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Digitalization in Energy: Case Study on "Cyber Resilience of Critical Energy Infrastructure"

Read more about Digitalization in Energy: Case Study on "Cyber Resilience of Critical Energy Infrastructure"

Digitalization is gaining more and more attention as a way to support and complement the energy transition process. Digitalization entails the use of digital technologies for existing processes, as it helps address existing challenges in new ways.

While using an integrated energy system with intelligent connected devices has many advantages, it also causes challenges. One of these challenges is the increased surface of attack and thus the related cybersecurity risk.

Thu, 28/12/2023 - 12:00

Market Forecast Tables 2023

Read more about Market Forecast Tables 2023

These tables show forest products production and trade forecasts for 2023 and 2024. These cover roundwood (logs, pulpwood and fuel wood), sawnwood (coniferous and non-coniferous), wood-based panels (plywood, particle board, OSB and fibreboard), pulp, paper and wood pellets. The forecast data are provided by national correspondents and approved at the meeting of the Committee on Forests and the Forest Industry.

Languages and translations

English

List of tables

List of Tables and Notes

Table 1 - Sawn Softwood

Table 2 - Sawn Hardwood (total)

Table 2a - Sawn Hardwood (temperate)

Table 2b - Sawn Hardwood (tropical)

Table 3 - Veneer Sheets

Table 4 - Plywood

Table 5 - Particle Board (excluding OSB)

Table 5a - Oriented Strand Board

Table 6 - Fibreboard

Table 6a - Hardboard

Table 6b - MDF/HDF

Table 6c - Other Fibreboard

Table 7 - Wood Pulp

Table 8 - Paper and Paperboard

Table 9 - Removals of wood in the rough

Table 9a - Removals of wood in the rough (softwood)

Table 9b - Removals of wood in the rough (hardwood)

Table 10 - Softwood sawlogs

Table 11 - Hardwood sawlogs

Table 11a - Hardwood logs (temperate)

Table 11b - Hardwood logs (tropical)

Table 12 - Pulpwood

Table 12a - Pulpwood (softwood)

Table 12b - Pulpwood (hardwood)

Table 12c - Wood Residues, Chips and Particles

Table 13 - Wood Pellets

Table 14 - Europe: Summary table of market forecasts for 2023 and 2024

Table 15 - North America: Summary table of market forecasts for 2023 and 2024

Source: UNECE Committee on Forests and the Forest Industry , November 2023, http://www.unece.org/forests/fpm/timbercommittee.html

Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia.

Data for the two latest years are forecasts.

In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.

In contrast to years prior to 2020, data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subregion.

Confidential data have not been included. Please inform secretariat in case you notice any confidential data which might have been included inadvertently.

Wherever the forecast data is incomplete, then data is repeated to avoid skewing.

For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.

Countries with nil, missing or confidential data for all years on a table are not shown.

Consumption figures are the sum of production and national imports minus national exports. Softwood = coniferous, hardwood = non-coniferous. United Kingdom production figures for OSB is secretariat estimate.

Uzbekistan – data extrapolated by the Secretariat based on national data for the first eight months 2023.

Poland - The trade turnover is based on data that includes the estimated value of trade turnover by entities exempt from the reporting obligation. These trade turnover figures are estimated at 3%. Roundwood: sawlogs and veneer logs and pulpwood and wood fuel - with removals from trees and shrubs outside the forest, including forest chips, with stump. Residues - production excluding recovered wood.

Softwood = coniferous, hardwood = non-coniferous

For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.

Countries with nil, missing or confidential data for all years on a table are not shown.

Table1


TABLE 1
	SAWN SOFTWOOD						SCIAGES CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	6,141	4,978	4,978	10,104	8,588	8,588	1,784	1,270	1,270	5,747	4,880	4,880	Autriche
Cyprus	33	34	34	1	1	1	32	33	33	0	0	0	Chypre
Czech Republic	2,965	2,343	2,470	4,720	3,776	4,040	583	414	350	2,338	1,847	1,920	République tchèque
Estonia	2,068	1,550	1,550	1,725	1,500	1,500	1,209	700	700	866	650	650	Estonie
Finland	2,938	2,420	2,420	11,200	10,300	10,400	305	20	20	8,567	7,900	8,000	Finlande
France	8,633	8,750	8,800	7,168	7,200	7,300	2,350	2,450	2,400	885	900	900	France
Germany	17,294	14,900	13,300	24,309	21,400	19,800	4,146	2,700	3,000	11,162	9,200	9,500	Allemagne
Hungary	788	902	918	85	96	86	717	821	842	14	15	11	Hongrie
Italy	4,790	4,302	4,302	400	400	400	4,608	4,157	4,157	217	255	255	Italie
Latvia	1,025	950	950	3,102	3,000	3,000	829	750	750	2,906	2,800	2,800	Lettonie
Luxembourg	71	122	122	39	39	39	43	91	91	11	8	8	Luxembourg
Malta	7	9	9	0	0	0	7	9	9	0	0	0	Malte
Montenegro	30	30	29	118	115	112	10	9	7	98	94	90	Monténégro
Netherlands	2,259	2,088	2,029	115	115	115	2,659	2,473	2,399	515	500	485	Pays-Bas
Poland	4,631	4,630	4,800	4,144	4,100	4,200	1,219	1,240	1,300	732	710	700	Pologne
Portugal	696	686	685	807	815	820	130	130	125	242	259	260	Portugal
Serbia	367	361	383	91	95	98	281	270	290	5	4	5	Serbie
Slovakia	847	810	860	1,430	1,360	1,400	480	450	460	1,063	1,000	1,000	Slovaquie
Slovenia	665	670	660	983	990	980	530	530	530	848	850	850	Slovénie
Spain	4,029	4,001	4,001	3,006	3,189	3,189	1,166	956	956	143	144	144	Espagne
Sweden	5,709	5,050	5,650	18,870	18,400	18,300	587	500	450	13,748	13,850	13,100	Suède
Switzerland	1,271	1,300	1,325	1,186	1,200	1,210	300	310	320	215	210	205	Suisse
United Kingdom	8,663	8,125	8,214	3,108	2,860	2,860	5,719	5,385	5,474	165	120	120	Royaume-Uni
Total Europe	75,919	69,011	68,490	96,712	89,540	88,439	29,694	25,668	25,934	50,487	46,197	45,883	Total Europe
Uzbekistan	2,256	1,498	1,498	0	0	0	2,256	1,498	1,498	0	0	0	Ouzbékistan
Total EECCA													Total EOCAC
Canada a	3,707	2,691	2,242	36,398	33,228	31,331	891	988	948	33,581	31,525	30,037	Canada a
United States a	87,925	87,155	88,151	64,039	64,178	64,399	26,202	25,492	26,149	2,316	2,515	2,397	Etats-Unis a
Total North America	91,632	89,846	90,393	100,437	97,406	95,730	27,093	26,480	27,097	35,898	34,040	32,434	Total Amérique du Nord
	a converted from nominal to actual size using factor of 0.72						a convertis du dimension nominale au véritable avec une facteur du 0.72

Table2


TABLE 2
	SAWN HARDWOOD (total)						SCIAGES NON-CONIFERES (total)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	310	222	222	238	202	202	217	140	140	145	120	120	Autriche
Cyprus	11	7	7	0	0	0	11	7	7	0	0	0	Chypre
Czech Republic	324	245	240	222	167	175	136	103	105	34	24	40	République tchèque
Estonia	232	125	125	175	125	125	147	60	60	90	60	60	Estonie
Finland	84	44	44	73	40	40	34	24	24	23	20	20	Finlande
France	1,124	1,140	1,150	1,446	1,300	1,400	264	420	350	586	580	600	France
Germany	693	650	650	997	800	800	395	300	300	699	450	450	Allemagne
Hungary	258	150	131	414	343	342	45	38	30	200	231	241	Hongrie
Italy	798	776	776	500	500	500	637	578	578	339	302	302	Italie
Latvia	5	105	105	720	800	800	54	55	55	769	750	750	Lettonie
Luxembourg	96	98	98	39	39	39	64	65	65	7	6	6	Luxembourg
Malta	7	8	9	0	0	0	7	8	9	0	0	0	Malte
Montenegro	11	8	10	39	35	34	2	1	1	30	28	25	Monténégro
Netherlands	238	213	203	34	34	34	314	289	279	110	110	110	Pays-Bas
Poland	495	470	500	487	450	460	267	270	300	259	250	260	Pologne
Portugal	369	295	290	182	185	190	287	200	190	100	90	90	Portugal
Serbia	172	215	225	343	370	385	64	60	70	235	215	230	Serbie
Slovakia	235	240	275	385	400	420	55	50	55	205	210	200	Slovaquie
Slovenia	106	145	145	143	145	145	83	80	80	121	80	80	Slovénie
Spain	425	467	467	302	321	321	175	193	193	53	47	47	Espagne
Sweden	142	140	140	100	100	100	83	80	80	41	40	40	Suède
Switzerland	78	79	81	52	53	54	50	51	52	24	25	25	Suisse
United Kingdom	807	810	810	37	40	40	787	790	790	17	20	20	Royaume-Uni
Total Europe	7,019	6,652	6,703	6,928	6,449	6,606	4,177	3,862	3,813	4,086	3,658	3,716	Total Europe
Uzbekistan	228	208	208	195	195	195	33	16	16	0	3	3	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,208	1,324	1,242	859	893	815	793	826	738	444	395	311	Canada
United States	14,647	14,835	15,217	17,637	17,827	18,214	798	805	820	3,788	3,797	3,817	Etats-Unis
Total North America	15,855	16,159	16,459	18,496	18,720	19,029	1,591	1,631	1,558	4,231	4,192	4,128	Total Amérique du Nord

Table 2a


TABLE 2a
	SAWN HARDWOOD (temperate)						SCIAGES NON-CONIFERES (zone tempérée)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	306	219	219	238	202	202	213	136	136	144	119	119	Autriche
Cyprus	9	5	5	0	0	0	8	5	5	0	0	0	Chypre
Czech Republic	307	229	223	222	167	175	119	86	88	34	24	40	République tchèque
Estonia	230	122	122	175	125	125	142	56	56	87	59	59	Estonie
Finland	80	40	40	73	40	40	26	16	16	19	16	16	Finlande
France	960	988	988	1,420	1,285	1,375	123	280	210	583	577	597	France
Germany	664	630	630	997	800	800	315	240	240	649	410	410	Allemagne
Hungary	257	147	127	414	343	342	43	35	26	200	230	241	Hongrie
Italy	819	791	791	495	495	495	476	423	423	152	127	127	Italie
Latvia	5	105	105	720	800	800	54	55	55	769	750	750	Lettonie
Luxembourg	92	96	96	39	39	39	60	63	63	7	6	6	Luxembourg
Malta	6	7	8	0	0	0	6	7	8	0	0	0	Malte
Montenegro	11	8	10	39	35	34	2	1	1	30	28	25	Monténégro
Netherlands	89	80	77	27	27	27	117	108	105	55	55	55	Pays-Bas
Poland	484	459	488	487	450	460	254	257	286	257	248	258	Pologne
Portugal	319	272	268	170	172	178	180	150	140	31	50	50	Portugal
Serbia	167	211	220	342	369	384	59	57	66	234	215	230	Serbie
Slovakia	235	240	275	385	400	420	55	50	55	205	210	200	Slovaquie
Slovenia	104	143	143	143	145	145	81	78	78	120	80	80	Slovénie
Spain	383	417	417	300	318	318	128	142	142	45	43	43	Espagne
Sweden	142	139	139	100	100	100	83	79	79	41	40	40	Suède
Switzerland	69	70	72	49	50	51	44	45	46	24	25	25	Suisse
United Kingdom	716	720	720	37	40	40	693	700	700	14	20	20	Royaume-Uni
Total Europe	6,453	6,138	6,183	6,872	6,402	6,550	3,281	3,069	3,025	3,700	3,334	3,392	Total Europe
Uzbekistan	227	207	207	195	195	195	33	15	15	0	3	3	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,191	1,316	1,236	859	893	815	762	805	715	430	382	294	Canada
United States	14,379	14,578	14,957	17,637	17,827	18,214	523	529	544	3,782	3,778	3,801	Etats-Unis
Total North America	15,569	15,893	16,193	18,496	18,720	19,029	1,285	1,334	1,259	4,212	4,160	4,095	Total Amérique du Nord

Table 2b

5.NC.T
	TABLE 2b
		SAWN HARDWOOD (tropical)						SCIAGES NON-CONIFERES (tropicale)
	Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption						Imports			Exports
	Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
	Austria	3	3	3	0	0	0	4	4	4	1	1	1	Autriche
	Bulgaria	0	0	0	0	0	0	0	0	0	0	0	0	Bulgarie
	Cyprus	3	2	2	0	0	0	3	2	2	0	0	0	Chypre
	Czech Republic	17	17	17	0	0	0	17	17	17	0	0	0	République tchèque
	Estonia	2	3	3	0	0	0	5	4	4	3	1	1	Estonie
	Finland	4	4	4	0	0	0	8	8	8	4	4	4	Finlande
	France	164	152	162	26	15	25	141	140	140	3	3	3	France
	Germany	29	20	20	0	0	0	79	60	60	50	40	40	Allemagne
	Hungary	2	3	4	0	0	0	2	4	4	0	0	0	Hongrie
	Italy	-21	-15	-15	5	5	5	161	154	154	187	175	175	Italie
	Luxembourg	4	2	2	0	0	0	4	2	2	0	0	0	Luxembourg
	Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
	Netherlands	149	133	126	7	7	7	197	181	174	55	55	55	Pays-Bas
	Poland	10	11	12	0	0	0	12	13	14	2	2	2	Pologne
	Portugal	50	23	22	12	13	12	107	50	50	69	40	40	Portugal
	Serbia	5	4	5	1	1	1	5	3	4	1	0	0	Serbie
	Slovenia	2	2	2	0	0	0	2	2	2	0	0	0	Slovénie
	Spain	42	49	49	2	2	2	47	50	50	7	4	4	Espagne
	Sweden	1	1	1	0	0	0	1	1	1	0	0	0	Suède
	Switzerland	9	9	9	3	3	3	6	6	6	0	0	0	Suisse
	United Kingdom	91	90	90	0	0	0	94	90	90	3	0	0	Royaume-Uni
	Total Europe	566	515	519	56	46	55	896	793	788	386	324	324	Total Europe
	Canada	17	8	7	0	0	0	31	21	23	14	13	16	Canada
	United States	269	257	260	0	0	0	275	276	276	6	19	16	Etats-Unis
	Total North America	286	266	266	0	0	0	305	297	299	20	31	32	Total Amérique du Nord

Table 3


TABLE 3
	VENEER SHEETS						FEUILLES DE PLACAGE
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	74	39	39	8	8	8	83	45	45	17	14	14	Autriche
Cyprus	1	1	1	0	0	0	1	1	1	0	0	0	Chypre
Czech Republic	28	28	27	28	16	17	58	53	50	58	41	40	République tchèque
Estonia	111	125	125	105	110	110	87	95	95	82	80	80	Estonie
Finland	27	21	21	190	160	160	12	10	10	175	149	149	Finlande
France	366	366	366	157	157	157	273	273	273	64	64	64	France
Germany	157	143	125	110	105	105	99	78	70	52	40	50	Allemagne
Hungary	23	25	20	13	18	13	39	39	39	28	31	32	Hongrie
Italy	344	308	308	107	107	107	274	234	234	37	33	33	Italie
Latvia	105	105	105	40	50	50	140	140	140	75	85	85	Lettonie
Luxembourg	1	0	0	0	0	0	1	0	0	0	0	0	Luxembourg
Malta	1	2	3	0	0	0	1	2	3	0	0	0	Malte
Netherlands	15	13	13	0	0	0	17	15	15	3	3	3	Pays-Bas
Poland	121	121	129	45	42	45	92	94	98	16	15	14	Pologne
Portugal	12	20	35	20	30	25	38	40	50	46	50	40	Portugal
Serbia	4	4	5	30	28	30	8	6	8	34	30	33	Serbie
Slovakia	17	25	25	21	25	25	27	30	30	31	30	30	Slovaquie
Slovenia	9	8	9	28	27	25	13	14	14	32	33	30	Slovénie
Spain	122	92	92	40	36	36	127	90	90	45	34	34	Espagne
Sweden	32	31	31	60	50	50	19	10	10	47	29	29	Suède
Switzerland	3	3	3	0	0	0	4	4	4	1	1	1	Suisse
United Kingdom	6	10	10	0	0	0	7	10	10	1	0	0	Royaume-Uni
Total Europe	1,577	1,490	1,491	1,002	969	962	1,419	1,283	1,288	843	762	760	Total Europe
Uzbekistan	4	4	4	3	3	3	2	1	1	0	0	0	Ouzbékistan
Total EECCA												0	Total EOCAC
Canada	204	262	267	581	581	581	212	218	230	590	537	544	Canada
United States	2,643	2,670	2,699	2,284	2,306	2,329	652	658	664	293	294	294	Etats-Unis
Total North America	2,847	2,932	2,966	2,866	2,887	2,910	864	876	894	883	831	838	Total Amérique du Nord
Note: Definition of veneers excludes domestic use for plywood.
La définition des placages exclus la conversion directe en contreplaqué.

Table 4


TABLE 4
	PLYWOOD						CONTREPLAQUES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	19	15	15	131	155	155	183	150	150	296	290	290	Autriche
Cyprus	14	15	15	0	0	0	14	15	15	0	0	0	Chypre
Czech Republic	193	116	123	240	236	238	230	115	115	277	235	230	République tchèque
Estonia	145	50	50	200	210	210	151	50	50	205	210	210	Estonie
Finland	297	240	240	1,110	940	940	87	60	60	900	760	760	Finlande
France	589	583	583	253	270	270	476	452	452	140	139	139	France
Germany	1,073	1,154	840	85	80	80	1,319	1,281	1,000	330	207	240	Allemagne
Hungary	136	110	107	60	61	63	138	138	138	62	90	94	Hongrie
Italy	602	537	537	288	290	290	525	442	442	211	195	195	Italie
Latvia	92	55	55	331	300	300	94	95	95	333	340	340	Lettonie
Luxembourg	33	29	29	0	0	0	33	29	29	0	0	0	Luxembourg
Malta	10	11	12	0	0	0	10	11	12	0	0	0	Malte
Montenegro	2	2	2	1	1	1	2	2	2	1	1	1	Monténégro
Netherlands	488	457	441	0	0	0	586	551	529	98	94	88	Pays-Bas
Poland	650	640	670	539	515	530	468	475	480	357	350	340	Pologne
Portugal	154	180	166	103	100	110	95	110	100	44	30	44	Portugal
Serbia	40	36	38	19	18	19	34	30	33	13	12	14	Serbie
Slovakia	67	63	63	153	150	150	59	59	59	146	146	146	Slovaquie
Slovenia	49	50	58	94	90	98	26	30	30	71	70	70	Slovénie
Spain	231	326	326	462	416	416	132	117	117	363	207	207	Espagne
Sweden	278	160	160	90	90	90	236	120	120	48	50	50	Suède
Switzerland	206	206	206	7	7	7	203	203	203	4	4	4	Suisse
United Kingdom	1,254	1,180	1,180	0	0	0	1,320	1,250	1,250	66	70	70	Royaume-Uni
Total Europe	6,623	6,215	5,916	4,166	3,930	3,967	6,422	5,786	5,482	3,965	3,501	3,532	Total Europe
Uzbekistan	62	46	46	0	0	0	63	47	47	0	0	0	Ouzbékistan
Total EECCA												0	Total EOCAC
Canada	2,174	2,028	2,123	1,604	1,557	1,526	1,224	1,058	1,241	654	587	644	Canada
United States	14,742	14,890	15,188	9,254	9,345	9,528	6,259	6,317	6,436	771	772	776	Etats-Unis
Total North America	16,916	16,918	17,311	10,858	10,902	11,054	7,483	7,375	7,677	1,425	1,359	1,420	Total Amérique du Nord

Table 5


TABLE 5
	PARTICLE BOARD (excluding OSB)						PANNEAUX DE PARTICULES (ne comprennent pas l'OSB)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	951	630	630	2,280	2,170	2,170	313	355	355	1,642	1,895	1,895	Autriche
Cyprus	49	46	46	0	0	0	49	46	46	0	0	0	Chypre
Czech Republic	793	811	835	962	866	910	530	484	485	699	538	560	République tchèque
Estonia	123	67	67	90	0	0	77	68	68	44	2	1	Estonie
Finland	113	75	75	54	54	54	85	44	44	26	23	23	Finlande
France	2,224	2,148	2,148	3,177	3,094	3,094	299	355	355	1,253	1,301	1,301	France
Germany	5,572	5,220	4,970	5,526	5,195	5,020	1,970	1,934	1,900	1,924	1,909	1,950	Allemagne
Hungary	408	384	379	447	428	438	264	282	272	303	326	331	Hongrie
Italy	3,070	2,813	2,813	2,646	2,500	2,500	956	821	821	532	508	508	Italie
Latvia	52	85	85	306	300	300	69	25	25	322	240	240	Lettonie
Luxembourg	20	12	12	0	0	0	21	13	13	1	1	1	Luxembourg
Malta	10	11	11	0	0	0	10	11	11	0	0	0	Malte
Montenegro	32	33	34	0	0	0	32	33	34	0	0	0	Monténégro
Netherlands	464	440	432	0	0	0	514	488	479	50	48	47	Pays-Bas
Poland	6,501	6,450	6,740	5,227	5,150	5,450	2,173	2,180	2,200	899	880	910	Pologne
Portugal	537	473	514	766	750	760	281	300	290	510	577	536	Portugal
Serbia	373	351	371	219	210	220	196	184	198	42	43	47	Serbie
Slovakia	352	343	340	676	675	675	148	140	137	473	473	472	Slovaquie
Slovenia	137	110	110	0	0	0	143	114	114	6	4	4	Slovénie
Spain	2,392	2,213	2,213	2,566	2,310	2,310	626	621	621	800	718	718	Espagne
Sweden	1,055	868	868	636	600	600	475	335	335	57	67	67	Suède
Switzerland	281	286	286	420	425	425	141	141	141	280	280	280	Suisse
United Kingdom	2,606	2,542	2,542	2,012	1,982	1,982	648	610	610	55	50	50	Royaume-Uni
Total Europe	28,115	26,410	26,521	28,012	26,710	26,908	10,021	9,584	9,555	9,917	9,883	9,942	Total Europe
Uzbekistan	880	542	542	252	252	252	654	317	317	26	27	27	Ouzbékistan
Total EECCA												27	Total EOCAC
Canada	1,466	1,886	1,894	1,625	2,032	2,012	552	504	491	710	650	609	Canada
United States	5,196	5,565	5,562	4,488	4,552	4,534	1,193	1,465	1,487	485	452	459	Etats-Unis
Total North America	6,663	7,451	7,456	6,113	6,584	6,546	1,745	1,969	1,978	1,195	1,102	1,068	Total Amérique du Nord
Data are calculated by subtracting OSB from the particleboard/OSB total - les données sont calculées en soustrayant les OSB du total des panneaux de particules et OSB.

Table 5a


TABLE 5a
	ORIENTED STRAND BOARD (OSB)						PANNEAUX STRUCTURAUX ORIENTES (OSB)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	205	135	135	0	0	0	212	140	140	7	5	5	Autriche
Cyprus	11	14	14	0	0	0	11	14	14	0	0	0	Chypre
Czech Republic	380	342	350	689	620	655	126	113	115	435	392	420	République tchèque
Estonia	55	32	32	0	0	0	55	32	32	1	0	0	Estonie
Finland	56	56	56	0	0	0	56	56	56	0	0	0	Finlande
France	427	522	522	302	406	406	222	165	165	96	49	49	France
Germany	1,316	1,238	1,130	1,164	1,105	1,080	679	669	600	526	536	550	Allemagne
Hungary	133	147	152	379	419	443	56	60	59	302	331	350	Hongrie
Italy	346	287	287	100	100	100	346	274	274	100	87	87	Italie
Latvia	196	165	165	674	650	650	76	75	75	554	560	560	Lettonie
Luxembourg	110	135	135	338	338	338	6	14	14	234	217	217	Luxembourg
Montenegro	2	2	2	0	0	0	2	2	2	0	0	0	Monténégro
Netherlands	222	222	227	0	0	0	286	286	292	64	64	65	Pays-Bas
Poland	655	650	760	647	650	750	302	320	350	294	320	340	Pologne
Portugal	46	37	41	0	0	0	50	40	45	4	3	4	Portugal
Serbia	40	35	41	0	0	0	41	36	42	1	1	1	Serbie
Slovakia	48	58	60	0	0	0	48	60	63	1	3	3	Slovaquie
Slovenia	31	24	24	0	0	0	33	26	26	2	2	2	Slovénie
Spain	26	15	15	3	3	3	35	33	33	12	20	20	Espagne
Sweden	94	92	92	0	0	0	97	95	95	3	3	3	Suède
Switzerland	95	95	95	0	0	0	96	96	96	1	1	1	Suisse
United Kingdom	773	758	758	598	598	598	365	350	350	190	190	190	Royaume-Uni
Total Europe	5,268	5,060	5,092	4,894	4,888	5,023	3,200	2,956	2,938	2,826	2,784	2,868	Total Europe
Uzbekistan	7	5	5	0	0	0	7	5	5	0	0	0	Ouzbékistan
Total EECCA												0	Total EOCAC
Canada	1,546	1,253	1,153	7,270	6,820	6,798	82	65	61	5,806	5,632	5,706	Canada
United States	19,658	19,834	20,197	13,592	13,783	14,059	6,198	6,236	6,326	132	185	188	Etats-Unis
Total North America	21,204	21,087	21,350	20,862	20,603	20,857	6,280	6,301	6,387	5,938	5,817	5,894	Total Amérique du Nord

Table 6


TABLE 6
	FIBREBOARD						PANNEAUX DE FIBRES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	421	386	386	470	395	395	331	308	308	381	316	316	Autriche
Cyprus	20	15	16	0	0	0	20	15	16	0	0	0	Chypre
Czech Republic	328	276	280	41	41	42	438	347	360	151	112	122	République tchèque
Estonia	70	46	47	75	40	40	65	46	47	70	40	40	Estonie
Finland	139	105	105	44	44	44	141	102	102	46	41	41	Finlande
France	828	915	915	1,238	1,035	1,035	721	772	772	1,130	892	892	France
Germany	3,791	3,437	3,325	5,194	4,900	4,800	1,590	1,543	1,470	2,993	3,006	2,945	Allemagne
Hungary	9	-17	-13	21	0	0	204	235	244	215	253	258	Hongrie
Italy	1,862	1,661	1,661	827	818	818	1,281	974	974	245	131	131	Italie
Latvia	60	50	40	48	50	50	62	65	65	50	65	75	Lettonie
Luxembourg	100	90	90	147	147	147	34	19	19	80	76	76	Luxembourg
Malta	6	7	7	0	0	0	6	7	7	0	0	0	Malte
Montenegro	32	32	33	0	0	0	32	32	33	0	0	0	Monténégro
Netherlands	332	310	296	29	29	29	465	431	412	162	150	145	Pays-Bas
Poland	3,808	3,765	4,020	4,960	4,920	5,080	590	585	630	1,743	1,740	1,690	Pologne
Portugal	534	485	529	526	520	560	338	315	335	330	350	366	Portugal
Serbia	74	74	88	19	20	22	71	73	88	16	19	22	Serbie
Slovakia	210	218	223	0	0	0	248	256	262	39	38	39	Slovaquie
Slovenia	24	15	15	132	120	125	28	25	30	136	130	140	Slovénie
Spain	920	894	894	1,430	1,287	1,287	462	355	355	972	748	748	Espagne
Sweden	301	260	260	0	0	0	425	360	360	124	100	100	Suède
Switzerland	238	238	238	97	97	97	308	308	308	167	167	167	Suisse
United Kingdom	1,692	1,630	1,630	856	850	850	895	840	840	60	60	60	Royaume-Uni
Total Europe	15,799	14,892	15,085	16,153	15,313	15,421	8,755	8,013	8,037	9,110	8,434	8,373	Total Europe
Uzbekistan	1,092	809	809	47	47	47	1,057	771	771	13	9	9	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,236	1,183	1,181	1,277	1,288	1,299	818	628	605	859	733	723	Canada
United States	8,684	8,749	8,888	6,362	6,420	6,571	3,359	3,289	3,310	1,038	960	993	Etats-Unis
Total North America	9,920	9,932	10,069	7,639	7,708	7,870	4,177	3,917	3,915	1,896	1,693	1,716	Total Amérique du Nord

Table 6a


TABLE 6a
	HARDBOARD						PANNEAUX DURS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	29	28	28	54	43	43	18	16	16	43	32	32	Autriche
Cyprus	2	1	2	0	0	0	2	1	2	0	0	0	Chypre
Czech Republic	43	45	45	0	0	0	61	59	60	18	14	15	République tchèque
Estonia	23	15	19	0	0	0	30	16	20	7	1	1	Estonie
Finland	23	21	21	44	44	44	21	15	15	41	38	38	Finlande
France	55	55	55	221	221	221	207	207	207	373	373	373	France
Germany	176	183	165	0	0	0	200	203	180	23	20	15	Allemagne
Hungary	27	41	45	2	0	0	65	81	85	40	40	40	Hongrie
Italy	280	280	280	16	16	16	283	283	283	19	19	19	Italie
Latvia	1	5	5	15	15	15	18	20	20	32	30	30	Lettonie
Luxembourg	-31	-12	-12	0	0	0	3	8	8	34	20	20	Luxembourg
Montenegro	1	1	1	0	0	0	1	1	1	0	0	0	Monténégro
Netherlands	44	41	39	0	0	0	63	58	56	19	17	17	Pays-Bas
Poland	-179	-120	-50	80	80	80	88	100	120	347	300	250	Pologne
Portugal	50	30	39	0	0	0	61	40	50	11	10	11	Portugal
Serbia	39	35	38	19	20	22	33	31	34	13	16	18	Serbie
Slovakia	21	20	21	0	0	0	21	21	22	1	1	1	Slovaquie
Slovenia	-1	0	1	0	0	0	4	2	4	4	2	3	Slovénie
Spain	17	15	15	32	29	29	46	46	46	61	60	60	Espagne
Sweden	47	30	30	0	0	0	116	110	110	70	80	80	Suède
Switzerland	19	19	19	0	0	0	24	24	24	5	5	5	Suisse
United Kingdom	101	90	90	0	0	0	110	100	100	9	10	10	Royaume-Uni
Total Europe	787	822	895	482	468	470	1,474	1,441	1,463	1,169	1,087	1,037	Total Europe
Uzbekistan	89	50	50	0	0	0	90	50	50	0	0	0	Ouzbékistan
Total EECCA													Total EOCAC
Canada	33	47	42	90	90	90	52	27	28	109	70	76	Canada
United States	481	509	514	437	504	509	259	255	258	215	250	253	Etats-Unis
Total North America	514	556	556	527	594	599	311	282	286	324	320	329	Total Amérique du Nord

Table 6b


TABLE 6b
	MDF/HDF
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	260	230	230	416	351	351	177	160	160	333	281	281	Autriche
Cyprus	16	12	12	0	0	0	16	12	12	0	0	0	Chypre
Czech Republic	199	157	160	41	41	42	180	135	140	22	19	22	République tchèque
Estonia	18	21	18	0	0	0	33	28	25	15	7	7	Estonie
Finland	82	67	67	0	0	0	86	70	70	4	3	3	Finlande
France	708	794	794	954	751	751	337	388	388	583	345	345	France
Germany	1,870	1,728	1,720	3,792	3,700	3,650	424	395	370	2,345	2,367	2,300	Allemagne
Hungary	-39	-65	-62	0	0	0	136	148	156	175	213	218	Hongrie
Italy	1,501	1,299	1,299	809	800	800	913	606	606	221	107	107	Italie
Latvia	52	40	30	33	35	35	22	25	25	2	20	30	Lettonie
Luxembourg	128	98	98	147	147	147	27	7	7	46	56	56	Luxembourg
Malta	5	5	5	0	0	0	5	5	5	0	0	0	Malte
Montenegro	31	31	32	0	0	0	31	31	32	0	0	0	Monténégro
Netherlands	220	205	196	0	0	0	361	336	322	141	131	126	Pays-Bas
Poland	3,066	3,020	3,130	3,052	3,030	3,100	470	450	470	456	460	440	Pologne
Portugal	447	440	465	494	500	530	257	260	265	305	320	330	Portugal
Serbia	31	35	46	0	0	0	34	38	50	3	3	4	Serbie
Slovakia	135	135	135	0	0	0	170	170	170	35	35	35	Slovaquie
Slovenia	24	15	14	132	120	125	24	23	26	131	128	137	Slovénie
Spain	835	821	821	1,334	1,201	1,201	397	302	302	897	682	682	Espagne
Sweden	254	225	225	0	0	0	284	230	230	30	5	5	Suède
Switzerland	24	24	24	97	97	97	88	88	88	161	161	161	Suisse
United Kingdom	1,553	1,510	1,510	856	850	850	739	700	700	42	40	40	Royaume-Uni
Total Europe	11,419	10,847	10,969	12,157	11,623	11,679	5,210	4,606	4,618	5,948	5,382	5,328	Total Europe
Uzbekistan	671	513	513	46	46	46	629	469	469	3	2	2	Ouzbékistan
Total EECCA													Total EOCAC
Canada	1,053	999	1,005	1,087	1,098	1,109	608	472	449	641	570	553	Canada
United States	5,156	5,228	5,226	2,746	2,778	2,786	2,939	2,874	2,866	529	424	426	Etats-Unis
Total North America	6,209	6,227	6,231	3,833	3,876	3,895	3,547	3,346	3,315	1,170	994	979	Total Amérique du Nord

Table 6c


TABLE 6c
	OTHER FIBREBOARD						AUTRES PANNEAUX DE FIBRES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	131	128	128	0	0	0	136	132	132	4	3	3	Autriche
Cyprus	2	2	2	0	0	0	3	2	2	0	0	0	Chypre
Czech Republic	86	74	75	0	0	0	197	154	160	111	80	85	République tchèque
Estonia	29	10	10	75	40	40	3	2	2	49	32	32	Estonie
Finland	33	17	17	0	0	0	34	17	17	0	0	0	Finlande
France	65	66	66	63	63	63	177	177	177	174	174	174	France
Germany	1,745	1,526	1,440	1,402	1,200	1,150	966	945	920	624	619	630	Allemagne
Hungary	21	7	4	19	0	0	3	7	4	0	0	0	Hongrie
Italy	82	82	82	3	3	3	85	85	85	6	6	6	Italie
Latvia	7	5	5	0	0	0	23	20	20	16	15	15	Lettonie
Luxembourg	4	4	4	0	0	0	4	4	4	0	0	0	Luxembourg
Malta	1	2	2	0	0	0	1	2	2	0	0	0	Malte
Netherlands	68	64	61	29	29	29	41	37	34	2	2	2	Pays-Bas
Poland	920	865	940	1,828	1,810	1,900	33	35	40	940	980	1,000	Pologne
Portugal	37	15	25	32	20	30	20	15	20	15	20	25	Portugal
Serbia	4	4	4	0	0	0	4	4	4	0	0	0	Serbie
Slovakia	54	63	67	0	0	0	57	65	70	3	2	3	Slovaquie
Slovenia	0	0	0	0	0	0	0	0	0	0	0	0	Slovénie
Spain	69	59	59	64	58	58	20	7	7	15	6	6	Espagne
Sweden	0	5	5	0	0	0	25	20	20	24	15	15	Suède
Switzerland	195	195	195	0	0	0	196	196	196	1	1	1	Suisse
United Kingdom	38	30	30	0	0	0	47	40	40	9	10	10	Royaume-Uni
Total Europe	3,592	3,223	3,221	3,514	3,222	3,272	2,071	1,965	1,956	1,993	1,965	2,007	Total Europe
Uzbekistan	331	246	246	2	2	2	339	252	252	10	7	7	Ouzbékistan
Total EECCA													Total EOCAC
Canada	150	137	134	100	100	100	158	129	128	108	92	94	Canada
United States	3,047	3,012	3,148	3,179	3,138	3,276	161	160	186	294	286	314	Etats-Unis
Total North America	3,196	3,149	3,282	3,279	3,238	3,376	319	289	314	402	378	408	Total Amérique du Nord

Table 7


TABLE 7
	WOOD PULP						PATE DE BOIS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 mt
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	2,209	1,950	2,030	1,977	1,700	1,800	630	610	630	399	360	400	Autriche
Czech Republic	847	688	700	640	525	540	324	259	260	117	96	100	République tchèque
Estonia	70	75	80	227	180	180	42	50	50	199	155	150	Estonie
Finland a	5,468	4,483	4,614	9,200	8,690	9,360	355	150	150	4,087	4,357	4,896	Finlande a
France	2,898	2,420	2,500	1,666	1,300	1,350	1,715	1,450	1,500	483	330	350	France
Germany	5,092	4,600	5,000	2,172	1,850	2,000	4,173	3,900	4,200	1,253	1,150	1,200	Allemagne
Hungary	205	206	214	66	77	87	141	133	131	3	3	4	Hongrie
Italy	3,466	3,466	3,466	223	223	223	3,536	3,536	3,536	293	293	293	Italie
Latvia	7	7	7	12	13	13	7	7	7	12	13	13	Lettonie
Netherlands	443	442	442	37	37	37	1,717	1,717	1,717	1,312	1,312	1,312	Pays-Bas
Poland	2,836	2,830	2,930	1,729	1,710	1,750	1,291	1,300	1,320	183	180	140	Pologne
Portugal	1,757	1,735	1,760	2,869	2,870	2,870	140	145	150	1,252	1,280	1,260	Portugal
Serbia	82	88	92	0	0	0	82	88	92	0	0	0	Serbie
Slovakia	700	700	715	692	700	725	173	170	170	166	170	180	Slovaquie
Slovenia	322	321	316	73	63	68	249	260	250	1	2	2	Slovénie
Spain	1,520	1,328	1,328	1,120	1,120	1,120	1,176	976	976	775	768	768	Espagne
Sweden	8,438	7,600	7,950	11,631	10,900	11,400	641	600	600	3,834	3,900	4,050	Suède
Switzerland	188	188	188	87	87	87	101	101	101	0	0	0	Suisse
United Kingdom	1,057	940	950	220	200	200	838	740	750	1	0	0	Royaume-Uni
Total Europe	37,604	34,067	35,282	34,641	32,244	33,809	17,333	16,193	16,590	14,369	14,369	15,118	Total Europe
Uzbekistan	38	28	28	1	1	1	37	28	28	0	0	0	Ouzbékistan
Total EECCA													Total EOCAC
Canada	6,007	5,851	5,616	14,200	13,102	12,638	472	582	640	8,665	7,833	7,662	Canada
United States	39,787	42,269	42,815	40,822	41,230	41,478	6,948	7,643	8,254	7,983	6,603	6,917	Etats-Unis
Total North America	45,794	48,121	48,431	55,022	54,332	54,116	7,420	8,224	8,894	16,648	14,436	14,579	Total Amérique du Nord
	a imports exclude dissolving pulp						a les importations excluent pâte à dissoudre

Table 8


TABLE 8
	PAPER AND PAPERBOARD						PAPIERS ET CARTONS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 mt
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	2,133	1,750	2,050	4,633	3,500	4,000	1,231	1,050	1,150	3,730	2,800	3,100	Autriche
Cyprus	56	48	48	0	0	0	56	48	48	0	0	0	Chypre
Czech Republic	1,467	1,234	1,258	938	769	785	1,531	1,286	1,312	1,002	822	838	République tchèque
Estonia	120	111	111	57	35	35	123	102	102	59	26	26	Estonie
Finland	514	475	460	7,200	5,990	6,150	333	275	280	7,019	5,790	5,970	Finlande
France	8,272	7,290	7,400	7,092	6,240	6,600	4,845	4,650	4,600	3,665	3,600	3,800	France
Germany	17,836	14,600	17,000	21,612	17,500	21,000	9,302	8,000	9,500	13,078	10,900	13,500	Allemagne
Hungary	1,213	1,167	1,212	1,057	1,003	1,034	877	892	898	720	727	721	Hongrie
Italy	11,390	11,390	11,390	8,696	8,696	8,696	5,800	5,800	5,800	3,106	3,106	3,106	Italie
Latvia	168	175	175	29	30	30	173	180	180	33	35	35	Lettonie
Luxembourg	26	14	14	0	0	0	27	15	15	1	1	1	Luxembourg
Malta	26	27	28	0	0	0	26	27	28	0	0	0	Malte
Netherlands	2,814	2,760	2,760	2,884	2,827	2,827	2,180	2,096	2,096	2,250	2,163	2,163	Pays-Bas
Poland	7,532	7,400	7,550	5,237	5,130	5,250	4,869	4,870	4,950	2,574	2,600	2,650	Pologne
Portugal	1,090	1,200	1,240	2,123	2,200	2,240	948	940	945	1,981	1,940	1,945	Portugal
Serbia	790	762	778	483	481	490	514	490	500	207	209	212	Serbie
Slovakia	565	575	600	967	975	1,000	457	450	475	859	850	875	Slovaquie
Slovenia	412	390	390	591	500	500	367	390	390	545	500	500	Slovénie
Spain	7,060	6,778	6,778	6,355	6,355	6,355	2,997	2,577	2,577	2,291	2,154	2,154	Espagne
Sweden	834	700	750	8,531	7,300	8,100	894	700	750	8,591	7,300	8,100	Suède
Switzerland	1,020	1,015	1,010	1,160	1,155	1,150	640	635	630	780	775	770	Suisse
United Kingdom	7,420	6,280	6,440	3,460	3,190	3,250	5,015	4,150	4,250	1,055	1,060	1,060	Royaume-Uni
Total Europe	72,758	66,140	69,442	83,103	73,876	79,492	43,204	39,623	41,476	53,549	47,358	51,526	Total Europe
Uzbekistan	335	297	297	142	142	142	214	171	171	21	17	17	Ouzbékistan
Total EECCA													Total EOCAC
Canada	5,505	6,069	6,231	9,094	9,124	9,155	2,516	2,242	2,235	6,105	5,298	5,159	Canada
United States	64,243	62,896	63,029	65,959	64,476	64,476	8,202	8,180	8,159	9,917	9,761	9,606	Etats-Unis
Total North America	69,748	68,964	69,260	75,053	73,600	73,631	10,718	10,423	10,395	16,023	15,059	14,765	Total Amérique du Nord

Table 9


TABLE 9
		REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
		TOTAL									TOTAL
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
		Total			Logs			Pulpwood a			Other b						Total
					Grumes			Bois de trituration a			Autre b
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		13,935	11,716	12,225	10,711	8,904	9,338	3,223	2,812	2,887	0	0	0	5,424	5,115	5,234	19,359	16,831	17,459	Autriche
Cyprus		3	2	2	2	2	2	0	0	0	0	0	0	11	9	8	14	11	10	Chypre
Czech Republic		20,708	15,535	14,897	14,635	10,617	10,106	5,965	4,804	4,675	108	113	115	4,405	3,965	3,900	25,113	19,499	18,797	République tchèque
Estonia		6,474	6,401	6,401	4,276	4,200	4,200	2,148	2,150	2,150	51	51	51	4,066	3,800	3,800	10,541	10,201	10,201	Estonie
Finland		56,246	53,397	55,435	25,699	22,749	23,412	30,547	30,648	32,023	0	0	0	9,340	9,340	9,340	65,586	62,737	64,775	Finlande
France		25,648	25,270	25,070	17,198	17,200	17,300	7,891	7,500	7,200	559	570	570	24,173	24,500	25,600	49,821	49,770	50,670	France
Germany		56,534	53,930	49,630	44,756	41,200	39,500	11,644	12,600	10,000	135	130	130	22,338	22,700	22,700	78,872	76,630	72,330	Allemagne
Hungary		2,901	2,881	2,881	1,410	1,374	1,399	912	995	1,008	579	512	475	3,626	3,284	3,397	6,527	6,165	6,278	Hongrie
Italy		2,838	3,540	3,540	1,890	1,890	1,890	316	1,018	1,018	632	632	632	10,839	10,839	10,839	13,677	14,379	14,379	Italie
Latvia		12,491	12,150	12,350	7,603	7,250	7,450	3,868	3,800	3,800	1,020	1,100	1,100	2,936	3,000	3,000	15,427	15,150	15,350	Lettonie
Luxembourg		231	197	193	147	144	133	56	38	38	27	15	22	40	45	43	271	242	235	Luxembourg
Montenegro		751	697	678	515	492	487	201	198	186	35	7	5	194	193	190	945	890	868	Monténégro
Netherlands		614	599	589	221	220	215	352	340	335	41	39	39	2,382	2,380	2,385	2,996	2,979	2,974	Pays-Bas
Poland		38,735	39,880	40,850	18,533	18,800	19,150	19,350	20,100	20,550	852	980	1,150	6,958	7,420	7,750	45,693	47,300	48,600	Pologne
Portugal		12,235	12,330	12,190	2,038	2,040	2,060	9,799	9,850	9,700	399	440	430	2,383	2,380	2,300	14,619	14,710	14,490	Portugal
Serbia		1,478	1,520	1,561	1,077	1,104	1,130	265	275	283	136	141	148	6,574	6,646	6,760	8,052	8,166	8,321	Serbie
Slovakia		6,827	6,820	6,880	4,130	4,080	4,100	2,672	2,710	2,750	25	30	30	609	610	650	7,435	7,430	7,530	Slovaquie
Slovenia		2,928	3,752	3,482	2,184	2,780	2,600	698	920	830	45	52	52	1,149	1,290	1,270	4,076	5,042	4,752	Slovénie
Spain		14,366	15,244	15,244	4,150	4,404	4,404	9,813	10,413	10,413	403	427	427	3,555	3,772	3,772	17,921	19,016	19,016	Espagne
Sweden		71,165	69,076	69,310	38,280	37,480	37,080	32,585	31,296	31,930	300	300	300	6,000	6,016	6,016	77,165	75,092	75,326	Suède
Switzerland		3,011	3,082	3,142	2,555	2,625	2,680	444	445	450	12	12	12	1,938	2,000	2,025	4,949	5,082	5,167	Suisse
United Kingdom		7,604	7,193	7,193	5,509	5,236	5,236	1,646	1,529	1,529	448	428	428	2,184	2,184	2,184	9,788	9,377	9,377	Royaume-Uni
Total Europe		357,723	345,212	343,742	207,519	194,791	193,872	144,397	144,441	143,754	5,807	5,980	6,116	121,124	121,488	123,163	478,847	466,699	466,905	Total Europe
Canada		142,131	140,499	140,499	124,900	123,350	123,350	15,040	14,864	14,864	2,190	2,285	2,285	1,683	1,908	1,908	143,814	142,407	142,407	Canada
United States		382,544	384,963	388,611	186,157	188,221	191,211	182,650	182,996	183,637	13,737	13,746	13,763	76,230	76,240	76,278	458,774	461,203	464,889	Etats-Unis
Total North America		524,675	525,462	529,110	311,057	311,571	314,561	197,690	197,861	198,501	15,927	16,031	16,048	77,913	78,148	78,186	602,587	603,610	607,296	Total Amérique du Nord
	a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
		therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
	b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
	c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
		used for energy purposes									à des fins energétiques

Table 9a


TABLE 9a
		REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
		SOFTWOOD									CONIFERES
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
		Total			Logs			Pulpwood a			Other b						Total
					Grumes			Bois de trituration a			Autre b
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		12,958	10,873	11,338	10,382	8,638	9,038	2,576	2,235	2,300	0	0	0	3,248	3,069	3,140	16,206	13,942	14,478	Autriche
Cyprus		2	2	2	2	2	2	0	0	0	0	0	0	10	8	7	12	10	9	Chypre
Czech Republic		19,440	14,455	13,825	14,019	10,094	9,589	5,316	4,253	4,125	105	109	111	3,610	3,249	3,200	23,050	17,704	17,025	République tchèque
Estonia		4,023	3,927	3,927	3,118	3,000	3,000	878	900	900	26	27	27	1,486	1,400	1,400	5,509	5,327	5,327	Estonie
Finland		47,408	45,464	47,590	24,662	21,700	22,351	22,746	23,764	25,239	0	0	0	4,593	4,593	4,593	52,001	50,057	52,183	Finlande
France		17,300	17,070	16,770	12,491	12,500	12,500	4,559	4,300	4,000	250	270	270	2,417	2,500	2,600	19,717	19,570	19,370	France
Germany		52,425	50,120	46,120	41,761	38,500	37,000	10,541	11,500	9,000	123	120	120	8,834	9,200	9,200	61,259	59,320	55,320	Allemagne
Hungary		688	759	743	175	201	208	411	488	481	102	70	53	383	294	333	1,071	1,053	1,076	Hongrie
Italy		1,797	2,502	2,502	1,169	1,169	1,169	148	853	853	480	480	480	1,180	1,180	1,180	2,977	3,682	3,682	Italie
Latvia		8,253	7,900	8,100	5,873	5,500	5,700	1,850	1,800	1,800	530	600	600	298	300	300	8,551	8,200	8,400	Lettonie
Luxembourg		162	143	145	124	122	115	10	6	8	27	15	22	17	11	12	178	154	158	Luxembourg
Montenegro		573	553	537	372	352	349	201	198	186	0	3	2	66	65	63	639	618	600	Monténégro
Netherlands		449	440	430	173	170	165	244	240	235	32	30	30	457	450	450	906	890	880	Pays-Bas
Poland		31,941	32,800	33,470	15,775	16,000	16,250	15,411	15,950	16,250	754	850	970	3,627	3,820	3,950	35,568	36,620	37,420	Pologne
Portugal		3,045	3,210	3,150	1,682	1,710	1,700	1,213	1,350	1,300	150	150	150	996	990	980	4,041	4,200	4,130	Portugal
Serbia		279	290	301	178	184	190	66	70	73	35	36	38	141	146	160	420	436	461	Serbie
Slovakia		3,325	3,160	3,120	2,559	2,430	2,400	748	710	700	18	20	20	259	260	275	3,584	3,420	3,395	Slovaquie
Slovenia		1,966	2,586	2,386	1,687	2,150	2,000	275	430	380	4	6	6	191	240	220	2,157	2,826	2,606	Slovénie
Spain		7,435	7,889	7,889	3,420	3,629	3,629	3,754	3,984	3,984	261	277	277	2,243	2,380	2,380	9,678	10,269	10,269	Espagne
Sweden		64,603	62,760	62,873	38,100	37,300	36,900	26,353	25,310	25,823	150	150	150	3,000	3,008	3,008	67,603	65,768	65,881	Suède
Switzerland		2,578	2,639	2,689	2,290	2,350	2,400	279	280	280	9	9	9	769	770	775	3,347	3,409	3,464	Suisse
United Kingdom		7,486	7,076	7,076	5,453	5,180	5,180	1,633	1,516	1,516	400	380	380	1,571	1,571	1,571	9,058	8,647	8,647	Royaume-Uni
Total Europe		288,136	276,619	274,984	185,467	172,881	171,836	99,212	100,136	99,433	3,458	3,602	3,715	39,396	39,504	39,798	327,533	316,123	314,781	Total Europe
Canada		114,659	112,907	112,907	110,046	108,424	108,424	4,229	4,021	4,021	384	462	462	806	946	946	115,465	113,853	113,853	Canada
United States		306,119	309,360	313,639	152,799	154,479	156,695	141,226	142,779	144,827	12,094	12,102	12,117	37,619	37,609	37,606	343,738	346,969	351,245	Etats-Unis
Total North America		420,778	422,267	426,546	262,845	262,903	265,119	145,455	146,800	148,848	12,478	12,564	12,579	38,425	38,555	38,552	459,203	460,822	465,098	Total Amérique du Nord
	a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
		therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
	b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
	c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
		used for energy purposes									à des fins energétiques

Table 9b


TABLE 9b
		REMOVALS OF WOOD IN THE ROUGH									QUANTITES ENLEVEES DE BOIS BRUT
		HARDWOOD									NON-CONIFERES
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
Country		Industrial wood - Bois industriels												Wood fuel c Bois de chauffage c						Pays
		Total			Logs			Pulpwood a			Other b						Total
					Grumes			Bois de trituration a			Autre b
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		977	843	887	329	266	300	647	577	587	0	0	0	2,176	2,046	2,094	3,153	2,889	2,981	Autriche
Cyprus		0	0	0	0	0	0	0	0	0	0	0	0	1	1	1	1	1	1	Chypre
Czech Republic		1,268	1,079	1,071	616	524	517	649	552	550	3	4	4	795	716	700	2,063	1,795	1,771	République tchèque
Estonia		2,452	2,474	2,474	1,158	1,200	1,200	1,270	1,250	1,250	24	24	24	2,580	2,400	2,400	5,032	4,874	4,874	Estonie
Finland		8,838	7,933	7,845	1,037	1,049	1,061	7,801	6,884	6,784	0	0	0	4,747	4,747	4,747	13,585	12,680	12,592	Finlande
France		8,348	8,200	8,300	4,707	4,700	4,800	3,332	3,200	3,200	309	300	300	21,756	22,000	23,000	30,104	30,200	31,300	France
Germany		4,110	3,810	3,510	2,995	2,700	2,500	1,103	1,100	1,000	12	10	10	13,504	13,500	13,500	17,613	17,310	17,010	Allemagne
Hungary		2,213	2,122	2,138	1,234	1,173	1,191	502	507	526	477	442	421	3,244	2,990	3,064	5,456	5,112	5,202	Hongrie
Italy		1,041	1,038	1,038	721	721	721	168	166	166	152	152	152	9,659	9,659	9,659	10,700	10,697	10,697	Italie
Latvia		4,238	4,250	4,250	1,730	1,750	1,750	2,018	2,000	2,000	490	500	500	2,638	2,700	2,700	6,876	6,950	6,950	Lettonie
Luxembourg		69	54	47	23	22	18	46	32	30	0	0	0	23	34	30	92	89	78	Luxembourg
Montenegro		178	144	141	143	140	138	0	0	0	35	4	3	128	128	127	306	272	268	Monténégro
Netherlands		165	159	159	48	50	50	108	100	100	9	9	9	1,925	1,930	1,935	2,090	2,089	2,094	Pays-Bas
Poland		6,794	7,080	7,380	2,757	2,800	2,900	3,939	4,150	4,300	98	130	180	3,331	3,600	3,800	10,125	10,680	11,180	Pologne
Portugal		9,190	9,120	9,040	356	330	360	8,586	8,500	8,400	249	290	280	1,387	1,390	1,320	10,578	10,510	10,360	Portugal
Serbia		1,199	1,230	1,260	899	920	940	199	205	210	101	105	110	6,433	6,500	6,600	7,632	7,730	7,860	Serbie
Slovakia		3,502	3,660	3,760	1,570	1,650	1,700	1,924	2,000	2,050	8	10	10	350	350	375	3,851	4,010	4,135	Slovaquie
Slovenia		962	1,166	1,096	497	630	600	424	490	450	41	46	46	957	1,050	1,050	1,919	2,216	2,146	Slovénie
Spain		6,931	7,354	7,354	730	775	775	6,059	6,429	6,429	142	151	151	1,312	1,392	1,392	8,243	8,746	8,746	Espagne
Sweden		6,562	6,316	6,437	180	180	180	6,232	5,986	6,107	150	150	150	3,000	3,008	3,008	9,562	9,324	9,445	Suède
Switzerland		433	443	453	265	275	280	165	165	170	3	3	3	1,169	1,230	1,250	1,602	1,673	1,703	Suisse
United Kingdom		118	117	117	56	56	56	13	13	13	48	48	48	613	613	613	730	730	730	Royaume-Uni
Total Europe		69,587	68,593	68,759	22,052	21,910	22,036	45,185	44,305	44,322	2,350	2,377	2,401	81,728	81,984	83,365	151,314	150,576	152,124	Total Europe
Canada		27,472	27,592	27,592	14,854	14,926	14,926	10,812	10,843	10,843	1,806	1,823	1,823	877	961	961	28,349	28,554	28,554	Canada
United States		76,425	75,603	74,972	33,358	33,742	34,516	41,424	40,217	38,810	1,643	1,644	1,646	38,611	38,631	38,672	115,036	114,234	113,644	Etats-Unis
Total North America		103,897	103,196	102,564	48,212	48,668	49,442	52,236	51,060	49,653	3,449	3,467	3,469	39,488	39,592	39,633	143,385	142,788	142,197	Total Amérique du Nord
	a	Pulpwood, round and split, as well as chips and particles produced directly								a	Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées
		therefrom and used as pulpwood									directement à partir des rondins et quartiers et utilisées comme bois de trituration
	b	Pitprops, poles, piling, posts etc.								b	Bois de mine, poteaux, pilotis, piquets etc.
	c	Including chips and particles produced from wood in the rough and								c	Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées
		used for energy purposes									à des fins energétiques

Table 10


TABLE 10
		SOFTWOOD SAWLOGS						GRUMES DE SCIAGES DES CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption a						Imports			Exports
Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		16,101	13,943	13,638	10,382	8,638	9,038	6,664	5,710	5,000	945	405	400	Autriche
Cyprus		2	2	2	2	2	2	0	0	0	0	0	0	Chypre
Czech Republic		8,002	6,511	6,962	14,019	10,094	9,589	411	596	715	6,428	4,178	3,343	République tchèque
Estonia		3,533	3,270	3,270	3,118	3,000	3,000	522	450	450	107	180	180	Estonie
Finland		24,310	21,336	21,991	24,662	21,700	22,351	127	79	83	479	443	443	Finlande
France		12,053	12,120	12,120	12,491	12,500	12,500	335	360	360	773	740	740	France
Germany		39,391	35,800	34,900	41,761	38,500	37,000	3,300	3,000	3,100	5,670	5,700	5,200	Allemagne
Hungary		175	201	208	175	201	208	0	0	0	0	0	0	Hongrie
Italy		1,645	1,396	1,396	1,169	1,169	1,169	580	457	457	104	230	230	Italie
Latvia		6,471	5,830	6,200	5,873	5,500	5,700	1,147	900	900	549	570	400	Lettonie
Luxembourg		465	403	396	124	122	115	693	424	424	352	143	143	Luxembourg
Montenegro		382	361	357	372	352	349	10	9	8	0	0	0	Monténégro
Netherlands		133	145	145	173	170	165	77	80	80	117	105	100	Pays-Bas
Poland		14,243	14,500	14,800	15,775	16,000	16,250	1,245	1,400	1,550	2,777	2,900	3,000	Pologne
Portugal		1,880	1,905	1,900	1,682	1,710	1,700	241	230	240	43	35	40	Portugal
Serbia		188	187	194	178	184	190	12	9	12	2	6	8	Serbie
Slovakia		3,059	3,030	3,100	2,559	2,430	2,400	900	950	1,000	400	350	300	Slovaquie
Slovenia		1,643	1,650	1,630	1,687	2,150	2,000	239	150	180	283	650	550	Slovénie
Spain		3,223	3,307	3,307	3,420	3,629	3,629	240	185	185	437	507	507	Espagne
Sweden		38,103	37,725	37,325	38,100	37,300	36,900	964	1,128	1,128	961	703	703	Suède
Switzerland		2,035	2,100	2,155	2,290	2,350	2,400	55	60	65	310	310	310	Suisse
United Kingdom		5,810	5,538	5,538	5,453	5,180	5,180	457	457	457	99	99	99	Royaume-Uni
Total Europe		182,849	171,260	171,534	185,467	172,881	171,836	18,218	16,634	16,394	20,836	18,255	16,696	Total Europe
Canada		105,870	103,492	103,916	110,046	108,424	108,424	1,346	1,402	1,309	5,522	6,333	5,816	Canada
United States		148,043	150,509	153,391	152,799	154,479	156,695	586	570	555	5,342	4,540	3,859	Etats-Unis
Total North America		253,913	254,001	257,307	262,845	262,903	265,119	1,931	1,972	1,864	10,863	10,873	9,675	Total Amérique du Nord
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11


TABLE 11
		HARDWOOD SAWLOGS (total)						GRUMES DE SCIAGES DES NON-CONIFERES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption a						Imports			Exports
Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		406	311	300	329	266	300	134	90	50	57	45	50	Autriche
Czech Republic		544	457	447	616	524	517	144	120	125	216	186	195	République tchèque
Estonia		1,187	1,244	1,244	1,158	1,200	1,200	46	60	60	16	16	16	Estonie
Finland		1,068	1,041	1,061	1,037	1,049	1,061	32	1	9	1	9	9	Finlande
France		3,453	4,020	4,120	4,707	4,700	4,800	116	120	120	1,370	800	800	France
Germany		2,532	2,290	2,130	2,995	2,700	2,500	111	110	110	574	520	480	Allemagne
Hungary		1,234	1,173	1,191	1,234	1,173	1,191	0	0	0	0	0	0	Hongrie
Italy		2,088	1,718	1,718	721	721	721	1,413	1,055	1,055	47	59	59	Italie
Latvia		1,221	1,190	1,410	1,730	1,750	1,750	87	40	60	596	600	400	Lettonie
Luxembourg		226	148	144	23	22	18	221	160	160	18	34	34	Luxembourg
Montenegro		143	140	138	143	140	138	0	0	0	0	0	0	Monténégro
Netherlands		54	60	60	48	50	50	54	60	60	48	50	50	Pays-Bas
Poland		2,687	2,730	2,830	2,757	2,800	2,900	80	80	80	150	150	150	Pologne
Portugal		997	885	925	356	330	360	663	580	590	22	25	25	Portugal
Serbia		894	922	946	899	920	940	15	20	28	20	18	22	Serbie
Slovakia		1,670	1,700	1,750	1,570	1,650	1,700	500	450	450	400	400	400	Slovaquie
Slovenia		281	290	280	497	630	600	31	30	30	247	370	350	Slovénie
Spain		833	854	854	730	775	775	164	174	174	61	94	94	Espagne
Sweden		217	217	217	180	180	180	37	37	37	0	0	0	Suède
Switzerland		145	155	160	265	275	280	35	40	40	155	160	160	Suisse
United Kingdom		78	77	77	56	56	56	26	26	26	5	5	5	Royaume-Uni
Total Europe		21,959	21,622	22,002	22,052	21,910	22,036	3,910	3,253	3,265	4,003	3,541	3,299	Total Europe
Canada		15,890	15,923	15,895	14,854	14,926	14,926	1,106	1,060	1,027	70	64	59	Canada
United States		31,550	32,311	33,431	33,358	33,742	34,516	221	156	156	2,028	1,587	1,241	Etats-Unis
Total North America		47,441	48,234	49,326	48,212	48,668	49,442	1,327	1,216	1,183	2,098	1,650	1,300	Total Amérique du Nord
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11a


TABLE 11a
		HARDWOOD LOGS (temperate)						GRUMES DE NON-CONIFERES (zone tempérée)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption a						Imports			Exports
Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		406	311	300	329	266	300	134	90	50	57	45	50	Autriche
Czech Republic		544	457	447	616	524	517	144	120	125	216	186	195	République tchèque
Estonia		1,187	1,244	1,244	1,158	1,200	1,200	46	60	60	16	16	16	Estonie
Finland		1,068	1,041	1,061	1,037	1,049	1,061	32	1	9	1	9	9	Finlande
France		3,412	3,978	4,078	4,707	4,700	4,800	72	75	75	1,367	797	797	France
Germany		2,527	2,285	2,125	2,995	2,700	2,500	101	100	100	569	515	475	Allemagne
Hungary		1,234	1,173	1,191	1,234	1,173	1,191	0	0	0	0	0	0	Hongrie
Italy		2,068	1,729	1,729	721	721	721	1,389	1,047	1,047	42	39	39	Italie
Latvia		1,221	1,190	1,410	1,730	1,750	1,750	87	40	60	596	600	400	Lettonie
Luxembourg		226	148	144	23	22	18	221	160	160	18	34	34	Luxembourg
Montenegro		143	140	138	143	140	138	0	0	0	0	0	0	Monténégro
Netherlands		46	55	55	48	50	50	42	50	50	44	45	45	Pays-Bas
Poland		2,685	2,727	2,827	2,757	2,800	2,900	78	77	77	150	150	150	Pologne
Portugal		981	870	912	356	330	360	642	560	571	17	20	19	Portugal
Serbia		893	921	945	899	920	940	14	19	27	20	18	22	Serbie
Slovakia		1,670	1,700	1,750	1,570	1,650	1,700	500	450	450	400	400	400	Slovaquie
Slovenia		280	290	280	497	630	600	30	30	30	247	370	350	Slovénie
Spain		827	847	847	730	775	775	158	167	167	61	94	94	Espagne
Sweden		217	217	217	180	180	180	37	37	37	0	0	0	Suède
Switzerland		145	155	160	265	275	280	35	40	40	155	160	160	Suisse
United Kingdom		76	75	75	56	56	56	24	24	24	5	5	5	Royaume-Uni
Total Europe		21,857	21,553	21,935	22,052	21,910	22,036	3,786	3,146	3,158	3,980	3,503	3,260	Total Europe
Canada		15,890	15,923	15,895	14,854	14,926	14,926	1,106	1,060	1,027	70	64	59	Canada
United States		31,549	32,308	33,429	33,358	33,742	34,516	219	152	154	2,027	1,586	1,240	Etats-Unis
Total North America		47,440	48,231	49,324	48,212	48,668	49,442	1,325	1,212	1,181	2,097	1,649	1,299	Total Amérique du Nord
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 11b


TABLE 11b
	HARDWOOD LOGS (tropical)						GRUMES DE NON-CONIFERES (tropicale)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Net Trade						Imports			Exports
Country	Commerce Net			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
France	-41	-42	-42				44	45	45	3	3	3	France
Germany	-5	-5	-5				10	10	10	5	5	5	Allemagne
Italy	-20	11	11				25	9	9	4	20	20	Italie
Netherlands	-8	-5	-5				12	10	10	4	5	5	Pays-Bas
Poland	-2	-3	-3				2	3	3	0	0	0	Pologne
Portugal	-16	-15	-13				21	20	19	5	5	6	Portugal
Serbia	-1	-1	-1				1	1	1	0	0	0	Serbie
Slovenia	-1	-0	-0				1	0	1	0	0	0	Slovénie
Spain	-6	-7	-7				6	7	7	0	0	0	Espagne
United Kingdom	-2	-2	-2				2	2	2	0	0	0	Royaume-Uni
Total Europe	-102	-69	-67				124	107	106	22	38	39	Total Europe
United States	-1	-3	-1				2	4	2	1	1	1	Etats-Unis
Total North America	-1	-3	-1				2	4	2	1	1	1	Total Amérique du Nord

Table12


TABLE 12
		PULPWOOD (total)						BOIS DE TRITURATION (total)
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
							1000 m3
		Apparent Consumption a						Imports			Exports
Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		13,844	12,627	12,592	11,047	9,212	9,287	3,676	4,070	4,020	879	655	715	Autriche
Cyprus		8	9	10	7	8	9	1	1	1	0	0	0	Chypre
Czech Republic		5,559	5,135	5,154	7,664	6,164	6,130	1,270	1,146	1,162	3,375	2,175	2,138	République tchèque
Estonia		3,117	2,380	2,435	6,548	6,550	6,550	256	330	285	3,687	4,500	4,400	Estonie
Finland		48,404	47,241	49,358	44,923	44,026	45,568	5,037	4,969	5,545	1,556	1,755	1,755	Finlande
France		24,495	24,350	24,050	24,257	24,000	23,700	2,527	2,600	2,600	2,289	2,250	2,250	France
Germany		26,555	26,580	23,090	27,936	27,100	23,500	4,474	3,870	3,770	5,855	4,390	4,180	Allemagne
Hungary		2,122	2,017	2,065	2,049	1,984	2,023	112	73	82	39	39	39	Hongrie
Italy		4,508	5,210	5,210	3,916	4,618	4,618	1,288	1,288	1,288	696	696	696	Italie
Latvia		5,540	5,150	5,150	9,484	8,800	8,800	1,084	950	950	5,028	4,600	4,600	Lettonie
Luxembourg		583	589	589	577	559	559	182	130	130	176	100	100	Luxembourg
Malta		2	3	3	0	0	0	2	3	3	0	0	0	Malte
Montenegro		245	241	227	245	241	227	0	0	0	0	0	0	Monténégro
Netherlands		604	1,100	1,095	1,267	1,240	1,230	289	100	105	952	240	240	Pays-Bas
Poland		35,250	36,265	37,135	33,531	34,600	35,450	3,652	3,660	3,710	1,933	1,995	2,025	Pologne
Portugal		15,954	15,330	15,365	11,664	11,720	11,590	4,657	4,000	4,140	368	390	365	Portugal
Serbia		981	1,007	1,045	967	1,000	1,033	15	8	13	1	1	1	Serbie
Slovakia		3,634	3,650	3,760	3,821	3,860	3,950	1,023	1,030	1,050	1,210	1,240	1,240	Slovaquie
Slovenia		926	770	790	2,058	2,280	2,230	625	490	530	1,757	2,000	1,970	Slovénie
Spain		13,959	14,358	14,358	14,383	15,261	15,261	1,435	1,564	1,564	1,859	2,467	2,467	Espagne
Sweden		55,632	54,193	54,727	50,015	48,196	48,730	7,036	7,750	7,750	1,419	1,753	1,753	Suède
Switzerland		1,823	1,824	1,829	1,216	1,217	1,222	795	795	795	188	188	188	Suisse
United Kingdom		4,590	4,471	4,471	4,293	4,175	4,175	406	405	405	109	109	109	Royaume-Uni
Total Europe		268,336	264,500	264,508	261,870	256,811	255,841	39,843	39,232	39,898	33,377	31,543	31,231	Total Europe
Canada		37,044	35,822	35,734	35,326	32,985	32,975	2,578	3,462	3,467	860	625	708	Canada
United States		238,450	239,587	240,850	244,912	246,110	247,536	348	324	308	6,809	6,848	6,994	Etats-Unis
Total North America		275,495	275,409	276,585	280,238	279,096	280,511	2,926	3,786	3,776	7,670	7,473	7,702	Total Amérique du Nord
	Includes wood residues, chips and particles for all purposes							Comprend les dechets de bois, plaquettes et particules pour toute utilisation
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12a


TABLE 12a
		PULPWOOD LOGS (ROUND AND SPLIT)						BOIS DE TRITURATION (RONDINS ET QUARTIERS)
		Softwood						Conifères
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		Apparent Consumption a						Imports			Exports
Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		3,681	3,895	3,850	2,576	2,235	2,300	1,312	1,750	1,700	206	90	150	Autriche
Czech Republic		3,927	3,744	3,675	5,316	4,253	4,125	811	811	830	2,200	1,320	1,280	République tchèque
Estonia		476	245	245	878	900	900	56	45	45	458	700	700	Estonie
Finland		22,913	24,189	25,835	22,746	23,764	25,239	1,163	1,410	1,581	996	985	985	Finlande
France		4,689	4,400	4,100	4,559	4,300	4,000	608	550	550	478	450	450	France
Germany		10,311	11,900	9,500	10,541	11,500	9,000	2,200	2,100	2,000	2,430	1,700	1,500	Allemagne
Hungary		411	488	481	411	488	481	0	0	0	0	0	0	Hongrie
Italy		148	853	853	148	853	853	0	0	0	0	0	0	Italie
Latvia		1,775	1,700	1,700	1,850	1,800	1,800	374	400	400	449	500	500	Lettonie
Luxembourg		-16	-18	-16	10	6	8	9	3	3	35	27	27	Luxembourg
Montenegro		201	198	186	201	198	186	0	0	0	0	0	0	Monténégro
Netherlands		146	150	145	244	240	235	70	80	85	168	170	175	Pays-Bas
Poland		15,378	15,900	16,300	15,411	15,950	16,250	1,428	1,500	1,650	1,462	1,550	1,600	Pologne
Portugal		1,323	1,430	1,375	1,213	1,350	1,300	122	100	90	12	20	15	Portugal
Serbia		66	70	74	66	70	73	0	0	1	0	0	0	Serbie
Slovakia		598	600	610	748	710	700	600	630	650	750	740	740	Slovaquie
Slovenia		264	200	220	275	430	380	268	170	200	278	400	360	Slovénie
Spain		3,369	3,467	3,467	3,754	3,984	3,984	179	138	138	564	655	655	Espagne
Sweden		28,513	27,431	27,944	26,353	25,310	25,823	3,114	3,269	3,269	954	1,148	1,148	Suède
Switzerland		209	210	210	279	280	280	20	20	20	90	90	90	Suisse
United Kingdom		1,894	1,776	1,776	1,633	1,516	1,516	291	291	291	31	31	31	Royaume-Uni
Total Europe		100,275	102,827	102,530	99,212	100,136	99,433	12,625	13,267	13,503	11,562	10,576	10,406	Total Europe
Canada		4,531	4,347	4,410	4,229	4,021	4,021	324	336	401	22	10	12	Canada
United States		141,231	142,785	144,831	141,226	142,779	144,827	5	6	4	0	0	0	Etats-Unis
Total North America		145,762	147,132	149,241	145,455	146,800	148,848	329	341	405	22	10	12	Total Amérique du Nord
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12b


TABLE 12b
		PULPWOOD LOGS (ROUND AND SPLIT)						BOIS DE TRITURATION (RONDINS ET QUARTIERS)
		Hardwood						Non-conifères
1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
		Apparent Consumption a						Imports			Exports
Country		Consommation Apparente a			Production			Imports - Importations			Exports - Exportations			Pays
		2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria		1,217	997	1,007	647	577	587	668	500	500	98	80	80	Autriche
Czech Republic		450	380	384	649	552	550	3	2	2	202	174	168	République tchèque
Estonia		363	200	250	1,270	1,250	1,250	154	250	200	1,060	1,300	1,200	Estonie
Finland		8,997	7,940	8,052	7,801	6,884	6,784	1,550	1,633	1,845	354	577	577	Finlande
France		2,386	2,250	2,250	3,332	3,200	3,200	43	50	50	989	1,000	1,000	France
Germany		1,116	1,180	1,090	1,103	1,100	1,000	259	270	270	246	190	180	Allemagne
Hungary		502	507	526	502	507	526	0	0	0	0	0	0	Hongrie
Italy		168	166	166	168	166	166	0	0	0	0	0	0	Italie
Latvia		172	200	200	2,018	2,000	2,000	244	100	100	2,090	1,900	1,900	Lettonie
Luxembourg		77	71	69	46	32	30	36	48	48	5	9	9	Luxembourg
Netherlands		62	50	55	108	100	100	21	20	20	67	70	65	Pays-Bas
Poland		4,424	4,635	4,785	3,939	4,150	4,300	560	560	560	75	75	75	Pologne
Portugal		10,495	10,300	10,260	8,586	8,500	8,400	2,100	2,000	2,050	191	200	190	Portugal
Serbia		199	205	210	199	205	210	0	0	0	0	0	0	Serbie
Slovakia		1,874	1,950	2,000	1,924	2,000	2,050	100	100	100	150	150	150	Slovaquie
Slovenia		137	120	130	424	490	450	84	80	90	371	450	410	Slovénie
Spain		5,422	5,288	5,288	6,059	6,429	6,429	269	291	291	906	1,432	1,432	Espagne
Sweden		8,517	8,412	8,533	6,232	5,986	6,107	2,313	2,481	2,481	28	55	55	Suède
Switzerland		128	128	133	165	165	170	3	3	3	40	40	40	Suisse
United Kingdom		23	22	22	13	13	13	18	18	18	9	9	9	Royaume-Uni
Total Europe		46,729	45,001	45,410	45,185	44,305	44,322	8,426	8,406	8,628	6,881	7,711	7,540	Total Europe
Canada		10,554	10,654	10,644	10,812	10,843	10,843	38	36	30	296	225	228	Canada
United States		41,407	40,200	38,795	41,424	40,217	38,810	58	32	18	75	50	33	Etats-Unis
Total North America		51,961	50,854	49,439	52,236	51,060	49,653	96	68	48	371	275	261	Total Amérique du Nord
	a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Table 12c


TABLE 12c
	WOOD RESIDUES, CHIPS AND PARTICLES						DECHETS DE BOIS, PLAQUETTES ET PARTICULES
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 m3
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	8,945	7,735	7,735	7,824	6,400	6,400	1,696	1,820	1,820	575	485	485	Autriche
Cyprus	8	9	10	7	8	9	1	1	1	0	0	0	Chypre
Czech Republic	1,182	1,011	1,094	1,699	1,359	1,454	456	333	330	973	681	690	République tchèque
Estonia	2,278	1,935	1,940	4,400	4,400	4,400	47	35	40	2,169	2,500	2,500	Estonie
Finland	16,494	15,112	15,471	14,376	13,378	13,545	2,324	1,926	2,119	206	193	193	Finlande
France	17,420	17,700	17,700	16,366	16,500	16,500	1,876	2,000	2,000	822	800	800	France
Germany	15,128	13,500	12,500	16,292	14,500	13,500	2,015	1,500	1,500	3,179	2,500	2,500	Allemagne
Hungary	1,209	1,022	1,057	1,137	989	1,015	112	73	82	39	39	39	Hongrie
Italy	4,192	4,192	4,192	3,600	3,600	3,600	1,288	1,288	1,288	696	696	696	Italie
Latvia	3,593	3,250	3,250	5,616	5,000	5,000	466	450	450	2,489	2,200	2,200	Lettonie
Luxembourg	522	536	536	521	521	521	137	79	79	136	64	64	Luxembourg
Malta	2	3	3	0	0	0	2	3	3	0	0	0	Malte
Montenegro	44	43	41	44	43	41	0	0	0	0	0	0	Monténégro
Netherlands	396	900	895	915	900	895	198	0	0	717	0	0	Pays-Bas
Poland	15,448	15,730	16,050	14,181	14,500	14,900	1,664	1,600	1,500	396	370	350	Pologne
Portugal	4,136	3,600	3,730	1,865	1,870	1,890	2,435	1,900	2,000	165	170	160	Portugal
Serbia	716	732	761	702	725	750	15	8	12	1	1	1	Serbie
Slovakia	1,162	1,100	1,150	1,149	1,150	1,200	323	300	300	310	350	350	Slovaquie
Slovenia	525	450	440	1,360	1,360	1,400	273	240	240	1,107	1,150	1,200	Slovénie
Spain	5,169	5,603	5,603	4,570	4,849	4,849	987	1,135	1,135	388	380	380	Espagne
Sweden	18,602	18,350	18,250	17,430	16,900	16,800	1,609	2,000	2,000	437	550	550	Suède
Switzerland	1,486	1,486	1,486	772	772	772	772	772	772	58	58	58	Suisse
United Kingdom	2,673	2,673	2,673	2,646	2,646	2,646	96	96	96	69	69	69	Royaume-Uni
Total Europe	121,332	116,673	116,568	117,472	112,370	112,087	18,793	17,559	17,767	14,933	13,256	13,285	Total Europe
Canada	21,959	20,821	20,680	20,285	18,121	18,111	2,216	3,090	3,037	542	390	467	Canada
United States	55,812	56,602	57,224	62,262	63,114	63,899	285	286	286	6,734	6,798	6,961	Etats-Unis
Total North America	77,771	77,423	77,904	82,547	81,235	82,010	2,500	3,376	3,323	7,277	7,188	7,428	Total Amérique du Nord

Table 13


TABLE 13
	WOOD PELLETS						GRANULES DE BOIS
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
						1000 mt
	Apparent Consumption						Imports			Exports
Country	Consommation Apparente			Production			Imports - Importations			Exports - Exportations			Pays
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
Austria	1,290	1,497	1,450	1,691	1,938	2,050	344	309	300	745	750	900	Autriche
Cyprus	8	5	5	0	0	0	8	5	5	0	0	0	Chypre
Czech Republic	234	215	225	540	459	482	38	38	40	344	282	296	République tchèque
Estonia	284	300	230	1,650	1,350	1,300	12	50	30	1,378	1,100	1,100	Estonie
Finland	530	541	562	360	380	405	188	163	160	18	2	3	Finlande
France	2,735	3,260	3,660	2,050	2,250	2,450	775	1,100	1,300	90	90	90	France
Germany	3,328	3,540	3,720	3,569	3,700	3,900	443	480	420	684	640	600	Allemagne
Hungary	63	44	50	62	43	49	11	13	12	11	12	12	Hongrie
Italy	2,359	2,359	2,359	450	450	450	1,916	1,916	1,916	7	7	7	Italie
Latvia	621	750	750	1,980	2,000	2,000	326	350	350	1,685	1,600	1,600	Lettonie
Luxembourg	61	72	72	63	63	63	17	11	11	19	2	2	Luxembourg
Malta	1	1	1	0	0	0	1	1	1	0	0	0	Malte
Montenegro	18	25	26	83	84	84	0	0	0	65	59	58	Monténégro
Netherlands	5,354	5,354	5,354	268	268	268	5,551	5,551	5,551	465	465	465	Pays-Bas
Poland	842	920	1,100	1,152	1,200	1,350	366	370	380	677	650	630	Pologne
Portugal	228	225	220	747	740	735	4	5	5	523	520	520	Portugal
Serbia	478	460	485	418	450	480	83	70	80	23	60	75	Serbie
Slovakia	22	175	175	390	450	450	47	75	75	415	350	350	Slovaquie
Slovenia	125	155	150	164	175	180	126	120	130	165	140	160	Slovénie
Spain	867	907	907	1,007	1,007	1,007	65	46	46	206	146	146	Espagne
Sweden	1,776	1,800	1,850	1,809	1,750	1,800	199	210	210	232	160	160	Suède
Switzerland	410	415	420	330	335	340	80	80	80	0	0	0	Suisse
United Kingdom	7,819	7,830	7,830	327	330	330	7,516	7,520	7,520	23	20	20	Royaume-Uni
Total Europe	29,451	30,850	31,601	19,110	19,422	20,173	18,114	18,482	18,621	7,774	7,055	7,194	Total Europe
Canada	368	420	179	3,830	3,830	3,830	31	52	56	3,493	3,462	3,707	Canada
United States	761	273	152	9,544	9,744	9,948	194	174	155	8,977	9,644	9,951	Etats-Unis
Total North America	1,129	694	331	13,374	13,574	13,778	225	226	211	12,470	13,106	13,659	Total Amérique du Nord

Table 14


TABLE 14
Europe: Summary table of market forecasts for 2023 and 2024
Europe: Tableau récapitulatif des prévisions du marché pour 2023 et 2024
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
	million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
	Apparent Consumption
	Consommation Apparente			Production			Imports - Importations			Exports - Exportations
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
	actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
	réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
Sawn softwood	75.92	69.01	68.49	96.71	89.54	88.44	29.69	25.67	25.93	50.49	46.20	45.88	Sciages conifères
Softwood logs a	182.85	171.26	171.53	185.47	172.88	171.84	18.22	16.63	16.39	20.84	18.25	16.70	Grumes de conifères a
Sawn hardwood	7.02	6.65	6.70	6.93	6.45	6.61	4.18	3.86	3.81	4.09	3.66	3.72	Sciages non-conifères
– temperate zone b	6.45	6.14	6.18	6.87	6.40	6.55	3.28	3.07	3.02	3.70	3.33	3.39	– zone tempérée b
– tropical zone b	0.57	0.51	0.52	0.06	0.05	0.06	0.90	0.79	0.79	0.39	0.32	0.32	– zone tropicale b
Hardwood logs a	21.96	21.62	22.00	22.05	21.91	22.04	3.91	3.25	3.26	4.00	3.54	3.30	Grumes de non-conifères a
– temperate zone b	21.86	21.55	21.93	22.05	21.91	22.04	3.79	3.15	3.16	3.98	3.50	3.26	– zone tempérée b
– tropical zone b	0.10	0.07	0.07				0.12	0.11	0.11	0.02	0.04	0.04	– zone tropicale b
Veneer sheets	1.58	1.49	1.49	1.00	0.97	0.96	1.42	1.28	1.29	0.84	0.76	0.76	Feuilles de placage
Plywood	6.62	6.21	5.92	4.17	3.93	3.97	6.42	5.79	5.48	3.96	3.50	3.53	Contreplaqués
Particle board (excluding OSB)	28.12	26.41	26.52	28.01	26.71	26.91	10.02	9.58	9.55	9.92	9.88	9.94	Pann. de particules (sauf OSB)
OSB	5.27	5.06	5.09	4.89	4.89	5.02	3.20	2.96	2.94	2.83	2.78	2.87	OSB
Fibreboard	15.80	14.89	15.09	16.15	15.31	15.42	8.76	8.01	8.04	9.11	8.43	8.37	Panneaux de fibres
– Hardboard	0.79	0.82	0.90	0.48	0.47	0.47	1.47	1.44	1.46	1.17	1.09	1.04	– Durs
– MDF	11.42	10.85	10.97	12.16	11.62	11.68	5.21	4.61	4.62	5.95	5.38	5.33	– MDF
– Other board	3.59	3.22	3.22	3.51	3.22	3.27	2.07	1.97	1.96	1.99	1.96	2.01	– Autres panneaux
Pulpwood a	268.34	264.50	264.51	261.87	256.81	255.84	39.84	39.23	39.90	33.38	31.54	31.23	Bois de trituration a
– Pulp logs	147.00	147.83	147.94	144.40	144.44	143.75	21.05	21.67	22.13	18.44	18.29	17.95	– Bois ronds de trituration
– softwood	100.28	102.83	102.53	99.21	100.14	99.43	12.63	13.27	13.50	11.56	10.58	10.41	– conifères
– hardwood	46.73	45.00	45.41	45.18	44.31	44.32	8.43	8.41	8.63	6.88	7.71	7.54	– non-conifères
– Residues, chips and particles	121.33	116.67	116.57	117.47	112.37	112.09	18.79	17.56	17.77	14.93	13.26	13.29	– Déchets, plaquettes et part.
Wood pulp	37.60	34.07	35.28	34.64	32.24	33.81	17.33	16.19	16.59	14.37	14.37	15.12	Pâte de bois
Paper and paperboard	72.76	66.14	69.44	83.10	73.88	79.49	43.20	39.62	41.48	53.55	47.36	51.53	Papiers et cartons
Wood Pellets	29.45	30.85	31.60	19.11	19.42	20.17	18.11	18.48	18.62	7.77	7.05	7.19	Granulés de bois
a Countries which did not provide trade data are included in consumption data							a La consommation comprend les pays qui n'ont pas fourni des données sur le commerce
b Trade figures by zone do not equal the total as some countries cannot provide data for both zones							b Les chiffres du commerce par zone ne correspondent pas aux totaux
							en raison du fait que certains pays ne peuvent les différencier.

Table 15


TABLE 15
North America: Summary table of market forecasts for 2023 and 2024
Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2023 et 2024
Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions
	million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques)
	Apparent Consumption
	Consommation Apparente			Production			Imports - Importations			Exports - Exportations
	2022	2023	2024	2022	2023	2024	2022	2023	2024	2022	2023	2024
	actual	forecasts		actual	forecasts		actual	forecasts		actual	forecasts
	réels	prévisions		réels	prévisions		réels	prévisions		réels	prévisions
Sawn softwood	91.63	89.85	90.39	100.44	97.41	95.73	27.09	26.48	27.10	35.90	34.04	32.43	Sciages conifères
Softwood logs	253.91	254.00	257.31	262.84	262.90	265.12	1.93	1.97	1.86	10.86	10.87	9.68	Grumes de conifères
Sawn hardwood	15.85	16.16	16.46	18.50	18.72	19.03	1.59	1.63	1.56	4.23	4.19	4.13	Sciages non-conifères
– temperate zone	15.57	15.89	16.19	18.50	18.72	19.03	1.29	1.33	1.26	4.21	4.16	4.10	– zone tempérée
– tropical zone	0.29	0.27	0.27	0.00	0.00	0.00	0.31	0.30	0.30	0.02	0.03	0.03	– zone tropicale
Hardwood logs	47.44	48.23	49.33	48.21	48.67	49.44	1.33	1.22	1.18	2.10	1.65	1.30	Grumes de non-conifères
– temperate zone	47.44	48.23	49.32	48.21	48.67	49.44	1.32	1.21	1.18	2.10	1.65	1.30	– zone tempérée
– tropical zone	0.00	0.00	0.00				0.00	0.00	0.00	0.00	0.00	0.00	– zone tropicale
Veneer sheets	2.85	2.93	2.97	2.87	2.89	2.91	0.86	0.88	0.89	0.88	0.83	0.84	Feuilles de placage
Plywood	16.92	16.92	17.31	10.86	10.90	11.05	7.48	7.37	7.68	1.43	1.36	1.42	Contreplaqués
Particle board (excluding OSB)	6.66	7.45	7.46	6.11	6.58	6.55	1.75	1.97	1.98	1.19	1.10	1.07	Pann. de particules (sauf OSB)
OSB	21.20	21.09	21.35	20.86	20.60	20.86	6.28	6.30	6.39	5.94	5.82	5.89	OSB
Fibreboard	9.92	9.93	10.07	7.64	7.71	7.87	4.18	3.92	3.92	1.90	1.69	1.72	Panneaux de fibres
– Hardboard	0.51	0.56	0.56	0.53	0.59	0.60	0.31	0.28	0.29	0.32	0.32	0.33	– Durs
– MDF	6.21	6.23	6.23	3.83	3.88	3.89	3.55	3.35	3.32	1.17	0.99	0.98	– MDF
– Other board	3.20	3.15	3.28	3.28	3.24	3.38	0.32	0.29	0.31	0.40	0.38	0.41	– Autres panneaux
Pulpwood	275.49	275.41	276.58	280.24	279.10	280.51	2.93	3.79	3.78	7.67	7.47	7.70	Bois de trituration
– Pulp logs	197.72	197.99	198.68	197.69	197.86	198.50	0.43	0.41	0.45	0.39	0.28	0.27	– Bois ronds de trituration
– softwood	145.76	147.13	149.24	145.45	146.80	148.85	0.33	0.34	0.41	0.02	0.01	0.01	– conifères
– hardwood	51.96	50.85	49.44	52.24	51.06	49.65	0.10	0.07	0.05	0.37	0.27	0.26	– non-conifères
– Residues, chips and particles	77.77	77.42	77.90	82.55	81.23	82.01	2.50	3.38	3.32	7.28	7.19	7.43	– Déchets, plaquettes et part.
Wood pulp	45.79	48.12	48.43	55.02	54.33	54.12	7.42	8.22	8.89	16.65	14.44	14.58	Pâte de bois
Paper and paperboard	69.75	68.96	69.26	75.05	73.60	73.63	10.72	10.42	10.39	16.02	15.06	14.77	Papiers et cartons
Wood pellets	1.13	0.69	0.33	13.37	13.57	13.78	0.23	0.23	0.21	12.47	13.11	13.66	Granulés de bois

List of Tables and Notes Table 1 - Sawn Softwood Table 2 - Sawn Hardwood (total) Table 2a - Sawn Hardwood (temperate) Table 2b - Sawn Hardwood (tropical) Table 3 - Veneer Sheets Table 4 - Plywood Table 5 - Particle Board (excluding OSB) Table 5a - Oriented Strand Board Table 6 - Fibreboard Table 6a - Hardboard Table 6b - MDF/HDF Table 6c - Other Fibreboard Table 7 - Wood Pulp Table 8 - Paper and Paperboard Table 9 - Removals of wood in the rough Table 9a - Removals of wood in the rough (softwood) Table 9b - Removals of wood in the rough (hardwood) Table 10 - Softwood sawlogs Table 11 - Hardwood sawlogs Table 11a - Hardwood logs (temperate) Table 11b - Hardwood logs (tropical) Table 12 - Pulpwood Table 12a - Pulpwood (softwood) Table 12b - Pulpwood (hardwood) Table 12c - Wood Residues, Chips and Particles Table 13 - Wood Pellets Table 14 - Europe: Summary table of market forecasts for 2023 and 2024 Table 15 - North America: Summary table of market forecasts for 2023 and 2024

Source: UNECE Committee on Forests and the Forest Industry , November 2023, http://www.unece.org/forests/fpm/timbercommittee.html

Notes: Data in italics are estimated by the secretariat. EECCA is Eastern Europe, Caucasus and Central Asia. Data for the two latest years are forecasts. In contrast to previous years, data are shown only for countries providing forecasts. Sub-regional totals are only for reporting countries.

For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year.

Softwood = coniferous, hardwood = non-coniferous For tables 1-13, data in italics are secretariat estimates or repeated data. All other data are from national sources and are of course estimates for the current and future year. Countries with nil, missing or confidential data for all years on a table are not shown.

Uzbekistan – data extrapolated by the Secretariat based on national data for the first eight months 2023. Poland - The trade turnover is based on data that includes the estimated value of trade turnover by entities exempt from the reporting obligation. These trade turnover figures are estimated at 3%. Roundwood: sawlogs and veneer logs and pulpwood and wood fuel - with removals from trees and shrubs outside the forest, including forest chips, with stump. Residues - production excluding recovered wood.

In contrast to years prior to 2020, data are shown only for countries providing forecasts. Sub-regional totals thus reflect only the reporting countries of the subreg Confidential data have not been included. Please inform secretariat in case you notice any confidential data which might have been included inadvertently.

Wherever the forecast data is incomplete, then data is repeated to avoid skewing.

Countries with nil, missing or confidential data for all years on a table are not shown. Consumption figures are the sum of production and national imports minus national exports. Softwood = coniferous, hardwood = non-coniferous. United Kingdom production figures for OSB is secretariat estimate.

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 6,141 4,978 4,978 10,104 8,588 8,588 1,784 1,270 1,270 5,747 4,880 4,880 Autriche Cyprus 33 34 34 1 1 1 32 33 33 0 0 0 Chypre Czech Republic 2,965 2,343 2,470 4,720 3,776 4,040 583 414 350 2,338 1,847 1,920 République tchèque Estonia 2,068 1,550 1,550 1,725 1,500 1,500 1,209 700 700 866 650 650 Estonie Finland 2,938 2,420 2,420 11,200 10,300 10,400 305 20 20 8,567 7,900 8,000 Finlande France 8,633 8,750 8,800 7,168 7,200 7,300 2,350 2,450 2,400 885 900 900 France Germany 17,294 14,900 13,300 24,309 21,400 19,800 4,146 2,700 3,000 11,162 9,200 9,500 Allemagne Hungary 788 902 918 85 96 86 717 821 842 14 15 11 Hongrie Italy 4,790 4,302 4,302 400 400 400 4,608 4,157 4,157 217 255 255 Italie Latvia 1,025 950 950 3,102 3,000 3,000 829 750 750 2,906 2,800 2,800 Lettonie Luxembourg 71 122 122 39 39 39 43 91 91 11 8 8 Luxembourg Malta 7 9 9 0 0 0 7 9 9 0 0 0 Malte Montenegro 30 30 29 118 115 112 10 9 7 98 94 90 Monténégro Netherlands 2,259 2,088 2,029 115 115 115 2,659 2,473 2,399 515 500 485 Pays-Bas Poland 4,631 4,630 4,800 4,144 4,100 4,200 1,219 1,240 1,300 732 710 700 Pologne Portugal 696 686 685 807 815 820 130 130 125 242 259 260 Portugal Serbia 367 361 383 91 95 98 281 270 290 5 4 5 Serbie Slovakia 847 810 860 1,430 1,360 1,400 480 450 460 1,063 1,000 1,000 Slovaquie Slovenia 665 670 660 983 990 980 530 530 530 848 850 850 Slovénie Spain 4,029 4,001 4,001 3,006 3,189 3,189 1,166 956 956 143 144 144 Espagne Sweden 5,709 5,050 5,650 18,870 18,400 18,300 587 500 450 13,748 13,850 13,100 Suède Switzerland 1,271 1,300 1,325 1,186 1,200 1,210 300 310 320 215 210 205 Suisse United Kingdom 8,663 8,125 8,214 3,108 2,860 2,860 5,719 5,385 5,474 165 120 120 Royaume-Uni Total Europe 75,919 69,011 68,490 96,712 89,540 88,439 29,694 25,668 25,934 50,487 46,197 45,883 Total Europe Uzbekistan 2,256 1,498 1,498 0 0 0 2,256 1,498 1,498 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada a 3,707 2,691 2,242 36,398 33,228 31,331 891 988 948 33,581 31,525 30,037 Canada a

United States a 87,925 87,155 88,151 64,039 64,178 64,399 26,202 25,492 26,149 2,316 2,515 2,397 Etats-Unis a

Total North America 91,632 89,846 90,393 100,437 97,406 95,730 27,093 26,480 27,097 35,898 34,040 32,434 Total Amérique du Nord a converted from nominal to actual size using factor of 0.72 a convertis du dimension nominale au véritable avec une facteur du 0.72

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 1 SAWN SOFTWOOD SCIAGES CONIFERES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 310 222 222 238 202 202 217 140 140 145 120 120 Autriche Cyprus 11 7 7 0 0 0 11 7 7 0 0 0 Chypre Czech Republic 324 245 240 222 167 175 136 103 105 34 24 40 République tchèque Estonia 232 125 125 175 125 125 147 60 60 90 60 60 Estonie Finland 84 44 44 73 40 40 34 24 24 23 20 20 Finlande France 1,124 1,140 1,150 1,446 1,300 1,400 264 420 350 586 580 600 France Germany 693 650 650 997 800 800 395 300 300 699 450 450 Allemagne Hungary 258 150 131 414 343 342 45 38 30 200 231 241 Hongrie Italy 798 776 776 500 500 500 637 578 578 339 302 302 Italie Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie Luxembourg 96 98 98 39 39 39 64 65 65 7 6 6 Luxembourg Malta 7 8 9 0 0 0 7 8 9 0 0 0 Malte Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro Netherlands 238 213 203 34 34 34 314 289 279 110 110 110 Pays-Bas Poland 495 470 500 487 450 460 267 270 300 259 250 260 Pologne Portugal 369 295 290 182 185 190 287 200 190 100 90 90 Portugal Serbia 172 215 225 343 370 385 64 60 70 235 215 230 Serbie Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie Slovenia 106 145 145 143 145 145 83 80 80 121 80 80 Slovénie Spain 425 467 467 302 321 321 175 193 193 53 47 47 Espagne Sweden 142 140 140 100 100 100 83 80 80 41 40 40 Suède Switzerland 78 79 81 52 53 54 50 51 52 24 25 25 Suisse United Kingdom 807 810 810 37 40 40 787 790 790 17 20 20 Royaume-Uni Total Europe 7,019 6,652 6,703 6,928 6,449 6,606 4,177 3,862 3,813 4,086 3,658 3,716 Total Europe Uzbekistan 228 208 208 195 195 195 33 16 16 0 3 3 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,208 1,324 1,242 859 893 815 793 826 738 444 395 311 Canada United States 14,647 14,835 15,217 17,637 17,827 18,214 798 805 820 3,788 3,797 3,817 Etats-Unis Total North America 15,855 16,159 16,459 18,496 18,720 19,029 1,591 1,631 1,558 4,231 4,192 4,128 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 2 SAWN HARDWOOD (total) SCIAGES NON-CONIFERES (total)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 306 219 219 238 202 202 213 136 136 144 119 119 Autriche Cyprus 9 5 5 0 0 0 8 5 5 0 0 0 Chypre Czech Republic 307 229 223 222 167 175 119 86 88 34 24 40 République tchèque Estonia 230 122 122 175 125 125 142 56 56 87 59 59 Estonie Finland 80 40 40 73 40 40 26 16 16 19 16 16 Finlande France 960 988 988 1,420 1,285 1,375 123 280 210 583 577 597 France Germany 664 630 630 997 800 800 315 240 240 649 410 410 Allemagne Hungary 257 147 127 414 343 342 43 35 26 200 230 241 Hongrie Italy 819 791 791 495 495 495 476 423 423 152 127 127 Italie Latvia 5 105 105 720 800 800 54 55 55 769 750 750 Lettonie Luxembourg 92 96 96 39 39 39 60 63 63 7 6 6 Luxembourg Malta 6 7 8 0 0 0 6 7 8 0 0 0 Malte Montenegro 11 8 10 39 35 34 2 1 1 30 28 25 Monténégro Netherlands 89 80 77 27 27 27 117 108 105 55 55 55 Pays-Bas Poland 484 459 488 487 450 460 254 257 286 257 248 258 Pologne Portugal 319 272 268 170 172 178 180 150 140 31 50 50 Portugal Serbia 167 211 220 342 369 384 59 57 66 234 215 230 Serbie Slovakia 235 240 275 385 400 420 55 50 55 205 210 200 Slovaquie Slovenia 104 143 143 143 145 145 81 78 78 120 80 80 Slovénie Spain 383 417 417 300 318 318 128 142 142 45 43 43 Espagne Sweden 142 139 139 100 100 100 83 79 79 41 40 40 Suède Switzerland 69 70 72 49 50 51 44 45 46 24 25 25 Suisse United Kingdom 716 720 720 37 40 40 693 700 700 14 20 20 Royaume-Uni Total Europe 6,453 6,138 6,183 6,872 6,402 6,550 3,281 3,069 3,025 3,700 3,334 3,392 Total Europe Uzbekistan 227 207 207 195 195 195 33 15 15 0 3 3 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,191 1,316 1,236 859 893 815 762 805 715 430 382 294 Canada United States 14,379 14,578 14,957 17,637 17,827 18,214 523 529 544 3,782 3,778 3,801 Etats-Unis Total North America 15,569 15,893 16,193 18,496 18,720 19,029 1,285 1,334 1,259 4,212 4,160 4,095 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 2a SAWN HARDWOOD (temperate) SCIAGES NON-CONIFERES (zone tempérée)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 3 3 3 0 0 0 4 4 4 1 1 1 Autriche Bulgaria 0 0 0 0 0 0 0 0 0 0 0 0 Bulgarie Cyprus 3 2 2 0 0 0 3 2 2 0 0 0 Chypre Czech Republic 17 17 17 0 0 0 17 17 17 0 0 0 République tchèque Estonia 2 3 3 0 0 0 5 4 4 3 1 1 Estonie Finland 4 4 4 0 0 0 8 8 8 4 4 4 Finlande France 164 152 162 26 15 25 141 140 140 3 3 3 France Germany 29 20 20 0 0 0 79 60 60 50 40 40 Allemagne Hungary 2 3 4 0 0 0 2 4 4 0 0 0 Hongrie Italy -21 -15 -15 5 5 5 161 154 154 187 175 175 Italie Luxembourg 4 2 2 0 0 0 4 2 2 0 0 0 Luxembourg Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte Netherlands 149 133 126 7 7 7 197 181 174 55 55 55 Pays-Bas Poland 10 11 12 0 0 0 12 13 14 2 2 2 Pologne Portugal 50 23 22 12 13 12 107 50 50 69 40 40 Portugal Serbia 5 4 5 1 1 1 5 3 4 1 0 0 Serbie Slovenia 2 2 2 0 0 0 2 2 2 0 0 0 Slovénie Spain 42 49 49 2 2 2 47 50 50 7 4 4 Espagne Sweden 1 1 1 0 0 0 1 1 1 0 0 0 Suède Switzerland 9 9 9 3 3 3 6 6 6 0 0 0 Suisse United Kingdom 91 90 90 0 0 0 94 90 90 3 0 0 Royaume-Uni Total Europe 566 515 519 56 46 55 896 793 788 386 324 324 Total Europe Canada 17 8 7 0 0 0 31 21 23 14 13 16 Canada United States 269 257 260 0 0 0 275 276 276 6 19 16 Etats-Unis Total North America 286 266 266 0 0 0 305 297 299 20 31 32 Total Amérique du Nord

1000 m3

Apparent Consumption Country Consommation Apparente Production Imports - Importations

TABLE 2b SAWN HARDWOOD (tropical) SCIAGES NON-CONIFERES (tropicale)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Exports - Exportations Pays

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 74 39 39 8 8 8 83 45 45 17 14 14 Autriche Cyprus 1 1 1 0 0 0 1 1 1 0 0 0 Chypre Czech Republic 28 28 27 28 16 17 58 53 50 58 41 40 République tchèque Estonia 111 125 125 105 110 110 87 95 95 82 80 80 Estonie Finland 27 21 21 190 160 160 12 10 10 175 149 149 Finlande France 366 366 366 157 157 157 273 273 273 64 64 64 France Germany 157 143 125 110 105 105 99 78 70 52 40 50 Allemagne Hungary 23 25 20 13 18 13 39 39 39 28 31 32 Hongrie Italy 344 308 308 107 107 107 274 234 234 37 33 33 Italie Latvia 105 105 105 40 50 50 140 140 140 75 85 85 Lettonie Luxembourg 1 0 0 0 0 0 1 0 0 0 0 0 Luxembourg Malta 1 2 3 0 0 0 1 2 3 0 0 0 Malte Netherlands 15 13 13 0 0 0 17 15 15 3 3 3 Pays-Bas Poland 121 121 129 45 42 45 92 94 98 16 15 14 Pologne Portugal 12 20 35 20 30 25 38 40 50 46 50 40 Portugal Serbia 4 4 5 30 28 30 8 6 8 34 30 33 Serbie Slovakia 17 25 25 21 25 25 27 30 30 31 30 30 Slovaquie Slovenia 9 8 9 28 27 25 13 14 14 32 33 30 Slovénie Spain 122 92 92 40 36 36 127 90 90 45 34 34 Espagne Sweden 32 31 31 60 50 50 19 10 10 47 29 29 Suède Switzerland 3 3 3 0 0 0 4 4 4 1 1 1 Suisse United Kingdom 6 10 10 0 0 0 7 10 10 1 0 0 Royaume-Uni Total Europe 1,577 1,490 1,491 1,002 969 962 1,419 1,283 1,288 843 762 760 Total Europe Uzbekistan 4 4 4 3 3 3 2 1 1 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 204 262 267 581 581 581 212 218 230 590 537 544 Canada United States 2,643 2,670 2,699 2,284 2,306 2,329 652 658 664 293 294 294 Etats-Unis Total North America 2,847 2,932 2,966 2,866 2,887 2,910 864 876 894 883 831 838 Total Amérique du Nord Note: Definition of veneers excludes domestic use for plywood. La définition des placages exclus la conversion directe en contreplaqué.

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 3 VENEER SHEETS FEUILLES DE PLACAGE

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 19 15 15 131 155 155 183 150 150 296 290 290 Autriche Cyprus 14 15 15 0 0 0 14 15 15 0 0 0 Chypre Czech Republic 193 116 123 240 236 238 230 115 115 277 235 230 République tchèque Estonia 145 50 50 200 210 210 151 50 50 205 210 210 Estonie Finland 297 240 240 1,110 940 940 87 60 60 900 760 760 Finlande France 589 583 583 253 270 270 476 452 452 140 139 139 France Germany 1,073 1,154 840 85 80 80 1,319 1,281 1,000 330 207 240 Allemagne Hungary 136 110 107 60 61 63 138 138 138 62 90 94 Hongrie Italy 602 537 537 288 290 290 525 442 442 211 195 195 Italie Latvia 92 55 55 331 300 300 94 95 95 333 340 340 Lettonie Luxembourg 33 29 29 0 0 0 33 29 29 0 0 0 Luxembourg Malta 10 11 12 0 0 0 10 11 12 0 0 0 Malte Montenegro 2 2 2 1 1 1 2 2 2 1 1 1 Monténégro Netherlands 488 457 441 0 0 0 586 551 529 98 94 88 Pays-Bas Poland 650 640 670 539 515 530 468 475 480 357 350 340 Pologne Portugal 154 180 166 103 100 110 95 110 100 44 30 44 Portugal Serbia 40 36 38 19 18 19 34 30 33 13 12 14 Serbie Slovakia 67 63 63 153 150 150 59 59 59 146 146 146 Slovaquie Slovenia 49 50 58 94 90 98 26 30 30 71 70 70 Slovénie Spain 231 326 326 462 416 416 132 117 117 363 207 207 Espagne Sweden 278 160 160 90 90 90 236 120 120 48 50 50 Suède Switzerland 206 206 206 7 7 7 203 203 203 4 4 4 Suisse United Kingdom 1,254 1,180 1,180 0 0 0 1,320 1,250 1,250 66 70 70 Royaume-Uni Total Europe 6,623 6,215 5,916 4,166 3,930 3,967 6,422 5,786 5,482 3,965 3,501 3,532 Total Europe Uzbekistan 62 46 46 0 0 0 63 47 47 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 2,174 2,028 2,123 1,604 1,557 1,526 1,224 1,058 1,241 654 587 644 Canada United States 14,742 14,890 15,188 9,254 9,345 9,528 6,259 6,317 6,436 771 772 776 Etats-Unis Total North America 16,916 16,918 17,311 10,858 10,902 11,054 7,483 7,375 7,677 1,425 1,359 1,420 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 4 PLYWOOD CONTREPLAQUES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 951 630 630 2,280 2,170 2,170 313 355 355 1,642 1,895 1,895 Autriche Cyprus 49 46 46 0 0 0 49 46 46 0 0 0 Chypre Czech Republic 793 811 835 962 866 910 530 484 485 699 538 560 République tchèque Estonia 123 67 67 90 0 0 77 68 68 44 2 1 Estonie Finland 113 75 75 54 54 54 85 44 44 26 23 23 Finlande France 2,224 2,148 2,148 3,177 3,094 3,094 299 355 355 1,253 1,301 1,301 France Germany 5,572 5,220 4,970 5,526 5,195 5,020 1,970 1,934 1,900 1,924 1,909 1,950 Allemagne Hungary 408 384 379 447 428 438 264 282 272 303 326 331 Hongrie Italy 3,070 2,813 2,813 2,646 2,500 2,500 956 821 821 532 508 508 Italie Latvia 52 85 85 306 300 300 69 25 25 322 240 240 Lettonie Luxembourg 20 12 12 0 0 0 21 13 13 1 1 1 Luxembourg Malta 10 11 11 0 0 0 10 11 11 0 0 0 Malte Montenegro 32 33 34 0 0 0 32 33 34 0 0 0 Monténégro Netherlands 464 440 432 0 0 0 514 488 479 50 48 47 Pays-Bas Poland 6,501 6,450 6,740 5,227 5,150 5,450 2,173 2,180 2,200 899 880 910 Pologne Portugal 537 473 514 766 750 760 281 300 290 510 577 536 Portugal Serbia 373 351 371 219 210 220 196 184 198 42 43 47 Serbie Slovakia 352 343 340 676 675 675 148 140 137 473 473 472 Slovaquie Slovenia 137 110 110 0 0 0 143 114 114 6 4 4 Slovénie Spain 2,392 2,213 2,213 2,566 2,310 2,310 626 621 621 800 718 718 Espagne Sweden 1,055 868 868 636 600 600 475 335 335 57 67 67 Suède Switzerland 281 286 286 420 425 425 141 141 141 280 280 280 Suisse United Kingdom 2,606 2,542 2,542 2,012 1,982 1,982 648 610 610 55 50 50 Royaume-Uni Total Europe 28,115 26,410 26,521 28,012 26,710 26,908 10,021 9,584 9,555 9,917 9,883 9,942 Total Europe Uzbekistan 880 542 542 252 252 252 654 317 317 26 27 27 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 27 Total EOCAC Canada 1,466 1,886 1,894 1,625 2,032 2,012 552 504 491 710 650 609 Canada United States 5,196 5,565 5,562 4,488 4,552 4,534 1,193 1,465 1,487 485 452 459 Etats-Unis Total North America 6,663 7,451 7,456 6,113 6,584 6,546 1,745 1,969 1,978 1,195 1,102 1,068 Total Amérique du Nord Data are calculated by subtracting OSB from the particleboard/OSB total - les données sont calculées en soustrayant les OSB du total des panneaux de particules et OSB.

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 5 PARTICLE BOARD (excluding OSB) PANNEAUX DE PARTICULES (ne comprennent pas l'OSB)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 205 135 135 0 0 0 212 140 140 7 5 5 Autriche Cyprus 11 14 14 0 0 0 11 14 14 0 0 0 Chypre Czech Republic 380 342 350 689 620 655 126 113 115 435 392 420 République tchèque Estonia 55 32 32 0 0 0 55 32 32 1 0 0 Estonie Finland 56 56 56 0 0 0 56 56 56 0 0 0 Finlande France 427 522 522 302 406 406 222 165 165 96 49 49 France Germany 1,316 1,238 1,130 1,164 1,105 1,080 679 669 600 526 536 550 Allemagne Hungary 133 147 152 379 419 443 56 60 59 302 331 350 Hongrie Italy 346 287 287 100 100 100 346 274 274 100 87 87 Italie Latvia 196 165 165 674 650 650 76 75 75 554 560 560 Lettonie Luxembourg 110 135 135 338 338 338 6 14 14 234 217 217 Luxembourg Montenegro 2 2 2 0 0 0 2 2 2 0 0 0 Monténégro Netherlands 222 222 227 0 0 0 286 286 292 64 64 65 Pays-Bas Poland 655 650 760 647 650 750 302 320 350 294 320 340 Pologne Portugal 46 37 41 0 0 0 50 40 45 4 3 4 Portugal Serbia 40 35 41 0 0 0 41 36 42 1 1 1 Serbie Slovakia 48 58 60 0 0 0 48 60 63 1 3 3 Slovaquie Slovenia 31 24 24 0 0 0 33 26 26 2 2 2 Slovénie Spain 26 15 15 3 3 3 35 33 33 12 20 20 Espagne Sweden 94 92 92 0 0 0 97 95 95 3 3 3 Suède Switzerland 95 95 95 0 0 0 96 96 96 1 1 1 Suisse United Kingdom 773 758 758 598 598 598 365 350 350 190 190 190 Royaume-Uni Total Europe 5,268 5,060 5,092 4,894 4,888 5,023 3,200 2,956 2,938 2,826 2,784 2,868 Total Europe Uzbekistan 7 5 5 0 0 0 7 5 5 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A 0 Total EOCAC Canada 1,546 1,253 1,153 7,270 6,820 6,798 82 65 61 5,806 5,632 5,706 Canada United States 19,658 19,834 20,197 13,592 13,783 14,059 6,198 6,236 6,326 132 185 188 Etats-Unis Total North America 21,204 21,087 21,350 20,862 20,603 20,857 6,280 6,301 6,387 5,938 5,817 5,894 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 5a ORIENTED STRAND BOARD (OSB) PANNEAUX STRUCTURAUX ORIENTES (OSB)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 421 386 386 470 395 395 331 308 308 381 316 316 Autriche Cyprus 20 15 16 0 0 0 20 15 16 0 0 0 Chypre Czech Republic 328 276 280 41 41 42 438 347 360 151 112 122 République tchèque Estonia 70 46 47 75 40 40 65 46 47 70 40 40 Estonie Finland 139 105 105 44 44 44 141 102 102 46 41 41 Finlande France 828 915 915 1,238 1,035 1,035 721 772 772 1,130 892 892 France Germany 3,791 3,437 3,325 5,194 4,900 4,800 1,590 1,543 1,470 2,993 3,006 2,945 Allemagne Hungary 9 -17 -13 21 0 0 204 235 244 215 253 258 Hongrie Italy 1,862 1,661 1,661 827 818 818 1,281 974 974 245 131 131 Italie Latvia 60 50 40 48 50 50 62 65 65 50 65 75 Lettonie Luxembourg 100 90 90 147 147 147 34 19 19 80 76 76 Luxembourg Malta 6 7 7 0 0 0 6 7 7 0 0 0 Malte Montenegro 32 32 33 0 0 0 32 32 33 0 0 0 Monténégro Netherlands 332 310 296 29 29 29 465 431 412 162 150 145 Pays-Bas Poland 3,808 3,765 4,020 4,960 4,920 5,080 590 585 630 1,743 1,740 1,690 Pologne Portugal 534 485 529 526 520 560 338 315 335 330 350 366 Portugal Serbia 74 74 88 19 20 22 71 73 88 16 19 22 Serbie Slovakia 210 218 223 0 0 0 248 256 262 39 38 39 Slovaquie Slovenia 24 15 15 132 120 125 28 25 30 136 130 140 Slovénie Spain 920 894 894 1,430 1,287 1,287 462 355 355 972 748 748 Espagne Sweden 301 260 260 0 0 0 425 360 360 124 100 100 Suède Switzerland 238 238 238 97 97 97 308 308 308 167 167 167 Suisse United Kingdom 1,692 1,630 1,630 856 850 850 895 840 840 60 60 60 Royaume-Uni Total Europe 15,799 14,892 15,085 16,153 15,313 15,421 8,755 8,013 8,037 9,110 8,434 8,373 Total Europe Uzbekistan 1,092 809 809 47 47 47 1,057 771 771 13 9 9 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,236 1,183 1,181 1,277 1,288 1,299 818 628 605 859 733 723 Canada United States 8,684 8,749 8,888 6,362 6,420 6,571 3,359 3,289 3,310 1,038 960 993 Etats-Unis Total North America 9,920 9,932 10,069 7,639 7,708 7,870 4,177 3,917 3,915 1,896 1,693 1,716 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6 FIBREBOARD PANNEAUX DE FIBRES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 29 28 28 54 43 43 18 16 16 43 32 32 Autriche Cyprus 2 1 2 0 0 0 2 1 2 0 0 0 Chypre Czech Republic 43 45 45 0 0 0 61 59 60 18 14 15 République tchèque Estonia 23 15 19 0 0 0 30 16 20 7 1 1 Estonie Finland 23 21 21 44 44 44 21 15 15 41 38 38 Finlande France 55 55 55 221 221 221 207 207 207 373 373 373 France Germany 176 183 165 0 0 0 200 203 180 23 20 15 Allemagne Hungary 27 41 45 2 0 0 65 81 85 40 40 40 Hongrie Italy 280 280 280 16 16 16 283 283 283 19 19 19 Italie Latvia 1 5 5 15 15 15 18 20 20 32 30 30 Lettonie Luxembourg -31 -12 -12 0 0 0 3 8 8 34 20 20 Luxembourg Montenegro 1 1 1 0 0 0 1 1 1 0 0 0 Monténégro Netherlands 44 41 39 0 0 0 63 58 56 19 17 17 Pays-Bas Poland -179 -120 -50 80 80 80 88 100 120 347 300 250 Pologne Portugal 50 30 39 0 0 0 61 40 50 11 10 11 Portugal Serbia 39 35 38 19 20 22 33 31 34 13 16 18 Serbie Slovakia 21 20 21 0 0 0 21 21 22 1 1 1 Slovaquie Slovenia -1 0 1 0 0 0 4 2 4 4 2 3 Slovénie Spain 17 15 15 32 29 29 46 46 46 61 60 60 Espagne Sweden 47 30 30 0 0 0 116 110 110 70 80 80 Suède Switzerland 19 19 19 0 0 0 24 24 24 5 5 5 Suisse United Kingdom 101 90 90 0 0 0 110 100 100 9 10 10 Royaume-Uni Total Europe 787 822 895 482 468 470 1,474 1,441 1,463 1,169 1,087 1,037 Total Europe Uzbekistan 89 50 50 0 0 0 90 50 50 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 33 47 42 90 90 90 52 27 28 109 70 76 Canada United States 481 509 514 437 504 509 259 255 258 215 250 253 Etats-Unis Total North America 514 556 556 527 594 599 311 282 286 324 320 329 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6a HARDBOARD PANNEAUX DURS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 260 230 230 416 351 351 177 160 160 333 281 281 Autriche Cyprus 16 12 12 0 0 0 16 12 12 0 0 0 Chypre Czech Republic 199 157 160 41 41 42 180 135 140 22 19 22 République tchèque Estonia 18 21 18 0 0 0 33 28 25 15 7 7 Estonie Finland 82 67 67 0 0 0 86 70 70 4 3 3 Finlande France 708 794 794 954 751 751 337 388 388 583 345 345 France Germany 1,870 1,728 1,720 3,792 3,700 3,650 424 395 370 2,345 2,367 2,300 Allemagne Hungary -39 -65 -62 0 0 0 136 148 156 175 213 218 Hongrie Italy 1,501 1,299 1,299 809 800 800 913 606 606 221 107 107 Italie Latvia 52 40 30 33 35 35 22 25 25 2 20 30 Lettonie Luxembourg 128 98 98 147 147 147 27 7 7 46 56 56 Luxembourg Malta 5 5 5 0 0 0 5 5 5 0 0 0 Malte Montenegro 31 31 32 0 0 0 31 31 32 0 0 0 Monténégro Netherlands 220 205 196 0 0 0 361 336 322 141 131 126 Pays-Bas Poland 3,066 3,020 3,130 3,052 3,030 3,100 470 450 470 456 460 440 Pologne Portugal 447 440 465 494 500 530 257 260 265 305 320 330 Portugal Serbia 31 35 46 0 0 0 34 38 50 3 3 4 Serbie Slovakia 135 135 135 0 0 0 170 170 170 35 35 35 Slovaquie Slovenia 24 15 14 132 120 125 24 23 26 131 128 137 Slovénie Spain 835 821 821 1,334 1,201 1,201 397 302 302 897 682 682 Espagne Sweden 254 225 225 0 0 0 284 230 230 30 5 5 Suède Switzerland 24 24 24 97 97 97 88 88 88 161 161 161 Suisse United Kingdom 1,553 1,510 1,510 856 850 850 739 700 700 42 40 40 Royaume-Uni Total Europe 11,419 10,847 10,969 12,157 11,623 11,679 5,210 4,606 4,618 5,948 5,382 5,328 Total Europe Uzbekistan 671 513 513 46 46 46 629 469 469 3 2 2 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 1,053 999 1,005 1,087 1,098 1,109 608 472 449 641 570 553 Canada United States 5,156 5,228 5,226 2,746 2,778 2,786 2,939 2,874 2,866 529 424 426 Etats-Unis Total North America 6,209 6,227 6,231 3,833 3,876 3,895 3,547 3,346 3,315 1,170 994 979 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6b MDF/HDF

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 131 128 128 0 0 0 136 132 132 4 3 3 Autriche Cyprus 2 2 2 0 0 0 3 2 2 0 0 0 Chypre Czech Republic 86 74 75 0 0 0 197 154 160 111 80 85 République tchèque Estonia 29 10 10 75 40 40 3 2 2 49 32 32 Estonie Finland 33 17 17 0 0 0 34 17 17 0 0 0 Finlande France 65 66 66 63 63 63 177 177 177 174 174 174 France Germany 1,745 1,526 1,440 1,402 1,200 1,150 966 945 920 624 619 630 Allemagne Hungary 21 7 4 19 0 0 3 7 4 0 0 0 Hongrie Italy 82 82 82 3 3 3 85 85 85 6 6 6 Italie Latvia 7 5 5 0 0 0 23 20 20 16 15 15 Lettonie Luxembourg 4 4 4 0 0 0 4 4 4 0 0 0 Luxembourg Malta 1 2 2 0 0 0 1 2 2 0 0 0 Malte Netherlands 68 64 61 29 29 29 41 37 34 2 2 2 Pays-Bas Poland 920 865 940 1,828 1,810 1,900 33 35 40 940 980 1,000 Pologne Portugal 37 15 25 32 20 30 20 15 20 15 20 25 Portugal Serbia 4 4 4 0 0 0 4 4 4 0 0 0 Serbie Slovakia 54 63 67 0 0 0 57 65 70 3 2 3 Slovaquie Slovenia 0 0 0 0 0 0 0 0 0 0 0 0 Slovénie Spain 69 59 59 64 58 58 20 7 7 15 6 6 Espagne Sweden 0 5 5 0 0 0 25 20 20 24 15 15 Suède Switzerland 195 195 195 0 0 0 196 196 196 1 1 1 Suisse United Kingdom 38 30 30 0 0 0 47 40 40 9 10 10 Royaume-Uni Total Europe 3,592 3,223 3,221 3,514 3,222 3,272 2,071 1,965 1,956 1,993 1,965 2,007 Total Europe Uzbekistan 331 246 246 2 2 2 339 252 252 10 7 7 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 150 137 134 100 100 100 158 129 128 108 92 94 Canada United States 3,047 3,012 3,148 3,179 3,138 3,276 161 160 186 294 286 314 Etats-Unis Total North America 3,196 3,149 3,282 3,279 3,238 3,376 319 289 314 402 378 408 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 6c OTHER FIBREBOARD AUTRES PANNEAUX DE FIBRES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 2,209 1,950 2,030 1,977 1,700 1,800 630 610 630 399 360 400 Autriche Czech Republic 847 688 700 640 525 540 324 259 260 117 96 100 République tchèque Estonia 70 75 80 227 180 180 42 50 50 199 155 150 Estonie Finland a 5,468 4,483 4,614 9,200 8,690 9,360 355 150 150 4,087 4,357 4,896 Finlande a

France 2,898 2,420 2,500 1,666 1,300 1,350 1,715 1,450 1,500 483 330 350 France Germany 5,092 4,600 5,000 2,172 1,850 2,000 4,173 3,900 4,200 1,253 1,150 1,200 Allemagne Hungary 205 206 214 66 77 87 141 133 131 3 3 4 Hongrie Italy 3,466 3,466 3,466 223 223 223 3,536 3,536 3,536 293 293 293 Italie Latvia 7 7 7 12 13 13 7 7 7 12 13 13 Lettonie Netherlands 443 442 442 37 37 37 1,717 1,717 1,717 1,312 1,312 1,312 Pays-Bas Poland 2,836 2,830 2,930 1,729 1,710 1,750 1,291 1,300 1,320 183 180 140 Pologne Portugal 1,757 1,735 1,760 2,869 2,870 2,870 140 145 150 1,252 1,280 1,260 Portugal Serbia 82 88 92 0 0 0 82 88 92 0 0 0 Serbie Slovakia 700 700 715 692 700 725 173 170 170 166 170 180 Slovaquie Slovenia 322 321 316 73 63 68 249 260 250 1 2 2 Slovénie Spain 1,520 1,328 1,328 1,120 1,120 1,120 1,176 976 976 775 768 768 Espagne Sweden 8,438 7,600 7,950 11,631 10,900 11,400 641 600 600 3,834 3,900 4,050 Suède Switzerland 188 188 188 87 87 87 101 101 101 0 0 0 Suisse United Kingdom 1,057 940 950 220 200 200 838 740 750 1 0 0 Royaume-Uni Total Europe 37,604 34,067 35,282 34,641 32,244 33,809 17,333 16,193 16,590 14,369 14,369 15,118 Total Europe Uzbekistan 38 28 28 1 1 1 37 28 28 0 0 0 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 6,007 5,851 5,616 14,200 13,102 12,638 472 582 640 8,665 7,833 7,662 Canada United States 39,787 42,269 42,815 40,822 41,230 41,478 6,948 7,643 8,254 7,983 6,603 6,917 Etats-Unis Total North America 45,794 48,121 48,431 55,022 54,332 54,116 7,420 8,224 8,894 16,648 14,436 14,579 Total Amérique du Nord

a imports exclude dissolving pulp a les importations excluent pâte à dissoudre

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 7 WOOD PULP PATE DE BOIS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 mt

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 2,133 1,750 2,050 4,633 3,500 4,000 1,231 1,050 1,150 3,730 2,800 3,100 Autriche Cyprus 56 48 48 0 0 0 56 48 48 0 0 0 Chypre Czech Republic 1,467 1,234 1,258 938 769 785 1,531 1,286 1,312 1,002 822 838 République tchèque Estonia 120 111 111 57 35 35 123 102 102 59 26 26 Estonie Finland 514 475 460 7,200 5,990 6,150 333 275 280 7,019 5,790 5,970 Finlande France 8,272 7,290 7,400 7,092 6,240 6,600 4,845 4,650 4,600 3,665 3,600 3,800 France Germany 17,836 14,600 17,000 21,612 17,500 21,000 9,302 8,000 9,500 13,078 10,900 13,500 Allemagne Hungary 1,213 1,167 1,212 1,057 1,003 1,034 877 892 898 720 727 721 Hongrie Italy 11,390 11,390 11,390 8,696 8,696 8,696 5,800 5,800 5,800 3,106 3,106 3,106 Italie Latvia 168 175 175 29 30 30 173 180 180 33 35 35 Lettonie Luxembourg 26 14 14 0 0 0 27 15 15 1 1 1 Luxembourg Malta 26 27 28 0 0 0 26 27 28 0 0 0 Malte Netherlands 2,814 2,760 2,760 2,884 2,827 2,827 2,180 2,096 2,096 2,250 2,163 2,163 Pays-Bas Poland 7,532 7,400 7,550 5,237 5,130 5,250 4,869 4,870 4,950 2,574 2,600 2,650 Pologne Portugal 1,090 1,200 1,240 2,123 2,200 2,240 948 940 945 1,981 1,940 1,945 Portugal Serbia 790 762 778 483 481 490 514 490 500 207 209 212 Serbie Slovakia 565 575 600 967 975 1,000 457 450 475 859 850 875 Slovaquie Slovenia 412 390 390 591 500 500 367 390 390 545 500 500 Slovénie Spain 7,060 6,778 6,778 6,355 6,355 6,355 2,997 2,577 2,577 2,291 2,154 2,154 Espagne Sweden 834 700 750 8,531 7,300 8,100 894 700 750 8,591 7,300 8,100 Suède Switzerland 1,020 1,015 1,010 1,160 1,155 1,150 640 635 630 780 775 770 Suisse United Kingdom 7,420 6,280 6,440 3,460 3,190 3,250 5,015 4,150 4,250 1,055 1,060 1,060 Royaume-Uni Total Europe 72,758 66,140 69,442 83,103 73,876 79,492 43,204 39,623 41,476 53,549 47,358 51,526 Total Europe Uzbekistan 335 297 297 142 142 142 214 171 171 21 17 17 Ouzbékistan Total EECCA #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A #N/A Total EOCAC Canada 5,505 6,069 6,231 9,094 9,124 9,155 2,516 2,242 2,235 6,105 5,298 5,159 Canada United States 64,243 62,896 63,029 65,959 64,476 64,476 8,202 8,180 8,159 9,917 9,761 9,606 Etats-Unis Total North America 69,748 68,964 69,260 75,053 73,600 73,631 10,718 10,423 10,395 16,023 15,059 14,765 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 8 PAPER AND PAPERBOARD PAPIERS ET CARTONS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 mt

Apparent Consumption

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 13,935 11,716 12,225 10,711 8,904 9,338 3,223 2,812 2,887 0 0 0 5,424 5,115 5,234 19,359 16,831 17,459 Autriche Cyprus 3 2 2 2 2 2 0 0 0 0 0 0 11 9 8 14 11 10 Chypre Czech Republic 20,708 15,535 14,897 14,635 10,617 10,106 5,965 4,804 4,675 108 113 115 4,405 3,965 3,900 25,113 19,499 18,797 République tchèque Estonia 6,474 6,401 6,401 4,276 4,200 4,200 2,148 2,150 2,150 51 51 51 4,066 3,800 3,800 10,541 10,201 10,201 Estonie Finland 56,246 53,397 55,435 25,699 22,749 23,412 30,547 30,648 32,023 0 0 0 9,340 9,340 9,340 65,586 62,737 64,775 Finlande France 25,648 25,270 25,070 17,198 17,200 17,300 7,891 7,500 7,200 559 570 570 24,173 24,500 25,600 49,821 49,770 50,670 France Germany 56,534 53,930 49,630 44,756 41,200 39,500 11,644 12,600 10,000 135 130 130 22,338 22,700 22,700 78,872 76,630 72,330 Allemagne Hungary 2,901 2,881 2,881 1,410 1,374 1,399 912 995 1,008 579 512 475 3,626 3,284 3,397 6,527 6,165 6,278 Hongrie Italy 2,838 3,540 3,540 1,890 1,890 1,890 316 1,018 1,018 632 632 632 10,839 10,839 10,839 13,677 14,379 14,379 Italie Latvia 12,491 12,150 12,350 7,603 7,250 7,450 3,868 3,800 3,800 1,020 1,100 1,100 2,936 3,000 3,000 15,427 15,150 15,350 Lettonie Luxembourg 231 197 193 147 144 133 56 38 38 27 15 22 40 45 43 271 242 235 Luxembourg Montenegro 751 697 678 515 492 487 201 198 186 35 7 5 194 193 190 945 890 868 Monténégro Netherlands 614 599 589 221 220 215 352 340 335 41 39 39 2,382 2,380 2,385 2,996 2,979 2,974 Pays-Bas Poland 38,735 39,880 40,850 18,533 18,800 19,150 19,350 20,100 20,550 852 980 1,150 6,958 7,420 7,750 45,693 47,300 48,600 Pologne Portugal 12,235 12,330 12,190 2,038 2,040 2,060 9,799 9,850 9,700 399 440 430 2,383 2,380 2,300 14,619 14,710 14,490 Portugal Serbia 1,478 1,520 1,561 1,077 1,104 1,130 265 275 283 136 141 148 6,574 6,646 6,760 8,052 8,166 8,321 Serbie Slovakia 6,827 6,820 6,880 4,130 4,080 4,100 2,672 2,710 2,750 25 30 30 609 610 650 7,435 7,430 7,530 Slovaquie Slovenia 2,928 3,752 3,482 2,184 2,780 2,600 698 920 830 45 52 52 1,149 1,290 1,270 4,076 5,042 4,752 Slovénie Spain 14,366 15,244 15,244 4,150 4,404 4,404 9,813 10,413 10,413 403 427 427 3,555 3,772 3,772 17,921 19,016 19,016 Espagne Sweden 71,165 69,076 69,310 38,280 37,480 37,080 32,585 31,296 31,930 300 300 300 6,000 6,016 6,016 77,165 75,092 75,326 Suède Switzerland 3,011 3,082 3,142 2,555 2,625 2,680 444 445 450 12 12 12 1,938 2,000 2,025 4,949 5,082 5,167 Suisse United Kingdom 7,604 7,193 7,193 5,509 5,236 5,236 1,646 1,529 1,529 448 428 428 2,184 2,184 2,184 9,788 9,377 9,377 Royaume-Uni Total Europe 357,723 345,212 343,742 207,519 194,791 193,872 144,397 144,441 143,754 5,807 5,980 6,116 121,124 121,488 123,163 478,847 466,699 466,905 Total Europe Canada 142,131 140,499 140,499 124,900 123,350 123,350 15,040 14,864 14,864 2,190 2,285 2,285 1,683 1,908 1,908 143,814 142,407 142,407 Canada United States 382,544 384,963 388,611 186,157 188,221 191,211 182,650 182,996 183,637 13,737 13,746 13,763 76,230 76,240 76,278 458,774 461,203 464,889 Etats-Unis Total North America 524,675 525,462 529,110 311,057 311,571 314,561 197,690 197,861 198,501 15,927 16,031 16,048 77,913 78,148 78,186 602,587 603,610 607,296 Total Amérique du Nord

a Pulpwood, round and split, as well as chips and particles produced directly a Bois de trituration, rondins et quartiers, ainse que plaquettes et particules fabriquées therefrom and used as pulpwood directement à partir des rondins et quartiers et utilisées comme bois de trituration

b Pitprops, poles, piling, posts etc. b Bois de mine, poteaux, pilotis, piquets etc. c Including chips and particles produced from wood in the rough and c Y compris plaquettes et particules fabriquées à partir du bois brut et utilisées

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

TABLE 9 REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT

TOTAL TOTAL 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Wood fuel c

Bois de chauffage c Pays

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 12,958 10,873 11,338 10,382 8,638 9,038 2,576 2,235 2,300 0 0 0 3,248 3,069 3,140 16,206 13,942 14,478 Autriche Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 10 8 7 12 10 9 Chypre Czech Republic 19,440 14,455 13,825 14,019 10,094 9,589 5,316 4,253 4,125 105 109 111 3,610 3,249 3,200 23,050 17,704 17,025 République tchèque Estonia 4,023 3,927 3,927 3,118 3,000 3,000 878 900 900 26 27 27 1,486 1,400 1,400 5,509 5,327 5,327 Estonie Finland 47,408 45,464 47,590 24,662 21,700 22,351 22,746 23,764 25,239 0 0 0 4,593 4,593 4,593 52,001 50,057 52,183 Finlande France 17,300 17,070 16,770 12,491 12,500 12,500 4,559 4,300 4,000 250 270 270 2,417 2,500 2,600 19,717 19,570 19,370 France Germany 52,425 50,120 46,120 41,761 38,500 37,000 10,541 11,500 9,000 123 120 120 8,834 9,200 9,200 61,259 59,320 55,320 Allemagne Hungary 688 759 743 175 201 208 411 488 481 102 70 53 383 294 333 1,071 1,053 1,076 Hongrie Italy 1,797 2,502 2,502 1,169 1,169 1,169 148 853 853 480 480 480 1,180 1,180 1,180 2,977 3,682 3,682 Italie Latvia 8,253 7,900 8,100 5,873 5,500 5,700 1,850 1,800 1,800 530 600 600 298 300 300 8,551 8,200 8,400 Lettonie Luxembourg 162 143 145 124 122 115 10 6 8 27 15 22 17 11 12 178 154 158 Luxembourg Montenegro 573 553 537 372 352 349 201 198 186 0 3 2 66 65 63 639 618 600 Monténégro Netherlands 449 440 430 173 170 165 244 240 235 32 30 30 457 450 450 906 890 880 Pays-Bas Poland 31,941 32,800 33,470 15,775 16,000 16,250 15,411 15,950 16,250 754 850 970 3,627 3,820 3,950 35,568 36,620 37,420 Pologne Portugal 3,045 3,210 3,150 1,682 1,710 1,700 1,213 1,350 1,300 150 150 150 996 990 980 4,041 4,200 4,130 Portugal Serbia 279 290 301 178 184 190 66 70 73 35 36 38 141 146 160 420 436 461 Serbie Slovakia 3,325 3,160 3,120 2,559 2,430 2,400 748 710 700 18 20 20 259 260 275 3,584 3,420 3,395 Slovaquie Slovenia 1,966 2,586 2,386 1,687 2,150 2,000 275 430 380 4 6 6 191 240 220 2,157 2,826 2,606 Slovénie Spain 7,435 7,889 7,889 3,420 3,629 3,629 3,754 3,984 3,984 261 277 277 2,243 2,380 2,380 9,678 10,269 10,269 Espagne Sweden 64,603 62,760 62,873 38,100 37,300 36,900 26,353 25,310 25,823 150 150 150 3,000 3,008 3,008 67,603 65,768 65,881 Suède Switzerland 2,578 2,639 2,689 2,290 2,350 2,400 279 280 280 9 9 9 769 770 775 3,347 3,409 3,464 Suisse United Kingdom 7,486 7,076 7,076 5,453 5,180 5,180 1,633 1,516 1,516 400 380 380 1,571 1,571 1,571 9,058 8,647 8,647 Royaume-Uni Total Europe 288,136 276,619 274,984 185,467 172,881 171,836 99,212 100,136 99,433 3,458 3,602 3,715 39,396 39,504 39,798 327,533 316,123 314,781 Total Europe Canada 114,659 112,907 112,907 110,046 108,424 108,424 4,229 4,021 4,021 384 462 462 806 946 946 115,465 113,853 113,853 Canada United States 306,119 309,360 313,639 152,799 154,479 156,695 141,226 142,779 144,827 12,094 12,102 12,117 37,619 37,609 37,606 343,738 346,969 351,245 Etats-Unis Total North America 420,778 422,267 426,546 262,845 262,903 265,119 145,455 146,800 148,848 12,478 12,564 12,579 38,425 38,555 38,552 459,203 460,822 465,098 Total Amérique du Nord

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

TABLE 9a REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT

SOFTWOOD CONIFERES 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Wood fuel c

Bois de chauffage c Pays

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 977 843 887 329 266 300 647 577 587 0 0 0 2,176 2,046 2,094 3,153 2,889 2,981 Autriche Cyprus 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 Chypre Czech Republic 1,268 1,079 1,071 616 524 517 649 552 550 3 4 4 795 716 700 2,063 1,795 1,771 République tchèque Estonia 2,452 2,474 2,474 1,158 1,200 1,200 1,270 1,250 1,250 24 24 24 2,580 2,400 2,400 5,032 4,874 4,874 Estonie Finland 8,838 7,933 7,845 1,037 1,049 1,061 7,801 6,884 6,784 0 0 0 4,747 4,747 4,747 13,585 12,680 12,592 Finlande France 8,348 8,200 8,300 4,707 4,700 4,800 3,332 3,200 3,200 309 300 300 21,756 22,000 23,000 30,104 30,200 31,300 France Germany 4,110 3,810 3,510 2,995 2,700 2,500 1,103 1,100 1,000 12 10 10 13,504 13,500 13,500 17,613 17,310 17,010 Allemagne Hungary 2,213 2,122 2,138 1,234 1,173 1,191 502 507 526 477 442 421 3,244 2,990 3,064 5,456 5,112 5,202 Hongrie Italy 1,041 1,038 1,038 721 721 721 168 166 166 152 152 152 9,659 9,659 9,659 10,700 10,697 10,697 Italie Latvia 4,238 4,250 4,250 1,730 1,750 1,750 2,018 2,000 2,000 490 500 500 2,638 2,700 2,700 6,876 6,950 6,950 Lettonie Luxembourg 69 54 47 23 22 18 46 32 30 0 0 0 23 34 30 92 89 78 Luxembourg Montenegro 178 144 141 143 140 138 0 0 0 35 4 3 128 128 127 306 272 268 Monténégro Netherlands 165 159 159 48 50 50 108 100 100 9 9 9 1,925 1,930 1,935 2,090 2,089 2,094 Pays-Bas Poland 6,794 7,080 7,380 2,757 2,800 2,900 3,939 4,150 4,300 98 130 180 3,331 3,600 3,800 10,125 10,680 11,180 Pologne Portugal 9,190 9,120 9,040 356 330 360 8,586 8,500 8,400 249 290 280 1,387 1,390 1,320 10,578 10,510 10,360 Portugal Serbia 1,199 1,230 1,260 899 920 940 199 205 210 101 105 110 6,433 6,500 6,600 7,632 7,730 7,860 Serbie Slovakia 3,502 3,660 3,760 1,570 1,650 1,700 1,924 2,000 2,050 8 10 10 350 350 375 3,851 4,010 4,135 Slovaquie Slovenia 962 1,166 1,096 497 630 600 424 490 450 41 46 46 957 1,050 1,050 1,919 2,216 2,146 Slovénie Spain 6,931 7,354 7,354 730 775 775 6,059 6,429 6,429 142 151 151 1,312 1,392 1,392 8,243 8,746 8,746 Espagne Sweden 6,562 6,316 6,437 180 180 180 6,232 5,986 6,107 150 150 150 3,000 3,008 3,008 9,562 9,324 9,445 Suède Switzerland 433 443 453 265 275 280 165 165 170 3 3 3 1,169 1,230 1,250 1,602 1,673 1,703 Suisse United Kingdom 118 117 117 56 56 56 13 13 13 48 48 48 613 613 613 730 730 730 Royaume-Uni Total Europe 69,587 68,593 68,759 22,052 21,910 22,036 45,185 44,305 44,322 2,350 2,377 2,401 81,728 81,984 83,365 151,314 150,576 152,124 Total Europe Canada 27,472 27,592 27,592 14,854 14,926 14,926 10,812 10,843 10,843 1,806 1,823 1,823 877 961 961 28,349 28,554 28,554 Canada United States 76,425 75,603 74,972 33,358 33,742 34,516 41,424 40,217 38,810 1,643 1,644 1,646 38,611 38,631 38,672 115,036 114,234 113,644 Etats-Unis Total North America 103,897 103,196 102,564 48,212 48,668 49,442 52,236 51,060 49,653 3,449 3,467 3,469 39,488 39,592 39,633 143,385 142,788 142,197 Total Amérique du Nord

used for energy purposes à des fins energétiques

Total Logs Pulpwood a Other b Total Grumes Bois de trituration a Autre bCountry

Industrial wood - Bois industriels

TABLE 9b REMOVALS OF WOOD IN THE ROUGH QUANTITES ENLEVEES DE BOIS BRUT

HARDWOOD NON-CONIFERES 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Wood fuel c

Bois de chauffage c Pays

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 16,101 13,943 13,638 10,382 8,638 9,038 6,664 5,710 5,000 945 405 400 Autriche Cyprus 2 2 2 2 2 2 0 0 0 0 0 0 Chypre Czech Republic 8,002 6,511 6,962 14,019 10,094 9,589 411 596 715 6,428 4,178 3,343 République tchèque Estonia 3,533 3,270 3,270 3,118 3,000 3,000 522 450 450 107 180 180 Estonie Finland 24,310 21,336 21,991 24,662 21,700 22,351 127 79 83 479 443 443 Finlande France 12,053 12,120 12,120 12,491 12,500 12,500 335 360 360 773 740 740 France Germany 39,391 35,800 34,900 41,761 38,500 37,000 3,300 3,000 3,100 5,670 5,700 5,200 Allemagne Hungary 175 201 208 175 201 208 0 0 0 0 0 0 Hongrie Italy 1,645 1,396 1,396 1,169 1,169 1,169 580 457 457 104 230 230 Italie Latvia 6,471 5,830 6,200 5,873 5,500 5,700 1,147 900 900 549 570 400 Lettonie Luxembourg 465 403 396 124 122 115 693 424 424 352 143 143 Luxembourg Montenegro 382 361 357 372 352 349 10 9 8 0 0 0 Monténégro Netherlands 133 145 145 173 170 165 77 80 80 117 105 100 Pays-Bas Poland 14,243 14,500 14,800 15,775 16,000 16,250 1,245 1,400 1,550 2,777 2,900 3,000 Pologne Portugal 1,880 1,905 1,900 1,682 1,710 1,700 241 230 240 43 35 40 Portugal Serbia 188 187 194 178 184 190 12 9 12 2 6 8 Serbie Slovakia 3,059 3,030 3,100 2,559 2,430 2,400 900 950 1,000 400 350 300 Slovaquie Slovenia 1,643 1,650 1,630 1,687 2,150 2,000 239 150 180 283 650 550 Slovénie Spain 3,223 3,307 3,307 3,420 3,629 3,629 240 185 185 437 507 507 Espagne Sweden 38,103 37,725 37,325 38,100 37,300 36,900 964 1,128 1,128 961 703 703 Suède Switzerland 2,035 2,100 2,155 2,290 2,350 2,400 55 60 65 310 310 310 Suisse United Kingdom 5,810 5,538 5,538 5,453 5,180 5,180 457 457 457 99 99 99 Royaume-Uni Total Europe 182,849 171,260 171,534 185,467 172,881 171,836 18,218 16,634 16,394 20,836 18,255 16,696 Total Europe Canada 105,870 103,492 103,916 110,046 108,424 108,424 1,346 1,402 1,309 5,522 6,333 5,816 Canada United States 148,043 150,509 153,391 152,799 154,479 156,695 586 570 555 5,342 4,540 3,859 Etats-Unis Total North America 253,913 254,001 257,307 262,845 262,903 265,119 1,931 1,972 1,864 10,863 10,873 9,675 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 10 SOFTWOOD SAWLOGS GRUMES DE SCIAGES DES CONIFERES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande France 3,453 4,020 4,120 4,707 4,700 4,800 116 120 120 1,370 800 800 France Germany 2,532 2,290 2,130 2,995 2,700 2,500 111 110 110 574 520 480 Allemagne Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie Italy 2,088 1,718 1,718 721 721 721 1,413 1,055 1,055 47 59 59 Italie Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro Netherlands 54 60 60 48 50 50 54 60 60 48 50 50 Pays-Bas Poland 2,687 2,730 2,830 2,757 2,800 2,900 80 80 80 150 150 150 Pologne Portugal 997 885 925 356 330 360 663 580 590 22 25 25 Portugal Serbia 894 922 946 899 920 940 15 20 28 20 18 22 Serbie Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie Slovenia 281 290 280 497 630 600 31 30 30 247 370 350 Slovénie Spain 833 854 854 730 775 775 164 174 174 61 94 94 Espagne Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse United Kingdom 78 77 77 56 56 56 26 26 26 5 5 5 Royaume-Uni Total Europe 21,959 21,622 22,002 22,052 21,910 22,036 3,910 3,253 3,265 4,003 3,541 3,299 Total Europe Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada United States 31,550 32,311 33,431 33,358 33,742 34,516 221 156 156 2,028 1,587 1,241 Etats-Unis Total North America 47,441 48,234 49,326 48,212 48,668 49,442 1,327 1,216 1,183 2,098 1,650 1,300 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 11 HARDWOOD SAWLOGS (total) GRUMES DE SCIAGES DES NON-CONIFERES

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 406 311 300 329 266 300 134 90 50 57 45 50 Autriche Czech Republic 544 457 447 616 524 517 144 120 125 216 186 195 République tchèque Estonia 1,187 1,244 1,244 1,158 1,200 1,200 46 60 60 16 16 16 Estonie Finland 1,068 1,041 1,061 1,037 1,049 1,061 32 1 9 1 9 9 Finlande France 3,412 3,978 4,078 4,707 4,700 4,800 72 75 75 1,367 797 797 France Germany 2,527 2,285 2,125 2,995 2,700 2,500 101 100 100 569 515 475 Allemagne Hungary 1,234 1,173 1,191 1,234 1,173 1,191 0 0 0 0 0 0 Hongrie Italy 2,068 1,729 1,729 721 721 721 1,389 1,047 1,047 42 39 39 Italie Latvia 1,221 1,190 1,410 1,730 1,750 1,750 87 40 60 596 600 400 Lettonie Luxembourg 226 148 144 23 22 18 221 160 160 18 34 34 Luxembourg Montenegro 143 140 138 143 140 138 0 0 0 0 0 0 Monténégro Netherlands 46 55 55 48 50 50 42 50 50 44 45 45 Pays-Bas Poland 2,685 2,727 2,827 2,757 2,800 2,900 78 77 77 150 150 150 Pologne Portugal 981 870 912 356 330 360 642 560 571 17 20 19 Portugal Serbia 893 921 945 899 920 940 14 19 27 20 18 22 Serbie Slovakia 1,670 1,700 1,750 1,570 1,650 1,700 500 450 450 400 400 400 Slovaquie Slovenia 280 290 280 497 630 600 30 30 30 247 370 350 Slovénie Spain 827 847 847 730 775 775 158 167 167 61 94 94 Espagne Sweden 217 217 217 180 180 180 37 37 37 0 0 0 Suède Switzerland 145 155 160 265 275 280 35 40 40 155 160 160 Suisse United Kingdom 76 75 75 56 56 56 24 24 24 5 5 5 Royaume-Uni Total Europe 21,857 21,553 21,935 22,052 21,910 22,036 3,786 3,146 3,158 3,980 3,503 3,260 Total Europe Canada 15,890 15,923 15,895 14,854 14,926 14,926 1,106 1,060 1,027 70 64 59 Canada United States 31,549 32,308 33,429 33,358 33,742 34,516 219 152 154 2,027 1,586 1,240 Etats-Unis Total North America 47,440 48,231 49,324 48,212 48,668 49,442 1,325 1,212 1,181 2,097 1,649 1,299 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 11a HARDWOOD LOGS (temperate) GRUMES DE NON-CONIFERES (zone tempérée)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 France -41 -42 -42 44 45 45 3 3 3 France Germany -5 -5 -5 10 10 10 5 5 5 Allemagne Italy -20 11 11 25 9 9 4 20 20 Italie Netherlands -8 -5 -5 12 10 10 4 5 5 Pays-Bas Poland -2 -3 -3 2 3 3 0 0 0 Pologne Portugal -16 -15 -13 21 20 19 5 5 6 Portugal Serbia -1 -1 -1 1 1 1 0 0 0 Serbie Slovenia -1 0 0 1 0 1 0 0 0 Slovénie Spain -6 -7 -7 6 7 7 0 0 0 Espagne United Kingdom -2 -2 -2 2 2 2 0 0 0 Royaume-Uni Total Europe -102 -69 -67 124 107 106 22 38 39 Total Europe United States -1 -3 -1 2 4 2 1 1 1 Etats-Unis Total North America -1 -3 -1 2 4 2 1 1 1 Total Amérique du Nord

Country Commerce Net Production Imports - Importations Exports - Exportations Pays

TABLE 11b HARDWOOD LOGS (tropical) GRUMES DE NON-CONIFERES (tropicale)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Net Trade

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 13,844 12,627 12,592 11,047 9,212 9,287 3,676 4,070 4,020 879 655 715 Autriche Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre Czech Republic 5,559 5,135 5,154 7,664 6,164 6,130 1,270 1,146 1,162 3,375 2,175 2,138 République tchèque Estonia 3,117 2,380 2,435 6,548 6,550 6,550 256 330 285 3,687 4,500 4,400 Estonie Finland 48,404 47,241 49,358 44,923 44,026 45,568 5,037 4,969 5,545 1,556 1,755 1,755 Finlande France 24,495 24,350 24,050 24,257 24,000 23,700 2,527 2,600 2,600 2,289 2,250 2,250 France Germany 26,555 26,580 23,090 27,936 27,100 23,500 4,474 3,870 3,770 5,855 4,390 4,180 Allemagne Hungary 2,122 2,017 2,065 2,049 1,984 2,023 112 73 82 39 39 39 Hongrie Italy 4,508 5,210 5,210 3,916 4,618 4,618 1,288 1,288 1,288 696 696 696 Italie Latvia 5,540 5,150 5,150 9,484 8,800 8,800 1,084 950 950 5,028 4,600 4,600 Lettonie Luxembourg 583 589 589 577 559 559 182 130 130 176 100 100 Luxembourg Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte Montenegro 245 241 227 245 241 227 0 0 0 0 0 0 Monténégro Netherlands 604 1,100 1,095 1,267 1,240 1,230 289 100 105 952 240 240 Pays-Bas Poland 35,250 36,265 37,135 33,531 34,600 35,450 3,652 3,660 3,710 1,933 1,995 2,025 Pologne Portugal 15,954 15,330 15,365 11,664 11,720 11,590 4,657 4,000 4,140 368 390 365 Portugal Serbia 981 1,007 1,045 967 1,000 1,033 15 8 13 1 1 1 Serbie Slovakia 3,634 3,650 3,760 3,821 3,860 3,950 1,023 1,030 1,050 1,210 1,240 1,240 Slovaquie Slovenia 926 770 790 2,058 2,280 2,230 625 490 530 1,757 2,000 1,970 Slovénie Spain 13,959 14,358 14,358 14,383 15,261 15,261 1,435 1,564 1,564 1,859 2,467 2,467 Espagne Sweden 55,632 54,193 54,727 50,015 48,196 48,730 7,036 7,750 7,750 1,419 1,753 1,753 Suède Switzerland 1,823 1,824 1,829 1,216 1,217 1,222 795 795 795 188 188 188 Suisse United Kingdom 4,590 4,471 4,471 4,293 4,175 4,175 406 405 405 109 109 109 Royaume-Uni Total Europe 268,336 264,500 264,508 261,870 256,811 255,841 39,843 39,232 39,898 33,377 31,543 31,231 Total Europe Canada 37,044 35,822 35,734 35,326 32,985 32,975 2,578 3,462 3,467 860 625 708 Canada United States 238,450 239,587 240,850 244,912 246,110 247,536 348 324 308 6,809 6,848 6,994 Etats-Unis Total North America 275,495 275,409 276,585 280,238 279,096 280,511 2,926 3,786 3,776 7,670 7,473 7,702 Total Amérique du Nord

Includes wood residues, chips and particles for all purposes Comprend les dechets de bois, plaquettes et particules pour toute utilisation a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Country Consommation Apparente a Production Imports - Importations Exports - Exportations Pays

TABLE 12 PULPWOOD (total) BOIS DE TRITURATION (total)

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 m3

Apparent Consumption a

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 3,681 3,895 3,850 2,576 2,235 2,300 1,312 1,750 1,700 206 90 150 Autriche Czech Republic 3,927 3,744 3,675 5,316 4,253 4,125 811 811 830 2,200 1,320 1,280 République tchèque Estonia 476 245 245 878 900 900 56 45 45 458 700 700 Estonie Finland 22,913 24,189 25,835 22,746 23,764 25,239 1,163 1,410 1,581 996 985 985 Finlande France 4,689 4,400 4,100 4,559 4,300 4,000 608 550 550 478 450 450 France Germany 10,311 11,900 9,500 10,541 11,500 9,000 2,200 2,100 2,000 2,430 1,700 1,500 Allemagne Hungary 411 488 481 411 488 481 0 0 0 0 0 0 Hongrie Italy 148 853 853 148 853 853 0 0 0 0 0 0 Italie Latvia 1,775 1,700 1,700 1,850 1,800 1,800 374 400 400 449 500 500 Lettonie Luxembourg -16 -18 -16 10 6 8 9 3 3 35 27 27 Luxembourg Montenegro 201 198 186 201 198 186 0 0 0 0 0 0 Monténégro Netherlands 146 150 145 244 240 235 70 80 85 168 170 175 Pays-Bas Poland 15,378 15,900 16,300 15,411 15,950 16,250 1,428 1,500 1,650 1,462 1,550 1,600 Pologne Portugal 1,323 1,430 1,375 1,213 1,350 1,300 122 100 90 12 20 15 Portugal Serbia 66 70 74 66 70 73 0 0 1 0 0 0 Serbie Slovakia 598 600 610 748 710 700 600 630 650 750 740 740 Slovaquie Slovenia 264 200 220 275 430 380 268 170 200 278 400 360 Slovénie Spain 3,369 3,467 3,467 3,754 3,984 3,984 179 138 138 564 655 655 Espagne Sweden 28,513 27,431 27,944 26,353 25,310 25,823 3,114 3,269 3,269 954 1,148 1,148 Suède Switzerland 209 210 210 279 280 280 20 20 20 90 90 90 Suisse United Kingdom 1,894 1,776 1,776 1,633 1,516 1,516 291 291 291 31 31 31 Royaume-Uni Total Europe 100,275 102,827 102,530 99,212 100,136 99,433 12,625 13,267 13,503 11,562 10,576 10,406 Total Europe Canada 4,531 4,347 4,410 4,229 4,021 4,021 324 336 401 22 10 12 Canada United States 141,231 142,785 144,831 141,226 142,779 144,827 5 6 4 0 0 0 Etats-Unis Total North America 145,762 147,132 149,241 145,455 146,800 148,848 329 341 405 22 10 12 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

TABLE 12a PULPWOOD LOGS (ROUND AND SPLIT) BOIS DE TRITURATION (RONDINS ET QUARTIERS)

Softwood Conifères 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 1,217 997 1,007 647 577 587 668 500 500 98 80 80 Autriche Czech Republic 450 380 384 649 552 550 3 2 2 202 174 168 République tchèque Estonia 363 200 250 1,270 1,250 1,250 154 250 200 1,060 1,300 1,200 Estonie Finland 8,997 7,940 8,052 7,801 6,884 6,784 1,550 1,633 1,845 354 577 577 Finlande France 2,386 2,250 2,250 3,332 3,200 3,200 43 50 50 989 1,000 1,000 France Germany 1,116 1,180 1,090 1,103 1,100 1,000 259 270 270 246 190 180 Allemagne Hungary 502 507 526 502 507 526 0 0 0 0 0 0 Hongrie Italy 168 166 166 168 166 166 0 0 0 0 0 0 Italie Latvia 172 200 200 2,018 2,000 2,000 244 100 100 2,090 1,900 1,900 Lettonie Luxembourg 77 71 69 46 32 30 36 48 48 5 9 9 Luxembourg Netherlands 62 50 55 108 100 100 21 20 20 67 70 65 Pays-Bas Poland 4,424 4,635 4,785 3,939 4,150 4,300 560 560 560 75 75 75 Pologne Portugal 10,495 10,300 10,260 8,586 8,500 8,400 2,100 2,000 2,050 191 200 190 Portugal Serbia 199 205 210 199 205 210 0 0 0 0 0 0 Serbie Slovakia 1,874 1,950 2,000 1,924 2,000 2,050 100 100 100 150 150 150 Slovaquie Slovenia 137 120 130 424 490 450 84 80 90 371 450 410 Slovénie Spain 5,422 5,288 5,288 6,059 6,429 6,429 269 291 291 906 1,432 1,432 Espagne Sweden 8,517 8,412 8,533 6,232 5,986 6,107 2,313 2,481 2,481 28 55 55 Suède Switzerland 128 128 133 165 165 170 3 3 3 40 40 40 Suisse United Kingdom 23 22 22 13 13 13 18 18 18 9 9 9 Royaume-Uni Total Europe 46,729 45,001 45,410 45,185 44,305 44,322 8,426 8,406 8,628 6,881 7,711 7,540 Total Europe Canada 10,554 10,654 10,644 10,812 10,843 10,843 38 36 30 296 225 228 Canada United States 41,407 40,200 38,795 41,424 40,217 38,810 58 32 18 75 50 33 Etats-Unis Total North America 51,961 50,854 49,439 52,236 51,060 49,653 96 68 48 371 275 261 Total Amérique du Nord

a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fournies des données sur la commerce

Pays Apparent Consumption a

Country Consommation Apparente a Production Imports - Importations Exports - Exportations

TABLE 12b PULPWOOD LOGS (ROUND AND SPLIT) BOIS DE TRITURATION (RONDINS ET QUARTIERS)

Hardwood Non-conifères 1000 m3 - Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 8,945 7,735 7,735 7,824 6,400 6,400 1,696 1,820 1,820 575 485 485 Autriche Cyprus 8 9 10 7 8 9 1 1 1 0 0 0 Chypre Czech Republic 1,182 1,011 1,094 1,699 1,359 1,454 456 333 330 973 681 690 République tchèque Estonia 2,278 1,935 1,940 4,400 4,400 4,400 47 35 40 2,169 2,500 2,500 Estonie Finland 16,494 15,112 15,471 14,376 13,378 13,545 2,324 1,926 2,119 206 193 193 Finlande France 17,420 17,700 17,700 16,366 16,500 16,500 1,876 2,000 2,000 822 800 800 France Germany 15,128 13,500 12,500 16,292 14,500 13,500 2,015 1,500 1,500 3,179 2,500 2,500 Allemagne Hungary 1,209 1,022 1,057 1,137 989 1,015 112 73 82 39 39 39 Hongrie Italy 4,192 4,192 4,192 3,600 3,600 3,600 1,288 1,288 1,288 696 696 696 Italie Latvia 3,593 3,250 3,250 5,616 5,000 5,000 466 450 450 2,489 2,200 2,200 Lettonie Luxembourg 522 536 536 521 521 521 137 79 79 136 64 64 Luxembourg Malta 2 3 3 0 0 0 2 3 3 0 0 0 Malte Montenegro 44 43 41 44 43 41 0 0 0 0 0 0 Monténégro Netherlands 396 900 895 915 900 895 198 0 0 717 0 0 Pays-Bas Poland 15,448 15,730 16,050 14,181 14,500 14,900 1,664 1,600 1,500 396 370 350 Pologne Portugal 4,136 3,600 3,730 1,865 1,870 1,890 2,435 1,900 2,000 165 170 160 Portugal Serbia 716 732 761 702 725 750 15 8 12 1 1 1 Serbie Slovakia 1,162 1,100 1,150 1,149 1,150 1,200 323 300 300 310 350 350 Slovaquie Slovenia 525 450 440 1,360 1,360 1,400 273 240 240 1,107 1,150 1,200 Slovénie Spain 5,169 5,603 5,603 4,570 4,849 4,849 987 1,135 1,135 388 380 380 Espagne Sweden 18,602 18,350 18,250 17,430 16,900 16,800 1,609 2,000 2,000 437 550 550 Suède Switzerland 1,486 1,486 1,486 772 772 772 772 772 772 58 58 58 Suisse United Kingdom 2,673 2,673 2,673 2,646 2,646 2,646 96 96 96 69 69 69 Royaume-Uni Total Europe 121,332 116,673 116,568 117,472 112,370 112,087 18,793 17,559 17,767 14,933 13,256 13,285 Total Europe Canada 21,959 20,821 20,680 20,285 18,121 18,111 2,216 3,090 3,037 542 390 467 Canada United States 55,812 56,602 57,224 62,262 63,114 63,899 285 286 286 6,734 6,798 6,961 Etats-Unis Total North America 77,771 77,423 77,904 82,547 81,235 82,010 2,500 3,376 3,323 7,277 7,188 7,428 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 12c WOOD RESIDUES, CHIPS AND PARTICLES DECHETS DE BOIS, PLAQUETTES ET PARTICULES Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

1000 m3

Apparent Consumption

Imports Exports

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 Austria 1,290 1,497 1,450 1,691 1,938 2,050 344 309 300 745 750 900 Autriche Cyprus 8 5 5 0 0 0 8 5 5 0 0 0 Chypre Czech Republic 234 215 225 540 459 482 38 38 40 344 282 296 République tchèque Estonia 284 300 230 1,650 1,350 1,300 12 50 30 1,378 1,100 1,100 Estonie Finland 530 541 562 360 380 405 188 163 160 18 2 3 Finlande France 2,735 3,260 3,660 2,050 2,250 2,450 775 1,100 1,300 90 90 90 France Germany 3,328 3,540 3,720 3,569 3,700 3,900 443 480 420 684 640 600 Allemagne Hungary 63 44 50 62 43 49 11 13 12 11 12 12 Hongrie Italy 2,359 2,359 2,359 450 450 450 1,916 1,916 1,916 7 7 7 Italie Latvia 621 750 750 1,980 2,000 2,000 326 350 350 1,685 1,600 1,600 Lettonie Luxembourg 61 72 72 63 63 63 17 11 11 19 2 2 Luxembourg Malta 1 1 1 0 0 0 1 1 1 0 0 0 Malte Montenegro 18 25 26 83 84 84 0 0 0 65 59 58 Monténégro Netherlands 5,354 5,354 5,354 268 268 268 5,551 5,551 5,551 465 465 465 Pays-Bas Poland 842 920 1,100 1,152 1,200 1,350 366 370 380 677 650 630 Pologne Portugal 228 225 220 747 740 735 4 5 5 523 520 520 Portugal Serbia 478 460 485 418 450 480 83 70 80 23 60 75 Serbie Slovakia 22 175 175 390 450 450 47 75 75 415 350 350 Slovaquie Slovenia 125 155 150 164 175 180 126 120 130 165 140 160 Slovénie Spain 867 907 907 1,007 1,007 1,007 65 46 46 206 146 146 Espagne Sweden 1,776 1,800 1,850 1,809 1,750 1,800 199 210 210 232 160 160 Suède Switzerland 410 415 420 330 335 340 80 80 80 0 0 0 Suisse United Kingdom 7,819 7,830 7,830 327 330 330 7,516 7,520 7,520 23 20 20 Royaume-Uni Total Europe 29,451 30,850 31,601 19,110 19,422 20,173 18,114 18,482 18,621 7,774 7,055 7,194 Total Europe Canada 368 420 179 3,830 3,830 3,830 31 52 56 3,493 3,462 3,707 Canada United States 761 273 152 9,544 9,744 9,948 194 174 155 8,977 9,644 9,951 Etats-Unis Total North America 1,129 694 331 13,374 13,574 13,778 225 226 211 12,470 13,106 13,659 Total Amérique du Nord

Country Consommation Apparente Production Imports - Importations Exports - Exportations Pays

TABLE 13 WOOD PELLETS GRANULES DE BOIS

Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions 1000 mt

Apparent Consumption

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 actual actual actual actual réels réels réels réels

Sawn softwood 75.92 69.01 68.49 96.71 89.54 88.44 29.69 25.67 25.93 50.49 46.20 45.88 Sciages conifères

Softwood logs a 182.85 171.26 171.53 185.47 172.88 171.84 18.22 16.63 16.39 20.84 18.25 16.70 Grumes de conifères a

Sawn hardwood 7.02 6.65 6.70 6.93 6.45 6.61 4.18 3.86 3.81 4.09 3.66 3.72 Sciages non-conifères

– temperate zone b 6.45 6.14 6.18 6.87 6.40 6.55 3.28 3.07 3.02 3.70 3.33 3.39 – zone tempérée b

– tropical zone b 0.57 0.51 0.52 0.06 0.05 0.06 0.90 0.79 0.79 0.39 0.32 0.32 – zone tropicale b

Hardwood logs a 21.96 21.62 22.00 22.05 21.91 22.04 3.91 3.25 3.26 4.00 3.54 3.30 Grumes de non-conifères a

– temperate zone b 21.86 21.55 21.93 22.05 21.91 22.04 3.79 3.15 3.16 3.98 3.50 3.26 – zone tempérée b

– tropical zone b 0.10 0.07 0.07 0.12 0.11 0.11 0.02 0.04 0.04 – zone tropicale b

Veneer sheets 1.58 1.49 1.49 1.00 0.97 0.96 1.42 1.28 1.29 0.84 0.76 0.76 Feuilles de placage

Plywood 6.62 6.21 5.92 4.17 3.93 3.97 6.42 5.79 5.48 3.96 3.50 3.53 Contreplaqués

Particle board (excluding OSB) 28.12 26.41 26.52 28.01 26.71 26.91 10.02 9.58 9.55 9.92 9.88 9.94 Pann. de particules (sauf OSB)

OSB 5.27 5.06 5.09 4.89 4.89 5.02 3.20 2.96 2.94 2.83 2.78 2.87 OSB

Fibreboard 15.80 14.89 15.09 16.15 15.31 15.42 8.76 8.01 8.04 9.11 8.43 8.37 Panneaux de fibres

– Hardboard 0.79 0.82 0.90 0.48 0.47 0.47 1.47 1.44 1.46 1.17 1.09 1.04 – Durs

– MDF 11.42 10.85 10.97 12.16 11.62 11.68 5.21 4.61 4.62 5.95 5.38 5.33 – MDF

– Other board 3.59 3.22 3.22 3.51 3.22 3.27 2.07 1.97 1.96 1.99 1.96 2.01 – Autres panneaux Pulpwood a 268.34 264.50 264.51 261.87 256.81 255.84 39.84 39.23 39.90 33.38 31.54 31.23 Bois de trituration a

– Pulp logs 147.00 147.83 147.94 144.40 144.44 143.75 21.05 21.67 22.13 18.44 18.29 17.95 – Bois ronds de trituration

– softwood 100.28 102.83 102.53 99.21 100.14 99.43 12.63 13.27 13.50 11.56 10.58 10.41 – conifères

– hardwood 46.73 45.00 45.41 45.18 44.31 44.32 8.43 8.41 8.63 6.88 7.71 7.54 – non-conifères

– Residues, chips and particles 121.33 116.67 116.57 117.47 112.37 112.09 18.79 17.56 17.77 14.93 13.26 13.29 – Déchets, plaquettes et part. Wood pulp 37.60 34.07 35.28 34.64 32.24 33.81 17.33 16.19 16.59 14.37 14.37 15.12 Pâte de bois

Paper and paperboard 72.76 66.14 69.44 83.10 73.88 79.49 43.20 39.62 41.48 53.55 47.36 51.53 Papiers et cartons

Wood Pellets 29.45 30.85 31.60 19.11 19.42 20.17 18.11 18.48 18.62 7.77 7.05 7.19 Granulés de bois a Countries which did not provide trade data are included in consumption data a La consommation comprend les pays qui n'ont pas fourni des données sur le commerce b Trade figures by zone do not equal the total as some countries cannot provide data for both zones b Les chiffres du commerce par zone ne correspondent pas aux totaux

en raison du fait que certains pays ne peuvent les différencier.

TABLE 14

Europe: Summary table of market forecasts for 2023 and 2024

Europe: Tableau récapitulatif des prévisions du marché pour 2023 et 2024 Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques) Apparent Consumption

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

2022 2023 2024 2022 2023 2024 2022 2023 2024 2022 2023 2024 actual actual actual actual réels réels réels réels

Sawn softwood 91.63 89.85 90.39 100.44 97.41 95.73 27.09 26.48 27.10 35.90 34.04 32.43 Sciages conifères

Softwood logs 253.91 254.00 257.31 262.84 262.90 265.12 1.93 1.97 1.86 10.86 10.87 9.68 Grumes de conifères

Sawn hardwood 15.85 16.16 16.46 18.50 18.72 19.03 1.59 1.63 1.56 4.23 4.19 4.13 Sciages non-conifères

– temperate zone 15.57 15.89 16.19 18.50 18.72 19.03 1.29 1.33 1.26 4.21 4.16 4.10 – zone tempérée

– tropical zone 0.29 0.27 0.27 0.00 0.00 0.00 0.31 0.30 0.30 0.02 0.03 0.03 – zone tropicale

Hardwood logs 47.44 48.23 49.33 48.21 48.67 49.44 1.33 1.22 1.18 2.10 1.65 1.30 Grumes de non-conifères

– temperate zone 47.44 48.23 49.32 48.21 48.67 49.44 1.32 1.21 1.18 2.10 1.65 1.30 – zone tempérée

– tropical zone 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 – zone tropicale Veneer sheets 2.85 2.93 2.97 2.87 2.89 2.91 0.86 0.88 0.89 0.88 0.83 0.84 Feuilles de placage

Plywood 16.92 16.92 17.31 10.86 10.90 11.05 7.48 7.37 7.68 1.43 1.36 1.42 Contreplaqués

Particle board (excluding OSB) 6.66 7.45 7.46 6.11 6.58 6.55 1.75 1.97 1.98 1.19 1.10 1.07 Pann. de particules (sauf OSB)

OSB 21.20 21.09 21.35 20.86 20.60 20.86 6.28 6.30 6.39 5.94 5.82 5.89 OSB

Fibreboard 9.92 9.93 10.07 7.64 7.71 7.87 4.18 3.92 3.92 1.90 1.69 1.72 Panneaux de fibres

– Hardboard 0.51 0.56 0.56 0.53 0.59 0.60 0.31 0.28 0.29 0.32 0.32 0.33 – Durs

– MDF 6.21 6.23 6.23 3.83 3.88 3.89 3.55 3.35 3.32 1.17 0.99 0.98 – MDF

– Other board 3.20 3.15 3.28 3.28 3.24 3.38 0.32 0.29 0.31 0.40 0.38 0.41 – Autres panneaux Pulpwood 275.49 275.41 276.58 280.24 279.10 280.51 2.93 3.79 3.78 7.67 7.47 7.70 Bois de trituration

– Pulp logs 197.72 197.99 198.68 197.69 197.86 198.50 0.43 0.41 0.45 0.39 0.28 0.27 – Bois ronds de trituration

– softwood 145.76 147.13 149.24 145.45 146.80 148.85 0.33 0.34 0.41 0.02 0.01 0.01 – conifères

– hardwood 51.96 50.85 49.44 52.24 51.06 49.65 0.10 0.07 0.05 0.37 0.27 0.26 – non-conifères

– Residues, chips and particles 77.77 77.42 77.90 82.55 81.23 82.01 2.50 3.38 3.32 7.28 7.19 7.43 – Déchets, plaquettes et part. Wood pulp 45.79 48.12 48.43 55.02 54.33 54.12 7.42 8.22 8.89 16.65 14.44 14.58 Pâte de bois

Paper and paperboard 69.75 68.96 69.26 75.05 73.60 73.63 10.72 10.42 10.39 16.02 15.06 14.77 Papiers et cartons

Wood pellets 1.13 0.69 0.33 13.37 13.57 13.78 0.23 0.23 0.21 12.47 13.11 13.66 Granulés de bois

TABLE 15

North America: Summary table of market forecasts for 2023 and 2024

Amérique du Nord: Tableau récapitulatif des prévisions du marché pour 2023 et 2024 Data only for those countries providing forecasts - Données uniquement pour les pays fournissant des prévisions

million m3 (pulp, paper and pellets million m.t. - pâte de bois, papiers et cartons, et granulés en millions de tonnes métriques) Apparent Consumption

Consommation Apparente Production Imports - Importations Exports - Exportations

forecasts forecasts forecasts forecasts prévisions prévisions prévisions prévisions

List of tables
Table1
Table2
Table 2a
Table 2b
Table 3
Table 4
Table 5
Table 5a
Table 6
Table 6a
Table 6b
Table 6c
Table 7
Table 8
Table 9
Table 9a
Table 9b
Table 10
Table 11
Table 11a
Table 11b
Table12
Table 12a
Table 12b
Table 12c
Table 13
Table 14
Table 15

Paper, Experimental CPI for lower and higher income households (U.S. Bureau of Labor Statistics)

Read more about Paper, Experimental CPI for lower and higher income households (U.S. Bureau of Labor Statistics)

This paper examines CPI indexes for subsets of the target population defined by the bottom and top of the income distribution and compares results with the target population. We use data from the Consumer Expenditure Surveys (CE) to construct biennial and monthly market basket shares for groups of respondents based on their reported income, in order to calculate CPIs using modified Laspeyres and Tornqvist formulas respectively. From 2003 to 2018, we find the Laspeyres index for the lowest income quartile population rose faster than the index for all urban consumers.

Languages and translations

English

BLS WORKING PAPERS U.S. Department of Labor U.S. Bureau of Labor Statistics Office of Prices and Living Conditions

Experimental CPI for lower and higher income households

Josh Klick Anya Stockburger U.S. Bureau of Labor Statistics

Working Paper 537 March 8, 2021

Experimental CPI for lower and higher income households1 Josh Klick, Anya Stockburger

Abstract This paper examines CPI indexes for subsets of the target population defined by the bottom and top of

the income distribution and compares results with the target population. We use data from the

Consumer Expenditure Surveys (CE) to construct biennial and monthly market basket shares for groups

of respondents based on their reported income, in order to calculate CPIs using modified Laspeyres and

Tornqvist formulas respectively. From 2003 to 2018, we find the Laspeyres index for the lowest income

quartile population rose faster than the index for all urban consumers. The Laspeyres index for the

highest income quartile population rose slower than the index for all urban consumers. Chained CPI

indexes for the income quartile populations rose slower than their Laspeyres counterparts. The measure

of consumer substitution was lowest for the lowest income quartile population; the difference between

the Laspeyres and Tornqvist index for the lowest income quartile population was less than half the

difference for all urban consumers.

Introduction The Consumer Price Index (CPI) measures the change in the cost of goods and services purchased by

consumers between two time periods. The target population for the headline CPI is the urban

population (CPI-U), however BLS also calculates estimates of price change for subsets of the target,

including those aged 62 years and older (R-CPI-E) and those earning most of their income from a select

list of wage-earning and clerical worker occupations (CPI-W). There is a lot of user interest in CPI indexes

for lower income households. This paper examines CPI indexes for subsets of the target population

defined by the bottom and top of the income distribution and compares results with the target

population. We use data from the Consumer Expenditure Surveys (CE) to construct biennial and monthly

market basket shares for groups of respondents based on their reported income, in order to calculate

CPIs using modified Laspeyres and Tornqvist formulas respectively.

Almost 25 years ago, BLS researchers Thesia Garner, David Johnson, and Mary Kokowski published

results for an experimental index for lower income households.2 The authors used CE Interview Survey

data from 1982-1984 and 1992-1994 to generate shares for lower income and lower expenditure

households to calculate Laspeyres, Paasche, and Fisher indexes from 1984 to 1994. They found little

difference in inflation between urban consumers and both lower income and lower expenditure

households. In order to register differences between urban consumers and any subset of the target

population, there must be significant differences in budget shares and price change differentials for

1 Many thanks to David Popko for his contributions to earlier versions of this research and to Chris Miller

and Greg Barbieri for their assistance compiling the data. We are also grateful to Robert Cage, Thesia

Garner, and Sara Stanley for their insightful comments that improved the paper.

2 https://www.bls.gov/opub/mlr/1996/09/art5full.pdf

those item categories. Without budget share differences, and relative price differences for those items,

any measure of price change will be the same across different population definitions.

In a 2002 BLS working paper, Rob Cage, Thesia Garner, and Javier Ruiz-Castillo constructed household

specific price indexes.3 Compared to the earlier Garner et al. results, they found greater differences in

inflation rates between urban consumers and the lower income population subset, perhaps because of

the inclusion of budget shares for categories, particularly food, collected in the CE Diary Survey. Leslie

McGranahan and Anna Paulson of the Federal Reserve Bank of Chicago conducted research over a

longer time period to study inflation for lower income consumers and found little long-term

differences.4 Similar to prior BLS studies, this research focused on changing weights to reflect different

consumption patterns across different population subsets.

More recently, several academic researchers have used scanner data linked with consumer information

to account for differences in consumer behavior at a much finer level than possible with BLS data. 5

When accounting for the heterogeneity across consumers at the lowest levels, these studies generally

find lower inflation rates for lower income consumers in the 1990s and early 2000s, and then higher

inflation rates for more recent time periods.

In this paper we review the background and issues with calculating CPIs for population subsets, define

two income-based populations (lowest and highest quartiles), and describe differences in their

demographic characteristics. In the results section, we present (i) a comparison of expenditure share

differences across the income-based populations, (ii) index results for both Laspeyres and Tornqvist

formulas, and (iii) a comparison of upper level substitution bias. We conclude with final observations

and remarks.

Background and Issues This section begins with a brief explanation of the methods to construct the CPI for the target

population, all-urban consumers. This foundation is helpful to explain how this methodology has been

adapted to construct CPIs for subsets of the target population, and the various drawbacks due to those

adaptations.

BLS selects cities to represent geographic strata (index areas) and sample units (goods or services) to

represent consumption item strata. With market basket revisions, BLS may change the number of strata

over time. As of January 2018, there were 32 index areas and 243 item strata. The product of these

strata create 7,776 elementary index cells for which prices are collected and then aggregated in two

stages.

At the first stage, changes in price are averaged across sampled units in each elementary index cell using

either a geometric mean or modified Laspeyres formula.6 The elementary index cells form the building

3 https://www.bls.gov/osmr/research-papers/2002/pdf/ec020030.pdf 4 https://www.chicagofed.org/publications/working-papers/2005/2005-20 5 Many examples include Broda and Romalis (2009), Broda, Leibtag, and Weinstein (2009), Agente and Lee (2017), Jaravel (2017), and Kaplan and Schulhofer-Wohl (2017). 6 The formula choice at the first stage of aggregation is based on the level of consumer substitution for that item category. Most goods and services use the geometric mean formula because consumers are generally able to substitute away from any particular item whose price is rising relative to others. Rent and Owner’s Equivalent Rent

blocks for the second stage of estimation. The same calculated elementary index cells are used as

building blocks to calculate the target (CPI-U) and subset (CPI-W and R-CPI-E) population indexes, as well

as the chained CPI (C-CPI-U) which uses a different aggregation formula and weights. The building blocks

for the target population are used as proxies for other populations. Building blocks are not produced

independently for the consumption patterns of the population subset of interest.

At the second stage, BLS uses market basket shares to combine price changes across elementary index

cells to calculate measures of aggregate price change. Market basket shares are calculated using data

collected by the Census Bureau on behalf of BLS in the CE Diary and Interview surveys. Several

adjustments are needed to modify CE data for CPI definitions of consumption, the most important of

which is an adjustment for expenditures on owned homes to estimate a consumption value.

Expenditures on the shelter component of the CPI include rent paid by renters and an estimate of the

rent homeowners would pay to live in their home (Owners’ Equivalent Rent). 7 BLS calculates market

basket shares independently for each population, and these shares are the only index construction

difference between populations. BLS uses the modified Laspeyres aggregation to calculate CPI-U indexes

(as well as the CPI-W and R-CPI-E indexes described below). It computes an arithmetic average price

change weighted by base period quantities. BLS uses the Tornqvist index formula to calculate the final

version of the C-CPI-U. It is a geometric average of component price changes weighted using the average

budget share for the previous and current month.

BLS has a long history of calculating indexes for subsets of the target population. The CPI-W is the oldest

measure of consumer inflation calculated by BLS.8 In the 1978 revision of the CPI, the urban population

was introduced as the target. The wage-earner and clerical worker population is a subset of the urban

population, where only CE respondents who work full-time and earn most of their income from a select

list of occupations are eligible for inclusion. Hence, when the CPI-U was introduced as the target

population the wage earner population became a subset of the target population. BLS produces another

index for a subset of the target population to measure price change for older consumers (R-CPI-E). This

series began in 1988 at the request of Congress and is published on a research basis. The reasons why

the R-CPI-E is published as a research index are listed in Table 1. These same caveats also apply to the

CPI-W, which was not reclassified as an experimental index when the CPI-U was introduced, or any other

population subset index calculated using the same methodology.

are calculated using a Laspeyres formula because consumers cannot easily move in response to changes in rent. There are a few other categories that use a Laspeyres formula due to the limited ability to substitute (such as prescription drugs). 7 For more information on the calculation of price change for rent and owners’ equivalent rent, see the factsheet https://www.bls.gov/cpi/factsheets/owners-equivalent-rent-and-rent.pdf 8 The First 100 Years of the Consumer Price Index: a methodological and political history. Darren Rippy. Monthly Labor Review. April 2014. https://www.bls.gov/opub/mlr/2014/article/the-first-hundred-years-of-the-consumer- price-index.htm

Table 1: Primary caveats with BLS approach to calculating indexes for subsets of the target population

Experimental weights: the CE sample is designed to produce reliable weights for the population living in urban areas. The smaller sample of CE respondents used to calculate weights for subsets of the target population are expected to have higher sampling error compared to the full sample of respondents used to calculate urban population weights. The CE sample is also designed to produce expenditure weights for Laspeyres indexes that pool data over 24 months. Tornqvist indexes require spending estimates every month and data limitations constrain the ability to construct reliable monthly weights for demographic subsets of the CE sample.

Areas and outlets priced: the sample of cities is designed to represent the population living in urban areas. Within cities, the sample of retail establishments and rental units are designed to represent the total population. To the extent that subsets of the target population live in different cities (or in different parts of cities) and shop at different stores, the urban samples may not be representative.

Items priced: for goods and services sold in a retail establishment, the unique items selected for pricing are based on sales data within the store. If a subset of the target population purchases different items than the general population, then the items selected for pricing may not be representative.

Rental units priced: the realized sample of rental units may have rent-determining characteristics that are not representative of a subset of the target population.9

Prices collected: there is only one set of prices collected. Any discount given to particular groups (such as senior-citizens or veterans) are used in the CPI only in proportion to their use by the urban population as a whole. This could understate the prevalence of this type of discount in an index specifically designed for a population subset.

BLS is researching improvements to methodology to measure price change for a subset of the target

population, drawing on the recent work in the international statistical community and academia. 10 In

particular, BLS is investigating a different treatment of owner occupied housing. While the concept of

owner’s equivalent rent is an appropriate conceptual approach for aggregate economic measurement, it

does not reflect price change experienced by individuals or households which is most useful for

escalation purposes. For homeowners with a mortgage, the imputed rent is used in place of mortgage

payments or other out-of-pocket expenses associated with owning a home. For populations with a large

9 Research by BLS in 2019 and 2020 have shown there is a statistically significant different in rent changes by type of structure of the housing unit, for example whether it is a single family home or an apartment building. Beyond geographic differences, there could be rental unit characteristics that should be controlled to produce unbiased estimates of price change for subpopulations. 10 The United Kingdom’s Office on National Statistics in particular has made several improvements in the calculation of subpopulation indexes that BLS is investigating, including democratic aggregation and a payments approach to expenditures on owner occupied housing. Academic research on consumer heterogeneity, such as work by Greg Kaplan and Xavier Jaravel, also provide valuable insights into potential biases in subpopulation indexes. A summary of this work is outside the scope of this paper.

share of home owners with no mortgage (such as the E population), this process imputes a larger

expense of owning a home than out-of-pocket spending.

BLS has been limited in its ability to assess the impact of the drawbacks listed in Table 1 on any

particular population subset. In particular, BLS does not have the data needed to research issues related

to the first stage of aggregation. Each of these caveats are unique and must be studied separately for

each population of interest. For example, using a single set of prices collected might be the most

important issue for the older subset, while the areas and outlets priced might be the most important

issue for the lower income subset. BLS has produced population subset indexes with these caveats for

many years, and there is growing interest in assessing the impact and potentially addressing these

drawbacks.

Methodology Price index number formula We calculate Laspeyres and Tornqvist indexes following BLS methodology as described in Formulas 1

and 2, respectively.11 The modified Laspeyres formula is a weighted arithmetic average of constituent

elementary index cell price changes. The weights as described in Formula 1 as aggregation weights can

be roughly interpreted as quantities corresponding to a 24 month reference period of consumer

expenditures. For example, monthly indexes calculated from January 2018 to December 2019 use

aggregation weights constructed from consumer spending in 2015 and 2016.

Formula 1: Modified Laspeyres Formula

𝐼𝑋𝑡 [𝐼,𝐴]

= 𝐼𝑋𝑡−1 [𝐼,𝐴]

∗ ∑ 𝐴𝑊

𝑏 [𝑖,𝑎] 𝐼𝑋𝑡

[𝑖,𝑎] [𝑖,𝑎]∈[𝐼,𝐴]

∑ 𝐴𝑊𝑏 [𝑖,𝑎] 𝐼𝑋𝑡−1

[𝑖,𝑎] [𝑖,𝑎]∈[𝐼,𝐴]

Where:

𝐼𝑋[𝐼,𝐴] is the All-Items, All-US aggregate index

𝐼𝑋[𝑖,𝑎]are the elementary index cells

t and t-1 are the current and previous months

𝐴𝑊𝑏 𝑖,𝑎 are the aggregation weights for elementary index cells, [i,a], based on a biennial

reference period, b

The Tornqvist index differs in both aggregation method and weights. The formula is a geometric average

of price change weighted by average budget shares from the current and previous month.

11 CPI Handbook of Methods, index calculation section. https://www.bls.gov/opub/hom/cpi/calculation.htm#index-calculation

Formula 2: Tornqvist formula

𝐼𝑋𝑡 [𝐼,𝐴] = 𝐼𝑋𝑡−1

[𝐼,𝐴] ∗ ∏ ( 𝐼𝑋𝑡

[𝑖,𝑎]

𝐼𝑋𝑡−1 [𝑖,𝑎]

)

𝑠𝑡 [𝑖,𝑎]

+𝑠𝑡−1 [𝑖,𝑎]

[𝑖,𝑎]∈[𝐼,𝐴]

Where 𝐼𝑋[𝐼,𝐴], 𝐼𝑋[𝑖,𝑎], t, and t-1 are defined as in Formula 1 and 𝑠[𝑖,𝑎] are the monthly expenditure

shares for the elementary index cells.

As noted in the background section, the CE sample is designed to produce expenditure estimates pooled

over a 24 month biennial reference period, b. To calculate monthly spending estimates used in the

Tornqvist index calculation (𝑠[𝑖,𝑎]), BLS uses a ratio allocation approach to allocate national spending on

an item category to index areas in order to minimize the number of elementary index cells with missing

expenditure data. Where cells are still missing after this procedure, annual expenditures are set to $0.01

(or monthly expenditures of 1/12th of a penny) to synthesize with the CPI-U procedure.12 The sparsity of

data is the primary reason Tornqvist indexes for W and E populations are currently not produced.

The different aggregation method and weights in the Laspeyres and Tornqvist formulas result in

different measures of price change. The resulting difference in inflation rates can be referred to as a

measure of consumer substitution bias. This bias in the CPI-U index is one of many summarized in

various reviews of CPI methodology.13 In short, consumers tend to respond to price changes by

substituting away from (or towards) items whose prices are rising (or falling) faster than average. Since

the modified Laspeyres formula holds quantities fixed for two years (as captured by 𝐴𝑊𝑏 𝑖,𝑎 in Formula

1), that index tends to overstate a true cost of living index when consumers exhibit substitution

behavior. A Tornqvist index uses an average budget share from the current and previous time period,

and reflects consumer substitution in response to relative price change. Tornqvist indexes (and other

indexes that use both current and previous period weights) are closer approximations of a cost of living

index than Laspeyres indexes (and other indexes with fixed weights). The generally upward bias in a

Laspeyres index is called substitution bias, and at the upper level is measured by the difference in

Tornqvist and Laspeyres indexes.14

Data and definitions In this study we use CE data from both the Diary and Interview surveys. Although expenditures for some

items are collected in both surveys, the CPI program selects one survey as the source for a particular

reference year. We use the same survey source as was used in the production calculation of weights for

the CPI-U index.

The time period of study is 2004 to 2018. BLS added an income imputation in 2004 that makes results

prior to that time period not comparable. Also, as of this research, BLS had published Tornqvist indexes

through July 2019, so we selected December 2018 as a terminus. The base period quantities used in the

12 The CPI Handbook of Methods, Final C-CPI-U calculation section https://www.bls.gov/opub/hom/cpi/calculation.htm#final-c-cpi-u 13 The Boskin Commission, CNSTAT At What Price, Moulton’s NBER paper are a few references. 14 Since there are two stages of index calculation, there are also two stages where consumer substitution bias can overstate inflation in a Laspeyres index. This measure of substitution bias is at the second stage, or upper level substitution bias.

modified Laspeyres formula are constructed using two years of CE data and updated in January of even

years. For example, data from 2001 and 2002 are compiled to create aggregation weights used in index

calculation from January 2004 through December 2005. We calculate Laspeyres indexes from December

2003 (2001/2002 weights) through December 2018 (2015/2016 weights). In order to preserve the same

base period, we calculate Tornqvist indexes starting in December 2003, and ending in December 2018.15

We made several adjustments to the data to account for minor CPI item structure changes over this

longitudinal time period.

In this paper we define lowest and highest income populations by income quartiles. There are many

other possible definitions of low and high income. We focused on a simple definition that ensures a

quarter of CE respondents nationally are classified in the populations of interest. In order to define the

income quartiles, we pooled respondents from each survey (Diary and Interview) by reference year,

then ranked by income, and then divided into income quartiles. Our definition of income is total before-

tax income, after imputation. We did not exclude households with incomes equal or below zero, but

that is a definitional change that could be considered in future research. The populations we present in

this report are the lowest income quartile and the highest income quartile.

Table 2 shows a comparison between these lowest and highest income quartile populations as well as

the urban, wage earners, and elderly populations. The median annual income of urban CE respondents

over this time period is $48,816. The wage earner and elderly subset of the urban population have

slightly lower median annual incomes. By tautology, there are larger differences in annual income when

grouping CE respondents by that variable. Looking at the quarter of CE respondents with the lowest and

highest income reported, the median annual income was $13,500 and $122,800 respectively.

Table 2: Annual income for Population Cohorts: 2004-2018

Variable Urban Wage

earner

Elderly Lowest

Income

Quartile

Highest

Income

Quartile

Mean Annual Income $67,109 $55,802 $51,156 $12,705 $155,045

Median Annual Income $47,920 $46,099 $33,313 $13,570 $124,362

Source: CE integrated data from 2004-2018, population weighted to represent consumer units in the

U.S.

We define the income bounds for the lowest and highest income quartile groupings in this paper based

on CE data. Alternatively, one could define the income thresholds using an external source of

information, for example to reflect a different benchmark income distribution of the population.

According to an analysis conducted in 2019 to study nonresponse bias in the CE, the population earning

less than $50,000 a year was over-represented by five to 20 percent when compared with the American

15 Tornqvist indexes were also calculated from December 1999 through December 2001, but are not presented here for ease of explication. The results in these two early years are similar to the rest of the time period presented in this paper.

Community Survey (ACS).16 The lowest and highest income quartiles in this paper might reflect lower

incomes than corresponding income distribution levels as measured by the ACS, even after adjusting for

different definitions of income. For example, CE respondents reporting an annual income in 2016 of less

than $25,000 were included in the lowest income quartile in this paper. Using ACS data, the lowest

quartile income cutoff is around $28,000. Similarly, based on the creation of the income quartile

variable, CE respondents reporting an annual income greater than about $93,000 were include in the

highest income quartile, compared to an ACS cutoff of around $110,000. This research could be

repeated for other definitions of income.

A comparison of other demographic information across populations is also helpful context to explain

differences in market basket shares. As shown in Table 3, relative to higher income respondents, lower

income respondents have lower rates of home ownership and educational attainment and lower rates

of labor force participation. Other demographic comparisons reveal the overlap in respondents included

in the elderly and the lowest income populations. The lowest income population is by definition 25

percent of the urban population. The elderly population is around 30 percent of the urban population,

36 percent of the lowest income population, and 16 percent of the highest income population. This is

likely the driving factor behind why, relative to higher income respondents, lower income respondents

are older, more likely to be retired, and have higher rates of home ownership without a mortgage.

Household size differences across populations are important to note and likely play an important role in

the median income differences in Table 2. Income was not adjusted for household size and future

research should control for household size to improve comparability across populations.17

16 A Nonresponse Bias Study of the Consumer Expenditure Survey for the Ten-Year Period 2007-2016; Krieger et al. https://www.reginfo.gov/public/do/DownloadDocument?objectID=101978401 17 There is a long literature using equivalence scales to adjust household income to account for different characteristics across households. Angela Daley, Thesia Garner, Shelley Phipps, Eva Sierminska, “Differences Across Place and Time in Household Expenditure Patterns: Implications for the Estimation of Equivalence Scales,” BLS Working Paper, 2020 https://www.bls.gov/osmr/research-papers/2020/pdf/ec200010.pdf

Table 3: Demographic Comparisons between Population Cohorts

Variable Urban Wage

earner

Elderly Lowest

income

quartile

Highest

income

quartile

Home Ownership

Percent Owner (incl. unknown mortgage status) 64.3% 56.0% 79.0% 41.3% 87.4%

Percent Owner with Mortgage 39.8% 39.8% 26.7% 13.0% 69.5%

Percent Owner no Mortgage 23.1% 14.4% 50.4% 26.6% 17.3%

Age and Household Size

Median Age of Householder 49 43 70 55 48

Mean Household Size 2.5 2.9 1.8 1.8 3.1

Education Level

Percent High School Diploma or Above 87.4% 83.5% 82.9% 75.6% 97.1%

Percent Associate's Degree or Above 42.0% 26.8% 35.5% 21.5% 67.8%

Employment Status

Percent Not Working/Any Reason 31.4% 12.8% 69.7% 58.0% 12.1%

Person Not Working Disabled or Taking Care

of Family 10.7% 8.5% 7.6% 20.2% 5.8%

Percent Not Working/Retired 19.0% 3.4% 61.9% 33.5% 5.8%

Source: CE integrated data from 2004-2018, population weighted to represent consumer units in the

U.S.

Results Using these examples, we constructed expenditure weights for lower and higher income populations

and used them as input to calculate Laspeyres and Tornqvist indexes. First we present a comparison of

expenditure weights for the population definitions, and then index results.

Expenditure Weights Recall a caveat to the method BLS uses to calculate indexes for subsets of the target is the potential for

increased sampling error of expenditure weights. This caution is particularly relevant for populations

defined by income. As we show in Table 4, biennial expenditure weights calculated for the 7,776

elementary index cells are rarely missing for the urban population (3 percent of the time during the

study period). The rate of missing cells is higher for subsets of the target population, and the highest for

the lowest income quartile population. The item structure is defined for the urban population, and some

item categories might be less relevant for a subset of the target population. For example there are

missing expenditures for the item category Sports vehicles (which includes bicycles, boats, and

snowmobiles) in 39 percent of the areas for the urban population and 86 percent of the areas for the

lowest income population. Very low (or no) expenditures might be an appropriate proxy for spending by

some populations on certain item categories. Nonetheless, the high rate of missing cells is a concerning

quality metric that should be further studied.

Table 4: Rate at which expenditure data are missing for elementary index cells (average 2004-2018)

Type of weights Urban Wage

earner

Elderly Lowest

Income

(Q1)

Highest

Income

(Q4)

Biennial expenditure weight 3% 9% 11% 17% 6%

Monthly expenditure weight 19% 44% 45% 55% 36%

Source: CPI expenditure weights from 2004 to 2018

As we stretch CE data further to calculate monthly expenditure weights for the Tornqvist index, the

number of elementary index cells with missing expenditure data increases substantially. BLS publishes a

Tornqvist index for the urban population, with a missing rate of 19%. In the past, the high rate of missing

data for subsets of the target population monthly expenditures has been a primary reason Tornqvist

indexes for the W and E populations have not been explored. This same caveat applies to populations

defined by income. Indeed, on average over half of the elementary index cells for monthly expenditure

weights are missing expenditure data for the lowest income quartile. Here, the imputation techniques

used for the urban population are applied to population subsets to enable calculation of Tornqvist

indexes. Imputation of missing expenditure data is another area that could be improved upon in future

research.

After imputing missing expenditures to fully populate the elementary index cells, there are several

notable differences in market basket shares between populations as displayed in Table 5. The eight

major group categories are presented, along with some notable subcategories. Although the market

basket shares vary over the time period of study, the comparison of 2015-2016 differences is illustrative

of general differences. Note these shares are not identical to CPI relative importances published on the

BLS website, which are inflation adjusted to reflect snapshots of weights used in CPI index calculation.

Food: Although spending shares on food in total by the lowest income quartile population is

similar to all households, more of their budget is spent on food at home rather than food away

from home (such as restaurants) and alcoholic beverages.

Housing: The share of spending (or consumption in the case of owners) on shelter (rent and

owner’s equivalent rent) is highest for the lowest income quartile population. Although there

are roughly twice as many renters in the lowest quartile compared to the highest quartile, their

budget share allocated to rent is more than four times as high18. Recall from the background

18 Recent research by BLS and Census Bureau linking CE data with rent subsidy information collected by the Department of Housing and Urban Development could have interesting implications for the budget share of rent

section that owner’s equivalent rent is an imputed cost of the shelter services provided by

owned homes. The imputed budget share for owner’s equivalent rent is the lowest for the

lowest income quartile population, but more than proportional to their smaller share of

homeowners. The lowest income quartile population also spends more on household utilities

than the other populations in Table 5 and less on household furnishings and lodging away from

home (including hotels and motels).

Recreation: The lowest income quartile population spends more of their budget share on

televisions than any other population presented in Table 5. With that one exception, spending

shares on all other recreation categories were lowest for the lowest income quartile than any

other population.

Education and communication: Spending shares on these item categories are very similar

between the urban population and the lowest income quartile population. The highest income

quartile population spends more of their budget share on education than the other populations

listed and the spending share of the older population is the lowest.

Apparel: Spending shares on jewelry and watches had the largest dispersion across the income

distribution. Spending shares on other apparel categories were fairly similar across the income

distribution and lowest for the older population.

Medical care: The older population spends the highest budget shares on all medical care

categories. The lowest income quartile population spends the least share on physician’s services

and health insurance.19 The impact of programs such as Medicaid and Medicare on the budget

shares for different populations is an interesting area for future research.

Transportation: Spending shares on transportation goods and services are the lowest for the

lowest income quartile population mostly due to differences in expenditures on vehicles and

vehicle maintenance and public transportation which includes all forms of non-private

transportation (such as fares for air, bus, train, ship, taxis, and ride sharing).

Other goods and services: Overall spending shares on other goods and services are highest for

the lowest income quartile population. This is due to larger spending shares on cigarettes and

for the lower income population. Future research should explore this particular impact of CE data quality on the calculation of weights specifically for a lower income population. Garret Christensen, Laura Erhard, Thesia Garner, Brett McBride, Nikolas Pharris-Ciurej, John Voorheis, “The promises and challenges of l inked rent data from the Consumer Expenditure Survey and Housing and Urban Development,” paper presented at the Joint Statistical Meetings Annual Conference 2019, Denver, Colorado, July 27–August 1, 2019 (U.S. Census Bureau, 2019). See https://www.census.gov/newsroom/press- kits/2019/jsm.html for conference proceedings, including links to all of the papers presented at the conference. 19 BLS uses an indirect method to measure the price change for health insurance. CE respondents report out-of- pocket spending on health insurance which is mostly allocated to the health care services that are covered by health insurance. The remainder is included in a health insurance retained earnings category which also includes the costs incurred by insurance companies to process claims. Since the factors used to allocate health insurance spending are fixed across populations, the lower overall budget shares of the lowest income quartile population on health insurance retained earnings can be accurately described as lower shares on out-of-pocket health insurance.

miscellaneous personal services (a category that includes legal, funeral, laundry, and banking

services).

Table 5: Distribution of total CPI market basket expenditures, snapshot of 2015-201620

Item Category All urban households (U)

62 years or older (E)

Lowest income quartile

Highest income quartile

Food, total 14.6% 12.4% 15.6% 14.2% Food at home 7.7% 7.1% 9.5% 6.7%

Food away from home 5.9% 4.5% 5.4% 6.3% Alcoholic beverages 1.0% 0.8% 0.7% 1.2%

Housing, total 41.0% 45.8% 45.2% 39.5% Shelter 30.5% 34.4% 34.6% 28.8%

Rent 7.5% 4.7% 15.6% 3.4%

Owner’s equivalent rent 23.0% 29.7% 19.1% 25.5% Household utilities 4.7% 5.0% 5.9% 3.8%

House furnishings and other household services 4.5% 4.8% 3.7% 5.1%

Lodging away from home 1.0% 1.0% 0.6% 1.4% Recreation 5.9% 5.7% 4.6% 6.7%

Education and communication, total 7.1% 4.4% 6.8% 8.0% Education 3.0% 0.8% 2.7% 4.4%

Communication 4.1% 3.7% 4.1% 3.6%

Apparel 3.2% 2.1% 2.9% 3.5% Medical care 8.5% 12.0% 8.2% 7.9%

Health insurance 1.0% 1.2% 0.8% 1.1% Professional services 3.3% 4.6% 3.0% 3.2%

Transportation, total 16.6% 14.7% 13.0% 17.2% Motor Fuel 3.7% 2.9% 3.4% 3.3%

Vehicles and vehicle maintenance 9.0% 8.0% 6.0% 9.7%

Motor vehicle insurance 2.1% 2.1% 2.4% 1.9% Public transportation 1.3% 1.2% 0.9% 1.7%

Other goods and services 3.2% 3.0% 3.6% 3.0%

Source: CE integrated data with CPI division adjustments based on CE data from 2015 – 2016.

Price Indexes We calculated Laspeyres indexes using biennial budget shares (expenditure weights), like the 2015-2016

shares described in the previous section. Indexes for the lowest and highest income quartile populations

are shown in graph 1, along with the indexes for the CPI-W and R-CPI-E for comparison purposes. We

show index results at the all items and major group levels in Table 6. The annualized percent change

over the time period of study (December 2001 to December 2018) is defined in Formula 3.

20 Due to rounding, the figures presented may not add to exactly 100%. The component items displayed are not exhaustive so the sum of their market basket shares may not equal the major group.

At the all items level, the annualized change in the lowest income quartile index is larger than that for

the urban population (and R-CPI-E index) and the annualized percent change for the highest income

quartile index is lower than that for the urban population. The annualized percent change in the lowest

income quartile index is greater than the urban population index for the education and communication,

other goods and services, housing, recreation, and transportation major groups. The annualized percent

change in the highest income quartile index is less than the urban population index for the other goods

and services, housing, recreation, and transportation major groups. As a reminder, these indexes differ

only in the market basket shares at the elementary index level.

Between 2002 and 2018, the 12-month change in the lowest income quartile index is consistently

greater than the urban population index, in 152 out of 169 months. The remaining 17 months occur in

2006, 2009, 2010, and 2011. Further study is needed to understand the cause of these months that are

different than the rest. Similarly, the 12-month change in the highest income quartile index is

consistently less than the urban population index. Future research should include variance estimation so

confidence intervals can be calculated to statistically compare these index results.

Formula 3: Annualized percent change

𝐴𝑛𝑛𝑢𝑎𝑙𝑖𝑧𝑒𝑑 𝑝𝑒𝑟𝑐𝑒𝑛𝑡 𝑐ℎ𝑎𝑛𝑔𝑒 = ( 𝑇𝑒𝑟𝑚𝑖𝑛𝑎𝑙 𝐼𝑛𝑑𝑒𝑥 𝑉𝑎𝑙𝑢𝑒

100 )

12 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑚𝑜𝑛𝑡ℎ𝑠⁄

Graph 1: Monthly Laspeyres indexes for lowest and highest income quartiles: December 2003 to

December 2018

Table 6: Laspeyres index annualized percent changes from December 2003 to December 2018

Item Category All urban households

(U)

62 years or older (E)

Wage earner (W)

Lowest income quartile

Highest income quartile

All items 2.07 2.17 2.06 2.25 1.97 Apparel 0.14 0.05 0.10 -0.09 0.23 Education and communication 1.39 0.69 0.86 1.84 1.77

Food and beverages 2.19 2.14 2.18 2.13 2.23 Other goods and services 2.65 2.52 3.07 3.03 2.25

Housing 2.31 2.32 2.36 2.45 2.17 Medical care 3.21 3.08 3.29 3.11 3.29 Recreation 0.70 1.17 0.54 0.92 0.63

Transportation 1.85 1.92 1.93 2.11 1.68

Indexes for subset populations differ from the urban population when there are meaningful differences

in budget shares and price change. The scatterplot in graph 2 displays the relationship between long

term price change (the percent change in indexes from December 2003 to December 2018) and the

difference in market basket shares (the ratio of 2015-2016 biennial shares for the lower income and

urban populations) for expenditures classes at the national level.21 The bolded x-axis shows the percent

change of the All Items, US City Average index over this time period (41.6%). They bolded y-axis shows

budget share ratios equal to one, where observations greater than one reflect greater spending shares

for the lowest income quartile compared to the urban population.

Graph 2: Relationship between price change and budget share differences for the lowest income

quartile and urban population

Source: Consumer expenditure survey data from 2015-2016, Consumer Price Index data from December

2001 and December 2018

The population in the lowest income quartile spent more than the urban population on rent and energy

services (which includes electricity), as a share of total spending, and the indexes for both of those items

rose faster than average over this time period (upper right quadrant of the graph). Conversely, the lower

income population had lower budget shares for items whose indexes rose slower than average (or fell)

such as private transportation, which includes new and used vehicles (lower left quadrant of the graph).

21 The 243 item strata that form the building blocks of CPI estimation are grouped into 70 expenditure classes.

Alcoholic beverages away from home

Rent of primary residence

Owners' equivalent rent of residences

Energy services

Appliances

Medicinal drugs

Private transportation

-100%

-50%

50%

100%

150%

200%

0 0.5 1 1.5 2 2.5

20 15

/2 01

6 b

u d

ge t s

h ar

e r

at io

Lo w

e st

in co

m e

q u

ar ti

le /

u rb

an p

o p

u la

ti o

Price change

2001-2018

These indexes likely contributed to a larger measure of price change for the lowest income quartile for

the housing and transportation major groups as well as the all items level.

Item categories in the upper left or lower right quadrants of the graph likely contributed to a smaller

measure of price change for the lowest income quartile. For example, the lowest income quartile

population spent less on owner’s equivalent rent and alcohol away from home whose prices rose faster

than average over the time period (upper left quadrant) and spent more on medicinal drugs and

appliances whose prices fell faster than the average over the time period (lower right quadrant). The

distribution of item categories across the four quadrants does not reveal a clear pattern between price

change and budget share differences.

The general patterns of price change between populations using a Laspeyres index also hold true for the

Tornqvist indexes. The lowest income quartile index displays the highest rate of inflation, and the

highest income quartile displays the lowest. Tornqvist indexes for the E and W populations were also

calculated, however note these are research indexes as opposed to the Laspeyres indexes for the E and

W populations which are produced by the BLS production systems. Graph 3 shows the Tornqvist indexes

and Table 7 shows the annualized percent changes at the all items level.22

22 Results at the major group level were presented for Laspeyres indexes to explain the differences at the all items level. This is possible because the Laspeyres index formula is consistent in aggregation, meaning the weighted sum of the major group level is equal to a direct calculation of the all items level. The Tornqvist index formula is not consistent in aggregation, therefore a presentation of major group level indexes would not necessarily explain differences at the all items level.

Graph 3: Monthly Tornqvist indexes for lowest and highest income quartiles: December 2003 to

December 2018

Table 7: Tornqvist index annualized percent changes: December 2003 – December 2018

All urban households

(U)

62 years or older (E)

Wage earner (W)

Lowest income quartile

(I1)

Highest income quartile

(I4)

Index value December 2018 (December 2003 = 100) 131.7 133.0 131.5 137.6 130.0 Annualized percent change 1.84% 1.91% 1.83% 2.14% 1.76%

For each population, the Tornqvist formula generally displays a lower measure of price change than the

Laspeyres index. The graphs in Appendix 1 and Table 8 show the difference in substitution bias between

the populations, defined as the difference in annual inflation rates measured by the Tornqvist and

Laspeyres indexes. The graphs in Appendix 1 shows the difference in the annual rate of change each

month for each population, and Table 8 shows the difference in the annualized percent changes over

the 16 year period.

100

105

110

115

120

125

130

135

140

145 2

00 31

2 00

40 7

2 00

50 2

2 00

50 9

2 00

60 4

2 00

61 1

2 00

70 6

2 00

80 1

2 00

80 8

2 00

90 3

2 00

91 0

2 01

00 5

2 01

01 2

2 01

10 7

2 01

20 2

2 01

20 9

2 01

30 4

2 01

31 1

2 01

40 6

2 01

50 1

2 01

50 8

2 01

60 3

2 01

61 0

2 01

70 5

2 01

71 2

2 01

80 7

C-CPI- lowest quartile C-CPI-U

C-CPI- highest quartile C-CPI-E

C-CPI-W

Table 8: Difference in Laspeyres and Tornqvist annualized percent change: December 2003 – December

2019

Annualized percent change All urban households

(U)

62 years or older (E)

Wage earner (W)

Lowest income quartile

Highest income quartile

Tornqvist 1.84% 1.91% 1.83% 2.14% 1.76% Laspeyres 2.07% 2.17% 2.06% 2.25% 1.97%

Substitution bias 0.23% 0.26% 0.23% 0.11% 0.21%

While the Tornqvist index for each population rises more slowly than its Laspeyres counterpart, the

difference in the rate of change is smallest for the lowest income quartile population. Indeed, the

measure of consumer substitution bias over the 2003 to 2018 time period for the lowest income quartile

population is less than half that of all urban consumers. The highest income quartile population had a

similar consumer substitution effect as all urban consumers. Future research should explore the extent

of consumer substitution (and the elasticity of substitution) across populations.

Summary and conclusion In this paper we present results of estimating CPI indexes for the lowest and highest income quartiles of

CE respondents. From 2003 to 2018, the Laspeyres index for the lowest income quartile population rose

faster than the index for all urban consumers. The Laspeyres index for the highest income quartile

population rose slower than the index for all urban consumers. Chained CPI indexes for the income

quartile populations rose slower than their Laspeyres counterparts. The measure of consumer

substitution was lowest for the lowest income quartile population; the difference between the

Laspeyres and Tornqvist index for the lowest income quartile population was less than half the

difference for all urban consumers.

We present these results with many caveats. Future research can improve upon the work of this paper

by redefining the income groups, either by using an equivalence scale to adjust for varying household

sizes, using externally defined income bands that are more representative of the population, or defining

income quartiles at the index area level (as opposed to nationally). Other improvements include using a

more sophisticated imputation methodology for missing expenditure weights (ideally sensitive to

population spending patterns) and calculation of variances to enable a statistical comparison of index

results. Additionally, the lowest income quartile population exhibits the largest number of missing

expenditure weights and the lowest measure of consumer substitution bias. Further research is needed

to understand the spending patterns of the lower income quartile subpopulation, which appear to be

unique from the W and E subpopulations previously defined.

Appendix 1: Difference in Laspeyres and Tornqvist annual percent change: December 2003 – December 2019

Paper, Household Cost Indexes: Prototype Methods and Results (U.S. Bureau of Labor Statistics)

Read more about Paper, Household Cost Indexes: Prototype Methods and Results (U.S. Bureau of Labor Statistics)

We estimate a family of price indexes known as Household Cost Indexes (HCI) using U.S. data. HCIs aim to measure the average inflation experiences of households as they purchase goods and services for consumption, and similar indexes are produced in the United Kingdom and New Zealand. These differ from the Bureau of Labor Statistics’ headline Consumer Price Index (CPI) products in two main respects. First, the upper-level aggregation of the HCIs weights households equally, unlike most headline CPIs which implicitly give more weight to higher expenditure households.

Languages and translations

English

BLS WORKING PAPERS U.S. Department of Labor U.S. Bureau of Labor Statistics Office of Prices and Living Conditions

Household Cost Indexes: Prototype Methods and Results

Robert S. Martin, U.S. Bureau of Labor Statistics Joshua Klick, U.S. Bureau of Labor Statistics William Johnson, U.S. Bureau of Labor Statistics Paul Liegey, U.S. Bureau of Labor Statistics

Working Paper 604 August 2023

Household Cost Indexes: Prototype Methods and

Results1

Robert S. Martin, Joshua Klick, William Johnson, Paul Liegey2

August 2023

Abstract

We estimate a family of price indexes known as Household Cost Indexes (HCI) using U.S.

data. HCIs aim to measure the average inflation experiences of households as they purchase

goods and services for consumption, and similar indexes are produced in the United Kingdom

and New Zealand. These differ from the Bureau of Labor Statistics’ headline Consumer Price

Index (CPI) products in two main respects. First, the upper-level aggregation of the HCIs weights

households equally, unlike most headline CPIs which implicitly give more weight to higher-

expenditure households. Second, the HCIs use the payments approach to value owner-occupied

housing services explicitly using household outlays. In contrast, the U.S. CPIs use rental

equivalence. The HCI for all urban consumers has an average 12-month change of 1.51% over

December 2011 to December 2021, compared to 1.86% for the CPI-U. Roughly 95% of the

difference is due to the payments approach.

Key Words: Price index; inflation; democratic aggregation; payments approach

JEL Codes: C43, E31

1 We thank Anya Stockburger, Robert Cage, Thesia I. Garner, and many others at the Bureau of Labor Statistics for helpful comments and guidance. 2 Division of Price and Index Number Research (Martin), Division of Consumer Price Indexes (Klick, Liegey), Division of Price Statistical Methods (Johnson), Bureau of Labor Statistics, 2 Massachusetts Ave., NE, Washington, DC 20212, USA. Emails: [email protected], [email protected], [email protected], [email protected]

1. Introduction

This article estimates Household Cost Indexes (HCIs) using U.S. data. Similar price

indexes are already produced in the United Kingdom (Office for National Statistics, 2017) and

New Zealand (Statistics New Zealand, 2020). HCIs measure the change in cash outflows

required, on average, for households to access the goods and services they purchase at a

constant quality. Like the headline and subpopulation Consumer Price Indexes (CPIs) produced

by the Bureau of Labor Statistics (BLS), the HCIs aim to capture price change for consumer

goods and services. However, the HCIs differ in two important methodological respects from

the CPIs. First, the upper-level aggregation of the HCIs weights households equally, whereas the

CPI market baskets implicitly give higher weight to higher-expenditure households.3 Second,

the HCIs use the payments approach to value services from owner-occupied housing, using

outlays on mortgage interest, property taxes, and the full reported value of insurance,

appliances, maintenance and repairs (i.e., what the household pays and when they pay it). The

CPIs, in contrast, use an implicit measure of owner-occupied housing consumption called rental

equivalence, all other goods and services (besides owner-occupied housing) are valued using

acquisition prices and expenditures (i.e., when the household acquired or took possession of

the good). For HCIs in principle, the payments approach should be applied more broadly, but

this paper focuses only on owner-occupied housing. In many cases, such as food, acquisition

and payment occur at the same time and involve the same values. We are ignoring household

outlays for the purchase of vehicles and other durable goods and instead are including the full

3 Households are still weighted by their sampling weight so that averages represent the population.

acquisition expenditures for these regardless of financing; including these in an HCI is left for a

future study.

We compute an HCI for the urban U.S. population covering the period December 2011

to December 2021. The HCI is based on the Lowe (modified Laspeyres) formula using average

annual household weights with about a two-year lag. From December 2012 to December 2021,

we find an average twelve-month inflation rate of 1.51 percent for the HCI-U, compared to 1.86

for the CPI-U and 1.73 for the Chained CPI-U. We find these empirical differences between the

HCIs and CPIs are primarily due to the HCI’s use of the payments approach, which we estimate

subtracts 0.39 percentage points per year on average relative to an index that uses rental

equivalence. This difference reflects both a lower weight for owner-occupied housing in the HCI

as well as lower inflation in explicit housing costs when compared to owner’s equivalent rent

inflation (as imputed from actual rent changes). In contrast, we estimate that equal household

weighting increases the index only about 0.05 percentage points per year on average compared

to an index which uses the standard expenditure weighting, but otherwise uses the same

methodology as the HCI.

CPIs are used in a wide variety of economic applications—as an overall macroeconomic

indicator, to deflate national accounts, to adjust marginal tax rates, and measure changes in the

cost-of-living representative of the entire economy. In such applications, measuring the change

in purchasing power of the average dollar of expenditure using an implicit consumption

concept like owner equivalent rent may be appropriate. In other cases, such comparing the

economic conditions of population subgroups, a measure tied to explicit outlays may be

attractive. One index cannot usually satisfy all needs, and in this sense the HCIs can provide

useful complementary information about the average household inflation experience.

2. Literature Review

Current BLS CPI methodology is based on market-level expenditure weights and the

rental equivalence approach to owner-occupied housing (Bureau of Labor Statistics, 2020).

Household-weighted aggregation and the payments approach differ substantially from current

BLS CPI methodology, though neither is new to the price index literature. Astin and Leyland

(2015) propose using these methods to better capture the inflation experiences of households.

They argue such a measurement is more credible for indexing monetary values, while a

traditional CPI is superior for macroeconomic analysis and inflation targeting. Based in part on

their research, the Office of National Statistics developed a set of HCIs for the United Kingdom

(Office for National Statistics, 2017). Statistics New Zealand publishes a similar set of indexes

called the Household Living-Costs Price Indexes. Research on a similar set of indexes for the U.S.

began with Cage, et. al. (2018).

Household-weighted aggregation (also known as democratic aggregation) has been

considered at least since Prais (1958). The topic has been developed and reviewed in Pollak

(1989), National Research Council (2002), International Labor Organization (2004, Chapter 18),

Ley (2005), and Martin (2022), among others. Spending patterns differ across the distribution of

total expenditure. To the extent that these differences coincide with expenditure categories

that have higher or lower inflation than average, a household-weighted index will differ from a

traditional expenditure-weighted one. Equally weighted indexes have been studied with U.S.

data in Kokoski (2000) and Hobijn, et. al. (2009). The latter is notable for statistically matching

the interview and diary components of the Consumer Expenditure Survey (CE), and we follow

many aspects of its approach. Our paper also builds on work from Cage, et. al. (2018) and

Martin (2022), the latter of which finds that household-weighted aggregation adds about 0.08

percentage points per year to inflation measured by a Lowe-type CPI from December 2001 to

June 2021.

Based in part on the observation from Boskin, et. al. (1998) that increases in the owner

equivalent rent component of the CPI could correspond to housing value appreciation, and that

owner-occupiers "should not be compensated for capital gains on their housing", Cage, et. al.

(2018) began exploring alternative methods for the BLS. The payments approach to owner-

occupied housing has been discussed at least since the 1989 version of the International Labor

Organization (ILO) CPI manual (as cited by Goodhart, 2001), and much of our initial approach

follows the 2004 version (International Labor Organization, 2004, Chapter 10). The payments

approach to owner-occupied housing focuses on the month-to-month outlays by households

rather than an upfront purchase price (the acquisition approach) or the implicit consumption

value (the use approach).4 In addition to the HCIs for the United Kingdom and New Zealand, the

payments approach is also used in the CPI for Ireland (Central Statistics Office, 2016). Mortgage

interest is also included in the housing component of the CPI for Canada (Statistics Canada,

2019), and was a part of the U.S. CPI housing component prior to 1983 (Gillingham and Lane,

1982). Diewert and Nakamura (2009) contains a conceptual comparison of the payments

4 Rental equivalence and user cost are both flavors of the use approach.

approach against other methods like the user cost approach and rental equivalence, while

Garner and Verbrugge (2009) compare methods empirically using the CE.

Astin and Leyland (2015) argue that the payments approach is superior for comparing

household inflation experiences and escalating payments. They make the case that because

rental equivalence is not tied to explicit outlays, an index which includes it as a large

component may be less tethered to the actual price movements that affect household budgets.

For some subpopulations, there can be large differences between implicit rents and explicit

cash flows. For instance, in Cage et. al. (2018), the subpopulation of households which receives

at least 50% of its before-tax income from Social Security has higher relative expenditures on

shelter (35-39%) when measured using rental equivalence than the overall urban population

(32%), but lower relative expenditures when measured using payments (16-23%). This is

because these households are disproportionately likely to be owner-occupiers without

mortgages, meaning their explicit housing outlays are limited to items like property taxes,

insurance, and maintenance.

Astin and Leyland (2015, 2023), as well as ILO (2003) advocate such an index for

escalation purposes, but this position is not universally held. Diewert and Shimzu (2021) argue

“it is not an index that can measure household consumption of the services of durable goods

because it focuses on the immediate costs associated with the purchase of durable goods and

ignores possible future benefits of these purchases.” The payments approach has also been

criticized in Goodhart (2001), Poole, Ptacek, and Verbrugge (2005), and elsewhere on the basis

that it doesn’t reflect consumption in an economic sense. We agree that a flow-of-service

method like rental equivalence is more appropriate for a macro-focused CPI or a representative

consumer’s cost-of-living index (See, e.g., Diewert 1976). However, we study the HCIs as

complementary series intended to capture explicit outlays of households rather than the

implicit consumption prices (in an economic theoretic sense) reflected in a traditional CPI,

though initially the distinction is limited to owner-occupied housing. The objective of our paper

is primarily to compare owner-occupied housing and household aggregation methods.

3. Methods and Data

Our methods for this paper are preliminary and based on utilizing existing BLS surveys or

publicly available data sources. Like the CPIs, the HCIs are constructed in two stages. First, basic

indexes are constructed for item-area strata (e.g., coffee in Washington, DC). These are then

aggregated using expenditure weights from the CE. As our initial version only applies the

payments approach to owner-occupied housing, the elementary indexes and underlying

household expenditures used in upper-level aggregation are largely the same. See Bureau of

Labor Statistics (2020) for more details. For housing, the owner equivalent rent elementary

indexes are replaced with indexes for property taxes, mortgage interest, and property

management services. In addition, to reflect payment amounts, we use the full reported value

of household expenditures on household appliances, maintenance and repair, and insurance

when constructing upper-level aggregation weights.5 Finally, we estimate equally weighted

averages of household expenditure shares based on matched CE Interview and Diary data and

use these in the second-stage aggregation.

5 This is different from the published CPI and C-CPI, which adjust these expenditures downward to reflect the likelihood they would be made by a renter.

3.A. Payments Approach Item Structure and Elementary Indexes

The payments approach for owner occupied housing reflects the housing-related cash

outflows of households. Compared to the CPI, the HCI item structure excludes owner’s

equivalent rent and includes three additional expenditure classes—property taxes, mortgage

interest, and other primary residence expenses. The payments approach also removes several

adjustments CPI makes to other category weights, which we discuss more later in this section.

Within property taxes and mortgage interest, we create new elementary item indexes

representing primary residences. These also serve as proxies for secondary residences. In the

CPI, the price index for owner’s equivalent rent of primary residences (numbered “01”) also

serves as the proxy for the unpriced item (numbered “09”) representing secondary residences.

A further item classification (see Table 1 for details) for other primary residence expenses

consists of ground rent, parking, and property management services. This category comprises

less than one half of one percent of the overall index weight, and we provisionally measure its

price change using the producer price index for final demand property management services as

a proxy. Finally, our objective, where possible, is to limit expenditures to those pertaining to

primary residences and vacation homes and exclude investment properties.

The rest of this section details the construction of the property tax and mortgage

interest payment indexes. We follow what is (to our knowledge) international practice by

including the interest component of mortgage payments (excluding second mortgages or home

equity lines of credit) and excluding the portion that goes toward principal reduction (and by

this reasoning down payments and cash purchases). From the 2004 ILO manual, only the

interest portion is considered a pure cash outflow; the principal portion immediately shows up

on the household’s balance sheet as an increase in assets, so it may be considered more like an

investment with a potential future return (International Labor Organization 2004, Chapter 10).

This view is not universal (see Astin and Leyland, 2015). However, including mortgage principal

presents additional technical challenges.6

Also following international practice, the mortgage interest and property tax payments

indexes derive conceptually from two sources of potential change: a rate (an interest rate or an

effective property tax rate) and the base to which the rate is applied (the debt level or the

dwelling value). Changes in rates alone do not capture changes in purchasing power

(International Labor Organization 2004, Chapter 10). Some users could be concerned about

allowing the effects of home prices given these could be associated with (eventual) financial

returns to households. In our view, there is a tradeoff between representing the explicit outlays

of households and controlling for investment using economic theory. Indeed, as noted by

Poole, Ptacek, and Verbrugge (2005), adjusting housing payments to account for investment

results in the user cost approach, which is another implicit housing cost concept. Empirically,

Garner and Verbrugge (2009) show that user costs can differ greatly from explicit payments.7

Our initial strategy, following international practice, aims to exclude the investment aspect of

housing ownership by excluding mortgage principal. Appendix A shows the decision to

6 The most straightforward method to estimate the proportional impact of changing interest rates on mortgage principal payments would involve plugging in aggregate (i.e., average) interest rates into a nonlinear function. In the sense of measuring a change in average payments across households, the potential bias of such a plug-in procedure from Jensen’s Inequality is unknown. 7 Garner and Verbrugge (2009) also find that user cost measures based on different underlying assumptions can differ greatly from each other and from implicit rents.

indirectly include home prices is significantly inflationary for the housing payments indexes and

suggests the decision to exclude mortgage principal is somewhat deflationary.

Finally, our preliminary results compute a single set of payments approach elementary

item indexes representing the U.S. urban population. We leave it to future research to extend

these methods to create elementary indexes by CPI geographic areas.

3.A.1. Mortgage Interest Payment Index

The mortgage interest payments index measures the proportional change in the interest

payment amount that would occur holding fixed the financing conditions—such as the loan

term and proportion of principal remaining. We aim to follow the recommendations in the

2004 ILO manual (Chapter 10), which is to use both a representative basket of interest rates

and a debt index, which holds “constant the age of the debt” between index periods

(International Labor Organization 2004, Chapter 10). Payments in each period are determined

by transactions occurring at many previous points in time, as mortgage loans are long-term

contracts. Consequently, our index is based on weighted averages of interest rates and house

prices corresponding to loans or debt of different ages. A fixed-basket approach has the

advantage of being feasible with aggregate interest rate and house price data, but the

disadvantage of not being micro-founded.8

8 We considered such a micro-founded approach which could, for example, average proportional changes in rates actually paid by households between the reference and comparison periods without fixing the loan age. Such an approach may be more appropriate for the U.S. market, which is dominated by 30-year fixed rate mortgages. However, basing such an approach on CE interest rate microdata misses any variation which occurs when a consumer unit moves from one house to another since consumer units are not followed.

Similar to Canada (Statistics Canada, 2019), we define the index as the product of a debt

index (which is influenced by home prices) and an interest rate index which compare payments

in the comparison period 𝑡 against the reference period 𝑠.9 The index is based on the model of

a thirty-year fixed rate mortgage, which dominates the U.S. market (about 75% of existing loans

as reported in the CE).10 It is written:

𝑃𝑀𝐼𝑃 = 𝑃𝐷𝑃𝑟 , (1)

where 𝑃𝐷 is the debt index and 𝑃𝑟 is the interest rate index. They are written

𝑃𝐷 = ∏ 𝐻

𝑡−𝑗

𝜓𝑏𝑗�̅� 𝑗=0

∏ 𝐻 𝑠−𝑗

𝜓𝑏𝑗�̅� 𝑗=0

(2)

and

𝑃𝑟 = ∏ 𝑟

𝑡−𝑗

𝜑𝑏𝑗𝜃−1 𝑗=0

∏ 𝑟 𝑠−𝑗

𝜑𝑏𝑗𝜃−1 𝑗=0

. (3)

The indexes measure change from period 𝑠 to period 𝑡 by weighting past home prices (relative

to a common base) and interest rates according to the relative importance of loans or debt

initiated in those months to the index periods 𝑡 and 𝑠.11

In these expressions, 𝐻𝜏 is a home price index for month 𝜏, 𝑟𝜏 is an average interest rate

for month 𝜏, 𝜓𝑏𝑗 is the population-weighted proportion of mortgagor-month observations with

debt of age 𝑗 (measured as the number of months since the property was acquired), and 𝜑𝑏𝑗 is

9 While our debt index is similar to the housing component of Canada’s mortgage interest index, their interest rate component is based on unit value-like averages using administrative banking data. 10 We ignore preferential treatment of mortgage interest in the tax code. 11 While the product of two geometric means with identical weights could be written as one geometric mean, writing the index as a product of two components makes for convenient discussion and analysis.

the population-weighted proportion of mortgagor-month observations with current loans of

age 𝑗 (measured as the number of months since the first payment) during the reference period

𝑏. The 𝜓 and 𝜑 parameters differ due to refinances. We use the proportion of mortgagors

(rather than the proportion of debt, which is closer to what Statistics Canada uses) in keeping

with the equal-weighting objective of the HCI. The parameter 𝜃 equals 360 to reflect the

number of potential payments in a thirty-year loan, while �̅� is set higher to allow for acquisition

periods to be earlier on refinanced properties. While not well bounded in theory, we set �̅�

equal to 408 to accommodate the beginning of our house price indexes in January 1975. This

covers about 97.5% of observations in our sample. We evaluate adjacent months 𝑡 and 𝑠. We

set 𝑏 as the fourth quarterly lag of the quarter containing month 𝑡. This reflects a realistic

production constraint for using CE data to construct the weights while keeping them as current

as possible. We use CE microdata on mortgage expenses and keep those observations with 30-

year fixed rate first mortgages on primary residences. We drop loan records that likely pertain

to non-housing expenditures (second mortgages and home equity lines of credit).

We use monthly averages of the weekly 30-year fixed mortgage rate averages from the

Freddie Mac Primary Mortgage Market Survey (PMMS), which are available only for the U.S.

market. We also use the Federal Housing Finance Agency’s (FHFA) All Transactions House Price

Index. This index is quarterly, and we interpolate monthly values using the natural spline in

SAS’s PROC EXPAND. The FHFA’s purchase only house price index is monthly and superior

conceptually for a debt index representing past home purchases. However, this series only goes

back to 1991, and would not be long enough to cover all loan ages in our sample.

3.A.2. Property Tax Payment Index

The property tax payment index measures the change in average property tax payments

for households. Our proposed method attempts to hold the aggregate quality of the housing

stock constant and uses annual data from the CE.12 Let 𝑋𝑠,𝑡 and 𝑉𝑠,𝑡 denote proportional growth

in population aggregates for property tax payments and owner-occupied housing unit values

between years 𝑠 and 𝑡, and let 𝐻𝑠,𝑡 be a constant-quality home price index between years 𝑠 and

𝑡. We use timeseries representing the entire U.S. and leave it for future research to extend the

method to geographic areas, which require more granular tax data than we currently have. We

compute the following:

𝑃𝑃𝑇𝑃 = 𝑋𝑠,𝑡 𝑉𝑠,𝑡

𝐻𝑠,𝑡 . (4)

Our method is similar to that of Statistics Canada and the Office for National Statistics,

which compute unit value indexes, or ratios of average property tax payments, though they do

so for different geographic areas. Let 𝑁𝑠,𝑡 be the growth in the number of owner-occupied

housing units between 𝑠 and 𝑡. A similar approach we explored with CE data computes

𝑃𝑃𝑇𝑈𝑉 = 𝑋𝑠,𝑡 𝑁𝑠,𝑡

. (5)

12 The CE asks homeowners the annual property taxes owed on their primary residence and adjusts these amounts if the property is partly used as a business. The CE also asks the consumer unit to estimate the market value of their primary residence. Investigating potentially more timely sources of property tax data is a task for future research.

where we use the number of owner-occupier consumer units to proxy for the number of

owner-occupied housing units.13 Equation (4) is equal to equation (5) divided by (𝑉𝑠,𝑡/𝑁𝑠,𝑡)/𝐻𝑠,𝑡

which is the growth in average home values deflated by the constant-quality home price index.

We interpret this ratio as a measure of change in dwelling quality which is relevant under the

assumption that the total housing market valuations 𝑉𝑠,𝑡 and the house price indexes 𝐻𝑠,𝑡

approximate changes in value and price as would be measured by tax assessors. We found that

the long-term trends of Eq. (4) and (5) were very similar. As in Canada and the U.K., we do not

attempt to control for potential differences in quality of municipal services.

Our preliminary efforts use annual property tax aggregates from the CE, as the survey

asks about annual tax obligations rather than monthly payments. The monthly expenditure

microdata include these figures divided by 12. We find that that using Equations (4) and (5) on

this average monthly data leads to substantial short-term sampling variation. For this reason,

we compute the property tax index at an annual frequency and interpolate monthly values

using a spline function. Statistics Canada and the Office for National Statistics, for instance,

update their property tax indexes once per year. The CE is not the ideal source for property tax

and housing value data, as data for a calendar year are released about nine months after that

year ends. For this reason, this paper’s analysis only covers through the end of 2021. Finding

timelier and larger samples using alternative data is an objective for future research.

13 In the CE, consumer units are equivalent to households in the vast majority of cases, but are defined by joint economic decision making rather than residence or familiar relationships.

3.B. Upper-level Aggregation

As in the CPI, we use CE data to derive upper-level aggregation weights, with some

important differences. As shown in Table 1, the set of eligible elementary item strata now

includes property taxes and mortgage interest and excludes owner equivalent rent. The

property tax and mortgage interest weight are derived from the monthly expenditures on those

items as collected by the CE. In addition, we use the full reported values of expenditures on

items like maintenance and repair, homeowner’s insurance, appliances, and household

furnishings. Under the rental equivalence approach, these items are scaled down for owner-

occupiers to reflect the likelihood of a renter making the same purchase. Table 2 compares

average housing-related relative importance across consumer units in different subpopulations

—by housing tenure, an indicator for being a wage earner or clerical worker (as in the CPI-W),

and an indicator for being elderly (age greater than or equal to 62, as in the R-CPI-E)14—both

under the payments approach and rental equivalence. In general, housing payments make up a

smaller share of overall spending under the payments approach than under rental equivalence.

For the urban population, for instance, housing under the payments approach amounts to

34.3% of the market basket on average, versus 42.9% on average under rental equivalence.

Interestingly, patterns of spending across some subpopulations differ by housing approach. For

instance, under rental equivalence, the average share going to housing among the elderly is

relatively high at 46.8%. Under the payments approach, however, the elderly have a high

proportion going to insurance, appliances, maintenance, and repairs (“other housing”), but

14 Consumer units were classified according to their reported demographic in their last interview in the sample.

relatively less going to mortgage interest, resulting in a total housing weight of 34.1%, slightly

less than the overall urban population (34.3%).

Table 1: Weights for Select Housing Items for the HCI Subsample in 2019

Payments Rental

Equivalence Code Description $ Bil. % RI* $ Bil. % RI*

HC01 Owner’s Equivalent Rent of Primary Residence NA NA 1,144.36 22.40 HC09 Unsampled Own. Equiv. Rent of Second. Res. NA NA 56.29 0.75 HD01 Tenants’ and Household Insurance 38.02 1.01 17.24 0.38 HH01 Floor Coverings 8.29 0.18 2.54 0.05 HK01 Major Appliances 17.05 0.39 2.38 0.06 HK09 Other Appliances 0.08 0.00 0.07 0.00 HM01 Tools, Hardware, and Supplies 17.23 0.43 11.67 0.26 HM09 Unsamp. Tools, Hardw., Outdoor Equip, Supp. 58.44 1.31 9.35 0.20 HP04 Repair of Household Items 46.52 0.83 4.14 0.08 HP09 Unsampled Household Operations 10.69 0.23 4.29 0.07 HR01 Property Tax of Primary Residence 199.70 4.51 NA NA HR09 Property Tax of Secondary Residence 8.61 0.16 NA NA HS01 Mortgage Interest of Primary Residence 211.64 4.26 NA NA HS09 Mortgage Interest of Secondary Residence 4.55 0.08 NA NA HT01 Other Owner Payments for Primary Residence 14.10 0.42 NA NA HT09 Other Owner Payments for Secondary Res. 1.29 0.02 NA NA * Average (equally-weighted) relative importance across consumer units.

Table 2: Average Household Relative Importance for Housing by Subpopulation (percent)

Category Urban Wage- earner Elderly

Own. w/ Mortgage

Own. w/o Mortgage Renter

Payments Approach Rent 9.2 13.0 6.3 0.1 0.2 31.8 Property Tax (Primary) 4.5 4.2 5.5 6.0 6.8 0.1 Property Tax (Secondary) 0.2 0.1 0.3 0.2 0.2 0.1 Mortgage Interest (Primary) 4.3 5.1 2.6 10.1 0.1 0.0 Mortgage Interest (Secondary) 0.1 0.1 0.1 0.1 0.2 0.0 Other Housing 16.0 14.8 19.4 16.9 22.0 8.8 Total Housing 34.3 37.2 34.1 33.2 29.5 40.9 Rental Equivalence Approach Rent 9.2 13.0 6.3 0.1 0.2 31.7 Owner’s Equiv. Rent (Primary) 22.4 20.5 28.2 30.4 32.8 0.4 Owner’s Equiv. Rent (Secondary) 0.7 0.4 1.2 0.7 1.2 0.4 Other Housing 10.6 10.4 11.1 10.9 12.0 8.7 Total Housing 42.9 44.3 46.8 42.1 46.1 41.2 Note: Cells show average December 2020 relative importance (2019 reference period weights price-updated to December 2020 values) across households meeting the HCI sample requirement. While expenditures cover a year, consumer units are classified according by attribute from their last collection quarter.

Our upper-level aggregation uses the Lowe formula, and same as the CPI (as of January

2023) the quantity weights pertain to annual expenditure reference periods which are updated

each year. The household-weighted aggregation starts from the CE Interview sample, as

consumer units contribute up to one year of data and the Interview comprises most eligible

expenditures. Eligible expenditures from the Diary survey are imputed to the Interview sample

using a matching procedure based on Hobijn, et. al. (2009), which is described further later in

this section and similar to that used in Martin (2022). The procedure matches eligible Diary

consumer units to an Interview consumer unit based on demographic characteristics that are

predictive of total expenditure. The second-stage aggregation is then based on the Lowe

formula with lagged expenditure weights.

𝑃𝐻𝐶𝐼 = ∑∑�̅�𝑎,𝑖,𝑣,𝑏𝑃𝑎,𝑖,𝑡,𝑣

𝑖∈ℐ𝑎∈𝒜

(6)

�̅�𝑎,𝑖{𝑣,𝑏} = (

𝐻𝑎,𝑏

𝐻𝑏 )𝐻𝑎,𝑏

−1 ∑ 𝜔ℎ

ℎ∈ℋ𝑎,𝑏

𝑠𝑖,𝑣,𝑏,ℎ

(7)

𝐻𝑎 = ∑ 𝜔ℎ

ℎ∈ℋ𝑎,𝑏

, 𝐻𝑏 = ∑ ∑ 𝜔ℎ

ℎ∈ℋ𝑎,𝑏𝑎∈𝒜

(8)

where 𝑎 indexes the geographic area, 𝑖 the item stratum, 𝑣 the index pivot month, 𝑏 the weight

reference period, and ℎ the consumer unit. The set of areas is 𝒜, the set of items ℐ, and the

set of consumer units in area 𝑎 during period 𝑏 is ℋ𝑎,𝑏. The elementary index between pivot

month 𝑣 and period 𝑡 for item 𝑖 in area 𝑎 is given by 𝑃𝑎,𝑖,𝑡,𝑣. The associated household-weighted

expenditure shares are �̅�𝑎,𝑖,𝑣,𝑏. These are equally (with respect to the population) weighted

averages of individual consumer unit annual expenditure shares 𝑠𝑖,𝑣,𝑏,ℎ, with 𝜔ℎbeing

household ℎ’s sampling weight. The weight reference period 𝑏 is the calendar year two years

prior to the calendar year containing month 𝑡, and the expenditure shares 𝑠𝑖,𝑣,𝑏,ℎ are price-

updated to represent period 𝑣 values using the ratio of the elementary index in month 𝑣 to its

average over period 𝑏.

Consumer units participate in the CE for up to four collection quarters, providing up to

twelve months of expenditures. Because participation is on a rolling basis and there is unit

nonresponse and occasional attrition, the number of observations exactly lining up with a single

calendar year is relatively small, often only a few hundred. Therefore, for the HCI, we define a

“reference year” sample differently than does either the CE or CPI. We assign a consumer unit

to a reference year 𝑏 if its last month of expenditure occurred during year 𝑏. So that each ℎ’s

expenditure basket reflects a whole year, we include only observations which completed all

four quarterly interviews, even if some of their expenditures occurred in the prior calendar

year. For the 2019 reference year, for instance, (used for indexes in 2021), we include

consumer units with at least one month occurring in 2019, meaning we include some

observations whose sample tenure started as early February 2018. With the four-quarter

requirement, this amounts to a sample of 3,063 unique consumer units (12,252 collection

quarters) representing our 2019 reference year. In comparison, 11,740 unique consumer units

(comprising 22,957 collection quarters) in the CE have expenditures recorded for the calendar

year 2019.15 For index subgroup definitions, we use consumer unit characteristics from their

final collection quarter.

As discussed in Martin (2022), including observations with periods less than one year

can distort household-weighted indexes due to greater variability in total expenditures and

lower average expenditure shares for less frequently purchased items. However, there is a

potential trade-off with the four-quarter requirement due to representativity. Table 3 shows

differences in the relative frequencies of a few consumer unit demographics. For the 2019

reference year, the HCI subsample has a greater proportion of owners and elderly than the full

sample of urban consumer units. At the same time, Table 2 shows there are differences in the

average expenditure shares on housing-related payments across these groups, suggesting

potential consequences for price indexes. For instance, the elderly spend relatively more on

property taxes than on mortgage interest, reflecting that they are disproportionately owners

without mortgages.

15 These sample sizes were calculated by counting the number of unique FAMID (or the consumer-unit specific portion of the FAMID) for a given expenditure reference period.

Table 3: Frequency of Consumer Unit Characteristics by Sample in 2019 (percent)

All Urban HCI Subsample

Owner with mortgage 37.3 41.4 Owner without mortgage 23.6 29.1 Renter 39.2 29.6 Wage earner 27.0 25.3 Elderly 30.8 37.7

Nevertheless, we find little evidence of a sample selection bias stemming from our HCI

eligibility criteria, at least over during sample period. Table 4 shows (comparing columns 2 and

3) the impact of using the CE subsample on major group-level weights is small relative to the

effect of using the payments approach or household aggregation. Additionally, we find

(Appendix C) that the sample selection impact on an expenditure-weighted version of the HCI-U

(corresponding to column 4 of Table 4) is minimal, about 0.01 percentage points per year.

Furthermore, our results show a CPI-like index calculated from these subsamples (with Diary

expenditures imputed as described in the next subsection), corresponding to column 3 of Table

4 closely matches the published CPI-U. These together imply our results are driven by the

payments approach and household-weighted aggregation, and not the reference period or CE

subsample. Our current method makes no adjustments to the CE sampling weights, which we

leave to future research. Such adjustments may be more important with more recent data than

our sample period, particularly with recent surges in mortgage interest rates.

There are a few other differences between our research indexes and official CPI

methods. Since the HCI is based on consumer unit-specific shares, which must be weakly

positive, we censor negative annual expenditures at zero.16 We also make some small item-

structure changes to simplify calculations using historical data. Finally, we omit weight-

smoothing procedures used in the CPI, including composite estimation for the item-area

weights, which are designed to lower their sampling variance across geographic areas. Our all

items, all areas CPI-U replications closely match the published indexes even without these

procedures, and our prototype procedure only estimates property tax and mortgage interest at

the national level. We leave it to future research to extend weight-smoothing procedures to the

HCIs.

Figure 1 below shows the December 2020 relative importance by major expenditure

group and select housing categories and compares them with the published shares for the CPI-

U. The HCI shares correspond to the 2019 weight reference year, while for the CPI they

correspond to the 2017-18 reference period. Table 4 tracks the change in relative importance

by major group as different HCI elements are activated. The effects of the payments approach

and household-weighted aggregation on the relative weights are significant, but sometimes

have offsetting effects. For instance, the overall housing weight in the HCI is smaller than the

CPI, as property tax, mortgage interest, and the increase in other housing outlays amounts to

less than the decrease due to the exclusion of OER. By itself, this decrease in housing weight

increases the weight allocated to other categories, like medical and recreation. At the same

time, however, household-weighted aggregation shifts weight toward households with lower

16 This affects items RC01 “Sports Vehicles, Including Bicycles”, TA02 “Used Cars and Trucks”, and TA09 “Unsampled New and Used Motor Vehicles.” The CPI counts returns or sales as negative expenditures.

total expenditures, further increasing the relative importance of rent and food while decreasing

that of transportation.

Figure 1: December 2020 Relative Importance for HCI-U and CPI-U

Panel a: HCI-U (2019 weights)

Panel b: CPI-U (2017-18 weights)

20.2%

9.2%

4.7%

4.3%

16.0%3.1%

14.2%

11.1%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Housing: Other Apparel

Transportation Medical

Recreation Educ. & Comm.

Other

15.2%

7.9%

24.3%

10.3% 2.7%

15.2%

8.9%

5.8%

6.8% 3.2%

Food & Bev. Housing: Rent

Housing: OER Housing: Other

Apparel Transportation

Medical Recreation

Educ. & Comm. Other

Table 4: December 2020 Relative Importance for Different Index Types (percent)

Major Group CPI-U (2) (3) (4) HCI-U

Food and Beverages 15.16 15.68 15.60 17.96 20.16 Housing 42.39 41.84 42.13 33.34 34.26 Apparel 2.66 2.70 2.67 3.07 3.15 Transportation 15.16 15.43 14.60 16.80 14.23 Medical 8.87 8.79 9.18 10.58 11.09 Recreation 5.80 5.80 6.16 7.08 6.59 Education and Comm. 6.81 6.72 6.57 7.61 6.76 Other 3.16 3.04 3.09 3.56 3.76 Methods* Reference Period 2017-18 2018-19 2019** 2019** 2019** CE Sample Full Full 4-quarter 4-quarter 4-quarter Aggregation Expenditure Expenditure Expenditure Expenditure Household Owner Occ. Housing REQ*** REQ*** REQ*** Payments Payments * Columns 2-5 also reflect other methodology changes and simplifications described in text. ** Under our sample eligibility criteria, this includes spending back to February 2018. *** REQ = Rental Equivalence

3.B.1. Interview-Diary Matching Procedure

As mentioned, the basis of our household average expenditure weights is the CE

Interview sample, which covers about three-quarters of the expenditure basket as traditionally

sourced by the CPI. We implement a statistical matching procedure based on Hobijn et al.

(2009) to impute the remaining proportion which CPI sources from the Diary.17 Similar

observations from the Diary sample provide the remaining expenditure data for each Interview

consumer unit, according to a model of expenditures as a function of demographic

characteristics. The dependent variable is expenditures on items which HCI (and the CPI)

sources from the Diary, but for which the Interview either collects the same item or has more

17 Garner, et. al. (2022) and Martin (2022) also use matching processes based on Hobijn, et. al. (2009).

aggregate data.18 The model is a convenient way of combining many characteristics according

to which linear combination most strongly predicts expenditures. We then use the predicted

values to form measures of distance between an Interview recipient and its potential Diary

donors. For our main results, the only attribute guaranteed to match between donor and

recipient is quintile group membership based on the distribution of annual before-tax income.19

For our results on housing tenure subpopulations, we also guarantee this attribute matches.

The matching procedure is many-to-one, as we draw four donor Diaries for each Interview in

each month with replacement. The procedure is implemented separately by month so that

weekly Diary donors are evenly distributed temporally over the recipient Interview’s sample

tenure. Due to the sample selection criteria outlined earlier, for reference year 2019, for

example, that means we are running monthly regressions from February 2018 to December

2019. The stratification and model estimation are done on the full Interview sample, not just

the four-quarter subsample.

First, we stratify both Interview and Diary consumer unit samples for the reference

period by the sample quintiles of annual before-tax income. For each month 𝑡 and quintile

grouping 𝑞, we use the Interview sample to estimate the regression

𝑦ℎ𝑡 = 𝒙ℎ𝑡𝜷𝑞𝑡 + 𝑢ℎ𝑡 ,

(9)

18 From Martin (2022), Table A2, these amount to about 80% of Diary-sourced expenditures in 2019. Alternatively, it might seem attractive to use the Diary sample to estimate Diary expenditures as a function of demographic characteristics, as we intend to impute these expenditures for the Interview sample. However, we find that characteristics explain relatively little variation in Diary expenditures, perhaps due to the short (week-long) recall period. 19 The Diary samples are small enough that conditioning on multiple characteristics quickly leads to empty cells. See Hobijn, et al. (2009) for more discussion.

where 𝑦ℎ𝑡 is logged expenditure of consumer unit h. The term 𝑢ℎ𝑡 is an error term, and 𝒙ℎ𝑡

include Census region, urban/rural, age, race, sex, and education of the reference person,

consumer unit size, the log of annual before-tax income (if positive), and an indicator for

whether income was negative.20 We use the least squares estimator weighted by the CE

sampling weight, finlwt21. Over the sample period, R-squared values for the quintile and

month-specific regressions averaged 0.17, while income quintile itself explained about 0.31 of

the variation in the dependent variable.

Let �̂�𝑞𝑡 be the slope estimate for quintile 𝑞 in month 𝑡. As household characteristics are

available and comparably defined in both surveys, we calculate predicted values �̂�ℎ𝑡 = 𝒙ℎ𝑡�̂�𝑞𝑡

for each Diary and Interview observation. For a given Interview observation ℎ and Diary

observation 𝑘, the distance metric is defined as

𝛿𝑡(ℎ, 𝑘) = |�̂�ℎ𝑡 − �̂�𝑘𝑡|.

(10)

Within each month and income quintile, we calculate 𝛿𝑡(ℎ, 𝑘) for all {ℎ, 𝑘} pairs. Then for each

Interview observation ℎ, we randomly select (with replacement) four 𝑘 from the twenty

smallest 𝛿𝑡(ℎ, 𝑘) out of all the Diary observations from the same month and income quintile.

The random component is intended to ensure a more even distribution of matches across Diary

observations. The detailed set of expenditures of the donor Diary is then assigned to the

recipient Interview. As one donor Diary is intended to represent one quarter of one month of

expenditure, but Diaries correspond to a one-week recall period, the donor Diary expenditures

20 These demographic variables technically pertain to the collection quarter or some other reference period, so we implicitly assume they represent the associated reference months. For the matching regressions, we allow a consumer unit’s attributes to vary by collection quarter.

are scaled by 13/12. This process is repeated for each Interview observation, for each month it

is in the sample.21 Since the Interview sample is much larger than the Diary on a per-month

basis, each Diary is matched with several Interviews. Further analysis of the matching

procedure is in Appendix B.

4. Results

We find the HCI-U follows similar patterns of acceleration and deceleration as the CPI-U,

but it has significantly lower average rates of growth during our sample period. The average 12-

month change in the HCI-U averages 1.51% versus 1.86% for the CPI-U, as shown in Table 5.

Figure 2 plots the index levels, showing markedly different trends between the CPI-U and HCI-U

from 2012-2020. The two indexes increased at a similar rate in 2021, averaging 4.6-4.7% year-

over-year growth throughout the year. Table 5 includes an index (U-EW-REQ) which uses

expenditure weighting and the rental equivalence approach but uses our CE subsample and

processing methods. It also includes a comparable series (U-EW-PAY) which instead uses the

payments approach but uses expenditure weighting as in the CPI. Comparisons of these indexes

and the HCI-U show the difference in trends and average growth reflects primarily the impact

of the payments approach. U-EW-PAY averages about 0.39 percentage points per year less than

U-EW-REQ, and in a single year (2016) averages 0.74 percentage points lower. In 2021, the

impact of the payments approach is to add 0.15 percentage points to the average 12-month

percent change, reflecting increasing home prices and interest rates. In 2022, we also expect

21 In the CPI, diary expenditures are multiplied by 13 to account for the difference in recall periods between weekly diaries and quarterly interviews. The scaling in our procedure is analogous in that an interview is matched with a total of 12 diaries each quarter, and with the scaling these also represent 13 weeks.

this effect to be positive and much larger in magnitude due to the large increase in mortgage

interest rates. In contrast, comparing HCI-U to U-EW-PAY shows the household-weighted

aggregation adding only slight amount to the overall average 12-month percent change

(0.05%), but yearly average differences are as high as 0.16 percentage points in 2017. In 2021,

household-weighted aggregation lowers HCI-U by 0.1 percentage points on average.

Figure 2: HCI-U and CPI-U Index Levels

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

lowe-u (ew, req) lowe-u (ew, pay) cpi-u hci-u

Table 5: HCI and CPI Average 12-month Percent Changes by Year

Year HCI-U CPI-U U-EW-

REQ U-EW-

PAY HCI-OM HCI-ONM HCI-RNT

2013 0.99% 1.47% 1.43% 0.86% 0.52% 1.22% 1.57% 2014 1.41% 1.62% 1.63% 1.27% 1.02% 1.65% 1.77% 2015 -0.44% 0.12% 0.15% -0.44% -0.88% -0.52% 0.27% 2016 0.56% 1.26% 1.24% 0.51% 0.10% 0.55% 1.19% 2017 1.76% 2.13% 2.13% 1.60% 1.41% 1.79% 2.24% 2018 2.36% 2.44% 2.42% 2.33% 2.32% 2.23% 2.52% 2019 1.39% 1.81% 1.81% 1.43% 1.30% 1.02% 1.80% 2020 0.93% 1.24% 1.21% 0.84% 0.65% 0.89% 1.31% 2021 4.62% 4.69% 4.58% 4.73% 4.54% 4.95% 4.44%

Average 1.51% 1.86% 1.84% 1.46% 1.22% 1.53% 1.90% Notes: U signifies urban population. U-EW-REQ is a CPI-like replication using the HCI sample and simplified expenditure processing methods, but expenditure-weighting and rental equivalence. Similarly, U-EW-PAY uses expenditure-weighting, but the payments approach. “OM” is owners with a mortgage, “ONM” is owners without a mortgage, and “RNT” is renters.

Table 6: International HCI and CPI Comparison, Average 12-month Percent Changes

Year UK-HCI* UK-CPIH* NZ-HLPI† NZ-CPI‡

2013 2.53% 2.31% 1.36% 1.13% 2014 1.48% 1.45% 1.65% 1.23% 2015 -0.08% 0.37% 0.72% 0.29% 2016 0.73% 1.00% 0.35% 0.65% 2017 2.67% 2.57% 1.78% 1.85% 2018 2.49% 2.30% 1.99% 1.60% 2019 1.99% 1.75% 1.43% 1.62% 2020 0.63% 1.00% 1.25% 1.72% 2021 2.51% 2.49% 3.11% 3.94%

Average 1.66% 1.69% 1.51% 1.56% *Office for National Statistics (2022). †Statistics New Zealand (May 2023). ‡Statistics New Zealand (June 2023).

For comparison, in Table 6, we list the average yearly inflation for headline HCI and CPI

inflation in the United Kingdom (U.K.) and New Zealand over our study period. We expect some

variation across these measures due to differences in methods and country-specific economic

conditions. New Zealand’s price index which is comparable to the HCI is known as the

Household Living-costs Price Index (HLPI). Both it and the UK’s HCI use a payments approach as

well as household-weighted aggregation. While the U.S. HCI-U tends to be among the lower

yearly averages, its long-term average is about the same as New Zealand’s HLPI. We are

particularly interested in the differences between a country’s HCI and CPI. For this, the U.K. is

the more comparable case, as its Consumer Price Index Including Owner-Occupied Housing

(CPIH) uses rental equivalence (Office for National Statistics, 2019). New Zealand’s CPI, on the

other hand uses the net acquisitions approach, which measures net additions to the housing

stock, including new construction and a excluding the value of land (Statistics New Zealand,

May 2023). The magnitudes of the yearly average HCI-CPI inflation differences for the U.S. (0.36

percentage points) are similar to the U.K. (0.21) and New Zealand (0.37). On the other hand,

the U.S. appears different in that the HCI (in terms of annual averages) is uniformly lower than

the CPI by 0.36 percentage points. In contrast, the U.K. and New Zealand’s HCI and HLPI

average 0.03 and 0.04 percentage points lower than their respective CPIs.

Figure 3 describes further how in the U.S., the actual outlays for owner-occupiers are

associated with lower inflation than would be implied by rental equivalence. Over the sample

period, the official index for owner’s equivalent rent increases 33.8% cumulatively, while our

sub-aggregate for owner’s payments (combining property tax, mortgage interest, and other

owner payments) increased only 11.5%. Within owner’s payments, the two major components,

the trend in the property tax index is similar to owner’s equivalent rent for most of the sample

period. However, the mortgage interest index trends flat, not yet picking up the sharp increases

in interest rates occurring in 2022 after our sample period ends.22 We also note that evolution

of the mortgage interest index is smoother than current average mortgage interest rates (from

the Freddie Mac PMMS), because the index is averaging over 30 years of past mortgage rates in

order to reflect current payments.

Figure 3: Owner’s Equivalent Rent vs. Owner’s Payments

Finally, we further illustrate the treatment of owned housing outlays by estimating HCI’s

for three subpopulations, owners with a mortgage (OM), owners without a mortgage (ONM),

and renters (RNT). We define these using the housing tenure value reported by the consumer

unit in their final interview. The final three columns of Table 5 show the average 12-month

percent changes, while Figure 4 plots the index levels. HCI-RNT, has average inflation of 1.9%

22 Our analysis is constrained by sourcing property tax payments from the CE, which as of June 2023 are only available through the first half of 2022. The average 12-month change for the mortgage interest index is 8.2% in 2022. Using the first half of 2022 property tax burden (X/V) as a crude forecast, we find an average change in the owner’s payments index of 10.0% in 2022 (versus 5.7% for owner’s equivalent rent), and an average change in the HCI-U of 8.7% (versus 8% for the CPI-U).

0.9

0.95

1.05

1.1

1.15

1.2

1.25

1.3

1.35

1.4

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Owner's Equiv. Rent (HC) Owner's Payments (HR, HS, HT)

Property Tax (HR) Mortgage Interest (HS)

Other Owner Payments (HT) 30-yr fix. rate (r. axis, %, PMMS)

and is closest to the CPI-U. While there may be overall weight differences between the urban

population and the subpopulation of renters, the evolution of owner’s equivalent rent is close

enough to the evolution of actual rent that this result is not surprising. In contrast, the HCI

inflation for owners is significantly lower, averaging 1.53% per year for those without a

mortgage and 1.22% per year for those with a mortgage. As with the urban indexes, the relative

rankings are not the same year to year. For instance, owners without mortgages had the

highest average inflation in 2021, 4.95%, versus 4.54% for owners with a mortgage and 4.44%

for renters.

Figure 4: HCIs for Housing Tenure Subpopulations

4.A. Alternative Treatments of Owner Payments for Housing

As discussed in Section 3.A, we follow international practice in excluding mortgage

principal and basing mortgage interest and property tax index changes on two sources: a

change in a rate (the interest rate or the effective property tax rate), and the change in a

monetary base (the debt level and the housing value). The appendix, including Figure 6 and

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

hci-om hci-onm hci-rnt

Figure 7, explore the sensitivity of the indexes to these decisions. Including mortgage principal

would raise the owner’s payments subindex (combining mortgage interest, property tax, and

other payments as in Figure 3) by 0.8 percentage points per year. Combined with the associated

weight increase to mortgages, this would result in an all-items HCI-U that is higher by 0.10

percentage points per year. The effect of home prices would be more substantial, lowering the

owner’s payments index by 4.0 percentage points per year and the all-items HCI-U by 0.38

percentage points per year.

5. Conclusions and Future Research

To the extent feasible with existing CPI and publicly available data, we compute HCIs for

the urban population and housing tenure subpopulations. Data constraints (specifically for the

property tax index) would prevent timely production of an HCI using these methods, but our

results still shed light on the relative impacts of different elements of HCI methodology. In

particular, we find the HCI differs from the CPI mainly because it uses the payments approach

for owner occupied housing, and only slightly because it weights households equally in its

upper-level aggregation. The payments approach tracks the actual outlays of homeowners,

which over our sample period of 2012 to 2021 have escalated at a lower trend than (imputed)

owner’s equivalent rent, resulting in lower inflation as measured by the HCI than as measured

by the CPI. We do not argue that the payments approach is superior from the standpoint of

measuring the cost-of-living as an economic theoretic concept or for use in monetary policy.

Rather, by reflecting the explicit outlays of owners, we show the HCI offers a measurement of

the household inflation experience which is empirically different than the CPI.

Future research could focus on many areas. First, the inclusion of principal reduction

payments in a mortgage index (or the capital component of housing more generally) remains an

area of debate and discussion (e.g., Astin and Leland 2023), which should be evaluated

empirically. In addition, our measures of price change for mortgage and property tax payments

use only national-level data. A natural next step would be to extend these to subnational

geographic areas, if relevant and feasible. Further down the road, exploring mortgage

microdata of the sort described by Bhutta, et. al. (2020) could be informative on different

experiences of subpopulations, to the extent that long enough histories can be obtained to

account for the long lives of mortgage loans. More timely and granular property tax data would

also improve the HCI. In addition, in principle, the payments approach could be extended to any

durable good where payment occurs over a long timeframe, with automobiles in particular

being a high priority. Martin (2022) suggests treating automobiles under an approach

consistent with the target of the index (payments, in our case) is critical if higher-frequency

household weights are to be taken seriously, such as for a monthly weighted superlative like

the C-CPI-U. Custom sampling weights should also be created to account for demographic

differences for the four-quarter sample of consumer units used for the HCIs, but further

analysis may also be warranted related to weight frequency and subsample selection. With

payments approach weighting of automobiles, for instance, perhaps infrequent purchase issue

discussed in Martin (2022) is less salient. Finally, the impact household-weighted aggregation

on the all-items index’s sampling variation or the potential of weight-smoothing techniques

have yet to be explored.

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Appendix

A. Alternative Mortgage Interest and Property Tax Indexes

The mortgage payments index which includes mortgage principal replaces the interest

rate component, Eq. (3), with the following representing change in full mortgage payments

between months 𝑠 and 𝑡:

𝑃𝑓 =

∏ [𝑓(𝑟𝑡−𝑗 , 𝜃 − 𝑗)] 𝜑𝑏𝑗𝜃−1

𝑗=0

∏ [𝑟𝑠−𝑗 , 𝜃 − 𝑗)] 𝜑𝑏𝑗𝜃−1

𝑗=0

. (11)

where 𝑓(𝑟, 𝜔) = 𝑟𝑅𝜔 (𝑅𝜔 − 1)⁄ , 𝜔 > 1, where 𝑅 = 1 + 𝑟. The function 𝑓 represents the

fixed mortgage payment as a proportion of the current debt amount. In this expression, the

interest rate 𝑟 is the annualized rate divided by 12 so that it corresponds to one month. Note,

when estimated using aggregate data, even if 𝑟𝑡−𝑗 equals an average interest rate across

households with loans of age 𝑗, the amount 𝑓(𝑟𝑡−𝑗 , 𝜃 − 𝑗) cannot be interpreted as an average

mortgage payment ratio across households due to Jensen’s inequality. The relationship

between 𝑓(𝑟𝑡−𝑗 , 𝜃 − 𝑗) and a true household average is unknown (at least to the authors) but

using such an average in a price index would require microdata tracking individual mortgagors

across loan changes including refinances (which we can observe in the CE) and new loans

(which we often do not observe due to address-based sampling). The mortgage payment

indexes without home prices remove the debt index component, Eq. (2), while the property tax

index without home prices is just the effective tax rate component, 𝑋𝑠,𝑡 𝑉𝑠,𝑡⁄ from Eq. (4).

Figure 5 shows the December 2020 relative importance for the baseline HCI-U (2019

reference period) along with the three versions which either include mortgage principal,

exclude home prices, or do both things simultaneously. Note, the relative importance reflects

not only the 2019 reference period expenditure weight, but also the price-updating to reflect

spending in the December 2020 pivot month. Including mortgage principal leads to an increase

in the mortgage payment weight from 4.3% to 7.2% when home prices are excluded, or from

4.1% to 6.9% when home prices are excluded. Accordingly, weight on all other spending

categories decreases slightly when mortgage principal is included. On net, the increase in the

total housing relative importance from including mortgage principal is only about 2 percentage

points. Additionally, even when including mortgage principal, the total weight for the housing

major group (36.3%) is still lower than when using rental equivalence (42.4%). As the inclusion

of home prices in the mortgage and property taxes affects only the price-updating, its effects

on the relative importance are much smaller.

Figure 6 plots the different Owner’s Payment subindexes (combining mortgage interest,

property taxes, etc., as in Figure 3) and compares them again against owner’s equivalent rent.

Adding mortgage principal increases the owner’s payments index by about 0.8 percentage

points per year when home prices are included, and about 1 percentage point per year when

home prices are excluded. Given the strong upward trend of home prices over the past several

decades, removing their lowers the payments index by 4.0 percentage points per year when

mortgage principal is excluded and by 6.6 percentage points per year when mortgage principal

is included, resulting in downward trends. Figure 7 tracks these payments indexes changes on

the all-items HCI-U, accounting for changes in both the elementary indexes and the aggregation

weights. The overall effect of mortgage principal is modest, adding 0.10 or 0.03 percentage

points per year depending on whether house prices are included. Home prices themselves have

a larger impact on the all-items index, decreasing it by either 0.38 or 0.45 percentage points per

year depending on whether mortgage principal is included.

Figure 5: December 2020 Relative Importance for HCI-U under Alternative Owner Payments

Panel a: HCI-U (baseline)

Panel b: HCI-U (with mortgage principal)

Panel c: HCI-U (no house prices)

Panel d: HCI-U (with mortgage principal, no

house prices)

20.2%

9.2%

4.7%

4.3%

16.0%3.1%

14.2%

11.1%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Housing: Other Apparel

Transportation Medical

Recreation Educ. & Comm.

Other

19.5%

9.2%

4.4%

7.2%

15.5% 3.0%

13.8%

10.8%

6.4%

6.5% 3.7%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Apparel Transportation

Medical Recreation

Educ. & Comm. Other

20.3%

9.2%

4.2%

4.1%

16.1%3.2%

14.3%

11.2%

6.6%

6.8% 3.8%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Housing: Other Apparel

Transportation Medical

Recreation Educ. & Comm.

Other

19.7%

9.2%

4.0%

6.9%

15.6% 3.1%

13.9%

10.9%

6.4%

6.6% 3.7%

Food & Bev. Housing: Rent

Housing: Prop. Tax Housing: Mortgage

Apparel Transportation

Medical Recreation

Educ. & Comm. Other

Figure 6: Alternative Versions of Owner’s Payments

Figure 7: HCI-U Under Alternative Versions of Owner’s Payments

B. Interview-Diary Matching Details

We base our household-averaged weights on the CE Interview sample but use a

statistical matching procedure to assign sets of weekly Diary expenditures to each Interview

0.7

0.8

0.9

1.1

1.2

1.3

1.4

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Owner's Equiv. Rent (HC) Paym. (HR, HS, HT) Paym. (with principal) Paym. (no home prices) Paym. (with principal, no home prices)

1.05

1.1

1.15

1.2

1.25

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

cpi-u hci-u hci-u (with principal) hci-u (no home prices) hci-u (with principal, no home prices)

consumer unit. Our procedure is similar in spirit to that of Hobijn, et. al. (2009), though that

paper models expenditure change (implied by a consumer-unit specific price index) rather than

expenditure levels. Modeling expenditure changes is attractive given the ultimate use of the

matched dataset for price indexes, but Martin (2022) finds demographics explain much less of

the variation in expenditure changes. We limit the dependent variable to categories collected in

both the Interview and the Diary to ensure that the correlations picked up by the model are

relevant to the expenditures we ultimately wish to impute. Over the sample period, R-squared

values for the quintile and month-specific regressions averaged 0.17, while income quintile

itself explained about 0.31 of the variation in the dependent variable. Figure 8 below plots the

average regression R-squared for each quintile, where the averaging is over the 23 months used

for each reference period. The figure shows that average R-squared for the income quintiles are

fairly stable over time, averaging about 0.23 for the 1st quintile, 0.17 for the second quintile,

0.13 for the third quintile, 0.11 for the fourth quintile, and 0.15 for the fifth quintile. The fits

(conditional on income quintile) are not particularly strong, which motivates matching an actual

diary’s expenditure set to an interview consumer unit rather than using regression fitted values.

Figure 8: Average R-Squared by Reference Period and Income Quintile

The rest of this section presents figures comparing the imputed weekly diary

expenditures to the actual. Figure 9 shows average imputed weekly expenditures for the

reference period track the actual averages well over time, always falling within 1% of the true

averages. Figure 10 compares average weekly Diary expenditures over time by major group. For

food and beverages, which is by far the largest category sourced from the Diary, the imputed

averages fall within 1% of the actual averages, and they fall within 10% for all other categories.

Figure 11 compares the deciles of weekly imputed Diary expenditures to those of the actual

Diary expenditures for the 2019 reference period (results are similar for other periods). The two

marginal distributions line up well—the imputed deciles are within a few dollars of the actual

deciles.

0.05

0.1

0.15

0.2

0.25

2010 2011 2012 2010 2013 2014 2015 2016 2010 2017 2018 2019

IQ1 IQ2 IQ3 IQ4 IQ5

Figure 9: Actual and Imputed Average Weekly Diary Expenditures by Reference Period

220

230

240

250

260

270

280

290

300

310

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

actual imputed

Figure 10: Average Weekly Diary Expenditures by Reference Period and Major Group

Panel a: Food and Beverages

Panel b: Housing

Panel c: Apparel

Panel d: Transportation

Panel e: Medical

Panel f: Recreation

Panel g: Education and Communication

Panel h: Other

100

150

200

actual imputed

Figure 11: Deciles of Actual and Imputed Weekly Diary Expenditures for 2019 Reference Year

In terms of joint distributions, the matching procedure also does a good job at

replicating average diary expenditures by several demographic characteristics, as shown in

Figure 12 for 2019. Not surprisingly, because income quintile is conditioned on, the procedure

replicates average expenditures by income quintile quite well. The procedure also does well

replicating average differences by housing tenure, age categories, Census region, presence of

children, and education categories, even though these characteristics are not explicitly

conditioned on in the matching process. In these cases, the match quality is being driven by the

correlation between these characteristics and income, as well as the extent to which similarity

in these characteristics across surveys is predictive of expenditures, and so leading to lower

distance between similarly attributed observations.

100

200

300

400

500

600

700

1 2 3 4 5 6 7 8 9

actual imputed

Figure 12: Average Weekly Diary Expenditures by Attribute, 2019 Reference Period

Panel a: Income Quintile

Panel b: Housing Tenure

Panel c: Age

Panel d: Presence of Children

Panel e: Census Region

Panel f: Education

100

200

300

400

500

600

1 2 3 4 5

actual imputed

100

200

300

400

Own w/ Mort.

Own w/o Mort.

Renter No cash rent

Student

actual imputed

100

150

200

250

300

350

<=61 >61

actual imputed

100

200

300

400

No kids Kids

actual imputed

260

270

280

290

300

310

320

330

NE MW S W

actual imputed

100

200

300

400

< H.S. H.S. & Some Coll.

>= Bachelors

actual imputed

C. All-items Indexes Using Different CE subsamples

Figure 13: Twelve-month inflation of CPI and indexes using payments approach by subsample

As a check of our sample requirement that consumer units contributing to the HCI have

four quarters of data in the CE survey, we compare all-items indexes (all using the payments

approach) with this eligibility requirement against all-items indexes without. For this

comparison, we examine expenditure-weighted aggregates across households, as equally-

weighted aggregates can be sensitive to weight frequency and overall dispersion in total

expenditures (Ley, 2005; Martin, 2022). We consider both the full CE sample for the reference

year, as well as for the full CE sample for the biennial period ending in the reference year, as

our HCI subsample also includes four-quarter households who entered the CE in the year prior

to the reference year. Figure 13 plots the twelve-month percent changes of these indexes as

well as the CPI-U for reference. Over this period, average inflation of the CPI-U is 1.86% per

-0.02

-0.01

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08 D

ec -1

M ay

-1 3

O ct

-1 3

M ar

-1 4

A u

g- 1

Ja n

-1 5

Ju n

-1 5

N o

v- 1

A p

r- 1

Se p

-1 6

Fe b

-1 7

Ju l-

1 7

D ec

-1 7

M ay

-1 8

O ct

-1 8

M ar

-1 9

A u

g- 1

Ja n

-2 0

Ju n

-2 0

N o

v- 2

A p

r- 2

Se p

-2 1

lowe-u (ew, pay, 4Q) lowe-u (ew, pay, full-be)

lowe-u (ew, pay, full-a) cpi-u

year. The payments approach index using the four-quarter sample averaged 1.46%, while the

indexes using the full annual and biennial samples averaged 1.47% and 1.46%, respectively.

Figure 14: Twelve-month inflation of HCI and indexes using payments approach by subsample

Figure 14 repeats the analysis in Figure 13, but compares the HCI-U and comparable

household-weighted indexes using the full annual or biennial CE samples. The HCI-U averaged

1.51% year-over-year, while the index using the full annual and full biennial samples averaged

1.50% and 1.51%, respectively, though larger differences occurred in 2021. Here, index

differences could reflect sample selection effects, but also likely reflect the mixed frequencies

of household weights underlying the full-sample indexes, as some consumer units have only a

few months or quarters of expenditure due to normal sample rotations and unit nonresponse.

Higher frequency expenditure shares tend to give less weight to less frequently purchased

items and more weight to more frequently purchased items (Martin, 2022). We do not want to

capture this latter effect because, in the case of the HCI’s, it is an artifact of using CPI weights

-0.02

-0.01

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

D ec

-1 2

M ay

-1 3

O ct

-1 3

M ar

-1 4

A u

g- 1

Ja n

-1 5

Ju n

-1 5

N o

v- 1

A p

r- 1

Se p

-1 6

Fe b

-1 7

Ju l-

1 7

D ec

-1 7

M ay

-1 8

O ct

-1 8

M ar

-1 9

A u

g- 1

Ja n

-2 0

Ju n

-2 0

N o

v- 2

A p

r- 2

Se p

-2 1

hci-u lowe-u (hw, pay, full-be) lowe-u (hw, pay, full-a)

for automobiles, which are measured by full purchase price at the time of acquisition, rather

than ongoing monthly payments. In 2021, when HCI-U (over the four-quarter sample) has

slightly higher inflation than the two full sample indexes. In 2021, vehicle price inflation was

high relative to the average inflation across all items, and the comparison in the figure is

consistent with the full-sample indexes giving too little weight to vehicles. A payments

approach for vehicles should mitigate this effect in the full samples.

Presentation, Thesia Garner (U.S. Bureau of Labor Statistics)

Read more about Presentation, Thesia Garner (U.S. Bureau of Labor Statistics)

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English

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Progress Report of the UNECE Task Force on Subjective Poverty Measures

Thesia I. Garner, PhD

Chair of UNECE Task Force on Subjective Poverty Measurement

and Chief Researcher, Office of Prices and Living Conditions

28–29 November: Meeting of the UNECE Group of Experts on Measuring Poverty and Inequality

Session D. “Subjective poverty” 28 November 2023 16:05 - 17:30

Geneva, Switzerland

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Appreciation to UNECE Expert Group on Poverty and Inequality

Task Force Members & Vania

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Progress Details ◼ Meeting of Task Force members (throughout 2023)

 Finalized what to include

 Identified leadership and contributors for writing chapters

◼ Final draft report

 Introduction

 Chapter 2. Focus on Subjective Poverty

 Chapter 3. Approaches for Measurement and Analysis

 Chapter 4. Methods for Data Collection and Guidance

 Chapter 5. Recommendations

 Appendices

– Appendix A. Survey of countries summary

– Appendix B. R computer code to produce Subjective Poverty Line as intersection of MIQ & income based on econometric estimation

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapters 1 & 2

◼ Chapter 1 Introduction -- background

◼ Chapter 2. Focus on Subjective Poverty

 Introduction

Definitions of subjective poverty

– Contrast to objective poverty

– Frameworks for subjective poverty

– Collection and analysis of subjective poverty at NSOs

– Collection and analysis of subjective poverty at International Agencies

Why measure

Evolution of subjective poverty measurement (literature review)

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 3 ◼ Approaches to measurement

Qualitative questions not focused on specific level of income (or consumption) – Identification

– Evaluation

– Prediction

Qualitative categorical focused on specific level of income (or consumption) – Evaluation

– Prediction

Money metric valuation question

◼ Analysis

Relationships

 Subjective poverty lines

Country/international organization examples

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Box 7. Example of Qualitative Categorical Evaluation Questions Focused on Income (Deleeck)

[EU-SILC participating countries] A household may have different sources of income and more than

one household member may contribute to it. Thinking of your household’s total income, is your

household able to make ends meet, namely, to pay for its usual necessary expenses?

• With great difficulty

• With difficulty

• With some difficulties

• Fairly easily

• Easily

• Very easily

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Box 10. Examples of Money Metric Valuation Questions, Minimum Income (MIQ)

[Brazil] Taking into account the current situation of your family, what would be the minimum

monthly income needed to “make ends meet”?

[Ukraine] What do you think: how much money (according to today’s price level) for one of your

household members is needed in order to not feel poor?

[Kyrgyz Republic] What is your opinion, how much money on average per month at today's price are

needed for the family with the same number of people as you have in order to avoid poverty?

[Moldova] What monthly cash income would meet the minimum needs of one person in order to

'live from day to day’?

[Belarus] In your opinion, what amount of money does your household need to have monthly to

meet[satisfy] the minimum needs of all its members?

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 4. Methods for Data Collection and Guidance - 1

◼ Survey frame and sample consideration

◼ Surveys – traditional versus alternative (e.g., rapid response)

◼ Administrative and registry data

◼ Sources of error – responses and representativeness

◼ Validity and relationship to other measure of poverty and economic well-being

◼ Time frame for data collection and release

◼ Cross sectional versus longitudinal data collection

◼ OECD subjective well-being guidelines

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 4. Methods for Data Collection and Guidance - 2

◼ Hypothetical assessments of subjective poverty

 Importance of question wording and examples

Frame and mode effects

What impacts responses (e.g., demographics, culture STiK)

◼ Lessons learned from COVID 19

 Subjective poverty in SEIA Questionnaires and Comparability Analysis

Overview of UNDP Socio-Economic Impact Assessments (SEIAs) for countries

in households of UNECE region

 Implications regarding COVID experience outbreak

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Money Metric Valuation and Use

◼ To meet the expenses you consider

necessary, what do you think is the minimum

income, before tax, a family like yours needs, on

a yearly basis, to make ends meet (if you are not

living with relatives, what are the minimum needs,

before tax, of an individual like you)?

◼ In your opinion, how much do you have to

spend each year in order to provide the basic

needs for your family? By basic needs I mean

barely adequate food, shelter, clothing and other

essential items required for daily living.

Version 1 (MIQ, 1988)

Version 2 (MSQ, 1988)

Su b

je ct

iv e

m in

im u

m in

co m

Actual income

Z*45

Intersection of MIQ and Income

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 5. Recommendations 1-3 Subjective measures of poverty should be included among the set of assessment tools used by countries …

▪ NOT to replace objective measures or multidimensional measures

▪ Serve as complements

▪ Countries with dashboards of poverty indicators should include subj assessments

Given their inclusion in EU-SILC, and their utility in identifying subjective poverty, NSOs use as standard for international comparisons...

▪ Deleeck questions - refer to level of financial difficulty (categorical)

▪ Minimum Income Question (money metric valuation)

Primary method to estimate subjective poverty lines …

▪ Utilize Minimum Income Question with

▪ Intersection approach (econometric estimation)

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Chapter 5. Recommendations 4-6 NSOs and analysts should consider the possible impacts of …

▪ Survey mode, context (framing), sampling methods, and working differences

▪ When analyzing subjective indicators like subjective poverty

▪ Countries with dashboards of poverty indicators should include subj assessments

NSOs and analysts should continue to demonstrate the utility of subjective poverty measures, considering...

▪ Issues of overlap with objective poverty measures and

▪ Policy applications

Subjective poverty measures should be …

▪ Disaggregated to at-risk groups

▪ Follow recommendations in UNECE’s guide to disaggregation

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Table A.1: Question Types Reported Being Asked by Country in UNECE (2021) Study

Qualitative Categorical Money

Metric Total # of

Subjective

Poverty

Questions

Other

Country Identification Evaluation Prediction Evaluation

Deprivation,

Social

Exclusion,

Well-being

Total # across

all Countries 4 42 6 40 45 22

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Appendix B. Estimation Example - Sample

◼ Sample data (N=1000):  income: monthly household income

 miq: response to the Minimum Income Question

 ordered: response to a categorical question (categories: 1-6)

 size: household size

 urban: degree of urbanisation with categories: capital city; other cities; towns; rural areas

 LNincome; LNmiq, and LNsize refer to natural logs of income, miq, and size variables.

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Income (eur/month)

D e n s ity

Actual income

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Minimum income (eur/month)

D e n s ity

Subjective minimum income

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Appendix B. R Code Provided to Produce SPL

16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Remaining Timeline

Time Task Status 20 Nov 2023 Draft report posted on wiki Completed

15 Dec 2023 Report finalized

Early Jan-Feb 2024

Send full report to Conference of European Statisticians (CES) Bureau for review

Mar-Apr 2024 Electronic UNECE wide consultation with all UN Member States – here is where we expect to receive comments from the countries

End of April- May 2024

Integrate comments and submit final report to the CES Bureau (CES Bureau meeting held in June)

May 2024 Submit final report to the CES plenary session for endorsement

17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Discussion & Questions

18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Contact

Thesia I. Garner

Chair of the UNECE Task Force on Subjective Poverty Measures and

Chief Researcher, Office of Prices and Living Conditions Bureau of Labor Statistics

Washington, DC 20212

[email protected]

mailto:[email protected]

Slide 1: Progress Report of the UNECE Task Force on Subjective Poverty Measures
Slide 2: Appreciation to UNECE Expert Group on Poverty and Inequality Task Force Members & Vania
Slide 3: Progress Details
Slide 4: Chapters 1 & 2
Slide 5: Chapter 3
Slide 6: Box 7. Example of Qualitative Categorical Evaluation Questions Focused on Income (Deleeck)
Slide 7: Box 10. Examples of Money Metric Valuation Questions, Minimum Income (MIQ)
Slide 8: Chapter 4. Methods for Data Collection and Guidance - 1
Slide 9: Chapter 4. Methods for Data Collection and Guidance - 2
Slide 10: Money Metric Valuation and Use
Slide 11
Slide 12
Slide 13: Table A.1: Question Types Reported Being Asked by Country in UNECE (2021) Study
Slide 14: Appendix B. Estimation Example - Sample
Slide 15: Appendix B. R Code Provided to Produce SPL
Slide 16: Remaining Timeline
Slide 17: Discussion & Questions
Slide 18: Contact

Russian

1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Отчет о работе Целевой группы ЕЭК ООН по субъективным показателям бедности

Тесия И. Гарнер, доктор наук Председатель Целевой группы ЕЭК ООН по измерению субъективной бедностии главный научный

сотрудник Управления по ценам и условиям жизни

28-29 ноября: Совещание Группы экспертов ЕЭК ООН по измерению бедности и неравенства

Сессия D. "Субъективная бедность" 28 ноября 2023 г. 16:05 - 17:30

Женева, Швейцария

2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Благодарность экспертной группе ЕЭК ООН по проблемам бедности и неравенства Члены целевой

группы и Ване

3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Подробности о ходе работ ◼ Встреча членов рабочей группы (в течение 2023 г.)

 Окончательно определились с тем, что включить

 Определены руководители и исполнители для написания глав

◼ Окончательный проект отчета

 Введение

 Глава 2. Фокус на субъективную бедность

 Глава 3. Подходы к измерению и анализу

 Глава 4. Методы сбора данных и руководства

 Глава 5. Рекомендации

 Приложения

– Приложение A. Резюме опроса стран

– Приложение B. Компьютерный код на языке R для получения субъективной черты бедности как пересечения MIQ и дохода на

основе эконометрической оценки

4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Главы 1 и 2

◼ Глава 1. Введение - история вопроса

◼ Глава 2. Фокус на субъективной бедности

Введение

Определения субъективной бедности

– Противопоставление объективной бедности

– Рамки субъективной бедности

– Сбор и анализ субъективной бедности в НСО

– Сбор и анализ субъективной бедности в международных агентствах

Зачем измерять

Эволюция измерения субъективной бедности (обзор литературы)

5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 3 ◼ Подходы к измерению

Качественные вопросы, не ориентированные на конкретный уровень дохода (или потребления)

– Идентификация – Оценка – Прогнозирование

Качественные категориальные, ориентированные на конкретный уровень дохода (или потребления)

– Оценка – Прогнозирование

Вопрос об оценке в денежной метрике

◼ Анализ Взаимоотношения Субъективные границы бедности Примеры стран/международных организаций

6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Вставка 7. Пример вопросов качественной категориальной оценки, ориентированных на доход (Deleeck)

[Страны-участницы ЕС-СИЛК] Домохозяйство может иметь различные источники дохода, и

несколько членов домохозяйства могут вносить в него свой вклад. Если подумать о совокупном

доходе Вашего домохозяйства, способно ли оно сводить концы с концами, то есть оплачивать

свои обычные необходимые расходы? • С большим трудом

• С трудом

• С некоторыми трудностями

• Достаточно легко

• Легко

• Очень легко

7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Вставка 10. Примеры вопросов по оценке денежной метрики, минимальный доход (MIQ)

[Бразилия] Принимая во внимание текущее положение Вашей семьи, каков должен быть

минимальный ежемесячный доход, необходимый для того, чтобы "свести концы с концами"?

[Украина] Как Вы считаете, сколько денег (по сегодняшнему уровню цен) необходимо для одного из

членов Вашего домохозяйства, чтобы не чувствовать себя бедным?

[Кыргызская Республика] Как Вы считаете, сколько денег в среднем в месяц по сегодняшним ценам

необходимо семье с таким же количеством человек, как у Вас, чтобы избежать бедности?

[Молдова] Какой ежемесячный денежный доход удовлетворял бы минимальные потребности

одного человека, чтобы «жить изо дня в день»?

[Беларусь] По Вашему мнению, какую сумму денег должно ежемесячно иметь Ваше домохозяйство,

чтобы удовлетворять минимальные потребности всех его членов?

8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 4. Методы сбора данных и рекомендации - 1

◼ Рассмотрение структуры и выборки опроса

◼ Опросы - традиционные и альтернативные (например, быстрого

реагирования)

◼ Административные и регистрационные данные

◼ Источники ошибок - ответы и репрезентативность

◼ Валидность и связь с другими показателями бедности и экономического

благосостояния

◼ Сроки сбора и выпуска данных

◼ Поперечный и продольный сбор данных

◼ Руководство по субъективному благополучию Организации экономического

сотрудничества и развития

9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 4. Методы сбора данных и рекомендации - 2

◼ Гипотетические оценки субъективной бедности

Важность формулировки вопроса и примеры

Эффекты рамок и режимов

Что влияет на ответы (например, демографические характеристики,

культура STiK)

◼ Уроки, извлеченные из COVID 19

Субъективная бедность в анкетах SEIA и анализ сопоставимости

Обзор оценок социально-экономического воздействия (ОСПВ) ПРООН для

стран региона ЕЭК ООН по домохозяйствам

Последствия вспышки заболевания, вызванной опытом COVID

10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Оценка и использование денежной метрики

◼ Как Вы считаете, какой минимальный доход

до вычета налогов необходим такой семье,

как Ваша, в год, чтобы свести концы с концами

(если Вы не живете с родственниками, каковы

минимальные потребности до вычета налогов

такого человека, как Вы)?

◼ Как Вы считаете, сколько Вам приходится

тратить в год, чтобы обеспечить основные

потребности своей семьи? Под основными

потребностями я понимаю едва ли

достаточное количество пищи, жилья, одежды

и других предметов первой необходимости,

необходимых для повседневной жизни.

Версия 1 (MIQ, 1988)

Версия 2 (MSQ, 1988)

Su b

je ct

iv e

m in

im u

m in

co m

Actual income

Z*45

Пересечение MIQ и дохода

11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 5. Рекомендации 1-3 Субъективные показатели бедности должны быть включены в набор инструментов оценки, используемых странами…

▪ НЕ должны заменять объективные или многомерные измерения

▪ Служат в качестве дополнения

▪ Страны, имеющие панели показателей бедности, должны включать субъективные оценки

Учитывая их включение в EU-SILC и полезность для определения субъективной бедности, НСО используют их в качестве стандарта для международных

сопоставлений...

▪ Вопросы Делека - относятся к уровню финансовых трудностей (категорические)

▪ Вопрос о минимальном доходе (оценка денежной метрики)

Первичный метод оценки субъективных границ бедности…

▪ Использовать вопрос о минимальном доходе с

▪ Пересекающимся подходом (эконометрическая оценка)

12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Глава 5. Рекомендации 4-6 НСО и аналитики должны учитывать возможные последствия…

▪ Способ проведения исследования, контекст (фрейминг), методы выборки и рабочие различия

▪ При анализе субъективных показателей, таких как субъективная бедность

▪ Страны, имеющие панели показателей бедности, должны включать в них субъективные оценки

НСО и аналитики должны продолжать демонстрировать полезность субъективных показателей бедности, учитывая...

▪ Вопросы совпадения с объективными показателями бедности и

▪ Применение политики

Субъективные показатели бедности должны быть…

▪ Дезагрегировано по группам риска

▪ Следовать рекомендациям руководства ЕЭК ООН по дезагрегированию

13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Таблица А.1: Типы вопросов, задаваемых по странам в исследовании ЕЭК ООН (2021)

Качественные Категориальные Денежная

метрика Общее

количество

вопросов о

субъективной

бедности

Прочее

Страна Идентификация Оценка Прогноз Оценка

Депривация,

социальное

отчуждение,

благосостоя

ние Общее

количест

во по

всем

странам

4 42 6 40 45 22

14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Приложение Б. Пример оценки - Образец

◼ Выборочные данные (N=1000):  доход: ежемесячный доход домохозяйства

 miq: ответ на вопрос о минимальном доходе

 упорядоченный: ответ на категорический вопрос (категории: 1-6)

 размер: размер домохозяйства

 город: степень урбанизации с категориями: столица; другие города; поселки; сельская местность

 LNincome; LNmiq и LNsize означают натуральные логарифмы переменных дохода, miq и размера.

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Income (eur/month)

D e n s ity

Actual income

0.00000

0.00025

0.00050

0.00075

0.00100

0 1000 2000 3000 4000

Minimum income (eur/month)

D e n s ity

Subjective minimum income

100

200

300

400

1 2 3 4 5 6

Ordered response

F re

q u e n c y

Self-reported category

100

200

300

1 2 3 4 5

Household size

F re

q u e n c y

Household size

100

200

300

capital city town rural

Type of location

F re

q u e n c y

Type of location

15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Приложение В. R-код, используемый для получения SPL

16 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Оставшиеся сроки

Время Задача Статус 20 ноября 2023 Проект отчета размещен в Вики Завершено

15 декабря 2023 Подготовка отчета завершена

Начало января - февраль 2024

Направить полный текст отчета на рассмотрение Бюро Конференции европейских статистиков (CES)

Март-апрель 2024

Электронные консультации с участием всех стран- членов ЕЭК ООН - здесь мы ожидаем получить комментарии от стран

Конец апреля - май 2024

Интеграция комментариев и представление окончательного отчета в Бюро КЕС (заседание Бюро КЕС состоялось в июне)

май 2024 Представить итоговый отчет на пленарное заседание КЕС для утверждения

17 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Обсуждение и вопросы

18 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Контакт

Thesia I. Garner

Chair of the UNECE Task Force on Subjective Poverty Measures and

Chief Researcher, Office of Prices and Living Conditions Bureau of Labor Statistics

Washington, DC 20212

[email protected]

mailto:[email protected]

Slide 1: Отчет о работе Целевой группы ЕЭК ООН по субъективным показателям бедности
Slide 2: Благодарность экспертной группе ЕЭК ООН по проблемам бедности и неравенства Члены целевой группы и Ване
Slide 3: Подробности о ходе работ
Slide 4: Главы 1 и 2
Slide 5: Глава 3
Slide 6: Вставка 7. Пример вопросов качественной категориальной оценки, ориентированных на доход (Deleeck)
Slide 7: Вставка 10. Примеры вопросов по оценке денежной метрики, минимальный доход (MIQ)
Slide 8: Глава 4. Методы сбора данных и рекомендации - 1
Slide 9: Глава 4. Методы сбора данных и рекомендации - 2
Slide 10: Оценка и использование денежной метрики
Slide 11
Slide 12
Slide 13: Таблица А.1: Типы вопросов, задаваемых по странам в исследовании ЕЭК ООН (2021)
Slide 14: Приложение Б. Пример оценки - Образец
Slide 15: Приложение В. R-код, используемый для получения SPL
Slide 16: Оставшиеся сроки
Slide 17: Обсуждение и вопросы
Slide 18: Контакт

JQ2022USA

Read more about JQ2022USA

JFSQ2022 Country Replies USA

Languages and translations

English

Cover

Joint Forest Sector Questionnaire
			2022
	DATA INPUT FILE
		Correspondent country:
		Reference year:		2022	Fill in the year
Name of person responsible for reply:

Official address (in full):

	Telephone:
	Fax:
	E-mail:

Manual

	The UNECE manual for the JFSQ for 2022 data is available on the UNECE website:
	https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-manual
	The definitions for the JFSQ for 2022 data are available on the UNECE website:
	https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-definitions

https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-definitions https://unece.org/forestry-timber/documents/2023/04/informal-documents/jfsq-2022-data-manual

conversion factors

JFSQ

JOINT FOREST SECTOR QUESTIONNAIRE

Conversion Factors

NOTE THESE ARE ONLY GENERAL FACTORS. IT WOULD BE PREFERABLE TO USE SPECIES- OR COUNTRY-SPECIFIC FACTORS

Multiply the quantity expressed in units on the right side of "per" with the factor to get the value expressed in units on left side of "per".

Items in BOLD RED text were added to the JFSQ in February 2023

Product Code

Product

JFSQ Quantity Unit

Results from UNECE/FAO/ITTO 2020 publication "Forest Product Conversion Factors"

UNECE/FAO Engineered Wood Products Questionnaire (last revised 2020)

Results from UNECE/FAO 2009 Conversion Factors Questionnaire (median)

FAO and UNECE Statistical Publications (Pre-2009)

volume to weight

volume/weight of finished product to volume of roundwood

Notes to Results

volume to weight

Notes to Results

volume to weight

volume/weight of finished product to volume of roundwood

Notes to Results

volume to weight

volume to area

volume/weight of finished product to volume of roundwood

m3 per MT

Roundwood equivalent

m3 per MT

Roundwood equivalent

m3 per MT

m3 per m2

Roundwood

equivalent

Europe

NA**

EECCA**

Europe

NA**

EECCA**

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3 ub

1.1

WOOD FUEL, INCLUDING WOOD FOR CHARCOAL

1000 m3 ub

1.38

1.1.C

Coniferous

1000 m3 ub

1.64

typical shipping weight

Green = 1.12

Based on 891 kg/m3 green, basic density of .41, and 20% moisture seasoned

1.60

1000 m3 ub

Seasoned = 1.82

Based on 407 kg/m3 dry, assuming 20% moisture

1.1.NC

Non-Coniferous

1000 m3 ub

1.11

typical shipping weight

Green=1.05

Based on 1137 kg/m3 green, specific gravity of .55, and 20% moisture seasoned

1.33

1000 m3 ub

Seasoned=1.43

1.2

INDUSTRIAL ROUNDWOOD

1000 m3 ub

1.2.C

Coniferous

1000 m3 ub

1.11

1.08

1.27

Averaged pulp and log

1.10

Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood

1.2.C.Fir

Fir (and Spruce)

1000 m3 ub

1.21

Austrian Energy Agency, 2009. weighted by share of standing inventory of European speices (57% spruce, 10% silver fir and remaining species)

1.2.C.Pine

Pine

1000 m3 ub

1.08

Austrian Energy Agency, 2009, weighted 25% Scots Pine, 2% maritime pine, 2% black pine and remaining species

1.2.NC

Non-Coniferous

1000 m3 ub

0.98

1.02

1.15

0.91

Based on 50/50 ratio of share of logs/pulpwood in industrial roundwood

1.2.NC.T

of which:Tropical

1000 m3 ub

AFRICA=1.31, ASIA=0.956, LA. AM= 0.847, World=1.12

Source: Fonseca "Measurement of Roundwood" 2005, ITTO Annual Review 2007, table 3-2-a Species weight averaged using m3/tonne from Fonseca 2005 and volume exported by species from each region as shown in ITTO 2007 (assumes that bark is removed)

1.37

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3 ub

1.04

0.96

1.12

Averaged C & NC

1.05

Based on 950 kg/m3 green. Bark is included in weight but not in volume.

1.2.1.C

Coniferous

1000 m3 ub

1.10

1.00

1.19

1.07

Based on 935 kg/m3 green. Bark is included in weight but not in volume.

1.43

1.2.1.NC

Non-Coniferous

1000 m3 ub

0.97

0.92

1.04

0.91

Based on 1093 kg/m3 green. Bark is included in weight but not in volume.

1.25

1.2.NC.Beech

Beech

1000 m3 ub

0.92

Austrian Energy Agency, 2009

1.2.NC.Birch

Birch

1000 m3 ub

0.88

Austrian Energy Agency, 2009

1.2.NC.Eucalyptus

Eucalyptus

1000 m3 ub

0.77

ATIBT, 1982

1.2.NC.Oak

Oak

1000 m3 ub

0.88

Austrian Energy Agency, 2009

1.2.NC.Poplar

Poplar

1000 m3 ub

1.06

Austrian Energy Agency, 2009

1.2.2

PULPWOOD (ROUND & SPLIT)

1000 m3 ub

1.05

1.14

1.30

Averaged C & NC

1.08

Based on 930 kg/m3 green. Bark is included in weight but not in volume.

1.48

1.2.2.C

Coniferous

1000 m3 ub

1.11

1.16

1.35

1.12

Based on 891 kg/m3 green. Bark is included in weight but not in volume.

1.54

1.2.2.NC

Non-Coniferous

1000 m3 ub

0.98

1.11

1.25

0.91

Based on 1095 kg/m3 green. Bark is included in weight but not in volume.

1.33

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3 ub

1.07

1.33

1.2.3.C

Coniferous

1000 m3 ub

1.11

1.16

1.35

used pulpwood data

1.12

same as 1.2.2.C

1.43

1.2.3.NC

Non-Coniferous

1000 m3 ub

0.98

1.11

1.25

0.91

same as 1.2.2.NC

1.25

WOOD CHARCOAL

1000 MT

6 m3rw/tonne

5.35

Does not include the use of any of the wood fiber to generate the heat to make (add about 30% if inputted wood fiber used to provide heat)

6.00

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

1.205

1.07

1.21

1.08

m3 /MT = green swe per odmt / avg delivered tonne/odmt, rwe= +1%

softwood=1.19

1.205

Based on swe/odmt of 2.41 and avg delivered mt / odmt of 2.0 in solid m3

1.60

1000 m3

hardwood = 1.05

1.123

Based on swe/odmt of 2.01 and avg delivered mt / odmt of 1.79 in solid m3

1000 m3

Woodchip, Green swe to oven-dry tonne m3/odmt

mix = 1.15

3.2

WOOD RESIDUES

1000 m3

1.205

1.07

1.21

1.08

Based on wood chips

Green=1.15

Based on wood chips

1.50

1000 m3

2.12

2.07

Seasoned = 2.12

2.07

Assumption for seasoned is based on average basic density of .42 from questionnaire and assumes 15% moisture content

3.2.1

of which: SAWDUST

1000 m3

1.205

1.07

1.21

1.08

Based on wood chips

RECOVERED POST-CONSUMER WOOD

1000 mt

Delivered MT (12-20% atmospheric moisture). Convert to dry weight for energy purposes (multiply by 0.88 - 0.80)

WOOD PELLETS AND OTHER AGGLOMERATES

1000 MT

5.1

WOOD PELLETS

1000 MT

1.54

1.45

1.54

1.51

1.44

nodata

m3/ton - bulk density, loose volume, 5-10% mcw- Equivalent - solid wood imput to bulk m3 pellets

1.51

1.44

Bulk (loose) volume, 5-10% moisture

5.2

OTHER AGGLOMERATES

1000 MT

1.12

nodata

2.32

nodata

m3/ton - Pressed logs and briquettes, bulk density, loose volume. Equivalent - m3rw/odmt

1.31

2.29

roundwood equivalent is m3rw/odmt, volume to weight is bulk (loose volume)

SAWNWOOD

1000 m3

1.6 / 1.82*

6.C

Coniferous

1000 m3

1.202

1.69

1.62

1.85

m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough

Green=1.202

RoughGreen=1.67

Green sawnwood based on basic density of .94, less bark (11%)

1.82

1000 m3

1.82

1.72

Nodata

1.69

2.05

Sawnwood dry rough

Dry = 1.99

RoughDry=1.99

Dry sawnwood weight based on basic density of .42, 4% shrinkage and 15% moisture content

1000 m3

2.26

2.08

nodata

Sawnwood dry planed

PlanedDry=2.13

6.C.Fir

Fir and Spruce

1000 m3

2.16

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.

6.C.Pine

Pine

1000 m3

1.72

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight). Weighted ratio of standing inventory.

6.NC

Non-Coniferous

1000 m3

1.04

1.89

1.79

nodata

Sawnwood green rough

Green=1.04

RoughGreen=1.86

Green sawnwood based on basic density of 1.09, less bark (12%)

1.43

1000 m3

1.43

nodata

2.01

1.92

nodata

m3/ton - Average Sawnwood shipping weight. Equivalent - Sawnwood green rough

Seasoned=1.50

RoughDry=2.01

Dry sawnwood weight based on basic density of .55, 5% shrinkage and 15% moisture content

1000 m3

3.25

3.38

nodata

Sawnwood dry planed

PlanedDry=2.81

6.NC.Ash

Ash

1000 m3

1.47

Wood Database (wood-database.com). Air-dry.

6.NC.Beech

Beech

1000 m3

1.42

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.Birch

Birch

1000 m3

1.47

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.Cherry

Cherry

1000 m3

1.62

Giordano, 1976, Tecnologia del legno. Air-dry. Prunus avium.

6.NC.Maple

Maple

1000 m3

1.35

Giordano, 1976, Tecnologia del legno. Air-dry

6.NC.Oak

Oak

1000 m3

1.38

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.Poplar

Poplar

1000 m3

2.29

Austrian Energy Agency, 2009. Dried weight (15% moisture content dry weight).

6.NC.T

of which:Tropical

1000 m3

1.38

Based on FP Conversion Factors (2019), Asia (720 kg / m3)

VENEER SHEETS

1000 m3

1.33

0.0025

1.9*

7.C

Coniferous

1000 m3

1.05

1.95

1.5

Green veneer based on the ratio from the old conversion factors

Green=1.20

1.5***

Green veneer based on basic density of .94, less bark (11%)

0.003

1000 m3

1.8

nodata

2.08

1.6

nodata

m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product

Seasoned=2.06

1.6***

Dry veneer weight based on basic density of .42, 9% shrinkage and 5% moisture content

7.NC

Non-Coniferous

1000 m3

1.15

nodata

2.11

1.89

Green veneer based on the ratio from the old conversion factors

Green=1.04

1.5***

Green veneer based on basic density of 1.09, less bark (11%)

0.001

1000 m3

1.7

nodata

2.25

nodata

m3/ton - Average panel shipping weight; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product

Seasoned=1.53

1.6***

Dry veneer weight based on basic density of .55, 11.5% shrinkage and 5% moisture content

7.NC.T

of which:Tropical

1000 m3

WOOD-BASED PANELS

1000 m3

1.6

8.1

PLYWOOD

1000 m3

1.54

0.105

2.3*

8,1.C

Coniferous

1000 m3

1.67

Nodata

2.16

1.92

nodata

1.69

2.12

dried, sanded, peeled

0.0165***

8.1.NC

Non-Coniferous

1000 m3

1.54

Nodata

2.54

2.14

nodata

1.54

1.92

dried, sanded, sliced

0.0215***

8.1.NC.T

of which:Tropical

1000 m3

8.1.1

of which: LAMINATED VENEER LUMBER

1000 m3

1.69

Same as coniferous plywood

8.1.1.C

Coniferous

1000 m3

1.69

Same as coniferous plywood

8.1.1.NC

Non-Coniferous

1000 m3

no data

8.1.1.NC.T

of which:Tropical

1000 m3

no data

8.2

PARTICLE BOARD (including OSB)

1000 m3

1.54

8.2x

PARTICLE BOARD (excluding OSB)

1000 m3

1.54

Nodata

1.51

1.54

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.53

1.50

0.018***

8.2.1

of which: OSB

1000 m3

1.64

Nodata

1.72

1.63

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.67

1.63

0.018***

8.3

FIBREBOARD

1000 m3

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

8.3.1

HARDBOARD

1000 m3

1.06

Nodata

2.2

1.77

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.06

1.93

solid wood per m3 of product

1.05

0.005
Alex McCusker: Alex McCusker: 0.003 per Conversion Factors Study

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

1.35

Nodata

1.80

1.53

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

1.37

1.70

solid wood per m3 of product

2.00

0.016

8.3.3

OTHER FIBREBOARD

1000 m3

3.85

Nodata

0.68

0.71

nodata

m3/ton - Based on Product based density; Roundwood equivalent - m3rw = cubic metre roundwood, m3p = cubic metre product.

3.44

0.71

solid wood per m3 of product, mostly insulating board

4.00

0.025

WOOD PULP

1000 MT

3.7

nodata

3.76

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.86

3.37

9.1

MECHANICAL AND SEMI-CHEMICAL

1000 MT

2.59

2.45

2.94

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

2.60

air-dried metric ton (mechanical 2.50, semi-chemical 2.70)

9..2

CHEMICAL

1000 MT

4.80

4.29

4.10

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.90

9.2.1

SULPHATE

1000 MT

4.50

nodata

4.60

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.57

air-dried metric ton (unbleached 4.63, bleached 4.50)

9.2.1.1

of which: bleached

1000 MT

4.50

nodata

4.90

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.50

air-dried metric ton

9.2.2

SULPHITE

1000 MT

4.73

nodata

4.15

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.83

air-dried metric ton (unbleached 4.64 and bleached 5.01)

9.3

DISSOLVING GRADES

1000 MT

4.46

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

5.65

air-dried metric ton

OTHER PULP

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

10.2

RECOVERED FIBRE PULP

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

RECOVERED PAPER

1000 MT

nodata

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

1.28 MT in per MT out

PAPER AND PAPERBOARD

1000 MT

3.85

nodata

4.15

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.6

3.37

12.1

GRAPHIC PAPERS

1000 MT

nodata

12.1.1

NEWSPRINT

1000 MT

2.80

2.50

3.15

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

2.80

air-dried metric ton

12.1.2

UNCOATED MECHANICAL

1000 MT

3.50

nodata

4.00

3.50

air-dried metric ton

12.1.3

UNCOATED WOODFREE

1000 MT

nodata

12.1.4

COATED PAPERS

1000 MT

3.50

nodata

4.00

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.95

air-dried metric ton

12.2

SANITARY AND HOUSEHOLD PAPERS

1000 MT

4.60

nodata

4.20

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.90

air-dried metric ton

12.3

PACKAGING MATERIALS

1000 MT

3.25

nodata

4.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.25

air-dried metric ton

12.3.1

CASE MATERIALS

1000 MT

4.20

nodata

4.00

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.20

air-dried metric ton

12.3.2

CARTONBOARD

1000 MT

4.00

nodata

4.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.00

air-dried metric ton

12.3.3

WRAPPING PAPERS

1000 MT

4.10

nodata

4.40

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.10

air-dried metric ton

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 MT

4.00

nodata

3.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

4.00

air-dried metric ton

12.4

OTHER PAPER AND PAPERBOARD N.E.S

1000 MT

3.48

nodata

3.30

m3sw/MT, where m3sw = cubic metre solid wood, and MT = tonne (in this case assumed air-dry – 10% moisture, wet basis)

3.48

air-dried metric ton

GLULAM AND CROSS-LAMINATED TIMBER

1000 m3

15.1

GLULAM

1000 m3

1.69

same as coniferous plywood

15.2

CROSS-LAMINATED TIMBER

1000 m3

2.00

I-BEAMS

1000 MT

1.68

222 linear meters per MT

For inverse relationships divide 1 by the factor given, e.g. to convert m3 of wood charcoal to mt divide 1 by m3/mt factor of 6 = 0.167

Notes:

Forest Measures

MT = metric tonnes (1000 kg)

Unit

m3/unit

m3 = cubic meters (solid volume)

1000 board feet (sawlogs)

4.53****

**** = obsolete - more recent figures would be:

m2 = square meters

1000 board feet (sawnwood - nominal)

2.36

for Oregon, Washington State, Alaska (west of Cascades), South East United States (Doyle region): 6.3

(s) = solid volume

1000 board feet (sawnwood - actual)

1.69

Inland Western North America, Great Lakes (North America), Eastern Canada: 5.7

1000 square feet (1/8 inch thickness)

0.295

Northeast United States Int 1/4": 5

Unit Conversion

cord

3.625

1 inch = 25.4 millimetres

cord (pulpwood)

2.55

1 square foot = 0.0929 square metre

cord (wood fuel)

2.12

1 pound = 0.454 kilograms

cubic foot

0.02832

1 short ton (2000 pounds) = 0.9072 metric ton

cubic foot (stacked)

0.01841

1 long ton (2240 pounds) = 1.016 metric ton

cunit

2.83

Bold = FAO published figure

fathom

6.1164

hoppus cubic foot

0.0222

* = ITTO

hoppus super(ficial) foot

0.00185

hoppus ton (50 hoppus cubic feet)

1.11

** NA = North America; EECCA = Eastern Europe, Caucasus and Central Asia

Petrograd Standard

4.672

stere

*** = Conversion Factor Study, US figures, rotary for conifer and sliced for non-conifer

stere (pulpwood)

0.72

stere (wood fuel)

0.65

Fonseca "Measurement of Roundwood" 2005. Estimated by Matt Fonseca based on regional knowledge of the scaling methods and timber types

prepared February 2004

updated 2007 with RWE factors

updated 2009 with provisional results of forest products conversion factors study

updated 2011 with results of forest products conversion factors study (DP49)

updated 2023 with results of 2019 UNECE/FAO/ITTO study - https://www.fao.org/documents/card/en/c/ca7952en

JQ1 Production

Country:

USA

Date:

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE JQ1

USDA Forest Service

4700 Old Kington Pike, Knoxville, TN 37919

Industrial Roundwood Balance

PRIMARY PRODUCTS

Telephone:

This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!

Discrepancies

Removals and Production

E-mail:

test for good numbers, missing number, bad number, negative number

Flag

Note

Product

Unit

2021

2022

2021

2022

2021

2022

Product

Unit

2021

2022

2021

2022

% change

Conversion factors

Code

Quantity

Code

Quantity

Roundwood

Industrial roundwood availability
McCusker 14/6/07: McCusker 14/6/07: minus 1.2.3 (other ind. RW) production

Missing data

missing data

m3 of wood in m3 or t of product

ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)

Recovered wood used in particle board

1448

-4757

-429%

Solid wood equivalent

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

453,530

458,774

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

Solid Wood Demand

agglomerate production

8,449

9,544

13%

2.4

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

71,111

76,230

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

Sawnwood production

80,705

81,676

1.1.C

Coniferous

1000 m3ub

33,760

37,619

1.1.C

Coniferous

1000 m3ub

veneer production

Missing data

missing data

1.1.NC

Non-Coniferous

1000 m3ub

37,351

38,611

1.1.NC

Non-Coniferous

1000 m3ub

plywood production

9,705

9,020

-7%

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

382,420

382,544

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

particle board production (incl OSB)

17,975

missing data

1.58

1.2.C

Coniferous

1000 m3ub

305,851

306,119

1.2.C

Coniferous

1000 m3ub

fibreboard production

missing data

1.8

1.2.NC

Non-Coniferous

1000 m3ub

76,569

76,425

1.2.NC

Non-Coniferous

1000 m3ub

mechanical/semi-chemical pulp production

4,046

missing data

2.5

1.2.NC.T

of which: Tropical

1000 m3ub

1.2.NC.T

of which: Tropical

1000 m3ub

chemical pulp production

44,411

missing data

4.9

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

183,401

186,157

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ub

dissolving pulp production

1,228

missing data

5.7

1.2.1.C

Coniferous

1000 m3ub

150,702

152,799

1.2.1.C

Coniferous

1000 m3ub

Availability

Solid Wood Demand

missing data

1.2.1.NC

Non-Coniferous

1000 m3ub

32,699

33,358

1.2.1.NC

Non-Coniferous

1000 m3ub

Difference (roundwood-demand)

missing data

positive = surplus

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

185,686

182,650

1.2.2

PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)

1000 m3ub

gap (demand/availability)

missing data

Negative number means not enough roundwood available

1.2.2.C

Coniferous

1000 m3ub

143,462

141,226

1.2.2.C

Coniferous

1000 m3ub

Positive number means more roundwood available than demanded

1.2.2.NC

Non-Coniferous

1000 m3ub

42,224

41,424

1.2.2.NC

Non-Coniferous

1000 m3ub

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

13,333

13,737

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2.3.C

Coniferous

1000 m3ub

11,687

12,094

1.2.3.C

Coniferous

1000 m3ub

% of particle board that is from recovered wood

35%

1.2.3.NC

Non-Coniferous

1000 m3ub

1,646

1,643

1.2.3.NC

Non-Coniferous

1000 m3ub

share of agglomerates produced from industrial roundwood residues

100%

PRODUCTION

usable industrial roundwood - amount of roundwood that is used, remainder leaves industry

98.5%

WOOD CHARCOAL

1000 t

…

WOOD CHARCOAL

1000 t

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

60,485

62,262

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

44,209

45,900

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

16,276

16,362

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

3.2.1

of which: Sawdust

1000 m3

8,890

8,937

3.2.1

of which: Sawdust

1000 m3

RECOVERED POST-CONSUMER WOOD

1000 t

...

…

RECOVERED POST-CONSUMER WOOD

1000 t

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

8,449

9,544

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

ERROR:#VALUE!

5.1

WOOD PELLETS

1000 t

8,449

9,544

5.1

WOOD PELLETS

1000 t

5.2

OTHER AGGLOMERATES

1000 t

…

...

5.2

OTHER AGGLOMERATES

1000 t

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

80,705

81,676

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

6.C

Coniferous

1000 m3

63,417

64,039

6.C

Coniferous

1000 m3

6.NC

Non-Coniferous

1000 m3

17,288

17,637

6.NC

Non-Coniferous

1000 m3

6.NC.T

of which: Tropical

1000 m3

6.NC.T

of which: Tropical

1000 m3

VENEER SHEETS

1000 m3

VENEER SHEETS

1000 m3

7.C

Coniferous

1000 m3

7.C

Coniferous

1000 m3

7.NC

Non-Coniferous

1000 m3

7.NC

Non-Coniferous

1000 m3

7.NC.T

of which: Tropical

1000 m3

7.NC.T

of which: Tropical

1000 m3

WOOD-BASED PANELS

1000 m3

27,680

9,020

WOOD-BASED PANELS

1000 m3

8.1

PLYWOOD

1000 m3

9,705

9,020

8.1

PLYWOOD

1000 m3

ERROR:#VALUE!

8.1.C

Coniferous

1000 m3

9,471

9,020

8.1.C

Coniferous

1000 m3

8.1.NC

Non-Coniferous

1000 m3

234

…

8.1.NC

Non-Coniferous

1000 m3

8.1.NC.T

of which: Tropical

1000 m3

8.1.NC.T

of which: Tropical

1000 m3

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

2,093

2,030

8.1.1

of which: Laminated Veneer Lumber (LVL)

1000 m3

ERROR:#VALUE!

8.1.1.C

Coniferous

1000 m3

2,093

2,030

8.1.1.C

Coniferous

1000 m3

8.1.1.NC

Non-Coniferous

1000 m3

...

…

8.1.1.NC

Non-Coniferous

1000 m3

8.1.1.NC.T

of which: Tropical

1000 m3

…

8.1.1.NC.T

of which: Tropical

1000 m3

8.2

PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD

1000 m3

17,975

8.2

PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD

1000 m3

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

13,839

13,592

8.2.1

of which: ORIENTED STRAND BOARD (OSB)

1000 m3

8.3

FIBREBOARD

1000 m3

8.3

FIBREBOARD

1000 m3

8.3.1

HARDBOARD

1000 m3

8.3.1

HARDBOARD

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

8.3.3

OTHER FIBREBOARD

1000 m3

8.3.3

OTHER FIBREBOARD

1000 m3

WOOD PULP

1000 t

49,685

WOOD PULP

1000 t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

4,046

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

1000 t

44,411

9.2

CHEMICAL WOOD PULP

1000 t

9.2.1

SULPHATE PULP

1000 t

44,167

9.2.1

SULPHATE PULP

1000 t

9.2.1.1

of which: BLEACHED

1000 t

20,262

9.2.1.1

of which: BLEACHED

1000 t

9.2.2

SULPHITE PULP

1000 t

244

9.2.2

SULPHITE PULP

1000 t

9.3

DISSOLVING GRADES

1000 t

1,228

9.3

DISSOLVING GRADES

1000 t

OTHER PULP

1000 t

30,072

31,250

OTHER PULP

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

108

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.2

RECOVERED FIBRE PULP

1000 t

29,964

31,154

10.2

RECOVERED FIBRE PULP

1000 t

RECOVERED PAPER

1000 t

45,037

44,828

RECOVERED PAPER

1000 t

PAPER AND PAPERBOARD

1000 t

67,475

PAPER AND PAPERBOARD

1000 t

12.1

GRAPHIC PAPERS

1000 t

8,296

12.1

GRAPHIC PAPERS

1000 t

-0

12.1.1

NEWSPRINT

1000 t

370

12.1.1

NEWSPRINT

1000 t

12.1.2

UNCOATED MECHANICAL

1000 t

394.6

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.3

UNCOATED WOODFREE

1000 t

4,775

12.1.3

UNCOATED WOODFREE

1000 t

12.1.4

COATED PAPERS

1000 t

2,757

12.1.4

COATED PAPERS

1000 t

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

6,928

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.3

PACKAGING MATERIALS

1000 t

50,948

12.3

PACKAGING MATERIALS

1000 t

12.3.1

CASE MATERIALS

1000 t

36,169

12.3.1

CASE MATERIALS

1000 t

12.3.2

CARTONBOARD

1000 t

8,482

12.3.2

CARTONBOARD

1000 t

12.3.3

WRAPPING PAPERS

1000 t

2,542

12.3.3

WRAPPING PAPERS

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

3,755

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

1,303

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

396

386

GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1

1000 m3

ERROR:#VALUE!

15.1

GLULAM

1000 m3

396

386

15.1

GLULAM

1000 m3

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

...

…

15.2

CROSS-LAMINATED TIMBER (CLT or X-LAM)

1000 m3

I BEAMS (I-JOISTS)1

1000 t

776

647

I BEAMS (I-JOISTS)1

1000 t

1 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here

m3ub = cubic metres solid volume underbark (i.e. excluding bark)

Please complete each cell if possible with

m3 = cubic metres solid volume

data (numerical value)

t = metric tonnes

or "…" for not available

or "0" for zero data

Notes: Sawnwood, nominal

https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ2 Trade

			61	62	61	62	91	92	91	92
		FOREST SECTOR QUESTIONNAIRE JQ2				Country:	USA		Date:
						Name of Official responsible for reply:																																											INTRA-EU	The difference might be caused by Intra-EU trade
		PRIMARY PRODUCTS				Official Address (in full):																																This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!											CHECK
		Trade				Telephone:			Fax:																			This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!																		ZERO CHECK 2 - if no value in Zero Check 1
						E-mail:																																			Country:	USA				verifies whether the JQ2 figures refers only to intra-EU trade
	Specify Currency and Unit of Value (e.g.:1000 USD):			1,000 US$							Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note		Trade		Discrepancies
Product		Unit of	I M P O R T				E X P O R T				Import				Export				Import				Export				Product			I M P O R T				E X P O R T				Product			Apparent Consumption		Related Notes		Product		Value per	I M P O R T		E X P O R T
code	Product	quantity	2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		2021		2022		code			2021		2022		2021		2022		code			2021	2022	2021	2022	code	Product	unit	2021	2022	2021	2022
			Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value				Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	898	205,803	1,021	245,538	9,415	2,149,547	7,425	1,948,240																	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	0	0	0	0	0	0	0	0	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	445,013	452,371			1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	126	30,914	81	22,463	1	1,146	4	5,230																	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	0	0	0	0	0	0	0	0	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	71,236	76,306			1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3
1.1.C	Coniferous Subashini NARASIMHAN: Subashini NARASIMHAN: All highlighted blue data have been changed in the Excel processed sheets and also in the DB on 31.7.2023	1000 m3ub	34	8,537	29	8,153	0	581	4	4,482																	1.1.C	Coniferous	1000 m3ub									1.1.C	Coniferous	1000 m3ub	33,793	37,644			1.1.C	Coniferous	NAC/m3
1.1.NC	Non-Coniferous	1000 m3ub	92	22,377	52	14,310	0	565	0	748																	1.1.NC	Non-Coniferous	1000 m3ub									1.1.NC	Non-Coniferous	1000 m3ub	37,443	38,662			1.1.NC	Non-Coniferous	NAC/m3
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	772	174,889	941	223,075	9,415	2,148,401	7,420	1,943,010																	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	0	0	0	0	0	0	0	0	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	373,777	376,064			1.2	INDUSTRIAL ROUNDWOOD	NAC/m3
1.2.C	Coniferous	1000 m3ub	484	149,132	600	198,107	7,280	1,316,788	5,286	1,039,206																	1.2.C	Coniferous	1000 m3ub									1.2.C	Coniferous	1000 m3ub	299,055	301,432			1.2.C	Coniferous	NAC/m3
1.2.NC	Non-Coniferous	1000 m3ub	288	25,756	341	24,968	2,135	831,613	2,134	903,803																	1.2.NC	Non-Coniferous	1000 m3ub									1.2.NC	Non-Coniferous	1000 m3ub	74,722	74,632			1.2.NC	Non-Coniferous	NAC/mt
1.2.NC.T	of which: Tropical1	1000 m3ub	8	1,420	18	1,950	6	2,053	4	1,183																	1.2.NC.T	of which: Tropical1	1000 m3ub									1.2.NC.T	of which: Tropical1	1000 m3ub	2	14			1.2.NC.T	of which: Tropical	1000 m3
2	WOOD CHARCOAL	1000 t	183	107,439	121	81,295	28	25,960	18	17,287																	2	WOOD CHARCOAL	1000 t									2	WOOD CHARCOAL	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			2	WOOD CHARCOAL	1000 m3
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	216	40,081	139	244,880	5,794	241,355	6,733	4,910,315																	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	0	0	-146	188,420	0	0	-2	4,598,885	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	54,907	55,668			3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3
3.1	WOOD CHIPS AND PARTICLES	1000 m3	80	17,732	116	26,045	5,773	232,637	6,721	303,738																	3.1	WOOD CHIPS AND PARTICLES	1000 m3									3.1	WOOD CHIPS AND PARTICLES	1000 m3	38,517	39,294			3.1	WOOD CHIPS AND PARTICLES	1000 mt
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	136	22,348	169	30,415	22	8,718	13	7,692																	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3									3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	16,391	16,518			3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt
3.2.1	of which: Sawdust	1000 m3			1	214			8	3,131																	3.2.1	of which: Sawdust	1000 m3									3.2.1	of which: Sawdust	1000 m3	8,890	8,929
4	RECOVERED POST-CONSUMER WOOD	1000 t			85	25,336			2	1,476																	4	RECOVERED POST-CONSUMER WOOD	1000 t									4	RECOVERED POST-CONSUMER WOOD	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			4	RECOVERED POST-CONSUMER WOOD	1000 mt
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	363	88,384	352	90,562	7,535	1,070,184	8,989	1,554,944																	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	0	0	0	0	0	0	0	0	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	1,276	906			5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3
5.1	WOOD PELLETS	1000 t	196	42,997	194	46,788	7,523	1,059,261	8,977	1,545,409																	5.1	WOOD PELLETS	1000 t									5.1	WOOD PELLETS	1000 t	1,122	761			5.1	WOOD PELLETS	NAC/m3
5.2	OTHER AGGLOMERATES	1000 t	167	45,387	157	43,774	13	10,923	12	9,535																	5.2	OTHER AGGLOMERATES	1000 t									5.2	OTHER AGGLOMERATES	1000 t	ERROR:#VALUE!	ERROR:#VALUE!			5.2	OTHER AGGLOMERATES	NAC/m3
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	38,164	13,728,885	37,190	12,273,652	7,254	3,559,309	7,005	3,533,102																	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	0	0	-0	0	0	0	-0	0	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	111,615	111,861			6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3
6.C	Coniferous	1000 m3	37,447	13,196,263	36,392	11,552,380	3,559	1,231,043	3,217	1,144,776																	6.C	Coniferous	1000 m3									6.C	Coniferous	1000 m3	97,305	97,214			6.C	Coniferous	NAC/m3
6.NC	Non-Coniferous	1000 m3	717	532,622	798	721,272	3,695	2,328,267	3,788	2,388,326																	6.NC	Non-Coniferous	1000 m3									6.NC	Non-Coniferous	1000 m3	14,310	14,647			6.NC	Non-Coniferous	NAC/m3
6.NC.T	of which: Tropical1	1000 m3	226	264,016	275	419,419	39	27,984	39	25,505																	6.NC.T	of which: Tropical1	1000 m3									6.NC.T	of which: Tropical1	1000 m3	186	236			6.NC.T	of which: Tropical	NAC/m3
7	VENEER SHEETS	1000 m3	671	484,150	0	560,912	281	360,099	0	392,195																	7	VENEER SHEETS	1000 m3	0	0	-652	0	0	0	-294	0	7	VENEER SHEETS	1000 m3	391	0			7	VENEER SHEETS	NAC/m3
7.C	Coniferous	1000 m3	606	306,918	585	319,117	81	37,424	82	38,911																	7.C	Coniferous	1000 m3									7.C	Coniferous	1000 m3	525	503			7.C	Coniferous	NAC/m3
7.NC	Non-Coniferous	1000 m3	65	177,232	67	241,795	200	322,675	211	353,284																	7.NC	Non-Coniferous	1000 m3									7.NC	Non-Coniferous	1000 m3	-134	-144			7.NC	Non-Coniferous	NAC/m3
7.NC.T	of which: Tropical	1000 m3	8	21,894	9	34,349	9	16,754	8	15,152																	7.NC.T	of which: Tropical	1000 m3									7.NC.T	of which: Tropical	1000 m3	-1	1			7.NC.T	of which: Tropical	NAC/m3
8	WOOD-BASED PANELS	1000 m3	20,455	9,839,884	16,927	9,966,936	2,134	830,395	2,278	898,403																	8	WOOD-BASED PANELS	1000 m3	0	0	-0	0	0	0	0	0	8	WOOD-BASED PANELS	1000 m3	46,001	23,669			8	WOOD-BASED PANELS	NAC/m3
8.1	PLYWOOD	1000 m3	8,086	4,066,342	6,259	4,318,139	759	363,164	771	358,879																	8.1	PLYWOOD	1000 m3	0	0	0	0	0	0	0	-0	8.1	PLYWOOD	1000 m3	17,031	14,508			8.1	PLYWOOD	NAC/m3
8.1.C	Coniferous	1000 m3	2,280	1,228,392	2,356	1,220,531	541	233,440	593	254,332																	8.1.C	Coniferous	1000 m3									8.1.C	Coniferous	1000 m3	11,210	10,783			8.1.C	Coniferous	NAC/m3
8.1.NC	Non-Coniferous	1000 m3	5,806	2,837,950	3,904	3,097,608	218	129,723	179	104,547																	8.1.NC	Non-Coniferous	1000 m3									8.1.NC	Non-Coniferous	1000 m3	5,821	ERROR:#VALUE!			8.1.NC	Non-Coniferous	NAC/m3
8.1.NC.T	of which: Tropical	1000 m3	786	516,649	1,013	831,465	21	11,360	43	20,703																	8.1.NC.T	of which: Tropical	1000 m3									8.1.NC.T	of which: Tropical	1000 m3	765	971			8.1.NC.T	of which: Tropical	NAC/m3
8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	…	…	159	135,667	…	…	62	33,345																	8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!	0	0.01	ERROR:#VALUE!	ERROR:#VALUE!	0	-0.01	8.1.1	of which: Laminated Veneer Lumber (LVL)	1000 m3	ERROR:#VALUE!	2,126
8.1.1.C	Coniferous	1000 m3	…	…	130	107,344	…	…	60	32,294																	8.1.1.C	Coniferous	1000 m3									8.1.1.C	Coniferous	1000 m3	ERROR:#VALUE!	2,100
8.1.1.NC	Non-Coniferous	1000 m3	…	…	28	28,323	…	…	2	1,051																	8.1.1.NC	Non-Coniferous	1000 m3									8.1.1.NC	Non-Coniferous	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
8.1.1.NC.T	of which: Tropical	1000 m3	…	…	26	24,658	…	…	0	184																	8.1.1.NC.T	of which: Tropical	1000 m3									8.1.1.NC.T	of which: Tropical	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	7,590	4,397,773	7,391	3,602,412	634	234,114	617	269,214																	8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3									8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	24,932	6,775			8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	6,128	4,065,184	6,198	3,222,448	146	58,742	132	59,284																	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3									8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	19,821	19,658			8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3
8.3	FIBREBOARD	1000 m3	4,780	1,375,769	3,277	2,046,385	741	233,117	890	270,310																	8.3	FIBREBOARD	1000 m3	0	0	-0	0	0	0	0	0	8.3	FIBREBOARD	1000 m3	4,038	2,387			8.3	FIBREBOARD	NAC/m3
8.3.1	HARDBOARD	1000 m3	243	141,339	249	180,815	255	94,419	224	85,718																	8.3.1	HARDBOARD	1000 m3									8.3.1	HARDBOARD	1000 m3	-12	25			8.3.1	HARDBOARD	NAC/mt
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	2,498	1,191,819	2,866	1,814,461	315	90,901	372	104,062																	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	2,183	2,494			8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt
8.3.3	OTHER FIBREBOARD	1000 m3	2,038	42,611	161	51,109	171	47,797	294	80,531																	8.3.3	OTHER FIBREBOARD	1000 m3									8.3.3	OTHER FIBREBOARD	1000 m3	1,867	-133			8.3.3	OTHER FIBREBOARD	NAC/mt
9	WOOD PULP	1000 t	6,036	3,858,758	6,948	4,801,537	7,621	6,054,396	7,983	7,283,702																	9	WOOD PULP	1000 t	0	0	0	0	0	0	0	0	9	WOOD PULP	1000 t	48,100	47,983			9	WOOD PULP	NAC/mt
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	137	47,577	207	123,673	211	124,222	239	142,122																	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	3,973	3,912			9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt
9.2	CHEMICAL WOOD PULP	1000 t	5,652	3,574,714	6,457	4,392,114	6,620	5,023,298	6,899	6,081,834																	9.2	CHEMICAL WOOD PULP	1000 t	0	0	0	0	0	0	0	0	9.2	CHEMICAL WOOD PULP	1000 t	43,443	43,431			9.2	CHEMICAL WOOD PULP	NAC/mt
9.2.1	SULPHATE PULP	1000 t	5,186	3,415,369	5,756	4,155,342	6,580	5,002,265	6,865	6,064,771																	9.2.1	SULPHATE PULP	1000 t									9.2.1	SULPHATE PULP	1000 t	42,773	42,565			9.2.1	SULPHATE PULP	NAC/mt
9.2.1.1	of which: BLEACHED	1000 t	5,043	3,316,872	5,592	4,051,886	6,218	4,767,898	6,607	5,896,472																	9.2.1.1	of which: BLEACHED	1000 t									9.2.1.1	of which: BLEACHED	1000 t	19,087	18,887			9.2.1.1	of which: BLEACHED	NAC/mt
9.2.2	SULPHITE PULP	1000 t	466	159,345	702	236,772	40	21,033	34	17,064																	9.2.2	SULPHITE PULP	1000 t									9.2.2	SULPHITE PULP	1000 t	670	866			9.2.2	SULPHITE PULP	NAC/mt
9.3	DISSOLVING GRADES	1000 t	247	236,467	283	285,750	790	906,875	845	1,059,746																	9.3	DISSOLVING GRADES	1000 t									9.3	DISSOLVING GRADES	1000 t	684	640			9.3	DISSOLVING GRADES	NAC/mt
10	OTHER PULP	1000 t	81	27,595	71	33,388	516	320,014	600	381,082																	10	OTHER PULP	1000 t	0	0	0	0	0	0	0	0	10	OTHER PULP	1000 t	29,637	30,721			10	OTHER PULP	NAC/mt
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	35	23,572	44	31,024	87	93,024	85	105,372																	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t									10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	56	55			10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt
10.2	RECOVERED FIBRE PULP	1000 t	46	4,023	27	2,364	429	226,990	515	275,710																	10.2	RECOVERED FIBRE PULP	1000 t									10.2	RECOVERED FIBRE PULP	1000 t	29,581	30,666			10.2	RECOVERED FIBRE PULP	NAC/mt
11	RECOVERED PAPER	1000 t	878	134,696	828	138,208	16,334	3,302,623	14,990	3,186,042																	11	RECOVERED PAPER	1000 t									11	RECOVERED PAPER	1000 t	29,581	30,666			11	RECOVERED PAPER	NAC/mt
12	PAPER AND PAPERBOARD	1000 t	8,223	8,031,344	8,202	10,372,687	10,077	9,137,947	9,917	10,037,653																	12	PAPER AND PAPERBOARD	1000 t	0	0	-0	0	0	0	0	0	12	PAPER AND PAPERBOARD	1000 t	65,621	63,780			12	PAPER AND PAPERBOARD	NAC/mt
12.1	GRAPHIC PAPERS	1000 t	4,609	3,635,105	4,536	5,024,247	1,283	1,288,913	957	1,116,460																	12.1	GRAPHIC PAPERS	1000 t	0	0	0	-0	0	0	0	0	12.1	GRAPHIC PAPERS	1000 t	11,622	11,757			12.1	GRAPHIC PAPERS	NAC/mt
12.1.1	NEWSPRINT	1000 t	1,065	557,210	286	184,077	120	68,813	67	43,961																	12.1.1	NEWSPRINT	1000 t									12.1.1	NEWSPRINT	1000 t	1,315	547			12.1.1	NEWSPRINT	NAC/mt
12.1.2	UNCOATED MECHANICAL	1000 t	1,195	787,757	1,204	1,028,678	59	62,303	60	69,424																	12.1.2	UNCOATED MECHANICAL	1000 t									12.1.2	UNCOATED MECHANICAL	1000 t	1,531	1,510			12.1.2	UNCOATED MECHANICAL	NAC/mt
12.1.3	UNCOATED WOODFREE	1000 t	986	1,011,189	1,152	1,412,886	331	423,467	282	431,055																	12.1.3	UNCOATED WOODFREE	1000 t									12.1.3	UNCOATED WOODFREE	1000 t	5,430	5,645			12.1.3	UNCOATED WOODFREE	NAC/mt
12.1.4	COATED PAPERS	1000 t	1,363	1,278,949	1,895	2,398,608	774	734,331	547	572,020																	12.1.4	COATED PAPERS	1000 t									12.1.4	COATED PAPERS	1000 t	3,346	4,055			12.1.4	COATED PAPERS	NAC/mt
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	275	375,570	322	516,593	172	223,342	176	264,718																	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t									12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	7,031	7,104			12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt
12.3	PACKAGING MATERIALS	1000 t	3,284	3,672,684	3,287	4,403,084	8,316	7,328,648	8,426	8,329,281																	12.3	PACKAGING MATERIALS	1000 t	0	0	0	-0	0	0	-0	0	12.3	PACKAGING MATERIALS	1000 t	45,917	43,934			12.3	PACKAGING MATERIALS	NAC/mt
12.3.1	CASE MATERIALS	1000 t	1,358	1,062,697	1,261	1,142,438	5,029	3,454,575	5,169	4,085,718																	12.3.1	CASE MATERIALS	1000 t									12.3.1	CASE MATERIALS	1000 t	32,498	30,381			12.3.1	CASE MATERIALS	NAC/mt
12.3.2	CARTONBOARD	1000 t	1,267	1,679,581	1,362	2,088,774	2,104	2,591,605	2,120	2,864,440																	12.3.2	CARTONBOARD	1000 t									12.3.2	CARTONBOARD	1000 t	7,645	5,779			12.3.2	CARTONBOARD	NAC/mt
12.3.3	WRAPPING PAPERS	1000 t	547	839,063	569	1,072,837	1,090	1,208,280	1,018	1,268,734																	12.3.3	WRAPPING PAPERS	1000 t									12.3.3	WRAPPING PAPERS	1000 t	1,998	1,995			12.3.3	WRAPPING PAPERS	NAC/mt
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	112	91,344	95	99,035	92	74,188	119	110,388																	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	3,775	5,780			12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	55	347,984	57	428,762	306	297,043	358	327,195																	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t									12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	1,052	986			12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/mt
15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2	1000 m3	...	…	37	65,253	…	…	2	2,887																	15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!	0	0	ERROR:#VALUE!	ERROR:#VALUE!	0	0	15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)1	1000 m3	ERROR:#VALUE!	421
15.1	GLULAM	1000 m3	…	…	18	41,410	…	…	2	2,816																	15.1	GLULAM	1000 m3									15.1	GLULAM	1000 m3	ERROR:#VALUE!	402
15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3	…	…	19	23,843	…	…	0	72																	15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3									15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	1000 m3	ERROR:#VALUE!	ERROR:#VALUE!
16	I BEAMS (I-JOISTS)2	1000 t	…	…	191	288,746	…	…	42	107,440																	16	I BEAMS (I-JOISTS)1	1000 t									16	I BEAMS (I-JOISTS)1	1000 t	ERROR:#VALUE!	796
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included here
	m3 = cubic metres solid volume		Please complete each cell if possible with
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)		data (numerical value)
	t = metric tonnes		or "…" for not available
			or "0" for zero data

https://www.fao.org/3/cb8216en/cb8216en.pdf

JQ3 Secondary PP Trade

			62	91	91
			Country:	USA	Date:
			Name of Official responsible for reply:
			ERROR:#REF!
			Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE JQ3
	SECONDARY PROCESSED PRODUCTS		Telephone/Fax:
	Trade		E-mail:	ERROR:#REF!
															This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
		Specify Currency and Unit of Value (e.g.:1000 US $):	1,000 US$													Discrepancies
						Flag	Flag	Flag	Flag	Note	Note	Note	Note
Product	Product	I M P O R T V A L U E		E X P O R T V A L U E		Import		Export		Import		Export		Product	Product	I M P O R T V A L U E		E X P O R T V A L U E
code		2021	2022	2021	2022	2021	2022	2021	2022	2021	2022	2021	2022	Code		2021	2022	2021	2022
13	SECONDARY WOOD PRODUCTS													13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	1,780,058	2,265,920	271,452	321,594									13.1	FURTHER PROCESSED SAWNWOOD	0	0	0	0
13.1.C	Coniferous	1,444,981	1,831,434	52,587	57,841									13.1.C	Coniferous
13.1.NC	Non-coniferous	335,078	434,486	218,865	263,753									13.1.NC	Non-coniferous
13.1.NC.T	of which: Tropical	72,628	111,968	3,668	3,843									13.1.NC.T	of which: Tropical
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	433,094	532,798	370,077	514,900									13.2	WOODEN WRAPPING AND PACKAGING MATERIAL
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	1,399,953	1,428,139	71,996	73,367									13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1	3,052,487	3,273,534	538,751	511,169									13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD1
13.5	WOODEN FURNITURE	25,434,020	27,484,945	1,862,401	2,224,718									13.5	WOODEN FURNITURE
13.6	PREFABRICATED BUILDINGS OF WOOD	152,392	211,759	35,038	43,016									13.6	PREFABRICATED BUILDINGS OF WOOD
13.7	OTHER MANUFACTURED WOOD PRODUCTS	1,861,141	1,919,923	209,009	223,679									13.7	OTHER MANUFACTURED WOOD PRODUCTS
14	SECONDARY PAPER PRODUCTS													14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	78,670	117,361	49,954	60,700									14.1	COMPOSITE PAPER AND PAPERBOARD
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	930,331	1,120,178	1,124,808	1,233,112									14.2	SPECIAL COATED PAPER AND PULP PRODUCTS
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	1,401,154	1,557,780	826,261	930,630									14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4	PACKAGING CARTONS, BOXES ETC.	3,041,464	3,506,777	2,257,086	2,409,528									14.4	PACKAGING CARTONS, BOXES ETC.
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	2,966,365	3,674,828	1,943,314	2,081,488									14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	2,479,418	2778405	1759537.11	1752296
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	503	16,616	10,606	69,038									14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	349,590	536,539	75,064	90,800									14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	136,853	343,268	98,106	169,354									14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE
1 In February 2023 this definition was updated to exclude Glulam, Cross-Laminated Timber and I-Beams which are now distinct items in the JFSQ (15.1, 15.2 and 16). This change was made to reflect the update of HS2022.
		Please complete each cell with
		data (numerical value)
		or "…" for not available
		or "0" for zero data

ECE-EU Species

Country:

Date:

Name of Official responsible for reply:

DISCREPANCIES

FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade

Official Address (in full):

Checks

- Checks that values reported on JQ2 match values reported on this sheet

Trade in Roundwood and Sawnwood by species

Telephone:

Fax:

- Checks that subitems are < or = to aggregate

E-mail:

Specify Currency and Unit of Value (e.g.:1000 national currency):

_______________________________

Flag

Note

I M P O R T

E X P O R T

Import

Export

Import

Export

I M P O R T

E X P O R T

Product

Classification

Unit of

2021

2022

2021

2022

2021

2022

2021

2022

2021

2022

2021

2022

Code

HS2022

CN2022

Product

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

1.2.C

4403.11/21/22/23/24/25/26

Industrial Roundwood, Coniferous

1000 m3ub

ERROR:#REF!

4403.21/22

of which: Pine (Pinus spp.)

1000 m3ub

4403 21 10

sawlogs and veneer logs

1000 m3ub

4403 21 90 4403 22 00

pulpwood and other industrial roundwood

1000 m3ub

4403.23/24

of which: Fir/Spruce (Abies spp., Picea spp.)

1000 m3ub

4403 23 10

sawlogs and veneer logs

1000 m3ub

4403 23 90 4403 24 00

pulpwood and other industrial roundwood

1000 m3ub

1.2.NC

4403.12/41/42/49/91/93/94 4403.95/96/97/98/99

Industrial Roundwood, Non-Coniferous

1000 m3ub

ERROR:#REF!

ex4403.12 4403.91

of which: Oak (Quercus spp.)

1000 m3ub

ex4403.12 4403.93/94

of which: Beech (Fagus spp.)

1000 m3ub

ex4403.12 4403.95/96

of which: Birch (Betula spp.)

1000 m3ub

4403 95 10

sawlogs and veneer logs

1000 m3ub

ex4403 12 00 4403 95 90 4403 96 00

pulpwood and other industrial roundwood

1000 m3ub

ex4403.12 4403.97

of which: Poplar/Aspen (Populus spp.)

1000 m3ub

ex4403.12 4403.98

of which: Eucalyptus (Eucalyptus spp.)

1000 m3ub

6.C

4406.11/91 4407.11/12/13/14/19

Sawnwood, Coniferous

1000 m3

ERROR:#REF!

4407.11 ex4407.13 ex4406.11/91

of which: Pine (Pinus spp.)

1000 m3

4407.12 ex4407.13/14 ex4406.11/91

of which: Fir/Spruce (Abies spp., Picea spp.)

1000 m3

6.NC

4406.12/92 4407.21/22/23/25/26/27/28/29/ 91/92/93/94/95/96/97/99

Sawnwood, Non-coniferous

1000 m3

ERROR:#REF!

ex4406.12/92 4407.91

of which: Oak (Quercus spp.)

1000 m3

ex4406.12/92 4407.92

of which: Beech (Fagus spp.)

1000 m3

ex4406.12/92 4407.93

of which: Maple (Acer spp.)

1000 m3

ex4406.12/92 4407.94

of which: Cherry (Prunus spp.)

1000 m3

ex4406.12/92 4407.95

of which: Ash (Fraxinus spp.)

1000 m3

ex4406.12/92 4407.96

of which: Birch (Betula spp.)

1000 m3

ex4406.12/92 4407.97

of which: Poplar/Aspen (Populus spp.)

1000 m3

Light blue cells are requested only for EU members using the Combined Nomenclature to fill in - other countries are welcome to do so if their trade classification nomenclature permits

Please note that information on tropical species trade is requested in questionnaire ITTO2 for ITTO member countries

"ex" codes indicate that only part of that trade classication code is used

m3ub = cubic metres underbark (i.e. excluding bark)

Please complete each cell if possible with

data (numerical value)

or "…" for not available

or "0" for zero data

ITTO1-Estimates

				Country:	0	Date:
				Name of Official responsible for reply:		0

				Official Address (in full):		0
	ITTO1
				Telephone:	0	Fax:	0
	FOREST SECTOR QUESTIONNAIRE			E-mail:		0
	Production and Trade Estimates for	2023
	Specify Currency and Unit of Value (e.g.:1000 US $):			__________

Product		Unit of	Production	Imports		Exports
Code	Product	quantity	Quantity	Quantity	Value	Quantity	Value

1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub
1.2.C	Coniferous	1000 m3ub
1.2.NC	Non-Coniferous	1000 m3ub
1.2.NC.T	of which: Tropical1	1000 m3ub
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3
6.C	Coniferous	1000 m3
6.NC	Non-Coniferous	1000 m3
6.NC.T	of which: Tropical1	1000 m3
7	VENEER SHEETS	1000 m3
7.C	Coniferous	1000 m3
7.NC	Non-Coniferous	1000 m3
7.NC.T	of which: Tropical	1000 m3
8.1	PLYWOOD	1000 m3
8.1.C	Coniferous	1000 m3
8.1.NC	Non-Coniferous	1000 m3
8.1.NC.T	of which: Tropical	1000 m3
1 Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.

	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO2-Species


						Country:	0	Date:
		ITTO2				Name of Official responsible for reply:		0

						Official Address (in full):		0
		FOREST SECTOR QUESTIONNAIRE
		Trade in Tropical Species				Telephone:	0	Fax:	0
						E-mail:		0
	Specify Currency and Unit of Value (e.g.:1000 US $):			1,000 US $

				I M P O R T				E X P O R T
Product	Classifications			2021		2022		2021		2022
	HS2022/HS2017/HS2012/HS2007	Scientific Name	Local/Trade Name	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
				(1000 m3)		(1000 m3)		(1000 m3)		(1000 m3)
1.2.NC.T	HS2022:
Industrial Roundwood, Tropical	ex4403.12 4403.41/42/49	Shorea spp.	Dark/light red meranti and meranti bakau	0	0	0	3	1	159	1	81
	HS2017:	Tectona grandis	Teak	0	0	0	119	0	0	0	62
	ex4403.12 4403.41/49	Other tropical	Other tropical	8	1,420	18	1,831	5	1,894	3	1,040
	HS2012/2007:
	ex4403.10 4403.41/49 ex4403.99
6.NC.T	HS2022:
Sawnwood, Tropical	ex4406.12/92 4407.21/22/23/25/26/27/28/29	Swietenia spp.	Mahogany	10	9,235	14	16,582	4	3,179	2	2,470
		Ocotea porosa & Ochroma pyramidale	Virola and Imbuia	7	4,200	7	4,193	19	14,263	15	11,961
	HS2017:	Shorea spp.	Dark/light red, white and yellow meranty, white luan/seraya, and bakau	7	8,880	9	14,121	0	146	1	443
	ex4406.12/92 4407.21/22/25/26/27/28/29	Ochroma pyramidale	Balsa	10	10,276	6	4,362
		Entandrophragma cylindricum	Sapelli /Sapele	27	26,988	42	43,240	4	3,084	4	2,536
	HS2012/2007:	Milicia excelsa, M. regia (syn. Chlorophora excelsa, C. regia)	Iroko	2	1,139	2	1,826	0	33	0	160
	ex4406.10/90 4407.21/22/25/26/27/28/30	Hymenaea courbaril	Jatoba/ Brazilian cherry	2	1,487	2	3,264
		Dipterocarpus spp.	Keruing	16	14,882	27	30,302
		Khaya spp.	Acajou d'afrique/ African mahogany	10	11,045	15	16,856
		Pouteria spp.	Aningre / Aniegre/ Anegre	0	87	0	67
		Tectona grandis	Teak	5	19,500	0	119			0	62
		Handroanthus spp.	Ipe	34	89,257	41	158,381
		Carapa spp.	Andiroba/ Padauk	1	943	2	2,644
		Cedrela odorata	Cedro/ Spanish cedar	5	4,901	6	5,629
		Other tropical	Other tropical	90	61,194	96	98,428	12	7,279	15	7,217
7.NC.T	HS2022:
Veneer Sheets, Tropical	4408.31/39	Shorea spp.	Dark/light red meranti and meranti bakau	0	387	14	683	3	5,136	2,574	3,918
	HS2017:	Other tropical	Other tropical	8	21,507	9,010	33,666	6	11,618	5,514	11,235
	4408.31/39
	HS2012/2007:
	4408.31/39 ex4408.90
8.1.NC.T	HS2022:
Plywood, Tropical	4412.31/41/51/91	Swietenia spp.	Mahogany	0	751	0	276
	HS2017:	Cedrela odorata	Cedro/ Spanish cedar	1	1,203	1	1,096	1	521
	4412.31 ex4412.94/99	Other tropical	Other tropical	784	514,695	1,012	829,947	21	10,838	42	20,092
	HS2012/2007:
	4412.31 ex4412.32/94/99	Other tropical	Other tropical		1000 square meters	172	2,931
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.

ITTO3-Miscellaneous

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE ITTO3

Miscellaneous Items

Telephone:

Fax:

(use additional paper if necessary)

E-mail:

Please enter current import tariff rates applied to tropical and non-tropical timber products. If available, please provide tariffs by the relevant customs classification category. If tariff levels have been reported in previous years, enter changes only. (Logs = JQ code 1.2, Sawn = JQ code 6, Veneer = JQ code 7, and Plywood = JQ code 8.1)

Current import tariff

Logs

Tropical:

Sawn

Tropical:

Veneer

Tropical:

Plywood

Tropical:

Non-Tropical:

Comments (if any):

Please comment on any quotas, incentives, disincentives, tariff/non-tariff barriers or other related factors which now or in future will significantly affect your production and trade of tropical timber products.

No icentive program for trade of tropical timber products

Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.

None

Please indicate any trends or changes expected in the species composition of your trade. How important are lesser-used tropical timber species and/or minor tropical forest products?

No change expected

Please indicate trends in domestic building activity, housing starts, mortgage/interest rates, substitution of non-tropical wood and/or non-wood products for tropical timbers, and any other domestic factors having a significant impact on tropical timber consumption in your country.

Please indicate the extent of foreign involvement in your timber sector (e.g. number and nationalities of concessionaires/mill (joint) owners, area of forest allocated, scale of investment, etc.).

Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.

Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.

TS-OB

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

ERROR:#REF!

P.OB

1000 m3

ERROR:#REF!

P.OB

1000 m3

1_C

ERROR:#REF!

P.OB

1000 m3

1_NC

ERROR:#REF!

P.OB

1000 m3

1_1

ERROR:#REF!

P.OB

1000 m3

1_1_C

ERROR:#REF!

P.OB

1000 m3

1_1_NC

ERROR:#REF!

P.OB

1000 m3

1_2

ERROR:#REF!

P.OB

1000 m3

1_2_C

ERROR:#REF!

P.OB

1000 m3

1_2_NC

ERROR:#REF!

P.OB

1000 m3

1_2_1

ERROR:#REF!

P.OB

1000 m3

1_2_1_C

ERROR:#REF!

P.OB

1000 m3

1_2_1_NC

ERROR:#REF!

P.OB

1000 m3

1_2_2

ERROR:#REF!

P.OB

1000 m3

1_2_2_C

ERROR:#REF!

P.OB

1000 m3

1_2_2_NC

ERROR:#REF!

P.OB

1000 m3

1_2_3

ERROR:#REF!

P.OB

1000 m3

1_2_3_C

ERROR:#REF!

P.OB

1000 m3

1_2_3_NC

ERROR:#REF!

TS-JQ1

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

1_C

ERROR:#REF!

1000 m3

1_NC

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_1_C

ERROR:#REF!

1000 m3

1_1_NC

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_1

ERROR:#REF!

1000 m3

1_2_1_C

ERROR:#REF!

1000 m3

1_2_1_NC

ERROR:#REF!

1000 m3

1_2_2

ERROR:#REF!

1000 m3

1_2_2_C

ERROR:#REF!

1000 m3

1_2_2_NC

ERROR:#REF!

1000 m3

1_2_3

ERROR:#REF!

1000 m3

1_2_3_C

ERROR:#REF!

1000 m3

1_2_3_NC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

TS-JQ2

Min:

80%

Max:

120%

Notes

JQ2

Country

Flow

Unit

Product

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_1

ERROR:#REF!

1000 m3

1_1

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2

ERROR:#REF!

1000 m3

1_2

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_C

ERROR:#REF!

1000 m3

1_2_C

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

1_2_NC_T

ERROR:#REF!

1000 m3

1_2_NC_T

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_1

ERROR:#REF!

1000 m3

3_1

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

3_2

ERROR:#REF!

1000 m3

3_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_1

ERROR:#REF!

1000 mt

4_1

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

4_2

ERROR:#REF!

1000 mt

4_2

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_C

ERROR:#REF!

1000 m3

5_C

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC

ERROR:#REF!

1000 m3

5_NC

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

5_NC_T

ERROR:#REF!

1000 m3

5_NC_T

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 m3

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1

ERROR:#REF!

1000 m3

6_1

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_C

ERROR:#REF!

1000 m3

6_1_C

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_1_NC_T

ERROR:#REF!

1000 m3

6_1_NC_T

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2

ERROR:#REF!

1000 m3

6_2

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_C

ERROR:#REF!

1000 m3

6_2_C

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_2_NC_T

ERROR:#REF!

1000 m3

6_2_NC_T

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3

ERROR:#REF!

1000 m3

6_3

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_3_1

ERROR:#REF!

1000 m3

6_3_1

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4

ERROR:#REF!

1000 m3

6_4

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_1

ERROR:#REF!

1000 m3

6_4_1

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_2

ERROR:#REF!

1000 m3

6_4_2

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

6_4_3

ERROR:#REF!

1000 m3

6_4_3

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_1

ERROR:#REF!

1000 mt

7_1

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_2

ERROR:#REF!

1000 mt

7_2

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3

ERROR:#REF!

1000 mt

7_3

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_1

ERROR:#REF!

1000 mt

7_3_1

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_2

ERROR:#REF!

1000 mt

7_3_2

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_3

ERROR:#REF!

1000 mt

7_3_3

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_3_4

ERROR:#REF!

1000 mt

7_3_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

7_4

ERROR:#REF!

1000 mt

7_4

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_1

ERROR:#REF!

1000 mt

8_1

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

8_2

ERROR:#REF!

1000 mt

8_2

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ERROR:#REF!

1000 mt

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1

ERROR:#REF!

1000 mt

10_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_1

ERROR:#REF!

1000 mt

10_1_1

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_2

ERROR:#REF!

1000 mt

10_1_2

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_3

ERROR:#REF!

1000 mt

10_1_3

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_1_4

ERROR:#REF!

1000 mt

10_1_4

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_2

ERROR:#REF!

1000 mt

10_2

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3

ERROR:#REF!

1000 mt

10_3

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_1

ERROR:#REF!

1000 mt

10_3_1

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_2

ERROR:#REF!

1000 mt

10_3_2

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_3

ERROR:#REF!

1000 mt

10_3_3

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_3_4

ERROR:#REF!

1000 mt

10_3_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

10_4

ERROR:#REF!

1000 mt

10_4

ERROR:#REF!

TS-JQ3

Min:

80%

Max:

120%

Notes

JQ3

Country

Flow

Unit

Product

ERROR:#REF!

1000 NAC

11_1

ERROR:#REF!

1000 NAC

11_1_C

ERROR:#REF!

1000 NAC

11_1_NC

ERROR:#REF!

1000 NAC

11_1_NC_T

ERROR:#REF!

1000 NAC

11_2

ERROR:#REF!

1000 NAC

11_3

ERROR:#REF!

1000 NAC

11_4

ERROR:#REF!

1000 NAC

11_5

ERROR:#REF!

1000 NAC

11_6

ERROR:#REF!

1000 NAC

11_7

ERROR:#REF!

1000 NAC

11_7_1

ERROR:#REF!

1000 NAC

12_1

ERROR:#REF!

1000 NAC

12_2

ERROR:#REF!

1000 NAC

12_3

ERROR:#REF!

1000 NAC

12_4

ERROR:#REF!

1000 NAC

12_5

ERROR:#REF!

1000 NAC

12_6

ERROR:#REF!

1000 NAC

12_6_1

ERROR:#REF!

1000 NAC

12_6_2

ERROR:#REF!

1000 NAC

12_6_3

ERROR:#REF!

1000 NAC

12_7

ERROR:#REF!

1000 NAC

12_7_1

ERROR:#REF!

1000 NAC

12_7_2

ERROR:#REF!

1000 NAC

12_7_3

ERROR:#REF!

1000 NAC

11_1

ERROR:#REF!

1000 NAC

11_1_C

ERROR:#REF!

1000 NAC

11_1_NC

ERROR:#REF!

1000 NAC

11_1_NC_T

ERROR:#REF!

1000 NAC

11_2

ERROR:#REF!

1000 NAC

11_3

ERROR:#REF!

1000 NAC

11_4

ERROR:#REF!

1000 NAC

11_5

ERROR:#REF!

1000 NAC

11_6

ERROR:#REF!

1000 NAC

11_7

ERROR:#REF!

1000 NAC

11_7_1

ERROR:#REF!

1000 NAC

12_1

ERROR:#REF!

1000 NAC

12_2

ERROR:#REF!

1000 NAC

12_3

ERROR:#REF!

1000 NAC

12_4

ERROR:#REF!

1000 NAC

12_5

ERROR:#REF!

1000 NAC

12_6

ERROR:#REF!

1000 NAC

12_6_1

ERROR:#REF!

1000 NAC

12_6_2

ERROR:#REF!

1000 NAC

12_6_3

ERROR:#REF!

TS-ECEEU

Min:

80%

Max:

120%

Notes

ECEEU

Country

Flow

Unit

Product

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_1

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_2

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_1

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_2

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_1

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_2

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_1_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_1

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_2

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_2_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_3

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_4

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_1_2_NC_5

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_1

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_C_2

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_1

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_2

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_3

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_4

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_5

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_6

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

€

ERROR:#REF!

1000 NAC

ST_5_NC_7

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#REF!

TS-EU1

Min:

80%

Max:

120%

Notes

EU1

Country

Flow

Unit

Product

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

1_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

1_1

ERROR:#REF!

EX_M

1000 m3

1_1

ERROR:#REF!

EX_X

1000 m3

1_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

1_1

ERROR:#REF!

EX_X

1000 m3

1_1

ERROR:#REF!

EX_M

1000 m3

1_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

1_2

ERROR:#REF!

EX_M

1000 m3

1_2

ERROR:#REF!

EX_X

1000 m3

1_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

1_2

ERROR:#REF!

EX_X

1000 m3

1_2

ERROR:#REF!

EX_M

1000 m3

1_2_C

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

1_2_C

ERROR:#REF!

EX_M

1000 m3

1_2_C

ERROR:#REF!

EX_X

1000 m3

1_2_C

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

1_2_C

ERROR:#REF!

EX_X

1000 m3

1_2_C

ERROR:#REF!

EX_M

1000 m3

1_2_NC

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

1_2_NC

ERROR:#REF!

EX_M

1000 m3

1_2_NC

ERROR:#REF!

EX_X

1000 m3

1_2_NC

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

1_2_NC

ERROR:#REF!

EX_X

1000 m3

1_2_NC

ERROR:#REF!

EX_M

1000 m3

1_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

1_2_NC_T

ERROR:#REF!

EX_M

1000 m3

1_2_NC_T

ERROR:#REF!

EX_X

1000 m3

1_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

1_2_NC_T

ERROR:#REF!

EX_X

1000 m3

1_2_NC_T

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

3_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

3_1

ERROR:#REF!

EX_M

1000 m3

3_1

ERROR:#REF!

EX_X

1000 m3

3_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

3_1

ERROR:#REF!

EX_X

1000 m3

3_1

ERROR:#REF!

EX_M

1000 m3

3_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

3_2

ERROR:#REF!

EX_M

1000 m3

3_2

ERROR:#REF!

EX_X

1000 m3

3_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

3_2

ERROR:#REF!

EX_X

1000 m3

3_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

4_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

4_1

ERROR:#REF!

EX_M

1000 mt

4_1

ERROR:#REF!

EX_X

1000 mt

4_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

4_1

ERROR:#REF!

EX_X

1000 mt

4_1

ERROR:#REF!

EX_M

1000 mt

4_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

4_2

ERROR:#REF!

EX_M

1000 mt

4_2

ERROR:#REF!

EX_X

1000 mt

4_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

4_2

ERROR:#REF!

EX_X

1000 mt

4_2

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

5_C

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

5_C

ERROR:#REF!

EX_M

1000 m3

5_C

ERROR:#REF!

EX_X

1000 m3

5_C

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

5_C

ERROR:#REF!

EX_X

1000 m3

5_C

ERROR:#REF!

EX_M

1000 m3

5_NC

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

5_NC

ERROR:#REF!

EX_M

1000 m3

5_NC

ERROR:#REF!

EX_X

1000 m3

5_NC

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

5_NC

ERROR:#REF!

EX_X

1000 m3

5_NC

ERROR:#REF!

EX_M

1000 m3

5_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

5_NC_T

ERROR:#REF!

EX_M

1000 m3

5_NC_T

ERROR:#REF!

EX_X

1000 m3

5_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

5_NC_T

ERROR:#REF!

EX_X

1000 m3

5_NC_T

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 m3

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 m3

ERROR:#REF!

EX_M

1000 m3

6_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_1

ERROR:#REF!

EX_M

1000 m3

6_1

ERROR:#REF!

EX_X

1000 m3

6_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_1

ERROR:#REF!

EX_X

1000 m3

6_1

ERROR:#REF!

EX_M

1000 m3

6_1_C

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_1_C

ERROR:#REF!

EX_M

1000 m3

6_1_C

ERROR:#REF!

EX_X

1000 m3

6_1_C

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_1_C

ERROR:#REF!

EX_X

1000 m3

6_1_C

ERROR:#REF!

EX_M

1000 m3

6_1_NC

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_1_NC

ERROR:#REF!

EX_M

1000 m3

6_1_NC

ERROR:#REF!

EX_X

1000 m3

6_1_NC

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_1_NC

ERROR:#REF!

EX_X

1000 m3

6_1_NC

ERROR:#REF!

EX_M

1000 m3

6_1_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_1_NC_T

ERROR:#REF!

EX_M

1000 m3

6_1_NC_T

ERROR:#REF!

EX_X

1000 m3

6_1_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_1_NC_T

ERROR:#REF!

EX_X

1000 m3

6_1_NC_T

ERROR:#REF!

EX_M

1000 m3

6_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_2

ERROR:#REF!

EX_M

1000 m3

6_2

ERROR:#REF!

EX_X

1000 m3

6_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_2

ERROR:#REF!

EX_X

1000 m3

6_2

ERROR:#REF!

EX_M

1000 m3

6_2_C

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_2_C

ERROR:#REF!

EX_M

1000 m3

6_2_C

ERROR:#REF!

EX_X

1000 m3

6_2_C

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_2_C

ERROR:#REF!

EX_X

1000 m3

6_2_C

ERROR:#REF!

EX_M

1000 m3

6_2_NC

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_2_NC

ERROR:#REF!

EX_M

1000 m3

6_2_NC

ERROR:#REF!

EX_X

1000 m3

6_2_NC

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_2_NC

ERROR:#REF!

EX_X

1000 m3

6_2_NC

ERROR:#REF!

EX_M

1000 m3

6_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_2_NC_T

ERROR:#REF!

EX_M

1000 m3

6_2_NC_T

ERROR:#REF!

EX_X

1000 m3

6_2_NC_T

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_2_NC_T

ERROR:#REF!

EX_X

1000 m3

6_2_NC_T

ERROR:#REF!

EX_M

1000 m3

6_3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_3

ERROR:#REF!

EX_M

1000 m3

6_3

ERROR:#REF!

EX_X

1000 m3

6_3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_3

ERROR:#REF!

EX_X

1000 m3

6_3

ERROR:#REF!

EX_M

1000 m3

6_3_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_3_1

ERROR:#REF!

EX_M

1000 m3

6_3_1

ERROR:#REF!

EX_X

1000 m3

6_3_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_3_1

ERROR:#REF!

EX_X

1000 m3

6_3_1

ERROR:#REF!

EX_M

1000 m3

6_4

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_4

ERROR:#REF!

EX_M

1000 m3

6_4

ERROR:#REF!

EX_X

1000 m3

6_4

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_4

ERROR:#REF!

EX_X

1000 m3

6_4

ERROR:#REF!

EX_M

1000 m3

6_4_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_4_1

ERROR:#REF!

EX_M

1000 m3

6_4_1

ERROR:#REF!

EX_X

1000 m3

6_4_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_4_1

ERROR:#REF!

EX_X

1000 m3

6_4_1

ERROR:#REF!

EX_M

1000 m3

6_4_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_4_2

ERROR:#REF!

EX_M

1000 m3

6_4_2

ERROR:#REF!

EX_X

1000 m3

6_4_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_4_2

ERROR:#REF!

EX_X

1000 m3

6_4_2

ERROR:#REF!

EX_M

1000 m3

6_4_3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

6_4_3

ERROR:#REF!

EX_M

1000 m3

6_4_3

ERROR:#REF!

EX_X

1000 m3

6_4_3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

6_4_3

ERROR:#REF!

EX_X

1000 m3

6_4_3

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

7_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_1

ERROR:#REF!

EX_M

1000 mt

7_1

ERROR:#REF!

EX_X

1000 mt

7_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_1

ERROR:#REF!

EX_X

1000 mt

7_1

ERROR:#REF!

EX_M

1000 mt

7_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_2

ERROR:#REF!

EX_M

1000 mt

7_2

ERROR:#REF!

EX_X

1000 mt

7_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_2

ERROR:#REF!

EX_X

1000 mt

7_2

ERROR:#REF!

EX_M

1000 mt

7_3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_3

ERROR:#REF!

EX_M

1000 mt

7_3

ERROR:#REF!

EX_X

1000 mt

7_3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_3

ERROR:#REF!

EX_X

1000 mt

7_3

ERROR:#REF!

EX_M

1000 mt

7_3_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_3_1

ERROR:#REF!

EX_M

1000 mt

7_3_1

ERROR:#REF!

EX_X

1000 mt

7_3_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_3_1

ERROR:#REF!

EX_X

1000 mt

7_3_1

ERROR:#REF!

EX_M

1000 mt

7_3_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_3_2

ERROR:#REF!

EX_M

1000 mt

7_3_2

ERROR:#REF!

EX_X

1000 mt

7_3_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_3_2

ERROR:#REF!

EX_X

1000 mt

7_3_2

ERROR:#REF!

EX_M

1000 mt

7_3_3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_3_3

ERROR:#REF!

EX_M

1000 mt

7_3_3

ERROR:#REF!

EX_X

1000 mt

7_3_3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_3_3

ERROR:#REF!

EX_X

1000 mt

7_3_3

ERROR:#REF!

EX_M

1000 mt

7_3_4

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_3_4

ERROR:#REF!

EX_M

1000 mt

7_3_4

ERROR:#REF!

EX_X

1000 mt

7_3_4

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_3_4

ERROR:#REF!

EX_X

1000 mt

7_3_4

ERROR:#REF!

EX_M

1000 mt

7_4

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

7_4

ERROR:#REF!

EX_M

1000 mt

7_4

ERROR:#REF!

EX_X

1000 mt

7_4

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

7_4

ERROR:#REF!

EX_X

1000 mt

7_4

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

8_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

8_1

ERROR:#REF!

EX_M

1000 mt

8_1

ERROR:#REF!

EX_X

1000 mt

8_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

8_1

ERROR:#REF!

EX_X

1000 mt

8_1

ERROR:#REF!

EX_M

1000 mt

8_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

8_2

ERROR:#REF!

EX_M

1000 mt

8_2

ERROR:#REF!

EX_X

1000 mt

8_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

8_2

ERROR:#REF!

EX_X

1000 mt

8_2

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

ERROR:#REF!

EX_M

1000 mt

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

ERROR:#REF!

EX_X

1000 mt

ERROR:#REF!

EX_M

1000 mt

10_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_1

ERROR:#REF!

EX_M

1000 mt

10_1

ERROR:#REF!

EX_X

1000 mt

10_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_1

ERROR:#REF!

EX_X

1000 mt

10_1

ERROR:#REF!

EX_M

1000 mt

10_1_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_1_1

ERROR:#REF!

EX_M

1000 mt

10_1_1

ERROR:#REF!

EX_X

1000 mt

10_1_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_1_1

ERROR:#REF!

EX_X

1000 mt

10_1_1

ERROR:#REF!

EX_M

1000 mt

10_1_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_1_2

ERROR:#REF!

EX_M

1000 mt

10_1_2

ERROR:#REF!

EX_X

1000 mt

10_1_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_1_2

ERROR:#REF!

EX_X

1000 mt

10_1_2

ERROR:#REF!

EX_M

1000 mt

10_1_3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_1_3

ERROR:#REF!

EX_M

1000 mt

10_1_3

ERROR:#REF!

EX_X

1000 mt

10_1_3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_1_3

ERROR:#REF!

EX_X

1000 mt

10_1_3

ERROR:#REF!

EX_M

1000 mt

10_1_4

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_1_4

ERROR:#REF!

EX_M

1000 mt

10_1_4

ERROR:#REF!

EX_X

1000 mt

10_1_4

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_1_4

ERROR:#REF!

EX_X

1000 mt

10_1_4

ERROR:#REF!

EX_M

1000 mt

10_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_2

ERROR:#REF!

EX_M

1000 mt

10_2

ERROR:#REF!

EX_X

1000 mt

10_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_2

ERROR:#REF!

EX_X

1000 mt

10_2

ERROR:#REF!

EX_M

1000 mt

10_3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_3

ERROR:#REF!

EX_M

1000 mt

10_3

ERROR:#REF!

EX_X

1000 mt

10_3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_3

ERROR:#REF!

EX_X

1000 mt

10_3

ERROR:#REF!

EX_M

1000 mt

10_3_1

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_3_1

ERROR:#REF!

EX_M

1000 mt

10_3_1

ERROR:#REF!

EX_X

1000 mt

10_3_1

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_3_1

ERROR:#REF!

EX_X

1000 mt

10_3_1

ERROR:#REF!

EX_M

1000 mt

10_3_2

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_3_2

ERROR:#REF!

EX_M

1000 mt

10_3_2

ERROR:#REF!

EX_X

1000 mt

10_3_2

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_3_2

ERROR:#REF!

EX_X

1000 mt

10_3_2

ERROR:#REF!

EX_M

1000 mt

10_3_3

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_3_3

ERROR:#REF!

EX_M

1000 mt

10_3_3

ERROR:#REF!

EX_X

1000 mt

10_3_3

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_3_3

ERROR:#REF!

EX_X

1000 mt

10_3_3

ERROR:#REF!

EX_M

1000 mt

10_3_4

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_3_4

ERROR:#REF!

EX_M

1000 mt

10_3_4

ERROR:#REF!

EX_X

1000 mt

10_3_4

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_3_4

ERROR:#REF!

EX_X

1000 mt

10_3_4

ERROR:#REF!

EX_M

1000 mt

10_4

ERROR:#REF!

€

ERROR:#REF!

EX_M

1000 NAC

10_4

ERROR:#REF!

EX_M

1000 mt

10_4

ERROR:#REF!

EX_X

1000 mt

10_4

ERROR:#REF!

€

ERROR:#REF!

EX_X

1000 NAC

10_4

ERROR:#REF!

EX_X

1000 mt

10_4

ERROR:#REF!

TS-EU2

Min:

80%

Max:

120%

Notes

EU2

Country

Flow

Unit

Product

ERROR:#REF!

1000 m3

EU2_1

ERROR:#REF!

1000 m3

EU2_1_C

ERROR:#REF!

1000 m3

EU2_1_NC

ERROR:#REF!

1000 m3

EU2_1_1

ERROR:#REF!

1000 m3

EU2_1_1_C

ERROR:#REF!

1000 m3

EU2_1_1_NC

ERROR:#REF!

1000 m3

EU2_1_2

ERROR:#REF!

1000 m3

EU2_1_2_C

ERROR:#REF!

1000 m3

EU2_1_2_NC

ERROR:#REF!

1000 m3

EU2_1_3

ERROR:#REF!

1000 m3

EU2_1_3_C

ERROR:#REF!

1000 m3

EU2_1_3_NC

ERROR:#REF!

Annex1 | JQ1-Corres.



			Last updated in 2016
		FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
		PRIMARY PRODUCTS
		Removals and Production
		CORRESPONDENCES to CPC Ver.2.1
		Central Product Classification Version 2.1 (CPC Ver. 2.1)
Product	Product
Code

REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1	ROUNDWOOD (WOOD IN THE ROUGH)	031
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	0313
1.1.C	Coniferous	03131
1.1.NC	Non-Coniferous	03132
1.2	INDUSTRIAL ROUNDWOOD	0311 0312
1.2.C	Coniferous	0311
1.2.NC	Non-Coniferous	0312
1.2.NC.T	of which: Tropical	ex0312
1.2.1	SAWLOGS AND VENEER LOGS	ex03110 ex03120
1.2.1.C	Coniferous	ex03110
1.2.1.NC	Non-Coniferous	ex03120
1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	ex03110 ex03120
1.2.2.C	Coniferous	ex03110
1.2.2.NC	Non-Coniferous	ex03120
1.2.3	OTHER INDUSTRIAL ROUNDWOOD	ex03110 ex03120
1.2.3.C	Coniferous	ex03110
1.2.3.NC	Non-Coniferous	ex03120
PRODUCTION
2	WOOD CHARCOAL	ex34510
3	WOOD CHIPS, PARTICLES AND RESIDUES	ex31230 ex39283
3.1	WOOD CHIPS AND PARTICLES	ex31230
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex39283
4	RECOVERED POST-CONSUMER WOOD	ex39283
5	WOOD PELLETS AND OTHER AGGLOMERATES	39281 39282
5.1	WOOD PELLETS	39281
5.2	OTHER AGGLOMERATES	39282
6	SAWNWOOD (INCLUDING SLEEPERS)	311 3132
6.C	Coniferous	31101 ex31109 ex3132
6.NC	Non-Coniferous	31102 ex31109 ex3132
6.NC.T	of which: Tropical	ex31102 ex31109 ex3132
7	VENEER SHEETS	3151
7.C	Coniferous	31511
7.NC	Non-Coniferous	31512
7.NC.T	of which: Tropical	ex31512
8	WOOD-BASED PANELS	3141 3142 3143 3144
8.1	PLYWOOD	3141 3142
8.1.C	Coniferous	31411 31421
8.1.NC	Non-Coniferous	31412 31422
8.1.NC.T	of which: Tropical	ex31412 ex31422
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	3143
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	31432
8.3	FIBREBOARD	3144
8.3.1	HARDBOARD	31442
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	31441
8.3.3	OTHER FIBREBOARD	31449
9	WOOD PULP	32111 32112 ex32113
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	ex32113
9.2	CHEMICAL WOOD PULP	32112
9.2.1	SULPHATE PULP	ex32112
9.2.1.1	of which: BLEACHED	ex32112
9.2.2	SULPHITE PULP	ex32112
9.3	DISSOLVING GRADES	32111
10	OTHER PULP	ex32113
10.1	PULP FROM FIBRES OTHER THAN WOOD	ex32113
10.2	RECOVERED FIBRE PULP	ex32113
11	RECOVERED PAPER	3924
12	PAPER AND PAPERBOARD	3212 3213 32142 32143 ex32149 32151 32198 ex32199
12.1	GRAPHIC PAPERS	3212 ex32143 ex32149
12.1.1	NEWSPRINT	32121
12.1.2	UNCOATED MECHANICAL	ex32122 ex32129
12.1.3	UNCOATED WOODFREE	32122 ex32129
12.1.4	COATED PAPERS	ex32143 ex32149
12.2	HOUSEHOLD AND SANITARY PAPERS	32131
12.3	PACKAGING MATERIALS	32132 ex32133 32134 32135 ex32136 ex32137 32142 32151 ex32143 ex32149
12.3.1	CASE MATERIALS	32132 32134 32135 ex32136
12.3.2	CARTONBOARD	ex32133 ex32136 ex32143 ex32149
12.3.3	WRAPPING PAPERS	ex32133 ex32136 ex32137 32142 32151
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	ex32136
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	ex32149 ex32133 ex32136 ex32137 32198 ex32199
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the CPC Ver.2.1 code is applicable.
For instance "ex31512" under product 7.NC.T means that only a part of CPC Ver.2.1 code 31512 refers to non-coniferous tropical veneer sheets.
In CPC, if only 3 or 4 digits are shown, then all sub-codes at lower degrees of aggregation are included (for example, 0313 includes 03131 and 03132).

Annex2 | JQ2-Corres.




			FOREST SECTOR QUESTIONNAIRE JQ2 (Supp. 1)
			PRIMARY PRODUCTS
			Trade
			CORRESPONDENCES to HS2022, HS2017, HS2012 and SITC Rev.4
			C l a s s i f i c a t i o n s
Product	Product
Code		HS2022	HS2017	HS2012	SITC Rev.4

1	ROUNDWOOD (WOOD IN THE ROUGH)	4401.11/12 44.03	4401.11/12 44.03	4401.10 44.03	245.01 247
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	4401.11/12	4401.11/12	4401.10	245.01
1.1.C	Coniferous	4401.11	4401.11	ex4401.10	ex245.01
1.1.NC	Non-Coniferous	4401.12	4401.12	ex4401.10	ex245.01
1.2	INDUSTRIAL ROUNDWOOD	44.03	44.03	44.03	247
1.2.C	Coniferous	4403.11/21/22/23/24/25/26	4403.11/21/22/23/24/25/26	ex4403.10 4403.20	ex247.3 247.4
1.2.NC	Non-Coniferous	4403.12/41/42/49/91/93/94/95/96/97/98/99	4403.12/41/49/91/93/94/95/96/97/98/99	ex4403.10 4403.41/49/91/92/99	ex247.3 247.5 247.9
1.2.NC.T	of which: Tropical1	ex4403.12 4403.41/42/49	4403.41/49	ex4403.10 4403.41/49 ex4403.99	ex247.3 247.5 ex247.9
2	WOOD CHARCOAL	4402.90	4402.90	4402.90	ex245.02
3	WOOD CHIPS, PARTICLES AND RESIDUES	4401.21/22 4401.41 ex4401.49	4401.21/22 ex4401.40	4401.21/22 ex4401.39	246.1 ex246.2
3.1	WOOD CHIPS AND PARTICLES	4401.21/22	4401.21/22	4401.21/22	246.1
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	4401.41 ex4401.49++	ex4401.40++	ex4401.39	ex246.2
3.2.1	of which: Sawdust	4401.41	ex4401.40++	ex4401.39	ex246.2
4	RECOVERED POST-CONSUMER WOOD	ex4401.49++	ex4401.40++	ex4401.39	ex246.2
5	WOOD PELLETS AND OTHER AGGLOMERATES	4401.31/32/39	4401.31/39	4401.31 ex4401.39	ex246.2
5.1	WOOD PELLETS	4401.31	4401.31	4401.31	ex246.2
5.2	OTHER AGGLOMERATES	4401.32/39	4401.39	ex4401.39	ex246.2
6	SAWNWOOD (INCLUDING SLEEPERS)	44.06 44.07	44.06 44.07	44.06 44.07	248.1 248.2 248.4
6.C	Coniferous	4406.11/91 4407.11/12/13/14/19	4406.11/91 4407.11/12/19	ex4406.10/90 4407.10	ex248.11 ex248.19 248.2
6.NC	Non-Coniferous	4406.12/92 4407.21/22/23/25/26/27/28/29/91/92/93/94/95/96/97/99	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99	ex4406.10/90 4407.21/22/25/26/27/28/29/91/92/93/94/95/99	ex248.11 ex248.19 248.4
6.NC.T	of which: Tropical1	ex4406.12/92 4407.21/22/23/25/26/27/28/29	4407.21/22/25/26/27/28/29	ex4406.10/90 4407.21/22/25/26/27/28/29 ex4407.99	ex248.11 ex248.19 ex248.4
7	VENEER SHEETS	44.08	44.08	44.08	634.1
7.C	Coniferous	4408.10	4408.10	4408.10	634.11
7.NC	Non-Coniferous	4408.31/39/90	4408.31/39/90	4408.31/39/90	634.12
7.NC.T	of which: Tropical	4408.31/39	4408.31/39	4408.31/39 ex4408.90	ex634.12
8	WOOD-BASED PANELS	44.10 44.11 4412.31/33/34/39/41/42/49/51/52/59/91/92/99	44.10 44.11 4412.31/33/34/39/94/99	44.10 44.11 4412.31/32/39/94/99	634.22/23/31/33/39 634.5
8.1	PLYWOOD	4412.31/33/34/39/41/42/49/51/52/59/91/92/99	4412.31/33/34/39/94/99	4412.31/32/39/94/99	634.31/33/39
8.1.C	Coniferous	4412.39/49/59/99	4412.39 ex4412.94 ex4412.99	4412.39 ex4412.94 ex.4412.99	ex634.31 ex634.33 ex634.39
8.1.NC	Non-Coniferous	4412.33/34/42/52/92	4412.31/33/34 ex4412.94 ex4412.99	4412.31/32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.1.NC.T	of which: Tropical	4412.31/41/51/91	4412.31 ex4412.94 ex4412.99	4412.31 ex4412.32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.1.1	of which: Laminated Veneer Lumber (LVL)	4412.41/42/49	ex4412.99	ex4412.99	ex634.39
8.1.1.C	Coniferous	4412.49	ex4412.99	ex4412.99	ex634.39
8.1.1.NC	Non-Coniferous	4412.41/42	ex4412.99	ex4412.99	ex634.39
8.1.1.NC.T	of which: Tropical	4412.41	ex4412.99	ex4412.99	ex634.39
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	44.10	44.10	44.10	634.22/23
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	4410.12	4410.12	4410.12	ex634.22
8.3	FIBREBOARD	44.11	44.11	44.11	634.5
8.3.1	HARDBOARD	4411.92	4411.92	4411.92	ex634.54 ex634.55
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	4411.12/13 ex4411.14*	4411.12/13 ex4411.14*	4411.12/13 ex4411.14*	ex634.54 ex634.55
8.3.3	OTHER FIBREBOARD	ex4411.14* 4411.93/94	ex4411.14* 4411.93/94	ex4411.14 4411.93/94	ex634.54 ex634.55
9	WOOD PULP	47.01/02/03/04/05	47.01/02/03/04/05	47.01/02/03/04/05	251.2 251.3 251.4 251.5 251.6 251.91
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	47.01 47.05	47.01 47.05	47.01 47.05	251.2 251.91
9.2	CHEMICAL WOOD PULP	47.03 47.04	47.03 47.04	47.03 47.04	251.4 251.5 251.6
9.2.1	SULPHATE PULP	47.03	47.03	47.03	251.4 251.5
9.2.1.1	of which: BLEACHED	4703.21/29	4703.21/29	4703.21/29	251.5
9.2.2	SULPHITE PULP	47.04	47.04	47.04	251.6
9.3	DISSOLVING GRADES	47.02	47.02	47.02	251.3
10	OTHER PULP	47.06	47.06	47.06	251.92
10.1	PULP FROM FIBRES OTHER THAN WOOD	4706.10/30/91/92/93	4706.10/30/91/92/93	4706.10/30/91/92/93	ex251.92
10.2	RECOVERED FIBRE PULP	4706.20	4706.20	4706.20	ex251.92
11	RECOVERED PAPER	47.07	47.07	47.07	251.1
12	PAPER AND PAPERBOARD	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	641.1 641.2 641.3 641.4 641.5 641.62/63/64/69/71/72/74/75/76/77/93 642.41
12.1	GRAPHIC PAPERS	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	641.1 641.21/22/26/29 641.3
12.1.1	NEWSPRINT	48.01	48.01	48.01	641.1
12.1.2	UNCOATED MECHANICAL	4802.61/62/69	4802.61/62/69	4802.61/62/69	641.29
12.1.3	UNCOATED WOODFREE	4802.10/20/54/55/56/57/58	4802.10/20/54/55/56/57/58	4802.10/20/54/55/56/57/58	641.21/22/26
12.1.4	COATED PAPERS	48.09 4810.13/14/19/22/29	48.09 4810.13/14/19/22/29	48.09 4810.13/14/19/22/29	641.3
12.2	HOUSEHOLD AND SANITARY PAPERS	48.03	48.03	48.03	641.63
12.3	PACKAGING MATERIALS	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	641.41/42/46 ex641.47 641.48/51/52 ex641.53 641.54/59/62/64/69/71/72/74/75/76/77
12.3.1	CASE MATERIALS	4804.11/19 4805.11/12/19/24/25/91	4804.11/19 4805.11/12/19/24/25/91	4804.11/19 4805.11/12/19/24/25/91	641.41/51/54 ex641.59
12.3.2	CARTONBOARD	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	ex641.47 641.48 ex641.59 641.75/76 ex641.77 641.71/72
12.3.3	WRAPPING PAPERS	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	641.42/46/52 ex641.53 641.62/64/69/74 ex641.77
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	4805.93	4805.93	4805.93	ex641.59
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	641.24 ex641.47 641.56 ex641.53 641.55/93 642.41
15	GLULAM AND CROSS-LAMINATED TIMBER (CLT or X-LAM)2	4418.81/82	ex4418.60	ex4418.60	ex635.39
15.1	GLULAM	4418.81	ex4418.60	ex4418.60	ex635.39
15.2	CROSS-LAMINATED TIMBER (CLT or X-LAM)	4418.82	ex4418.60	ex4418.60	ex635.39
16	I BEAMS (I-JOISTS)2	4418.83	ex4418.60	ex4418.60	ex635.39
1Please include the non-coniferous non-tropical species exported by tropical countries or imported from tropical countries.
2 Glulam, CLT and I Beams are classified as secondary wood products but for ease of reporting are included in JQ1 and JQ2
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017/HS2022 or SITC Rev.4 code is applicable.
For instance "ex4401.49" under product 3.2 means that only a part of HS2022 code 4401.49 refers to wood residues coming from wood processing (the other part coded under 4401.49 is recovered post-consumer wood).
++ Please use your judgement or, as a default, assign half of 4401.49 to item 3.2 and half to item 4 (note different quantity units)
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.

SentData

Country	Flow	Year	Unit	Product	Conc	Data value
ERROR:#REF!	P	2021	1000 m3	1	ERROR:#REF!	453530.087745588	JQ1
ERROR:#REF!	P	2021	1000 m3	1_C	ERROR:#REF!	71110.54
ERROR:#REF!	P	2021	1000 m3	1_NC	ERROR:#REF!	33759.55
ERROR:#REF!	P	2021	1000 m3	1_1	ERROR:#REF!	37350.99
ERROR:#REF!	P	2021	1000 m3	1_1_C	ERROR:#REF!	382419.547745588
ERROR:#REF!	P	2021	1000 m3	1_1_NC	ERROR:#REF!	305851
ERROR:#REF!	P	2021	1000 m3	1_2	ERROR:#REF!	76568.5477455878
ERROR:#REF!	P	2021	1000 m3	1_2_C	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	1_2_NC	ERROR:#REF!	183400.547745588
ERROR:#REF!	P	2021	1000 m3	1_2_1	ERROR:#REF!	150702
ERROR:#REF!	P	2021	1000 m3	1_2_1_C	ERROR:#REF!	32698.5477455878
ERROR:#REF!	P	2021	1000 m3	1_2_1_NC	ERROR:#REF!	185686
ERROR:#REF!	P	2021	1000 m3	1_2_2	ERROR:#REF!	143462
ERROR:#REF!	P	2021	1000 m3	1_2_2_C	ERROR:#REF!	42224
ERROR:#REF!	P	2021	1000 m3	1_2_2_NC	ERROR:#REF!	13333
ERROR:#REF!	P	2021	1000 m3	1_2_3	ERROR:#REF!	11687
ERROR:#REF!	P	2021	1000 m3	1_2_3_C	ERROR:#REF!	1646
ERROR:#REF!	P	2021	1000 m3	1_2_3_NC	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	2	ERROR:#REF!	60485.27
ERROR:#REF!	P	2021	1000 m3	3	ERROR:#REF!	44209.21
ERROR:#REF!	P	2021	1000 m3	3_1	ERROR:#REF!	16276.06
ERROR:#REF!	P	2021	1000 m3	3_2	ERROR:#REF!	...
ERROR:#REF!	P	2021	1000 mt	4	ERROR:#REF!	8448.6
ERROR:#REF!	P	2021	1000 mt	4_1	ERROR:#REF!	8448.6
ERROR:#REF!	P	2021	1000 mt	4_2	ERROR:#REF!	…
ERROR:#REF!	P	2021	1000 m3	5	ERROR:#REF!	80705
ERROR:#REF!	P	2021	1000 m3	5_C	ERROR:#REF!	63417
ERROR:#REF!	P	2021	1000 m3	5_NC	ERROR:#REF!	17288
ERROR:#REF!	P	2021	1000 m3	5_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1_C	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1_NC	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_1_NC_T	ERROR:#REF!	27679.8579911906
ERROR:#REF!	P	2021	1000 m3	6_2	ERROR:#REF!	9704.8579911906
ERROR:#REF!	P	2021	1000 m3	6_2_C	ERROR:#REF!	9470.8579911906
ERROR:#REF!	P	2021	1000 m3	6_2_NC	ERROR:#REF!	234
ERROR:#REF!	P	2021	1000 m3	6_2_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_3	ERROR:#REF!	17975
ERROR:#REF!	P	2021	1000 m3	6_3_1	ERROR:#REF!	13839
ERROR:#REF!	P	2021	1000 m3	6_4	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_4_1	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_4_2	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 m3	6_4_3	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	7	ERROR:#REF!	49685
ERROR:#REF!	P	2021	1000 mt	7_1	ERROR:#REF!	4046
ERROR:#REF!	P	2021	1000 mt	7_2	ERROR:#REF!	44411
ERROR:#REF!	P	2021	1000 mt	7_3	ERROR:#REF!	44167
ERROR:#REF!	P	2021	1000 mt	7_3_1	ERROR:#REF!	20262
ERROR:#REF!	P	2021	1000 mt	7_3_2	ERROR:#REF!	244
ERROR:#REF!	P	2021	1000 mt	7_3_3	ERROR:#REF!	1228
ERROR:#REF!	P	2021	1000 mt	7_3_4	ERROR:#REF!	30072
ERROR:#REF!	P	2021	1000 mt	7_4	ERROR:#REF!	108
ERROR:#REF!	P	2021	1000 mt	8	ERROR:#REF!	29964
ERROR:#REF!	P	2021	1000 mt	8_1	ERROR:#REF!	45037
ERROR:#REF!	P	2021	1000 mt	8_2	ERROR:#REF!	67475.31
ERROR:#REF!	P	2021	1000 mt	9	ERROR:#REF!	8296
ERROR:#REF!	P	2021	1000 mt	10	ERROR:#REF!	370
ERROR:#REF!	P	2021	1000 mt	10_1	ERROR:#REF!	394.6
ERROR:#REF!	P	2021	1000 mt	10_1_1	ERROR:#REF!	4774.51
ERROR:#REF!	P	2021	1000 mt	10_1_2	ERROR:#REF!	2756.93
ERROR:#REF!	P	2021	1000 mt	10_1_3	ERROR:#REF!	6928.17
ERROR:#REF!	P	2021	1000 mt	10_1_4	ERROR:#REF!	50948.42
ERROR:#REF!	P	2021	1000 mt	10_2	ERROR:#REF!	36169.47
ERROR:#REF!	P	2021	1000 mt	10_3	ERROR:#REF!	8482.18
ERROR:#REF!	P	2021	1000 mt	10_3_1	ERROR:#REF!	2541.93
ERROR:#REF!	P	2021	1000 mt	10_3_2	ERROR:#REF!	3754.84
ERROR:#REF!	P	2021	1000 mt	10_3_3	ERROR:#REF!	1302.72
ERROR:#REF!	P	2021	1000 mt	10_3_4	ERROR:#REF!	0
ERROR:#REF!	P	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	2022	1000 m3	1	ERROR:#REF!	458773.695388199
ERROR:#REF!	P	2022	1000 m3	1_C	ERROR:#REF!	76230
ERROR:#REF!	P	2022	1000 m3	1_NC	ERROR:#REF!	37619
ERROR:#REF!	P	2022	1000 m3	1_1	ERROR:#REF!	38611
ERROR:#REF!	P	2022	1000 m3	1_1_C	ERROR:#REF!	382543.695388199
ERROR:#REF!	P	2022	1000 m3	1_1_NC	ERROR:#REF!	306118.695388199
ERROR:#REF!	P	2022	1000 m3	1_2	ERROR:#REF!	76425
ERROR:#REF!	P	2022	1000 m3	1_2_C	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	1_2_NC	ERROR:#REF!	186156.695388199
ERROR:#REF!	P	2022	1000 m3	1_2_1	ERROR:#REF!	152798.695388199
ERROR:#REF!	P	2022	1000 m3	1_2_1_C	ERROR:#REF!	33358
ERROR:#REF!	P	2022	1000 m3	1_2_1_NC	ERROR:#REF!	182650
ERROR:#REF!	P	2022	1000 m3	1_2_2	ERROR:#REF!	141226
ERROR:#REF!	P	2022	1000 m3	1_2_2_C	ERROR:#REF!	41424
ERROR:#REF!	P	2022	1000 m3	1_2_2_NC	ERROR:#REF!	13737
ERROR:#REF!	P	2022	1000 m3	1_2_3	ERROR:#REF!	12094
ERROR:#REF!	P	2022	1000 m3	1_2_3_C	ERROR:#REF!	1643
ERROR:#REF!	P	2022	1000 m3	1_2_3_NC	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	2	ERROR:#REF!	62262
ERROR:#REF!	P	2022	1000 m3	3	ERROR:#REF!	45900
ERROR:#REF!	P	2022	1000 m3	3_1	ERROR:#REF!	16362
ERROR:#REF!	P	2022	1000 m3	3_2	ERROR:#REF!	…
ERROR:#REF!	P	2022	1000 mt	4	ERROR:#REF!	9544
ERROR:#REF!	P	2022	1000 mt	4_1	ERROR:#REF!	9544
ERROR:#REF!	P	2022	1000 mt	4_2	ERROR:#REF!	...
ERROR:#REF!	P	2022	1000 m3	5	ERROR:#REF!	81676
ERROR:#REF!	P	2022	1000 m3	5_C	ERROR:#REF!	64039
ERROR:#REF!	P	2022	1000 m3	5_NC	ERROR:#REF!	17637
ERROR:#REF!	P	2022	1000 m3	5_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1_C	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1_NC	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_1_NC_T	ERROR:#REF!	9020.0459873934
ERROR:#REF!	P	2022	1000 m3	6_2	ERROR:#REF!	9020.0459873934
ERROR:#REF!	P	2022	1000 m3	6_2_C	ERROR:#REF!	9020.0459873934
ERROR:#REF!	P	2022	1000 m3	6_2_NC	ERROR:#REF!	…
ERROR:#REF!	P	2022	1000 m3	6_2_NC_T	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_3_1	ERROR:#REF!	13592
ERROR:#REF!	P	2022	1000 m3	6_4	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_4_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_4_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 m3	6_4_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	7_3_4	ERROR:#REF!	31250
ERROR:#REF!	P	2022	1000 mt	7_4	ERROR:#REF!	96
ERROR:#REF!	P	2022	1000 mt	8	ERROR:#REF!	31154
ERROR:#REF!	P	2022	1000 mt	8_1	ERROR:#REF!	44828
ERROR:#REF!	P	2022	1000 mt	8_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	9	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_1_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_1_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_1_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_1_4	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_3_1	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_3_2	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_3_3	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_3_4	ERROR:#REF!	0
ERROR:#REF!	P	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	1	ERROR:#REF!	ERROR:#REF!	JQ2
ERROR:#REF!	M	2021	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	11_1	ERROR:#REF!	1444980.53	JQ3
ERROR:#REF!	M	0	1000 NAC	11_1_C	ERROR:#REF!	335077.53
ERROR:#REF!	M	0	1000 NAC	11_1_NC	ERROR:#REF!	72627.89
ERROR:#REF!	M	0	1000 NAC	11_1_NC_T	ERROR:#REF!	433093.8
ERROR:#REF!	M	0	1000 NAC	11_2	ERROR:#REF!	1399952.62
ERROR:#REF!	M	0	1000 NAC	11_3	ERROR:#REF!	3052486.93
ERROR:#REF!	M	0	1000 NAC	11_4	ERROR:#REF!	25434019.73
ERROR:#REF!	M	0	1000 NAC	11_5	ERROR:#REF!	152392.3
ERROR:#REF!	M	0	1000 NAC	11_6	ERROR:#REF!	1861141.27
ERROR:#REF!	M	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	11_7_1	ERROR:#REF!	78670.24
ERROR:#REF!	M	0	1000 NAC	12_1	ERROR:#REF!	1401153.82
ERROR:#REF!	M	0	1000 NAC	12_2	ERROR:#REF!	3041463.97
ERROR:#REF!	M	0	1000 NAC	12_3	ERROR:#REF!	2966364.71
ERROR:#REF!	M	0	1000 NAC	12_4	ERROR:#REF!	503.39
ERROR:#REF!	M	0	1000 NAC	12_5	ERROR:#REF!	349590.35
ERROR:#REF!	M	0	1000 NAC	12_6	ERROR:#REF!	136853.01
ERROR:#REF!	M	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	11_1	ERROR:#REF!	1831434
ERROR:#REF!	M	0	1000 NAC	11_1_C	ERROR:#REF!	434486
ERROR:#REF!	M	0	1000 NAC	11_1_NC	ERROR:#REF!	111968
ERROR:#REF!	M	0	1000 NAC	11_1_NC_T	ERROR:#REF!	532798
ERROR:#REF!	M	0	1000 NAC	11_2	ERROR:#REF!	1428139
ERROR:#REF!	M	0	1000 NAC	11_3	ERROR:#REF!	3273534
ERROR:#REF!	M	0	1000 NAC	11_4	ERROR:#REF!	27484945
ERROR:#REF!	M	0	1000 NAC	11_5	ERROR:#REF!	211759
ERROR:#REF!	M	0	1000 NAC	11_6	ERROR:#REF!	1919923
ERROR:#REF!	M	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	11_7_1	ERROR:#REF!	117361
ERROR:#REF!	M	0	1000 NAC	12_1	ERROR:#REF!	1557780
ERROR:#REF!	M	0	1000 NAC	12_2	ERROR:#REF!	3506777
ERROR:#REF!	M	0	1000 NAC	12_3	ERROR:#REF!	3674828
ERROR:#REF!	M	0	1000 NAC	12_4	ERROR:#REF!	16616
ERROR:#REF!	M	0	1000 NAC	12_5	ERROR:#REF!	536539
ERROR:#REF!	M	0	1000 NAC	12_6	ERROR:#REF!	343268
ERROR:#REF!	M	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	M	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	11_1	ERROR:#REF!	52587.01
ERROR:#REF!	X	0	1000 NAC	11_1_C	ERROR:#REF!	218864.53
ERROR:#REF!	X	0	1000 NAC	11_1_NC	ERROR:#REF!	3667.65
ERROR:#REF!	X	0	1000 NAC	11_1_NC_T	ERROR:#REF!	370076.93
ERROR:#REF!	X	0	1000 NAC	11_2	ERROR:#REF!	71996.3
ERROR:#REF!	X	0	1000 NAC	11_3	ERROR:#REF!	538750.66
ERROR:#REF!	X	0	1000 NAC	11_4	ERROR:#REF!	1862400.66
ERROR:#REF!	X	0	1000 NAC	11_5	ERROR:#REF!	35037.9
ERROR:#REF!	X	0	1000 NAC	11_6	ERROR:#REF!	209009.15
ERROR:#REF!	X	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	11_7_1	ERROR:#REF!	49953.75
ERROR:#REF!	X	0	1000 NAC	12_1	ERROR:#REF!	826261.36
ERROR:#REF!	X	0	1000 NAC	12_2	ERROR:#REF!	2257086.44
ERROR:#REF!	X	0	1000 NAC	12_3	ERROR:#REF!	1943313.85
ERROR:#REF!	X	0	1000 NAC	12_4	ERROR:#REF!	10605.98
ERROR:#REF!	X	0	1000 NAC	12_5	ERROR:#REF!	75064.42
ERROR:#REF!	X	0	1000 NAC	12_6	ERROR:#REF!	98106.34
ERROR:#REF!	X	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	11_1	ERROR:#REF!	57841
ERROR:#REF!	X	0	1000 NAC	11_1_C	ERROR:#REF!	263753
ERROR:#REF!	X	0	1000 NAC	11_1_NC	ERROR:#REF!	3843
ERROR:#REF!	X	0	1000 NAC	11_1_NC_T	ERROR:#REF!	514900
ERROR:#REF!	X	0	1000 NAC	11_2	ERROR:#REF!	73367
ERROR:#REF!	X	0	1000 NAC	11_3	ERROR:#REF!	511169
ERROR:#REF!	X	0	1000 NAC	11_4	ERROR:#REF!	2224718
ERROR:#REF!	X	0	1000 NAC	11_5	ERROR:#REF!	43016
ERROR:#REF!	X	0	1000 NAC	11_6	ERROR:#REF!	223679
ERROR:#REF!	X	0	1000 NAC	11_7	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	11_7_1	ERROR:#REF!	60700
ERROR:#REF!	X	0	1000 NAC	12_1	ERROR:#REF!	930630
ERROR:#REF!	X	0	1000 NAC	12_2	ERROR:#REF!	2409528
ERROR:#REF!	X	0	1000 NAC	12_3	ERROR:#REF!	2081488
ERROR:#REF!	X	0	1000 NAC	12_4	ERROR:#REF!	69038
ERROR:#REF!	X	0	1000 NAC	12_5	ERROR:#REF!	90800
ERROR:#REF!	X	0	1000 NAC	12_6	ERROR:#REF!	169354
ERROR:#REF!	X	0	1000 NAC	12_6_1	ERROR:#REF!	0
ERROR:#REF!	X	0	1000 NAC	12_6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	0	1000 NAC	12_6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C	ERROR:#REF!	0	ECEEU
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2021	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	M	2022	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2021	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 m3	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_1_2_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_C	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_C_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_C_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_1	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_2	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_3	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_4	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_5	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_6	ERROR:#REF!	0
ERROR:#REF!	X	2022	1000 NAC	ST_5_NC_7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1	ERROR:#REF!	ERROR:#REF!	EU1
ERROR:#REF!	EX_M	2021	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2021	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_M	2022	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2021	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 m3	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 mt	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	1_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	5_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_1_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_2_NC_T	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	6_4_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	7_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	8	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	8_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	8_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	9	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_1_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_3_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	EX_X	2022	1000 NAC	10_4	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1	ERROR:#REF!	ERROR:#REF!	EU2
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P	ERROR:#REF!	1000 m3	EU2_1_3_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1	ERROR:#REF!	ERROR:#REF!	OB
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_1_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_2_NC	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_C	ERROR:#REF!	ERROR:#REF!
ERROR:#REF!	P.OB	ERROR:#REF!	1000 m3	1_2_3_NC	ERROR:#REF!	ERROR:#REF!

Database

Country	Flow	Year	Unit	Product	conc
ERROR:#REF!	P	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2015	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2015	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2015	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2015	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2015	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2014	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2014	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2014	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2014	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2014	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2013	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2013	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2013	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2013	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2013	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_1_NC_T	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	11_7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_6_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	12_7_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	M	2012	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 m3	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_C_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_1_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_2_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_1_2_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_C	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_C_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_C_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_1	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_2	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_3	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_4	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_5	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_6	ERROR:#REF!
ERROR:#REF!	X	2012	1000 NAC	ST_5_NC_7	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_M	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	8	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	9	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	5_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2_C	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2_NC	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	6_4_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	7_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	8	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	8_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	8_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	9	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_1_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_1	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_2	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_3	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_3_4	ERROR:#REF!
ERROR:#REF!	EX_X	2012	1000 NAC	10_4	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_1	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_1_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_2	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_2_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_3	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_3_C	ERROR:#REF!
ERROR:#REF!	P	2012	1000 m3	EU2_1_3_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P.OB	2012	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_2_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_2_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_3_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	1_2_3_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_3_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	7_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	8	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	8_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	8_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	9	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_1_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_1	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_2	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_3	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_3_4	ERROR:#REF!
ERROR:#REF!	P	2011	1000 mt	10_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	1_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	4_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	4_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	5_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_1_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2_C	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2_NC	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_2_NC_T	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 m3	6_4_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_1	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_2	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_3	ERROR:#REF!
ERROR:#REF!	M	2011	1000 mt	7_3_4	ERROR:#REF!
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ERROR:#REF! Presentation, Liana Fox (United States Census Bureau) Read more about Presentation, Liana Fox (United States Census Bureau) National Experimental Wellbeing Statistics, Liana Fox, United States Census Bureau Languages and translations English National Experimental Well-being Statistics (NEWS) Combining Survey and Administrative Data to Improve Income and Poverty Statistics Liana E. Fox U.S. Census Bureau UNECE Group of Experts on Measuring Poverty and Inequality November 28-29, 2023 Any opinions and conclusions expressed herein are those of the authors and do not reflect the views of the U.S. Census Bureau. The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data used to produce this product (Data Management System (DMS) number: P-7524052, Disclosure Review Board (DRB) approval number: CDRB-FY23-SEHSD003-025). 1 Attribution • Adam Bee, Joshua Mitchell, Nikolas Mittag, Jonathan Rothbaum, Carl Sanders, Lawrence Schmidt, and Matthew Unrath 2 Income and Poverty Estimates • Household survey nonresponse is increasing • 11% in 2013 to 31% in 2023 (March Current Population Survey) • For those that respond to the survey, many do not answer income questions • ~45% of income in official poverty estimate imputed for nonresponse • For those that answer income questions, many underreport • We estimate 1.1 percentage points fewer people in poverty (~3.5 million people) than official estimates 3 What is NEWS? 4 • Rethink how we produce income and resource statistics • What is the best possible estimate given all the data currently available at Census for a given income/resource statistic? • Address multiple sources of bias simultaneously • Apply research on addressing each How Does NEWS Do This? • Pull together all available data: survey, census, administrative records, commercial (third-party) data • Often need linked data to address bias correctly • Do everything in a transparent, replicable, evidence-based manner • Engage research community • Will create linked microdata and code database for access in FSRDCs • Code will be shared publicly (subject to disclosure constraints) 5 What Have We Done? 6 • Version 1 Release – February 14 • Proof of concept • 1 year • Mirror income and poverty releases – money income (no taxes, credits, in- kind benefits) • Present methods and approach for feedback • Paper and estimates available at • https://www.census.gov/data/experimental-data-products/national- experimental-wellbeing-statistics.html https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html Measurement Challenges Survey Data 1. Unit Nonresponse Bias • Not answering the survey • Poverty biased down by 0.3-0.5 percentage points during the pandemic (Bee and Rothbaum, 2022) 2. Item Nonresponse Bias • Not answering income questions (~45 percent of income in the CPS ASEC is imputed!) • Poverty biased down by 0.5-1 percentage points (Bollinger et al., 2019; Hokayem et al., 2022) 3. Mis- and underreporting • Not answering accurately • Poverty biased up by 2.5 percentage points for individuals 65+ (Bee and Mitchell, 2017) Biases can have different signs and magnitudes which can vary by group 7 Measurement Challenges Administrative Data 1. Selection into administrative data • Not everyone has to file taxes or gets a W-2 or other information return • Larrimore, Mortenson, and Splinter (2020) estimate poverty from administrative data, but must impute the existence and poverty status of 4-6 million people 2. Administrative data “nonresponse” • Some information not reported that should have been • Under-the-table jobs without a W-2, for example – 5% of adults in CPS ASEC report wage and salary earnings on the survey with no W-2 3. Administrative mis- and underreporting • Not always 100% accurate • Unreported tips, underreported self-employment earnings (refer to IRS tax gap analyses) 8 Measurement Challenges Administrative Data 4. Conceptual misalignment • Administrative not always measuring what we want • W-2s historically do not have earnings used to pay for health insurance premiums – understate true earnings (Census also doesn’t get this information when it’s available) 5. Incomplete data coverage • Data not available for individuals or places 6. Selection into linkage • Not all individuals can be linked across data sources (refer to Bond et al., 2014) 9 Addressing the Measurement Challenges 10 Step Description Measurement Challenge Related Work Weighting Use address-level data for all occupied housing units to weight respondent, linked sample to be representative of the target universe of households Survey unit nonresponse Selection into administrative data Administrative data “nonresponse” Selection into linkage Rothbaum et al. (2021) Rothbaum and Bee (2022) Imputation Survey earnings Impute survey earnings conditional on survey and administrative information Survey item nonresponse Hokayem et al. (2022) Admin gross earnings Impute gross earnings when missing in administrative data Administrative data “nonresponse” Conceptual misalignment Incomplete data coverage Means-tested program data Impute means-tested program data for states for which administrative data is not available Incomplete data coverage Fox et al. (2022) Nonfiler income Impute unemployment insurance compensation, interest, and dividends for nonfilers Selection into administrative data Incomplete data coverage Rothbaum (2023) Estimation Combine survey and admin earnings Combine survey and administrative wage and salary earnings according to the NEWS earnings measurement error model Survey mis- and underreporting Administrative mis- and underreporting Bee et al. (2023) Income replacement Use survey and administrative data, imputed income, and earnings from the measurement error model to construct household and family income Survey mis- and underreporting Administrative mis- and underreporting Bee and Mitchell (2017) Address-Linked Data (Weighting) 11 Survey Housing Units (Occupied) Master Address File Black Knight IRMF Link Addresses to People (MAFID→PIK) MAFARF 1040 Tax Returns MAFID Linked Individuals at Occupied Units W-2s 1040 Tax Returns Information Returns (IRMF) IRS Data SSA Data Social Security/OASDI Payments (PHUS) SSI Payments (SSR) State Data (from partner states) LEHDPIK Firm Data (LBD) EIN Job-Level Match Decennial Censuses Geographic Summaries of Characteristics ACS 5-Year Files IRMF MAFARF Numident MAFID Housing Unit Information EIN EIN EIN EIN Numident PIK W-2s PIK 1040 Tax Returns Geographic ID (State, County, Tract) 1099-Rs PIK Decennial Censuses PIK Links by Geography Links by Address Links to People in Adrecs at the Addresses Links jobs to each other and to firms Estimation Combining Survey and Admin Earnings • Five sources of wage and salary earnings information 1. Survey 2. W-2s 3. Detailed Earnings Records 4. LEHD 5. 1040 wage and salary 12 The Full Picture – Wage and Salary Earnings 13 1. Use job-level Information to get “best possible” administrative job-level earnings 2. Compare to 1040 to check for missing earnings (at tax-unit level) 1040 W-2 DER LEHD Best Job Earnings Best Adrec Earnings Final Earnings Estimate Survey 3. Compare to survey and decide for which individuals to use adrec or survey earnings 4. Final “best” estimate of earnings for each individual/household If LEHD is missing (or has apparent data quality issues), impute gross earnings conditional on administrative and survey information for each job (up to 2) How to combine survey and administrative earnings? Improve survey imputes Different Earnings Sources W-2 vs. Survey Responses 14 Source: O'Hara et al. (2017) using the 2011 ACS linked to 2010 W-2 records. Cluster around 45° line Noisy “Mean-reverting” Survey Earnings Use • 21 percent of individuals • More often for: • Workers in real estate and construction • Younger workers (25-44 year-olds) • Less often for: • Workers in retail, education, management, and health care • Older workers (65+) • Black workers 15 Household Income in 2018: NEWS Estimate Relative to Survey 16 Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and third-party data. Household Income in 2018: NEWS Relative to Survey by Age 17 Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and third-party data. Results • Overall, median household income was 6.3 percent higher than in the survey estimate, and poverty was 1.1 percentage points lower. • Results driven by individuals age 65 and over: • Median household income was 27.3 percent higher than in the survey estimate • Poverty is 3.3 percentage points lower than the survey estimate. • No significant impact on median household income for householders under 65 or on child poverty. 18 Future Plans • More years • Not all adrecs are available in all years • Not all survey variables are available in all years • More geographies • Use ACS – less detailed information makes combining surveys and adrecs more difficult • More income/resource concepts • Include taxes, credits, and in-kind transfers • Supplemental Poverty Measure • Address more sources of measurement error • Self-employment earnings • Income at the very top of the distribution (top 0.1%, 0.01%,...) • Further investigate assumptions, issues for other subgroups of interest • Non-citizens, homeless/unhoused (or those with unstable living arrangements), group quarters • Feedback into surveys to improve questions and processing 19 Feedback Paper and estimates available at: https://www.census.gov/data/experimental-data-products/national- experimental-wellbeing-statistics.html Please e-mail any comments, concerns, suggestions, and feedback to: [email protected] 20 https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html mailto:[email protected] Slide 1: National Experimental Well-being Statistics (NEWS) Combining Survey and Administrative Data to Improve Income and Poverty Statistics Slide 2: Attribution Slide 3: Income and Poverty Estimates Slide 4: What is NEWS? Slide 5: How Does NEWS Do This? Slide 6: What Have We Done? Slide 7: Measurement Challenges Survey Data Slide 8: Measurement Challenges Administrative Data Slide 9: Measurement Challenges Administrative Data Slide 10: Addressing the Measurement Challenges Slide 11: Address-Linked Data (Weighting) Slide 12: Estimation Combining Survey and Admin Earnings Slide 13: The Full Picture – Wage and Salary Earnings Slide 14: Different Earnings Sources W-2 vs. Survey Responses Slide 15: Survey Earnings Use Slide 16: Household Income in 2018: NEWS Estimate Relative to Survey Slide 17: Household Income in 2018: NEWS Relative to Survey by Age Slide 18: Results Slide 19: Future Plans Slide 20: Feedback Russian Национальная экспериментальная статистика благосостояния (NEWS) Объединение данных обследований и административных данных для улучшения статистики доходов и бедности Лиана Э. Фокс Бюро переписи населения США Группа экспертов ЕЭК ООН по измерению бедности и неравенства 28-29 ноября 2023 г. Любые мнения и выводы, выраженные в данном документе, принадлежат авторам и не отражают точку зрения Бюро переписи населения США. Бюро переписи населения проверило данный информационный продукт с целью обеспечения надлежащего доступа, использования и защиты от разглашения конфиденциальных исходных данных, использованных для создания данного продукта (номер системы управления данными (DMS): P-7524052, номер одобрения Disclosure Review Board (DRB): CDRB-FY23- SEHSD003-025). 1 Атрибуция • Адам Би, Джошуа Митчелл, Николас Миттаг, Джонатан Ротбаум, Карл Сандерс, Лоренс Шмидт и Мэтью Унрат 2 Оценки доходов и уровня бедности • Неотвечаемость при обследовании домохозяйств растетот 11% в 2013 году до 31% в 2023 году (мартовское текущее обследование населения) • Среди тех, кто ответил на вопросы обследования, многие не отвечают на вопросы о доходах • ~ 45% дохода в официальной оценке бедности, вмененного за отсутствие ответов • Среди тех, кто отвечает на вопросы о доходах, многие занижают данные • По нашим оценкам, число людей, живущих в бедности, на 1,1 процентного пункта (~3,5 млн. человек) меньше, чем по официальным оценкам 3 Что такое NEWS? 4 • Переосмысление методов подготовки статистики доходов и ресурсов • Какова наилучшая возможная оценка с учетом всех данных, имеющихся в настоящее время в распоряжении Census, для данной статистики доходов/ресурсов? • Одновременное устранение нескольких источников предубеждений • Применять исследования для решения каждой Kак это делает NEWS? • Собирает воедино все имеющиеся данные: опросы, переписи, административные записи, коммерческие (сторонние) данные • Для корректного решения проблемы смещения часто требуются связанные данные • Делает все прозрачно, воспроизводимо, на основе фактических данных • Привлекает исследовательское сообщество • Будет создана связанная база микроданных и кодов для доступа к ней в федеральных центрах данных статистических исследований • Код будет размещен в открытом доступе (с учетом ограничений на раскрытие информации) 5 Что мы сделали? 6 • Выпуск версии 1 - 14 февраля 14 • Proof of concept • 1 год • Зеркало доходов и релизы бедности - денежные доходы (без налогов, кредитов, неденежных льгот) • Представить методы и подход к обратной связи • Документ и расчеты доступны по адресу https://www.census.gov/data/experimental-data-products/national- experimental-wellbeing-statistics.html https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html Проблемы, связанные с измерениями Данные опроса 1. Непредвзятость ответов подразделений • Отказ от ответа на опрос • Снижение уровня бедности на 0,3-0,5 процентных пункта во время пандемии (Bee and Rothbaum, 2022) 2. Непредвзятость ответов на вопросы • Отказ от ответов на вопросы о доходах (~45% доходов в CPS ASEC являются вымышленными!) • Снижение уровня бедности на 0,5-1 процентный пункт (Bollinger et al., 2019; Hokayem et al., 2022) 3. Искажение и занижение данных • Неточные ответы • Бедность смещена в сторону увеличения на 2,5 процентных пункта для лиц 65+ (Bee and Mitchell, 2017) Предвзятость может иметь различные знаки и величины, которые могут варьироваться в зависимости от группы 7 Проблемы измерения Административные данные 1. Выборка по административным данным • Не все должны подавать налоги или получать W-2 или другую информационную декларацию • Ларримор, Мортенсон и Сплинтер (2020) оценивают уровень бедности на основе административных данных, но при этом им приходится вменять существование и статус бедности 4-6 млн. человек 2. Административные данные "неответы" • Не представлена некоторая информация, которая должна была бы быть представлена • Работа "под столом" без W-2, например - 5% взрослых в CPS ASEC сообщают о заработках в рамках опроса без W-2 3. Административные искажения и занижения • Не всегда 100% точность • Незарегистрированные чаевые, заниженные доходы от самозанятости (см. анализ налоговых пробелов IRS) 8 Проблемы измерения Административные данные 4. Концептуальное рассогласование • Администрация не всегда измеряет то, что мы хотим • В W-2 исторически не указываются доходы, использованные для уплаты взносов на медицинское страхование, что занижает истинные доходы (перепись населения также не получает эту информацию, когда она доступна) 5. Неполный охват данных • Данные по отдельным лицам или местам отсутствуют 6. Отбор в систему связи • Не все лица могут быть связаны между собой в разных источниках данных (см. Bond et al., 2014) 9 Решение проблем, связанных с измерениями 10 Шаг Описание Проблема измерения Похожие работы Взвешивание Использование данных об адресах всех занятых единиц жилья для взвешивания респондентов, связанной выборки для обеспечения репрезентативности целевой совокупности домохозяйств Неотвечающие единицы обследования Выбор в административные данные"Неотвечающие" административные данные Выборка в систему связей Ротбаум и др. (2021) Ротбаум и Би (2022) Импутация Доходы от проведения опроса Вмененный заработок по результатам опроса, обусловленный данными опроса и административной информацией Неотвечающие элементы обследования Хокайем и др. (2022) Валовой заработок администратора Исчисление валового заработка в случае его отсутствия в административных данных "Неответы" административных данныхКонцептуальное несоответствиеНеполный охват данных Данные по программам с выплатой пособий Вмененные данные по программам с оплатой по средствам для штатов, по которым отсутствуют административные данные Неполный охват данных Фокс и др. (2022) Доходы неплательщиков Вычет компенсации по страхованию от безработицы, процентов и дивидендов для неплательщиков Выборка в административных данныхНеполный охват данных Ротбаум и др. (2023) Оценка Совмещайте заработок на опросах и администрировании Объединение данных обследования и административных доходов от заработной платы в соответствии с моделью ошибки измерения доходов NEWS Искажение и занижение данных в обследованияхАдминистративные искажения и занижения данных Би и др. (2023) Замещение дохода Использование данных обследований и Искажение и занижение данных в Би и Митчелл (2017) Адресно-связанные данные (взвешивание) 11 Опрос Жилищные единицы(занято) Главная адресная картотека Черный рыцарь IRMF Link Addresses to People (MAFID→PIK) MAFARF Налоговые декларации 1040 MAFID Связанные физические лица в Занятые единицы W-2s Налоговые декларации 1040 Возврат информации (IRMF) Данные налоговой службы SSA Data Платежи по социальному обеспечению/OASDI (PHUS) Выплаты по SSI (SSR) Данные по государствам(от государств-партнеров) LEHDPIK Firm Data (LBD) EIN Соответствие должности и уровня квалификаци и Десятилет ние переписи Краткие географические данныехарактеристик Файлы ACS за 5 лет IRMF MAFARF Numident MAFID Жилищное подразделение Информация EIN EIN EIN EIN Numident PIK W-2s PIK Налоговые декларации 1040 Географический идентификатор(штат, округ, район) 1099-Rs PIK Десятилетние переписи PIK Ссылки по географическому принципу Ссылки по адресу Ссылки на людей в Adrecs по адресам Связывает рабочие места друг с другом между собой и с фирмами Оценка Объединение результатов опроса и административного заработка • Пять источников информации о заработной плате 1. Обследование 2. W-2s 3. Подробный учет доходов 4. LEHD - Продольная динамика "работодатель – домохозяйство” 5. 1040 заработная плата и оклад 12 Полная картина - заработная плата и оклад 13 1. Использование информации об уровне должности для получения "максимально возможного" административного заработка на уровне должности 2. Сравнение с 1040 для проверки отсутствующих доходов (на уровне налоговых единиц) 1040 W-2 DER LEHD Лучший заработо к на работе Лучший заработо к Adrec Окончат ельная оценка прибыли Обследование 3. Сравните с результатами опроса и решите, для каких индивидуумов использовать adrec или доходы от опроса 4. Окончательная "наилучшая" оценка доходов для каждого человека/домохозяйства Если LEHD отсутствует (или имеются явные проблемы с качеством данных), то для каждого рабочего места (до 2) произведите интерполяцию валового заработка на основе административной и опросной информации Как совместить опрос и административный заработок?Улучшение вменений при обследовании Различные источники доходовW-2 в сравнении с ответами на вопросы анкеты 14 Источник: O'Hara et al. (2017) с использованием данных ACS 2011 года, связанных с записями W-2 2010 года. Кластер вокруг линии 45° Шумно “Среднереверсивный" Использование заработков по результатам опроса • 21% лиц • Чаще всего для: • Работников, занятых в сфере недвижимости и строительства • Более молодых работников (25-44 года) • Реже для: • Работников розничной торговли, образования, управления и здравоохранения • Пожилых работников (65+) • Темнокожих работников 15 Доходы населения в 2018 году: Оценка NEWS относительно опроса 16 Источник: Ежегодное социально-экономическое приложение к обследованию населения (2019 Current Population Survey Annual Social and Economic Supplement), связанное с административными данными, данными десятилетней переписи населения и данными сторонних организаций. Доходы населения в 2018 г: НОВИНКИ относительно опроса по возрасту 17 Источник: Ежегодное социально-экономическое приложение к обследованию населения (2019 Current Population Survey Annual Social and Economic Supplement), связанное с административными данными, данными десятилетней переписи населения и данными сторонних организаций. Результаты • В целом медианный доход домохозяйств был на 6,3% выше, чем в оценочном исследовании, а уровень бедности - на 1,1 процентного пункта ниже. • Результаты обусловлены лицами в возрасте 65 лет и старше: • Медианный доход домохозяйства был на 27,3% выше, чем в оценочном исследовании • Уровень бедности на 3,3 процентных пункта ниже, чем в оценке исследования. • Существенного влияния на медианный доход домохозяйств для лиц моложе 65 лет и на уровень детской бедности не было. 18 Планы на будущее • Несколько лет • Не все адресаты доступны во все годы • Не все переменные исследования доступны во все годы • Больше географий • Использование ACS - менее подробная информация затрудняет объединение опросов и адресов • Больше концепций доходов/ресурсов • Включает налоги, кредиты и трансферты в натуральной форме • Дополнительный показатель бедности • Устранение дополнительных источников ошибок измерения • Доходы от самозанятости • Доходы на самом верху распределения (верхние 0,1%, 0,01%,..) • Дальнейшее исследование допущений и проблем для других интересующих подгрупп • Неграждане, бездомные/не имеющие жилья (или лица с нестабильными жилищными условиями), групповые квартиры • Обратная связь с опросами для улучшения вопросов и обработки 19 Обратная связь Документ и расчеты доступны по адресу : https://www.census.gov/data/experimental-data-products/national- experimental-wellbeing-statistics.html Все комментарии, замечания, предложения и отзывы направляйте по адресу : [email protected] 20 https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html https://www.census.gov/data/experimental-data-products/national-experimental-wellbeing-statistics.html mailto:[email protected] Slide 1: Национальная экспериментальная статистика благосостояния (NEWS) Объединение данных обследований и административных данных для улучшения статистики доходов и бедности Slide 2: Атрибуция Slide 3: Оценки доходов и уровня бедности Slide 4: Что такое NEWS? Slide 5: Kак это делает NEWS? Slide 6: Что мы сделали? Slide 7: Проблемы, связанные с измерениями Данные опроса Slide 8: Проблемы измерения Административные данные Slide 9: Проблемы измерения Административные данные Slide 10: Решение проблем, связанных с измерениями Slide 11: Адресно-связанные данные (взвешивание) Slide 12: Оценка Объединение результатов опроса и административного заработка Slide 13: Полная картина - заработная плата и оклад Slide 14: Различные источники доходовW-2 в сравнении с ответами на вопросы анкеты Slide 15: Использование заработков по результатам опроса Slide 16: Доходы населения в 2018 году: Оценка NEWS относительно опроса Slide 17: Доходы населения в 2018 г: НОВИНКИ относительно опроса по возрасту Slide 18: Результаты Slide 19: Планы на будущее Slide 20: Обратная связь Presentation, Thesia Garner (U.S. Bureau of Labor Statistics) Read more about Presentation, Thesia Garner (U.S. Bureau of Labor Statistics) Languages and translations English 1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Expanding the family of U.S. Consumer Price Indexes Thesia I. Garner, Bill Johnson, Joshua Klick, Paul Liegey, Robert Martin, Anya Stockburger U.S. Bureau of Labor Statistics UNECE Group of Experts on Measuring Poverty and Inequality November 28, 2023 2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Outline Introduction and Motivation Income-based indexes Household Cost Indexes Next steps 3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov CPI Family of Indexes Official Indexes CPI-U Chained CPI-U CPI-W Research Indexes-https://www.bls.gov/cpi/research-series/ R-CPI-U-RS R-CPI-E R-HICP R-COICOP R-CPI-Income Household Cost Index? R-C-CPI-Income https://www.bls.gov/cpi/research-series/ 4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Motivation ◼Headline consumer price indexes summarize a range of household experiences ◼ Increased need for data granularity pertaining to demographic groups in particular Recent recommendations by Committee on National Statistics, interest from Federal Reserve Bank, data users, and media ◼ Interest in inflation from the household perspective rather than the “macro” perspective Inspired by the United Kingdom, New Zealand, and Australia 5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Methods Overview Index Prices / Rents Expenditure Weights CPI-U, C-CPI-U (official) - Outlets and items selected to represent urban households - Owned housing is measured using owner equivalent rent (OER) - Consumer Expenditure Surveys (CE) Diary and Interview: sum expenditures for urban households CPIs by Income - Same as CPI-U and C-CPI-U - Group CE respondents by quintile of equivalized income, sum expenditures separately for each group HCI-U - Same as CPI-U except owned housing is measured using payments approach - Create weights for each CE respondent and average equally (“democratic”) over urban population. 6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Key Results ◼ CPIs by equivalized income quintiles: Over 2005-2022, average annual inflation for the lowest quintile was about 0.3 percentage points higher than for the highest quintile. ◼Household cost index: using a payments approach and household-weighted (“democratic”) aggregation, average annual inflation for the urban population was about 0.35 percentage lower than the CPI. 7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income 0% 5% 10% 15% 20% 25% 30% Rent Food at home Motor fuel Owner's equivalent rent Vehicles and maintenance Food away from home Recreation Q1 U Q5 8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Annualized Inflation Rates by Income Quintile Based on CPIs Lowe Formula, December 2005 - December 2022 2.60 2.54 2.47 2.41 2.33 2.43 2.1 2.2 2.3 2.4 2.5 2.6 2.7 Q1 Q2 Q3 Q4 Q5 Income Quintiles Urban 9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Items contributing to inflation gap (2022) CPI-U 8%; Q1 8.2%; Q5 7.7% -10 -5 0 5 10 15 20 Rent primary residence(HA01) Gasoline (all types)(TB01) Electricity(HF01) Utility (piped) gas service(HF02) Cigarettes(GA01) Motor vehicle insurance(TE01) Limited service meals/snacks(FV02) Juices and drinks(FN03) Cable & satellite tv/radio(RA02) Chicken(FF01) Club membership (RB02) Child care & nursery school(EB03) Owners' rent secondary res.(HC09) Leased cars and trucks(TA03) Full service meals and snacks(FV01) Commercial Health Insurance(ME01) Owners' rent primary residence(HC01) Lodging away from home(HB02) Airline fare(TG01) New vehicles(TA01) Q1 > Q5 Q1 < Q5 10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov HCI-U and CPI-U index levels Average 12-month % change CPI-U 1.86% HCI-U (Payments Approach + Household-weighted Aggregation) 1.51% HCI-U (Payments Approach Only) 1.46% 1 1.05 1.1 1.15 1.2 1.25 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov December 2020 relative importance 20.2% 9.2% 4.7% 4.3% 16.0%3.1% 14.2% 11.1% 6.6% 6.8% 3.8% Food & Bev. Housing: Rent Housing: OER Housing: Prop. Tax Housing: Mortgage Housing: Other Apparel Transportation Medical Recreation Educ. & Comm. Other 15.2% 7.9% 24.3% 10.3% 2.7% 15.2% 8.9% 5.8% 6.8% 3.2% HCI-U (2019 weights) CPI-U (2017-18 weights) 12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov HCI-U housing components versus OER 0.9 1 1.1 1.2 1.3 1.4 1.5 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Owner's Payments (HR, HS, HT) Mortgage Interest (HS) Owner's Equiv. Rent (HC) Property Tax (HR) Other Owner Payments (HT) 13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Limitations and Future Research ◼Data does not reflect lower-level heterogeneity (e.g. specific prices paid by households or groups) ◼Ongoing discussions on payments approach methods for HCI e.g., should mortgage payments reflect principal as well as interest? ◼ Continued refinement of methods ◼What is the impact on poverty measurement? 14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Further Reading ◼ CPI by Income Publications: Initial working paper, Spotlight on Statistics Home page: https://www.bls.gov/cpi/research-series/r-cpi-i.htm ◼ Household Cost Index Working paper https://www.bls.gov/osmr/research-papers/2021/ec210030.htm https://www.bls.gov/spotlight/2022/inflation-experiences-for-lower-and-higher-income-households/home.htm https://www.bls.gov/cpi/research-series/r-cpi-i.htm https://www.bls.gov/osmr/research-papers/2023/pdf/ec230040.pdf 15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Thank you! Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions U.S. Bureau of Labor Statistics [email protected] Slide 1: Expanding the family of U.S. Consumer Price Indexes Slide 2: Outline Slide 3: CPI Family of Indexes Slide 4: Motivation Slide 5: Methods Overview Slide 6: Key Results Slide 7: Snapshot of spending weights by population, 2019-2020 biennial expenditure weight share, equivalized income Slide 8: Annualized Inflation Rates by Income Quintile Based on CPIs Lowe Formula, December 2005 - December 2022 Slide 9: Items contributing to inflation gap (2022) CPI-U 8%; Q1 8.2%; Q5 7.7% Slide 10: HCI-U and CPI-U index levels Slide 11: December 2020 relative importance Slide 12: HCI-U housing components versus OER Slide 13: Limitations and Future Research Slide 14: Further Reading Slide 15: Thank you! Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions U.S. Bureau of Labor Statistics [email protected] Russian 1 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Расширяя семейство индексов потребительских цен США Thesia I. Garner, Bill Johnson, Joshua Klick, Paul Liegey, Robert Martin, Anya Stockburger Федеральное Бюро Статистики Труда США Группа экспертов ЕЭК ООН по измерению бедности и неравенства 28 ноября 2023 2 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Содержание Вступление и мотивация Индексы на основании доходов Индексы расходов домохозяйства Дальнейшие шаги 3 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Семейство ИПЦ (CPI) (индексов потребительских цен) Официальные индексы ИПЦ-U Сцепленные ИПЦ-U ИПЦ-W Исследование индексов-https://www.bls.gov/cpi/research-series/ R-ИПЦ-U-RS R-ИПЦ-E R-HICP R-COICOP R-ИПЦ-Доход Индекс расходов домохозяйства? R-C-ИПЦ-Доход https://www.bls.gov/cpi/research-series/ 4 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Мотивация ◼Индексы потребительских цен обобщают спект расходов домохозяйства ◼ Возросла потребность в подробных данных, особенно в отношении демографических групп Недавние рекомендации Комитета национальной статистики, процентная ставка Федерального банка резервов, пользователей данных и СМИ ◼Процентная ставка инфляции с точки зрения именно домохозяйств в отличии от «макро» переспективы Вдохновленные примером Соединенного Королевства, Новой Зеландии и Австралии 5 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Обзор методов Индекс Цены/ Ренты Вес расходов ИПЦ-U, C-ИПЦ-U (официальный) - Магазины и товары выбранные для представления городских домохозяйств - Собственное жилье измеряется с использованием эквивалентной аренды для собственника (OER) - Обследование потребительских расходов (CE) Дневники и интервью: сумма расходов для городских домохозяйств ИПЦ по доходам - Также как в ИПЦ-U и C-ИПЦ-U - Респонденты группы CE по квинтилям эквивалентного дохода, сумма расходов отдельно по каждой группе HCI-U (индекс расходов домохозяйства) - Также как в ИПЦ-U только собственное жилье измеряется с использованием платежного подхода - Создать весы для каждого респондента CE, распределить поровну (демократически) на городское население. 6 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Ключевые результаты ◼ИПЦ по квинтилям эквивалентного дохода: В течение 2005- 2022гг средняя годовая инфляция для самого низкого квинтиля составила примерно на 0,3 процентных пункта выше, чем для самого высокого квинтиля. ◼Индекс расходов домохозяйства: используя платежный подход и взвешенное по домохозяйствам (демократическое) обобщение, средняя годовая инфляция городского населения была примерно на 0,35% ниже, чем в ИПЦ 7 — U.S. BUREAU OF LABOR STATISTICS • bls.gov График весов расходов по группам населения, 2019-2020гг доля двухлетних расходов, эквивалентный доход 0% 5% 10% 15% 20% 25% 30% Rent Food at home Motor fuel Owner's equivalent rent Vehicles and maintenance Food away from home Recreation Q1 U Q5 8 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Ежегодный уровень инфляции по квинтилям дохода На основании ИПЦ, индекс Лоу, декабрь 2005 – декабрь 2022 2.60 2.54 2.47 2.41 2.33 2.43 2.1 2.2 2.3 2.4 2.5 2.6 2.7 Q1 Q2 Q3 Q4 Q5 Income Quintiles Urban 9 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Что влияет на инфляционный разрыв (2022) ИПЦ-U 8%; Q1 8.2%; Q5 7.7% -10 -5 0 5 10 15 20 Rent primary residence(HA01) Gasoline (all types)(TB01) Electricity(HF01) Utility (piped) gas service(HF02) Cigarettes(GA01) Motor vehicle insurance(TE01) Limited service meals/snacks(FV02) Juices and drinks(FN03) Cable & satellite tv/radio(RA02) Chicken(FF01) Club membership (RB02) Child care & nursery school(EB03) Owners' rent secondary res.(HC09) Leased cars and trucks(TA03) Full service meals and snacks(FV01) Commercial Health Insurance(ME01) Owners' rent primary residence(HC01) Lodging away from home(HB02) Airline fare(TG01) New vehicles(TA01) Q1 > Q5 Q1 < Q5 10 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Уровни индексов HCI-U и CPI-U Average 12-month % change CPI-U 1.86% HCI-U (Payments Approach + Household-weighted Aggregation) 1.51% HCI-U (Payments Approach Only) 1.46% 1 1.05 1.1 1.15 1.2 1.25 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 11 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Декабрь 2020г, относительная важность 20.2% 9.2% 4.7% 4.3% 16.0%3.1% 14.2% 11.1% 6.6% 6.8% 3.8% Food & Bev. Housing: Rent Housing: OER Housing: Prop. Tax Housing: Mortgage Housing: Other Apparel Transportation Medical Recreation Educ. & Comm. Other 15.2% 7.9% 24.3% 10.3% 2.7% 15.2% 8.9% 5.8% 6.8% 3.2% HCI-U (2019 weights) CPI-U (2017-18 weights) 12 — U.S. BUREAU OF LABOR STATISTICS • bls.gov HCI-U компоненты жилья в противовес OER 0.9 1 1.1 1.2 1.3 1.4 1.5 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 Owner's Payments (HR, HS, HT) Mortgage Interest (HS) Owner's Equiv. Rent (HC) Property Tax (HR) Other Owner Payments (HT) 13 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Ограничения и дальнейшие исследования ◼Данные не отражают низкоуровневую неоднородность (напр. конкретные цены, которые оплачивают домохозяйства или группы) ◼Идут дискуссии по методу платежного подхода для HCI Напр. должны ли выплаты за ипотеку отражать основную задолженность наравне с процентами? ◼Продолжается работа над улучшением методологии ◼ Как это отражается на измерении бедности? 14 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Что еще почитать ◼ИПЦ по доходу Публикации: Initial working paper, Spotlight on Statistics Домашняя страница: https://www.bls.gov/cpi/research-series/r-cpi-i.htm ◼ Индекс расходов домохозяйства (HCI) Рабочий доклад https://www.bls.gov/osmr/research-papers/2021/ec210030.htm https://www.bls.gov/spotlight/2022/inflation-experiences-for-lower-and-higher-income-households/home.htm https://www.bls.gov/cpi/research-series/r-cpi-i.htm https://www.bls.gov/osmr/research-papers/2023/pdf/ec230040.pdf 15 — U.S. BUREAU OF LABOR STATISTICS • bls.gov Спасибо! Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions U.S. Bureau of Labor Statistics [email protected] Slide 1: Расширяя семейство индексов потребительских цен США Slide 2: Содержание Slide 3: Семейство ИПЦ (CPI) (индексов потребительских цен) Slide 4: Мотивация Slide 5: Обзор методов Slide 6: Ключевые результаты Slide 7: График весов расходов по группам населения, 2019-2020гг доля двухлетних расходов, эквивалентный доход Slide 8: Ежегодный уровень инфляции по квинтилям дохода На основании ИПЦ, индекс Лоу, декабрь 2005 – декабрь 2022 Slide 9: Что влияет на инфляционный разрыв (2022) ИПЦ-U 8%; Q1 8.2%; Q5 7.7% Slide 10: Уровни индексов HCI-U и CPI-U Slide 11: Декабрь 2020г, относительная важность Slide 12: HCI-U компоненты жилья в противовес OER Slide 13: Ограничения и дальнейшие исследования Slide 14: Что еще почитать Slide 15: Спасибо! Thesia I. Garner Chief Researcher, Office of Prices and Living Conditions U.S. Bureau of Labor Statistics [email protected] Progress report of the UNECE Task Force on subjective poverty measures, Thesia Garner (U.S. Bureau of Labor Statistics) Read more about Progress report of the UNECE Task Force on subjective poverty measures, Thesia Garner (U.S. Bureau of Labor Statistics) Objective poverty measures alone are not sufficient to understand the complexity of poverty and that subjective measures can complement them in important ways, especially with regard to reaching the poorest and making their voice heard. Given this fact, during the 2019 Conference of European Statisticians Bureau meeting, subjective poverty measurement was selected as a topic for in-depth review (/ECE/CES/2019/14/Add.13). Languages and translations English 1 UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE Subjective Poverty Report prepared by the UNECE Task Force on Subjective Poverty Measures 2 Acknowledgements This Report has been prepared by the UNECE Task Force on Subjective Poverty Measures, which consisted of the following members representing national statistical offices, international organizations, and academia: Thesia Garner, U.S. Bureau of Labor Statistics – Chair of the Task Force Nikki Graf, U.S. Bureau of Labor Statistics Jake Schild, U.S. Bureau of Labor Statistics Andrew Heisz, Statistics Canada Kimberly Newman, Statistics Canada Christine Laporte, Statistics Canada Eric Olson, Statistics Canada Alex Miller, Statistics Canada Rania Abdulla, Statistics Canada Rana Maarouf, Statistics Canada Jarl Quitzau, Statistics Demark Daniel Gustafsson, Statistics Demark Yafit Alfandari, Israel Ellys Monahan, Office for National Statistics Ellys Croal, Office for National Statistics Tim Vizard, Office for National Statistics Andrew Zelinsky, Office for National Statistics Anna Szukiełoć-Bieńkuńska, Statistics Poland Maria Vyshnikova, Belarus João Hallak Neto, Brazilian Institute of Geography and Statistics (IBGE) Leonardo Santos de Oliveira, Brazilian Institute of Geography and Statistics (IBGE) Agata Kaczmarek-Firth, Eurostat Estefania Alaminos Aguilera, Eurostat Carlotta Balestra, OECD Elena Danilova-Cross, UNDP Regional Bureau for Europe and CIS Esther Dzifa Bansah UNDP Regional Bureau for Europe and CIS Alexander Kirianov, CIS-Stat Gerardo Leyva, INEGI Mexico Adriana Pérez, INEGI Mexico Gwyther Rees, UNICEF Siraj Mahmudlu, UNICEF Sabina Alkire, OPHI Fanni Kovesdi, OPHI Tomas Zelinsky Durham University (United Kingdom) Martina Mysikova Institute of Sociology of the Czech Academy of Sciences 3 Table of Contents Chapter 1. INTRODUCTION ........................................................................................................................... 6 Chapter 2. FOCUS ON SUBJECTIVE POVERTY ................................................................................................ 8 I. INTRODUCTION ................................................................................................................................. 8 II. DEFINITION OF SUBJECTIVE POVERTY .............................................................................................. 9 A. Contrast to objective poverty ..................................................................................................... 10 B. Frameworks for subjective poverty ............................................................................................ 11 C. Collection and analysis of subjective poverty at National Statistical Offices ............................. 13 D. Collection and analysis of subjective poverty at International Agencies ................................... 13 III. WHY MEASURE SUBJECTIVE POVERTY AND A BRIEF REVIEW OF THE LITERATURE ................... 14 A. Why measure subjective poverty? ............................................................................................. 14 B. Evolution of subjective poverty measurement ........................................................................... 16 Chapter 3. APPROACHES FOR MEASUREMENT AND ANALYSIS .................................................................. 19 I. APPROACHES TO MEASUREMENT .................................................................................................. 19 A. Qualitative Questions not Focused on Specific Levels of Income (or Consumption) ................. 20 Identification ................................................................................................................................... 20 Evaluation ........................................................................................................................................ 21 Prediction ........................................................................................................................................ 23 B. Qualitative Categorical Questions Focused on Specific Income (or Consumption) ................... 24 Evaluation ........................................................................................................................................ 24 Prediction ........................................................................................................................................ 26 C. Money Metric Valuation Questions ............................................................................................ 26 II. ANALYSIS ......................................................................................................................................... 28 A. Relationships ............................................................................................................................... 28 B. Subjective Poverty Lines ............................................................................................................. 29 Leyden Poverty Line based on Money Metric Evaluation Question ............................................... 30 Intersection Method Based on the Minimum Income Question .................................................... 30 Quasi Leyden Poverty Line Based on the Deleeck Question ........................................................... 33 An Approach Based on Proportional Odds Logistic Regression ...................................................... 34 An Approach Based on Dichotomized Data .................................................................................... 35 C. Country/international organization examples ............................................................................ 37 Chapter 4. STATCAN contribution ............................................................................................................... 37 4 Methods of data collection and guidelines............................................................................................. 37 Survey Frame and sample considerations .......................................................................................... 38 Traditional surveys .............................................................................................................................. 39 Case Study 1: National Survey of Self-reported Well-being (ENBIARE) 2021 of Mexico................. 40 Omnibus Survey .................................................................................................................................. 43 Case Study 2: The Quality of Life framework for Canada................................................................ 44 Opinion Poll Survey ............................................................................................................................. 44 Rapid response.................................................................................................................................... 44 Case Study 3: The U.S. Census Bureau Household Pulse Survey Financial Well-being Question ... 45 Web-panel........................................................................................................................................... 46 Crowdsourced surveys ........................................................................................................................ 46 Case Study 4: Using crowdsourced data ......................................................................................... 46 Administrative and registry data ........................................................................................................ 47 Case Study 5: Use of administrative data for sampling and calibration of EU-SILC at Statistics Denmark .......................................................................................................................................... 47 Sources of error: concerns with response and representativeness ................................................... 48 Validity and relationship to other measures of poverty and economic well-being ........................... 49 Quality reports and validating data................................................................................................. 49 Advantages of subjective poverty measures .................................................................................. 50 Disadvantages of subjective poverty measures .............................................................................. 50 Differences in personal opinion ...................................................................................................... 51 Timeframe for data collection and release ......................................................................................... 51 Cross-sectional versus longitudinal data collection ............................................................................ 52 OECD subjective well-being guidelines ............................................................................................... 52 Hypothetical assessments of subjective poverty .................................................................................... 53 What is the role of question wording? ............................................................................................... 54 Statistics Canada ............................................................................................................................. 54 Cognitive tests Bureau of Labor Statistics ....................................................................................... 55 Framing and mode effects .................................................................................................................. 56 Subjective poverty and the evolution of measures ............................................................................ 57 Case Study 5: Subjective assessments versus objective measures of poverty – discussion of the definitions of selected poverty measures based on the Polish edition of the EU-SILC survey ....... 57 What is the role of defining minimums in assessing one’s subjective poverty position? .................. 61 What is the role of geographic differences in prices? ........................................................................ 62 5 What is the role of household composition and assumptions regarding sharing? ............................ 64 What is the role of Social Transfers in Kind (STIK)? ............................................................................ 65 What is the role of housing wealth and imputed rent? ..................................................................... 66 What is the role of differences in “culture” and religion? .................................................................. 67 Concluding remarks on hypothetical questions ................................................................................. 69 Lessons learned from COVID-19 ............................................................................................................. 69 Subjective Poverty in SEIA Questionnaires and Comparability Analysis ............................................ 70 Poverty defined in a fully subjective way (direct self-identification as poor, feeling of poverty) ... 72 Perceived financial difficulties ......................................................................................................... 72 Subjective poverty line approach – perceived poverty line ............................................................ 72 Subjective poverty lines assessed with the use of statistical methods (so-called objectivised, quasi-subjective poverty lines) ....................................................................................................... 72 Perception of poverty as a social phenomenon .............................................................................. 72 Other Approaches ........................................................................................................................... 73 An overview of UNDP Socio-Economic Impact Assessments (SEIAs) for households in countries of UNECE region ...................................................................................................................................... 73 Case study 6: Self-assessed Financial Well-being: comparing objective and subjective measures 75 Overlaps in Dimensions of Poverty ................................................................................................. 76 Implications regarding experience with COVID outbreak .................................................................. 77 Conclusion ............................................................................................................................................... 78 Chapter 5. RECOMMENDATIONS ................................................................................................................ 78 Appendix ..................................................................................................................................................... 82 6 Chapter 1. INTRODUCTION Objective poverty measures alone are not sufficient to understand the complexity of poverty and that subjective measures can complement them in important ways, especially with regard to reaching the poorest and making their voice heard. Given this fact, during the 2019 Conference of European Statisticians Bureau meeting, subjective poverty measurement was selected as a topic for in-depth review (/ECE/CES/2019/14/Add.13). This was followed up by an in-depth review of subjective poverty measures which was presented before the Bureau of the Conference of European Statisticians (CES) in October 2021. This was largely based on a paper prepared by Statistics Poland summarizing survey responses from National Statistical Offices from 52 countries, with additional information regarding international activities. Reference is also made to another study which was conducted by the United Nations Development Programme of 15 countries/territory in Europe and Central Asia region. This study was conducted during the COVID-19 outbreak in 2020. A summary of the in-depth review follows (from document ECE/CES/BUR/2021/OCT/2): 1. Both the literature review and research practices indicate different ways of understanding and defining the term subjective poverty. This indicates a need to clarify terminology and develop a system of concepts related to the measurement of subjective poverty. 2. At present, both at national and international level, objective indicators play a dominant role in monitoring the phenomenon of poverty, and statistical offices give priority to the production of these data. The measurement of subjective poverty is generally very limited or not considered at all. 3. In the framework of “official statistics”, direct self-identification as poor is very rarely used. In most countries, household surveys include questions on subjective assessments of living standards, which can provide a basis for calculating indirect measures of subjective poverty. However, in practice these data are not fully exploited for the analysis of subjective poverty. 4. The omission of the subjective approach, as complementary to the objective measurement, significantly weakens the diagnosis of poverty. In this context it seems important to disseminate knowledge on the usefulness and interpretation of subjective data on poverty. 5. Taking into consideration the conclusions of the review of methods used to measure subjective poverty and the opinion of National Statistical Offices on the usefulness of work in this area at international level, it is proposed to develop a guide on methods for measuring subjective poverty and to agree on a short list of harmonised subjective poverty indicators for international comparisons. To ensure the implementation of these tasks it is proposed to establish under the umbrella of the Conference of European Statisticians a Task Force on Subjective Poverty Measurement. 7 The Bureau asked the UNECE Secretariat, together with the Steering Group on Measuring Poverty and Inequality, to prepare a proposal for follow-up work addressing the priority areas raised in the in-depth review, considering the discussions on subjective poverty at the meeting of the Group of Experts on Measuring Poverty and Inequality in December 2021. During the December meeting it was suggested that a task force be created to consider going beyond quantitative approaches to measuring poverty to include qualitative measures as well. The UNECE Secretariat together with the Steering Group on Measuring Poverty and Inequality prepared terms of reference for the Task Force on Subjective Poverty Measures. The objective of the Task Force was to develop a guide on measuring subjective poverty, including a set of subjective poverty indicators that could be used for international comparison. As noted from CES Bureau discussions in October 2021 and February 2022, the proposed list of subjective poverty indicators to be developed should be coherent, holistic, and short. The indicators should relate to existing international work, i.e., to the measuring of subjective perception of living conditions defined in the EU Survey on Income and Living Conditions (EU-SILC), and to the OECD guidelines on measuring subjective well-being. The proposed guide on measuring subjective poverty should include a list of indicators, the related conceptual considerations, and guidelines on how to develop the indicators. In follow-up, electronic consultations with the CES member States on the in-depth review of subjective poverty measures were conducted in April-May 2022 (for reference, see ECE/CES/2022/9/Add.1, 31 May 2022). The following 13 countries replied to the electronic consultation: Austria, Belarus, Canada, Costa Rica, Denmark, Finland, Hungary, Lithuania, Mexico, Poland, Russian Federation, Turkey, and Ukraine. A summary of comments from these consultations follows: 1. All responding countries welcomed the outcome of the in-depth review paper and expressed support for further steps in the area. 2. The proposal to develop a guide on measuring subjective poverty containing description of approaches and best practices, system of indicators and methodology behind their measurement as well as further recommendations for statistical services concerning international comparisons was highly valued. 3. Poverty in general as well as subjective poverty are complex phenomena. Clarified terminology and unambiguous interpretation are preconditional for international harmonisation. Different economic, social, political, and cultural conditions across countries should be taken into consideration when measuring subjective poverty. 4. The use of the subjective approach as complementary to the objective measurement can be a very useful and efficient diagnostic tool of poverty. It allows for a better understanding of what poverty means to people and verifying whether objective evaluations of poverty are consistent with social experience. At the same time, nationally and at the policy level having more than one measure of poverty could be challenging and likely to require a large dissemination effort to make use of additional measures of poverty sufficiently widespread. 8 5. There was some agreement that the proposed list of subjective poverty indicators to be developed should be coherent, holistic, and short. According to Members of the Task Force and experts responding to the survey and electronic consultation with National Statistical Offices representatives, subjective poverty measurement is not an alternative to objective poverty measurement but should be considered as complementary. The subjective approach shows the problem of poverty from a completely different perspective than the objective one. Applying a subjective approach allows for a better understanding of what poverty means to people, as well as to verify whether objective evaluations of poverty are consistent with the social perception of this phenomenon. Subjective measures also provide information on 'public moods,' which can influence people's behaviour in both the economic, social and political spheres. Statistical analyses related to the use of subjective and quasi-subjective measures may also be used to verify and even construct measures of an objective nature (e.g., the consensus method for constructing deprivation indices, verification of equivalence scales used). The purpose of this guide is to enrich the subjective assessment of poverty by improving the understanding of what people think it means to be poor and by going beyond a purely economic approach to poverty measurement. This guide builds upon existing UNECE networks of experts in measuring poverty and inequality and follows the methodological work under the Conference that has led to the publication of the Guide on poverty measurement in 2017 and the Guide on disaggregated poverty measures in 2020. Chapter 2. FOCUS ON SUBJECTIVE POVERTY I. INTRODUCTION Scholars across different disciplines of the social sciences agree that poverty is a multidimensional phenomenon. It is well recognized that traditional resource-based indicators (e.g., income compared to an official poverty line) alone cannot fully capture the complex nature of well-being, and thus ignoring other than the traditional or objective income/expenditure-based poverty measures can distort the overall picture. Like objective measures, the focus of this report is poverty defined in terms of people not having economic resources to realize a set of basic “functionings” or minimum level or standard of living (Sen 1985, 1993).1 But how to determine whether this minimum level has been achieved can be measured using subjective measures, not just objective ones.2 Like for other measures of 1 An alternative conceptualization of poverty is based on the scarcity theory (Mullainathan and Shafir, 2013). Following this theory, poverty can be defined as “the gap between one's needs and the resources available to fulfil them” (Mani et al, 2013, 976). Identifying one’s need and this gap is based on subjective assessments and can be used to define poverty. 2 There is much research on the dynamic relationship between the subjective and objective measures. For example, many sociologists write about it regarding social boundaries and identity, for example Lamon and Mizrachi (2012), Mizrachi and Zawdu (2012), and Harold et al. (2021). Blanchflower and Bryson (2023) explore the role COVID-19 and the Great Recession had on objective and subjective well-being. 9 poverty, this achievement can be influenced by many factors (see Figure 1). While poverty can be approached from various perspectives, including domains such as human rights or sustainable development, for example, the UNECE Task Force on Subjective Poverty determined that its primary focus would be on economic poverty. The challenge for National Statistical Offices is to develop measures that can tie various aspects of poverty together, and that then could be used by governments to determine how effective policies are in supporting people in meeting minimum needs. We propose that subjective measures be included among the set of assessment tools used by countries. We are not proposing that these replace objective measures or multidimensional measures; rather that these be included in the arsenal used by countries to assess poverty. The Stiglitz et al. (2009) report cites the need for wider perspective and recommends that objective and subjective measures of well-being be included in a dashboard. The OECD references this report and its recommendations as a motivation behind collecting subjective well-being data (OECD, 2013). Additionally following the report, Eurostat developed the EU-SILC ad-hoc module on “wellbeing” in 2013. All of which has led to the creation of the OECD Better Life initiative (2023) which includes objective and subjective measures but no measure of poverty specifically. The primary purpose of this chapter is to provide an overview of the theoretical and conceptual background of subjective poverty measurement. II. DEFINITION OF SUBJECTIVE POVERTY To understand the concept of subjective poverty, we start with a description of what is subjective, emphasizing its relevance within the context of welfare. Something is subjective if it reflects one’s personal views, experiences, preferences, attitudes, values, or background and arises out of one’s own perceptions. In developing these perceptions, individuals compare their perceived status against their own standards of desirability. These perceptions are F ure 1. Co cept u e the e tio or ea ure e t o poverty From arel an den Bosch, I Ashgate Publishing, ampshire, England, 2001, page 6. Economic resources Set of feasible functionings ( capabilities) Realized functionings Subjective welfare (Dis)abilities and circumstances Preferences Personal standards and expectations https://ec.europa.eu/eurostat/web/income-and-living-conditions/database/modules http://ec.europa.eu/eurostat/web/income-and-living-conditions/database?node_code=ilc_pwb 10 influenced by each respondent’s own income/expenditures/wealth, personality, family influences (e.g., background such as religion, disability of family members), and subjective well-being (e.g., happiness, life satisfaction in general) plus views regarding one’s community, society at large, and the general economy. Along these lines, many people now are familiar with the more broadly defined concept of “subjective well-being,” which focuses on life satisfaction or happiness (Mahoney 2023). Indicators of subjective poverty can be seen as complements to indicators of subjective well-being, with both drawing on how to measure these.3 An early contribution to the quantification of happiness in surveys was Cantrilʼs (1965) idea of the “ladder of life.” With reference to subjective well-being, for example see Diener (1984), Kashdan (2004). Early applications of subjective welfare concepts in economics included van Praag (1968), Kapteyn and van Praag (1976), and Easterlin (1974). Though the origins of subjective welfare come from happiness or life satisfaction, we focus here on subjective economic welfare and specifically subjective poverty. The determination of whether an individual or household is poor is based on their situation compared to a standard which could be objectively or subjectively determined and could be assessed in terms of a money-metric response (e.g., with respect to levels of income, expenditures, consumption, or wealth) or qualitative categorical response (e.g., one’s perception of being poor or satisfaction with one’s income). For subjective poverty, measures do not rely on any externally given absolute or relative resource-based threshold or measure. Rather, they rely on individuals’ own assessments of their economic situation, or that of others’ economic situations. For example, being in poverty based on a subjective measure means could mean being below a subjectively defined national threshold, experiencing a state of being that is less than that of others, or experiencing a state of being that is less than one’s own standard such as reporting having great difficulty making ends meet. The majority of subjective assessments, particularly those associated with poverty, reflect the respondent’s own situation; however, other questions refer to hypothetical situations or families. Assessments referring to another’s living conditions or expectations regarding minimum living standards are often referred to as hypothetical or consensual. In this report we consider hypothetical/consensual measures as a type of method for assessing subjective poverty. A detailed discussion comparing the use of the respondent’s own situation or a hypothetical one is provided in Chapter IV. A. Co tra t to objective poverty Subjective and objective assessments of poverty are related; however, they are distinct. When considered together, they provide a more comprehensive view of poverty. Objective approaches are typically based on household income, expenditures, consumption, wealth, access to or possession of various goods or services or “attainment” of certain observable and “objectively” measurable variables. On the other hand, subjective approaches rely on respondents’ self-assessments of their own or another’s financial and/or material situations and reflect all circumstances of their living conditions. With subjective measures there are particular concerns about methodological issues such as comparability (across people and time), validity, reproducibility, and generalizability cross-nationally. While objective 3 See Simona-Moussa (2020) for a recent study of subjective wellbeing and measures of vulnerability to poverty considered together. 11 measures, such as a specific income level, can be influenced by these same circumstances, the reporting of this income is not expected to be influenced by one’s self-assessment of one’s financial situation. The objective approach is typically the preferred option by national and international statistics offices as the data are often readily available from large-scale household surveys and cross-country comparisons are more easily understood; however, (low) income only represents one dimension of poverty. To produce valid and practical poverty standards for a country, subjective assessments are also needed. These assessments provide insight into how well people are faring personally and adapting to policies to alleviate poverty. In addition, they can be used as indicators of economic insecurity or vulnerability regarding needs that are unmet by current policies.4 For example, a family may have income that is just above an objectively defined poverty threshold, but still may have difficulty meeting its material needs due to circumstances not accounted for in this objective measure. In this case, a subjective measure can provide additional information for the development of policies to improve the economic well-being of such families that income alone has not been able to address. B. Fra ework or ubjective poverty Recent UNECE studies have proposed alternative frameworks to group questions that can be used for the measurement of subjective poverty. The UNECE Guide on Poverty Measurement (2017) proposed grouping questions into three groups: (1) ability to meet various needs focused on financial restrictions faced by the household; (2) considering oneself as poor via individual self-assessment; and (3) income necessary to make ends meet and households’ minimum perceived needs. In a 2021 report published by the Conference of European Statisticians, Statistics Poland presents a framework based on responses to a survey on current country practices for measuring subjective poverty (2021). They classify questions as (1) direct identification, (2) perceived financial difficulty, and (3) a subjective poverty line approach. The subjective poverty line approach is divided into two subcategories: perceived poverty line and statistical methods. The purpose of subjective poverty questions is to provide a subjective measure of the welfare space, where the “welfare space” is defined as economic poverty. To measure the welfare space, we first need to operationalize it. Ravallion (2014) suggested there are two approaches to measuring subjective poverty based on responses. The first approach asks for a money metric of subjective welfare, and the second approach uses qualitative categories in the welfare space. Adopting Ravallion’s suggestion, we propose a framework for thinking about subjective poverty questions based on the same two approaches. Our framework aligns closely with the work by Statistics Poland and the UNECE proposal, while also taking into consideration the qualitative categorical classification proposed by the OECD in their 2023 report, Subjective Well-being Measurement: Current Practices and New Frontiers.5 4 For an example, see Duboux and Papuchon (2019a,b) and Bertolini et al. (2017). 5 Alternative frameworks are available when discussing subjective wellbeing more generally, rather than subjective poverty specifically. For example, Ryff (1989) discusses wellbeing questions using the framework of eudaimonic (psychological) and 12 Money metric questions ask respondents to report a specific monetary value. The subject of these questions is typically income or expenditures with respect to some attribute, such as ability to make ends meet, satisfaction, or adequacy of consumption, and were designed for estimation of subjective poverty lines.6 Though attempts have been made to apply simpler methods, such as averaging responses to subjective quantitative questions (such as respondents reported minimum income to meet basic needs), or contrasting the responses directly to the actual income (comparing respondents actual income to their reported minimum incomes), these (naïve) methods lead to less reliable results. This is because individuals often misperceive the true minimum income. Econometric methods have been developed that are based on the intersection of actual and reported minimum incomes that produce reliable results (Knight and Gunatilaka, 2012; Garner and Short, 2005). It is the multidimensionality of factors considered by respondents and the heterogeneity in their answers that predetermines the necessity to apply appropriate econometric techniques to analyze the subjective quantitative questions.7 In contrast, qualitative questions rely on categorical responses, rather a specific monetary value, and typically ask respondents about perceptions of their (or a hypothetical household’s) material, financial, or economic situation. For instance, does the respondent consider his/her family to be poor? Yes or No. The goal of such questions is for respondents to assess their situations holistically as opposed to providing a particular income or expenditure. When assessing their financial or economic situation, respondents are expected (and sometimes asked specifically) to consider factors such as income sufficiency, the extent of their savings and other financial assets, their ability to repay debt, and their capacity to cover unexpected expenses. Within the concept of qualitative questions, we further operationalize the welfare space by specifying three subcategories or groups based on what the question is asking of the respondent: evaluation, identification, and prediction. More detailed descriptions of the money metric and qualitative categorial questions, as well as examples, are provided in Chapter IV Section A. hedonic (life satisfaction, negative affect, and positive affect). In their 2013 report “Subjective Well-Being: Measuring appiness, Suffering, and Other Dimensions of Experience,” the National Academies of Science (NAS) build of Ryff’s framework. They classify subjective wellbeing questions as evaluative, experienced, and eudaimonic. The 2023 OECD report, Subjective Well- being Measurement: Current Practices and New Frontiers, presents a similar framework, classifying questions as evaluative, affective, and eudaimonic (page 6) as follows. (1) Life evaluation: Evaluative measures of subjective well-being refer to the general assessments people make of their lives, or specific aspects of it, and is most commonly captured through an indicator asking respondents to reflect on how satisfied they are with their lives (i.e. life satisfaction). Domain satisfaction measures, relating to how satisfied one is with various aspects of one’s life, also fall under the evaluative heading. (2) Affect: Affective measures capture people’s feelings, emotions or states, often measured with respect to a defined time period (e.g., “over the course of yesterday”, etc.). )3) Eudaimonia: Eudaimonia can be thought of as psychological flourishing, operationalised in the Guidelines as a measure of feeling one’s life has purpose or meaning, though also containing aspects of autonomy, competence and self-actualisation. 6 While subjective monetary measures that ask about income or expenditures might be more useful in developed countries, measures focusing on consumption could be more relevant for lesser developed ones. Consumption-based measures typically focus on one’s assessment of the value of consumption needed for the respondent to feel well-off and account for not just income but all resources available, for example, home production and uses of credit and access to wealth. 7 See Chapter IV Section B for an overview of the most common estimation procedures. 13 C. Collectio a a aly o ubjective poverty at Natio al Stati tical Office Measurement and analysis of subjective poverty tend to be neglected or omitted by most National Statistical Offices. This was the conclusion of Statistics Poland based on an in-depth review of current country practices for measuring subjective poverty that was tasked by the Bureau of the Conference of European Statisticians, under the auspices of the United Nations Economic Commission for Europe (UNECE 2021).8 Seven of the 52 countries surveyed did not report collecting any information or conducting any work related to subjective poverty.9 Among the remaining 45 countries, all reported asking subjective poverty questions, but only a small subset of these regularly produce, analyze, and publish data in this area. However, 37 of the respondents saw a need to prepare a guide providing an overview of the methods used to measure subjective poverty, and 34 countries were in favor of working on a short list of subjective poverty indicators for international comparison. Another study with data collected from national statistical offices was conducted by the United Nations Development Programme (UNDP). The focus of this study was Socio-Economic Impact Assessments (SEIAs) of households and their response to COVID-19 (Danilova-Cross 2022). Information was collected from 15 countries with six of them reporting the collection and use of subjective poverty measurement;10 five of these embarked on the collection of primary data to support the measurement; and one, Serbia, reporting making use of subjective poverty measures in its annual national surveys. In the surveys, households were asked questions to assess their perceptions of the Covid-19 pandemic on changes in the household levels of income, their ability to meet material and non-material needs or household expenses as they fall due. This approach "gave a voice to respondents and sought to determine poverty criteria on the basis of their opinions and experiences resulting from the pandemic. Employing this method in socio-economic impact assessments is of particular importance as it helps gauge where economic hardship is being experienced in the face of a global pandemic” (page 6). It should be noted that the results of the study conducted by Statistics Poland and the one conducted by the UNDP (Danilova-Cross 2022) are based on National Statistical Offices regarding country specific measurement and analysis. Several statistical offices have conducted analyses in an experimental capacity or commissioned research to be done by individuals outside of their agency. Much of this work is cited and discussed in the brief review of the literature provided in the next chapter. D. Collectio a a aly o ubjective poverty at I ter atio al A e c e In contrast to the lack of work in this area by National Statistical Offices, several international 8 A copy of the report can be found via the following link: https://unece.org/sites/default/files/2021-10/02_In- depth_review_Subjective_poverty.pdf. 9 These seven countries are Azerbaijan, Czech Republic, Dominican Republic, Georgia, Japan, Mongolia, and United States. Although the Czech Republic did not report collecting data related to subjective poverty, they participate in the European Union Statistics on Income and Living Conditions Survey, which does collect data related to subjective poverty. It should also be noted, after this survey was conducted the United States began collecting data related to subjective poverty via the Household Pulse Survey. For more information about this question see Garner et al. (2020). 10 These six include: yrgyz Republic, Moldova, Serbia, Tajikistan, Ukraine, and Uzbekistan. https://unece.org/sites/default/files/2021-10/02_In-depth_review_Subjective_poverty.pdf https://unece.org/sites/default/files/2021-10/02_In-depth_review_Subjective_poverty.pdf 14 organizations have demonstrated positive practices in measuring some aspects of subjective poverty. Two agencies in particular are Eurostat and the OECD. At the European level, EU-SILC11 is the EU reference source for comparative statistics on income, social inclusion and living conditions.12 The EU-SILC survey, which is managed by Eurostat (European Commission), is a household and individual data collection which output is harmonised as it is regulated by legislations.13 Among the different variables that EU-SILC collects, some of them (e.g., in the field of subjective assessments of living standards, questions about making ends meet) constitute a potential data source for measuring some aspects of subjective poverty at the European level (e.g., estimating quasi-subjective poverty lines or calculating indirect measures of subjective poverty). On the basis of EU-SILC data, analytical work in the area of subjective poverty has been carried out by various research centres (Zelinsky et al., 2022). In addition, Eurostat, on the basis of a harmonised question included in EU-SILC, calculates and publishes on its website the indicator “Inability to make ends meet” as a monetary measure of subjective poverty.14 This makes it possible to compare, at the European level15, measures of objective poverty with people’s feelings of subjective economic poverty, identified as stress in the survey. The OECD has been collecting evidence on subjective poverty through Compare your Income (CYI), a web-based interactive tool that allows users to explore income statistics and compare how well or badly off they are, and test whether their perceptions are in line with the actual situation in their country.16 The web-tool was launched in 2015 and has so far, collected more than 2 million entries. Over the course of years, the web-tool attracted a varied audience, thanks to the fact that it covers all OECD countries (except Colombia, for which internationally comparable income data are currently missing), is available in eight languages, and has been widely promoted. The OECD uses the data from the CYI in two ways. First, subjective poverty lines and equivalence scales are derived and compared with the equivalence scale use by the OECD for official reporting. The results of this analysis are unpublished at the time of writing this report. Second, although not focused on subjective poverty, data on perceptions of income inequality across countries have been published in an OECD report, Does Inequality Matter?: How People Perceive Economic Disparities and Social Mobility (2021). III. WHY MEASURE SUBJECTIVE POVERTY AND A BRIEF REVIEW OF THE LITERATURE A. Why ea ure ubjective poverty? The conventional and the most commonly adopted approach to measuring poverty is based on an indirect or so-called “welfarist” approach. This approach relies on the assumption that 11 EU-SILC - the European Union Statistics on Income and Living Conditions Survey 12 See: https://ec.europa.eu/eurostat/web/income-and-living-conditions/overview 13 In addition, Eurostat issues yearly methodological guidelines which provide extended explanations and recommendations on the implementation of the data collection. 14 See: https://ec.europa.eu/eurostat/databrowser/view/ILC_MDES09__custom_6666774/default/table?lang=en 15 EU-SILC provides cross-sectional and longitudinal data for the 27 European Members States, Iceland, Norway, Switzerland, Albania, Kosovo, Montenegro, North Macedonia, Serbia and Türkiye. 16 See: https://www.oecd.org/wise/compare-your-income.htm https://ec.europa.eu/eurostat/web/income-and-living-conditions/legislation https://circabc.europa.eu/ui/group/853b48e6-a00f-4d22-87db-c40bafd0161d/library/334d943f-6f71-4f4b-9c7e-a6767a3fe164?p=1&n=-1&sort=name_DESC 15 individuals are rational and can reasonably be considered the most capable assessors of the kind of life and pursuits that optimize their personal satisfaction and happiness (Duclos and Araar, 2006). Within this conceptual framework, assessments of poverty are typically based on measures of income or resources. As these indicators are observed and generally considered objectively measurable, we can also refer to it as to the objective approach. In this context, along with an additional set of assumptions, income is seen as a measure of individual welfare as all welfare-relevant goods and services can be purchased through market transactions. When based on resources to include in-kind transfers and home production, the attainment of an individual’s welfare is not limited to market transactions. Shortfalls in income or resources can be interpreted as shortfalls in economic welfare or poverty. Nanda and Banerjee (2021) as well as van Praag and Ferrer-i-Carbonell (2006) point out that objective measures of poverty based on income may not be appropriate for developing nations because such societies are not completely “monetarized” and there is a considerable amount of home production and in-kind transfers. For such countries, the broader resource measure would be more appropriate. Or as suggested by Ravallion (2016), consumption could be a better measure of welfare particularly when considered in terms of individual’s subjective evaluation of the adequacy of their consumption. Furthermore, there is no generally agreed objective standard for where to draw the income threshold that defines poverty. To build an argument for the addition of subjective measures to assess poverty, we again to turn Sen (2007) who noted: “ – p – w w k w x p w p .” This definition is founded within his argument that welfare should be thought of in terms of a person’s capabilities or the functionings, not just income, that a person is able to achieve (Sen, 1985, 1993). Based on this approach, someone is poor when they have limited freedoms or chances of realizing their own lifestyle. Also noted by Sen (1992, p. 107) is the following warning, “We are not entirely free to characterize poverty in any way we like…There are some clear associations that constrain the nature of the concept [i.e., poverty].” Given this guidance and wisdom, one could attempt the Sisyphean task of trying to define “limited freedoms” in order to establish a poverty threshold or the task could be given to the people via subjective poverty questions, thereby establishing poverty criteria on the basis of public opinion. Alternatively, one can ask about financial difficulty, minimum income, and other subjective poverty questions directly as measures of a person’s ability or inability to lead a decent – minimally acceptable – life.17 Regardless of the subjective measures selected, drawing upon the UNECE (2020) recommendation for deprivation measures (28.1), a key criterion is that the measures be “based on clear and explicit theory or normative definition of poverty to ensure that the questions used are valid indicators of poverty as opposed to unrelated concepts of 17 See Van den Bosch (2001) for a discussion of this options in his treatise on Identifying the Poor Using subjective and consensual measures. 16 general wellbeing or happiness.” It should be noted that although we provide an argument for measuring subjective poverty, we do not advocate for subjective poverty to replace objective measures. Rather, measures of subjective poverty should be seen as complements to objective measures. This recommendation aligns with the recommendations made by the Commission on the Measurement of Economic Performance and Social Progress (Stiglitz et al., 2009). Their report emphasizes the importance of developing robust measures of social connections, political voice, and insecurity that can predict life satisfaction, using both objective and subjective data. And, in addition, the report highlights the need for statistical offices to incorporate objective and subjective indicators that capture people’s life evaluations, hedonic experiences, and priorities in their surveys. B. Evolutio o ubjective poverty ea ure e t Early subjective well-being questions and measures were modified to a narrow definition of economic welfare. For example, the Cantril ladder was designed to ask respondents to rank themselves on a ladder with steps numbered from zero at the bottom to ten at the top, supposing that the top of the ladder represents the best possible life, and the bottom of the ladder represents the worst possible life. This scale has been used to assess subjective well- being with results currently included in the OECD WISE dashboard for countries.18 An example of using such a ladder for subjective qualitative poverty measurement is a rich/poor scale included in the Eurobarometer survey for the first time in 1976 (Riffault, 1991). The ladder included seven rungs with the bottom rung representing “poor”. An example of directly labelling the rungs with respective to poverty explicitly (e.g., “poor”, “borderline”, “non-poor”) was used by Mangahas (1995). In the economics literature, these types of questions are also referred to as the Economic Welfare Question or Economic Ladder Question (Ravallion and Lokshin, 2002, Ravallion, 2014). The current European survey EU–Statistics on Income and Living conditions (EU-SILC) applies a 6-point scale question asking households to self-evaluate their ability to make ends meet with respect to their income, which is a monetary version of the question that can be used to assess subjective poverty. The most common approach to identify poor populations using qualitative categorical responses to an Economic Ladder Question is to set an arbitrary threshold based on one or more bottom ladder rungs (Carletto and Zezza, 2006, Mysíková et al., 2019). Though a threshold must be selected by the researcher (i.e., a category below which the household is identified as poor), the advantage is that such an approach does not require specifying a monetary value of the subjective poverty line (Duvoux and Papuchon, 2019). Attempts to estimate the subjective poverty line based on the categorical welfare ladder questions are less frequent (Piasecki and Bieńkuńska 2018, Pradhan and Ravallion, 2000, Želinský et al., 2020, see section III.B). The references cited represent three different estimation methods; however, only the method by Pradhan and Ravallion – explained below – has been used in several other papers, but mostly by the same group of authors. 18 See https://www.oecd.org/wise/measuring-well-being-and-progress.htm 17 Pradhan and Ravallion (2000) proposed employing a different type of qualitative categorical question, the Consumption Adequacy Question (CAQ). Using this question avoids asking respondents (in Jamaica and Nepal; Lokshin et al., 2006, applied the CAQ in Madagascar) about a precise amount of income needed to make ends meet. ouseholds, especially in rural areas, may have different concepts of income, making the answers to Minimum Income Questions (MIQ) less comparable. The raised issues concerned inclusion of cash income only versus other components of total income such as imputed income from own housing and production (e.g., a family farm) or production costs. Therefore, instead of asking about minimum income, respondents were asked to evaluate if their consumption of various commodities (food, housing, clothing) was adequate or not. Thus, this approach drops the monetary component and applies categorical questions instead to facilitate respondents’ answers. Another source of objections to subjective indicators arises from latent heterogeneity, a phenomenon occurring when people with similar observable characteristics (for example, age, income, education) but different latent personality traits provide different responses to subjective welfare questions (Ravallion, 2014). In other words, people may employ different criteria when assessing their well-being, as they may hold distinct perceptions of what constitutes “wealth” or “poverty” and what signifies satisfaction or dissatisfaction in their lives (Beegle et al., 2012). As further argued by Ravallion, even individuals with similar observable and latent personality traits may use different criteria to assess their welfare, which can be influenced by a “frame-of-reference” bias (Ravallion, 2008). To address this concern, Beegle et al. (2012) used vignettes to test for bias due to latent heterogeneity in individual scales of subjective welfare. Respondents were asked: “Imagine a 6-step ladder where on the bottom, the first step, stand the poorest people, and the highest step, the sixth, stand the rich. On which step are you today?” In a later section of the questionnaire, respondents were asked to place four vignettes of hypothetical families on the six-step ladder and then to place themselves on the same scale. Their findings demonstrate the presence of a frame-of-reference effect on individuals' SWB, indicating that people from diverse socioeconomic backgrounds consistently employ distinct scales when responding to inquiries about their welfare. Nevertheless, their results indicate that this factor is not a significant source of bias in producing subjective poverty lines. In contrast, money metric questions, at times, ask individuals to state a concrete amount that represents a certain living standard. While asking for very specific amounts of money, objections to such questions also arise. The concept of a money metric approach to measure subjective economic poverty was first introduced by van Praag (1968, 1971), with the Income Evaluation Question (IEQ). The IEQ asked respondents to provide explicit income values that they considered “very bad” to “very good”, with a number of options in between. The answers to the IEQ from all respondents were fitted to a utility function with the formula of the log- normal distribution function (van Praag and apteyn, 1994). The derived poverty line is referred to as the Leyden Poverty Line (LPL). Such questions were primarily designed to be used for econometric modelling of subjective poverty lines, which then would be compared to respondents’ actual income. The foundation of a model-based approach to produce subjective poverty lines is the MIQ, a specific case of IEQ. MIQ, and again a monetary-based subjective question ( apteyn et al., 18 1988, apteyn, 1994, Goedhart et al., 1977) asks what income is needed to make ends meet. The Subjective Poverty Line (SPL) is econometrically estimated such that the expected minimum income equals actual income across the population rather than at the individual household level (see section III.B for estimation details). Objectively measured income normalised by the SPL is used as the welfare indicator, i.e., actual income below SPL identifies the subjectively poor population. Flik and van Praag (1991) compared the LPL and SPL and concluded that LPL seems to be theoretically superior to the SPL given the fact that IEQ is a multi-level question, while MIQ is a one-level question, which makes the latter more likely to be subject to random response fluctuations. Simplified methods based on averaging responses to questions of subjectively evaluated living standards or comparing the responses directly to the actual income (referred to as the individual method) are less common but have been applied, for instance, by rooman and off (2004), Thijssen and Wildeboer Schut (2005), and Mysíková et al. (2019). These latter approaches are presumed to produce less reliable subjective poverty measures than those based on the model-based subjective poverty lines. The simplified or naïve methods have been criticized for “heterogeneity, such that people at the same standard of living can give different answers on subjective welfare” (Ravallion, 2014, pp. 146–147; Pittau and Zelli, 2023). One way to control for this heterogeneity is to use monetary-based subjective questions to estimate model-based subjective poverty lines (Goedhart et al., 1977, apteyn et al., 1988). Rather than derive the subjective poverty lines based on income, Morissette and Poulin (1991) for Canada and Garner and Short (2003, 2004) for the U.S., used a similar question, the Minimum Spending Question (MSQ) to assess poverty based on subjective questions. For the U.S., Garner and Short (2005) compared MSQ-based lines to household expenditure outlays. They concluded that such a question resulted in poverty thresholds/ rates similar to those based on NAS methods (NRC 1995). A similar approach was introduced by the Centre for Social Policy (CSP). For this approach, the subjective line is derived based on the MIQ question but is only applied to a subsample of respondents (Deleeck, 1977, Deleeck et al., 1984). The subsample is selected based on a monetary, categorical question that asks respondents to evaluate on a 6-point scale how they can make ends meet with their actual household disposable income. This question is known as a “Deleeck” question (also included in EU-SILC survey, see Chapter III, Box 7) and the derived poverty line as a CSP poverty line. The method only selects respondents who classified themselves as making ends meet “with some difficulty”, as these are assumed to be on the margin of poverty and consequently to have the best knowledge of the situation. After excluding outliers, the CSP poverty line is derived as an average value of the minimum between the actual household income and the reported subjective minimum income (from MIQ). The selection of the subsample assumes that the poverty line must be determined by respondents who are at the border of poverty as these have the best knowledge of the situation. Some researchers considered this assumption to be too strong and disagreed with the strong dependence of the poverty line on the choice of the subsample of respondents, especially because the reference group could possibly include only a few people (Flik and van Praag, 1991). Alternative methods and modifications broadly based on LPL, SPL or CSP lines have 19 been further developed in the literature. Subsequent literature raised concerns about how respondents interpret the MIQ (Garner and de os, 1995) and that the concept of income may not be well-defined for respondents, especially in developing countries (Pradhan and Ravallion, 2000).19 De os and Garner (1991) analyzed the relationship between expenditures and responses to MIQ. Consequently, perceptions of minimum expenditures started to supplement or supplant income. Garner and Short (2003, 2004) discussed a notion that respondents consider a higher living standard when answering the MIQ than the MSQ. The reasons might be that respondents could include savings or loan payments in the minimum income, while they are asked to focus specifically on spending and basic necessities such as food, shelter, clothing and other essential items for daily living in the MSQ. The MIQ refers to a broader set of needs than MSQ. Therefore, they suggested the higher MIQ-based line as representing a “social minimum standard”, while the lower MSQ-based line could be considered a “subsistence minimum standard”. The difference between MIQ-based and MSQ-based SPLs was shown on the U.S. data. Similar to the CSP method in that qualitative and quantitative questions were used together to estimate the poverty line, Pradhan and Ravallion (2000) used responses to the Consumption Adequacy Question (CAQ) in combination with actual reports of consumption. Specifically, respondents were asked to evaluate if their consumption of various commodities (food, housing, clothing) was adequate or not. Two methods were used to estimate the subjective poverty line, both based on regressions. Method (1) anchors the subjective poverty line to the perceived adequacy of food consumption alone; Method (2) also includes non-food consumption, but the approach is the same in both cases. The difference is that in Method (2) Pradhan and Ravallion also estimate a reduced-form Engel curve to make “an allowance for the remaining components of spending which is an estimate of the expected value for someone consuming the subjective poverty line level for core expenditure.” Chapter 3. APPROACHES FOR MEASUREMENT AND ANALYSIS I. APPROACHES TO MEASUREMENT Following the framework developed in the Chapter 2, we provide a discussion of the various approaches to measuring subjective poverty. We divide qualitative categorical response questions into three groups: identification, evaluation, and prediction. The first two align closely with what are considered standard notions of poverty, while prediction more closely aligns poverty with economic insecurity or vulnerability. In contrast a money metric question requires a specific money value response. A description of each type of question is provided in this section. To help elucidate this framework, along with the descriptions we provide examples of subjective poverty questions, we limit our presentation to the country responses to 19 This is the case especially for subsistence farmers, who are a significant group of poor, but may not impute income/expenditure for the produce which they use for their own consumption. 20 the 2021 UNECE survey developed by Statistics Poland. Figure 1 presents the number of countries asking subjective poverty questions by type as collected in the 2021 UNECE survey. The pie chart in the center of the figure shows 29 countries report asking only monetary subjective poverty questions, 3 countries report asking only non-monetary questions, and 13 countries report asking both types of questions. Among country representatives who reported asking monetary questions, as well as countries who reported asking non-monetary questions, “evaluation” was the most frequently reported subcategory, with 40 countries reporting monetary evaluation questions and 14 countries reporting non-monetary evaluation questions. A more detailed breakdown of the questions can be found in Table A.1, which provides counts of the number of questions by type, by country. Figure 1: Number of Countries Asking Subjective Poverty Questions by Type Note: Data comes from the responses to the UNECE survey developed by Statistics Poland. Several responses to the survey fell more into the area of measuring deprivation, social exclusion, or well-being, rather than subjective poverty, which are outside the guidelines of this Task Force. Therefore, we did not include them in our analysis; however, we do make a record of these responses in Table A.1. They are classified as “other.” 22 countries reported asking at least one question that fell outside the scope of subjective poverty. A. Qual tative Que tio ot Focu e o Spec c Level o I co e (or Co u ptio ) Identification “Identification” is the most direct way of collecting data on subjective poverty. This type of question asks respondents to identify themselves as poor or experiencing poverty in a qualitative sense based on a categorical response. Countries can then use the responses to this Qualitative Categorical 23 Money Metric 1 Both 21 4 42 6 Identification Evaluation Prediction 22 Valuation 21 question to produce simple statistics to describe the subjective poverty status of their population. Only four of the 52 countries (i.e., Columbia, Israel, yrgyz Republic, and iet Nam) reported questions in which the respondent was asked to identify themselves or their household as poor or feeling at risk of poverty. There was no standard question wording across countries. See Box 1 for examples of questions from Columbia and yrgyz Republic. Box 1. Examples of Qualitative Categorical Identification Questions [Colombia] Do you consider yourself poor? • Yes • No [Kyrgyz Republic] How do you assess the circumstances of your household? • Rich • Average • Poor • Very poor Evaluation Qualitative categorical evaluation questions ask respondents to assess their economic or financial situation holistically with respect to some attribute such as satisfaction. 14 countries report asking a categorical evaluation question, with the most frequently used question wording asking respondents about their current financial situation. See Box 2 for examples. Canada, ungary, Norway, and Switzerland reported asking questions using this phrasing.20 Five countries asked respondents to indicate their level of satisfaction with their financial situation using a scale from 0 to 10; however, the scales were not uniformly defined. Canada designates their scale as “very dissatisfied” (0) to “very satisfied” (10), whereas the other countries have scales that range from “not at all satisfied” to “completely/very satisfied” (10). In addition to the 0 to 10 scale, Canada also includes a satisfaction question where the responses follow a 5-point Likert scale.21 Even though the questions are worded similarly across countries, because the scales are defined differently, cross-country comparisons, specifically with Canada, are not possible. Box 2. Examples of Qualitative Categorical Evaluation Questions, Current Financial Situation [Canada] How do you feel about your finances? 0 – Very dissatisfied … 10 – Very Satisfied [Switzerland] In general, how satisfied are you with the current financial situation of your household? 20 In the UNECE CIS report (2023), Kazakhstan was also identified as asking a categorial evaluation question with wording focused on satisfaction with one’s financial situation. 21 The 5-point Likert scale used by Canada was (1) very satisfied, (2) satisfied, (3) neither satisfied nor dissatisfied, (4) dissatisfied, and (5) very dissatisfied. 22 0 – Not satisfied at all … 10 – Completely satisfied The next most common qualitative categorical question is to ask respondents how they perceive their current financial or economic situation compared to a reference point in the past. See Box 3 for examples. Two countries, Colombia and Ukraine, ask respondents to consider “12 months ago” and “the last 12 months,” respectively. In contrast, Belarus and Finland use the “previous year” as the reference point, with Finland specifying the calendar year in the question. The different wording can result in different reference periods. For example, consider an individual being interviewed in December of 2020. A respondent asked to consider the last calendar year (all of 2019) will likely answer differently than if their reference point was the previous 12 months (December 2019 through December 2020) or even 12 months ago (December 2019). All counties use a 5-point Likert scale for responses. Box 3. Examples of Qualitative Categorical Evaluation Questions, Current Financial Situation Compared to the Past [Columbia] How do you consider the economic situation of your household compared to 12 months ago? (1) Much better (2) Better (3) Same (4) Worse (5) Much worse [Finland] Compared to the previous year, that is [20XX-1], has your financial situation: (1) Changed significantly for the better (2) Changed somewhat for the better (3) Remained unchanged (4) Changed somewhat for the worse (5) Changed significantly for the worse Another frequently reported qualitative categorical question asked respondents to select a phrase from a set of options that best describes their current financial situation. See Box 4 for examples. Denmark, Lithuania, and Netherlands all report asking this type of question. The phrases respondents select from can provide a detailed picture of their financial situation. For example, one of the options Lithuania offers is “we are having to draw on our savings.” owever, similar to the problem previously encountered when asking respondents how they feel about their financial situation, cross-country comparisons are only possible if the response options are worded in a comparable manner. Box 4. Examples of Qualitative Categorical Evaluation Questions, Describe Current Financial Situation [Denmark] How is the present financial situation of your household, or in other words: 23 • Do you spend more than you earn? • Do you find it difficult to make ends meet? • Are you able to put money aside? [Lithuania] Which of these statements best described the current financial situation of your household: • We are saving a lot • We are saving a little • We are just managing to make ends meet on our income • We are having to draw on our savings • We are running into debt Prediction The final type of qualitative categorical question is “prediction,” which asks respondents to consider how they think their current financial, material, or economic situation will change over a specified period. See Box 6 for examples. Four countries report asking this type of question: Belarus, Colombia, ungary, and Ukraine, and all four use the next twelve months or next year as the prediction period. owever, a country could also ask about the next six months, two years, or even longer, depending on whether they are interested in measuring respondents’ short- or long-run perceptions. Box 6. Examples of Qualitative Categorical Evaluation Questions, Prediction [Columbia] W k ’ w k in 12 months compared to now? • Much better • Better • Same • Worse • Much worse [Ukraine] How do you think the material status of your household could change for the next 12 months? • It will get better • It will remain without any changes • It will get worse • It is difficult to specify [Belarus] How do you think the material situation of your household will change next year? • It will get better • It will remain without any changes It will get worse 24 As with the previous questions, the question wording and response options were not standardized across countries. Both Belarus and Ukraine report asking respondents to evaluate potential change in their material situation over the next 12 months, but Belarus asks respondents to consider how the material situation “will change” over the next year, whereas Ukraine asks respondents to consider how things “could change.” Although the wording is only slightly different, the choice of “will” or “could” may impact how a respondent evaluates the future. Both Colombia and ungary also ask respondents to consider how their financial situation will change over the next 12 months but provide different response options. Colombia uses a 5-point Likert scale, whereas ungary only uses a 3-point Likert scale.22 Other types of qualitative categorical questions refer to money in particular. These are presented in the next section B. Qual tative Cate or cal Que tio Focu e o Spec c I co e (or Co u ptio ) Evaluation Qualitative categorical evaluation questions ask respondents to evaluate their income with respect to some attribute, such as ability to make ends meet, satisfaction, or adequacy of consumption. Responses to these types of questions are categorial and can be used to create simple statistics to describe the subjective poverty status of a country’s population. Responses to these evaluation questions can also be combined with money metric valuation questions, questions that require the respondent to report a specific dollar value such as the minimum income question, to create a subjective poverty threshold.23 See Section II. B in this chapter for more information regarding the estimation of such thresholds. Forty countries report asking at least one qualitative categorical question that was focused on income in particular. The overwhelming popularity of this type of question is, in part, a result of it being included in the EU-SILC. The exact wording of the question reported by the EU- SILC countries is slightly different but follows the same general pattern of asking respondents to evaluate their ability to make ends meet with respect to their income. EU-SILC survey offers response options following a 6-point Likert scale. See Box 7 for an example.24 This type of question is also known within the literature as a Deleeck question. Box 7. Examples of Qualitative Categorical Evaluation Questions Focused on Income, EU-SILC Countries [EU-SILC participating countries] A household may have different sources of income and more than one household member may contribute to . T k ’ 22 Colombia’s response options are “much better,” “better,” “the same,” “worse,” and “much worse.” ungary’s response options are “it will get better,” “it will not change,” and “it will get worse.” Colombia’s 5-point Likert scale could be converted to a 3-point Likert scale to make the responses comparable to Hungary. 23 This approach is also known as the Deleek Method of measuring subjective poverty. See Flik and Praag (1991) for more details about this method. 24 The example provided is the suggested wording of the monetary evaluation question provided by the 2021 EU- SILC Guidelines. Each country’s statistical office must translate it into their country’s official language, so the exact wording may vary from country to country. 25 income, is your household able to make ends meet, namely, to pay for its usual necessary expenses? • With great difficulty • With difficulty • With some difficulties • Fairly easily • Easily • Very easily Of 12 non-EU countries that reported asking a qualitative categorical evaluation type question focused on inomce, five of which (Armenia, Brazil, Russian Federation, Turkey, and Ukraine) report asking a question that is akin to the one asked in the EU-SILC. Respondents are asked to evaluate their ability to make ends meet with respect to their income. Additionally, the response options that were reported follow the 6-point Likert scale. Since these countries and those participating in the EU-SILC asked similar income evaluation questions with similar response options, it is possible for subjective measures of the ability to make ends meet to be compared across these countries as well as the EU-SILC participating countries. A closely related qualitative categorical question asks respondents to evaluate their income, but instead of asking respondents about their ability to make ends meet, respondents are asked to describe their current income by selecting from a list of descriptions. Belarus, Colombia, Mexico, New Zealand, Ukraine, and Uzbekistan report asking this type of question; however, response options are substantially different, making cross-country comparison difficult. See Box 8 for examples. Box 8. Examples of Qualitative Categorical Evaluation Questions Focused on Making Ends Meet, Descriptive Responses [Belarus] How do you assess the total income of your household? • Income is barely enough to buy food. • Income is enough to buy food, but it is difficult to buy clothes and other necessary goods and services. • Income is enough to buy food, clothes and other necessary goods and services but it is difficult to buy durables (TV, refrigerator, other). • Income is enough to buy durables, but expensive goods (car, etc.) are difficult to buy. • Income is enough to buy everything we think we need. [Columbia] Y … • is not enough to cover minimum expenses. • is enough to cover the minimum expenses. • covers more than the minimum expenses. 26 The remaining questions classified as qualitative categorical evaluation focused on income or a related resource measure are either unique to the country or only asked by one other country. For example, Belarus reports asking respondents “how satisfied” they are with their money income. Costa Rica provides respondents with a reference household and asks them to evaluate whether the monthly income for the household is enough to live on. Both the Netherlands and Slovakia ask respondents how their income has changed compared to the previous year. Prediction Similar to the earlier qualitative question that did not refer to income specifically, the qualitative income-focused version of “prediction” asks respondents to evaluate how their income will change over a specific period in the future, eor will be in some future period. Only two countries, Canada and Netherlands, reported asking thes types of question. See Box 9 for the specific question wording. Box 9. Examples of Qualitative Income-focused Prediction Questions [Canada] I x w k [ ’ ] w increase, decrease, or stay the same? • Increase • Decrease • Same [Netherlands] Do you expect your income/total household income to increase, stay the same or decrease over the next 12 months? • Increase • Stay the same • Decrease [Canada] Taking all of the various sources of retirement income into account for your household (including government sources as well as personal and occupational pensions and provisions), how adequate do you think your household income in retirement will be to maintain your st ? W …? • More than adequate • Adequate • Barely adequate • Inadequate • Very inadequate C. Mo ey Metr c Valuatio Que tio Money metric valuation questions ask respondents to provide a specific value of income or money they think is necessary for the specified situation. 22 countries report asking a valuation question. 17 of these countries report asking respondents to provide the minimum 27 income they believe is needed to “make ends meet,”25 “meet the basic needs,”26 or “cover all normally necessary expenses”27.28 Of the 5 remaining countries, three report asking similar questions but set the reference for the minimum at different points. This type of question is referred to in the literature as a Minimum Income Question (MIQ). See Box 10 for examples. yrgyz Republic and Ukraine set the minimum at avoiding poverty instead of making ends meet.29 Republic of Moldova asks two questions; the first asks for the minimum income needed to live day-to-day, and the second asks for the minimum income needed for a decent life. Although some of the reference points are similar, such as “making ends meet,” “avoiding poverty,” and “live from day-to-day,” it is not guaranteed that they will evoke the same image for a respondent. Thus, responses to these questions should not be compared across countries and cannot be used to create the same subjective poverty threshold. Box 10. Examples of Money Metric Valuation Questions, Minimum Income Question (MIQ) [Brazil] Taking into account the current situation of your family, what would be the minimum “ k ”? [Ukraine] W k: w ( ’ p ) your household members is needed in order to not feel poor? [Kyrgyz Republic] What is your opinion, how much money on average per month at today's price are needed for the family with the same number of people as you have in order to avoid poverty? [Moldova] What monthly cash income would meet the minimum needs of one person in order to 'live from day to day’? [Belarus] In your opinion, what amount of money does your household need to have monthly to meet[satisfy] the minimum needs of all its members? The remaining two countries, Armenia and ungary, do not ask respondents to report only the minimum income needed to make ends meet or avoid poverty. Instead, they ask respondents to report the income needed for a variety of living standards. This type of question is also referred to in the literature as an Income Evaluation Question (IEQ). See Box 11 for the specific question wording. Brazil and Turkey also report asking a multi-point valuation 25 Austria, Belgium, Brazil, Cyprus, Germany, Ireland, Italy, Lithuania, Luxembourg, Malta, Republic of North Macedonia, Russian Federation, and Spain use the phrase “make ends meet.” 26 Costa Rica uses the phrase “meet the basic needs,” and Belarus uses the phrase “meet the minimum needs.” 27 Switzerland and Turkey use the phrase “cover all normally necessary expenses.” 28 A few of the countries that report asking this type of question indicate that it is asked as part of the EU-SILC. However, not all the EU-SILC countries that responded to the survey reported a valuation question. 12 of the 29 EU- SILC countries that participated in the survey reported asking a minimum income question. Hungary does not report asking a minimum income question but does report asking a valuation question. The remaining 16 countries did not report asking any type of valuation question. Because these countries did not report any valuation questions, we do not include them in the analysis, even though the EU-SILC was reported to include a minimum income question at the time of the survey. 29 yrgyz Republic sets the minimum income at what is needed “to avoid poverty,” whereas Ukraine sets the minimum at what “is needed to order not to feel like the poor.” 28 question using a similar five- and three-point scale, respectively. Box 11. Example of Money Metric Valuation Questions, Income Evaluation Question (IEQ) [Armenia] How much money does your family need monthly to make ends meet (survive)? How much money does your family need monthly to live well? How much money does your family need to live very well in a month? [Hungary] What (net) amount of income do you think your household would need in a month … • a very low standard of living? • a low standard of living? • an average standard of living? • a high standard of living? • a very high standard of living? II. ANALYSIS The literature provides numerous examples of applications of estimation techniques in relation to subjective welfare or subjective poverty. Some of these assess factors related to subjective welfare and search for determinants that explain the variation in responses. Others are applied to estimate subjective poverty lines that allow for the identification of subjectively poor subpopulations and, hence, the subjective poverty rates. After a brief overview of relevant determinants of subjective poverty in the literature, we introduce several estimation techniques to derive subjective poverty lines with respect to different types of subjective poverty questions. A. Relatio h p The empirics concur on the fact that there is a positive correlation between income level and subjective welfare (e.g., errera et al., 2006), and in turn subjectively based poverty. When analyzing responses to questions that ultimately are used to assess subjective poverty, these relationships need to be acknowledged and accounted for in measurement. A huge stream of literature focuses on the relationship between income and subjective welfare, mostly defined in a broader sense, e.g., in terms of happiness and/or life satisfaction (e.g., Easterlin, 2001). The correlation was found to be stronger in developing countries than in developed ones ( errera et al., 2006). owever, it was also realized that the correlation is not perfect and that it is not only current own income that matters (Ravallion and Lokshin, 2002), but also past incomes, income expectations and aspirations, and/or relative/comparison incomes (Clark and Oswald, 1996). The empirical literature broadly analyses factors of subjective poverty, where survey responses have been regressed on individual and household characteristics. Besides income, other factors 29 such as household size, age and gender composition, education and employment status, and regional dummies are commonly controlled for in model estimations. For an example of a wide list of analyzed characteristics, Ravallion and Lokshin (2002) examined how the answers to a nine-rung economic welfare question (with the rungs ranging from “poor” to “rich”) varied with various variables grouped in three areas: (i) supplementary objective indicators of personal or household circumstances (expenditure, assets and durables, education, health, employment status, age and marital status), also utilizing the panel nature of the applied data (past incomes); (ii) measures of relative income (variables measuring the individual’s relative position within certain reference groups, e.g., position within the respondent’s household or within the locality where they live); and (iii) attitudinal variables (e.g., expectations about future welfare, perceived insecurity of employment, and whether the government cares about people), which, however, may have raised concerns about endogeneity. Some of the variables might affect subjective welfare through effects on expected future income or perceived riskiness of individuals’ current incomes. Lower subjective welfare of divorced or widowed individuals may stem from perceived lower economic security. Relative income within one’s locality were found to account for almost all the variance attributable to geographic effects; people in richer areas felt relatively worse off. Ravallion and Lokshin (2002) concluded that “results clearly reject any notion that one only gets noise from the answers to subjective questions. owever, it is also unclear whether the systematic factors that influence self-rated welfare will all be deemed relevant to the types of inter-personal welfare comparisons that are required for making specific policy choices.” (p. 1471). The type of regression modelling utilized will be based on how the dependent variable is defined. When subjective welfare is represented by ordinal data from a welfare ladder question, ordered probit regression models are typically applied. When continuous data is used as the dependent variable, such as with the MIQ, standard OLS regression is commonly applied. Researchers have mostly agreed that if regression models are used to estimate subjective poverty lines, covariates, such as household size, should be included in order to get unbiased estimates of other variables (Garner and de os, 1995). B. Subjective Poverty L e In this section we present an overview of the two most known approaches to estimate subjective poverty lines based on money metric valuation questions: the Leyden Poverty Line based on Income Evaluation Question (IEQ) and the Subjective Poverty Line based on Minimum Income Question (MIQ). Though both the approaches were developed around the 1970s, the latter gained more interest in the literature because of the availability of the questions in recent surveys. While the IEQ was rarely included, the MIQ was asked annually in the EU-SILC up to 2020.30 30 The related variable is likely to be collected every six years in the EU-SILC 6-yealy rolling module 2026 on “over- indebtedness, consumption and wealth”. This module will be legally adopted by the end of 2024. The module will be collected every six years starting in 2026. 30 Leyden Poverty Line based on Money Metric Evaluation Question The construction of the Leyden Poverty Line (LPL) relies on estimating parameters of the individual welfare function of income (income utility function), which is typically based on the so-called IEQ. The IEQ (presented in Box 11) asks respondents to report what they consider to be ( ) /( ) /( ) income, in their circumstances (van Praag, 1968, 1971). The amounts corresponding to these categories are used to form the individual welfare function, and this function is further used as a basis for estimating the LPL (see Box 12). Within this framework, it is necessary to decide upon the value of a parameter 𝛼 – the welfare (utility) level under which a household is considered poor. Ultimately, a household is considered poor if the total household income falls below a certain level of welfare (𝛼). Note that the parameter 𝛼 is arbitrarily chosen. Box 12. Leyden Poverty Line The individual poverty line yαi is defined by solving (Flik and van Praag, 1991):    =      −  i iiy )ln( , (1) where α is the welfare (utility) level below which a household is considered poor, Ф(∙) denotes the cumulative distribution function of the standard normal distribution; i and i are the mean and standard deviation estimated from responses to the IEQ. Assuming that )ln()ln( 210 iii sy  ++= , (2) we get: )()ln()ln()ln( 1 210  −+++= iii syy . (3) Fixing  at the population average  , the log of national LPL can be computed as: ( ) 1 1 20 1 )()ln( ln    − ++ = −  s y . (4) A specific LPL can be found for each value of household size. In addition, further household characteristics can be included in the equation. Intersection Method Based on the Minimum Income Question Intermediate approaches developed in the 1990s aimed to identify cost and/or utility functions based on subjective money metric valuation questions. The most well-known approach derives the Subjective Poverty Line based on subjective valuations of MIQ (Box 10), first introduced by Goedhart et al. (1977). It is model-based in the sense that individual’s responses do not directly generate the poverty line ( eptayen et al., 1988). There were attempts to define the poverty threshold as anyone whose actual income was lower than their reported subjective minimum; however, as people at the same standard of living can provide different answers to the MIQ. This heterogeneity must be accounted for because it would lead to inconsistencies in the poverty measures otherwise (Pradhan and Ravallion, 2000, Ravallion, 2014). It has been shown that there exists a positive relationship between the expected answer to MIQ and actual income. More generally, the income effect on subjective welfare has been identified as robust across countries, within countries, and over time in the literature (Stevenson and 31 Wolfers, 2008; Clark et al., 2008). The conditions the existence of SPL on subjective minimum income being an increasing function of actual income, more concretely, a concave function as illustrated by Figure II.1. The intersection (Z) of the lines representing the equality of minimum and actual incomes (i.e., the 45‐degree line in Figure II.1) determines the Subjective Poverty Line. The intersection point assumes that only respondents with actual incomes equal to their subjective minimum incomes have a realistic idea of the minimum income level. Richer respondents tend to overestimate their minimum necessary income while poorer respondents tend to do the opposite. Figure II.1 Subjective Poverty Line based on Minimum Income Question Source: Illustrative picture. Notes: Z is the estimated Subjective Poverty Line. The seminal paper by Goedhart et al. (1977) estimated the subjective minimum income as a function of actual income and household size only, but the authors suggested that “any quantifiable factor that has a measurable effect” might have been incorporated (p. 518). Subsequent studies extended the set of explanatory variables as differentiating factors for the subjective poverty lines (e.g., García‐Carro and Sánchez‐Sellero, 2019; Mysíková et al., 2021, 2022; Želinský, 2022). These commonly included employment status, sex, age, education, and degree of urbanization. Discussions on the inclusion of explanatory or control variables mostly argue that even if a variable causing a significant effect is not accepted as a factor differentiating the poverty line, it should be included in order to obtain unbiased estimates of other variables (e.g., Garner and de os, 1995). Though effects caused by differences in Su b je ct iv e m in im u m in co m e Actual income Z* Z45 32 personality, tastes, lifestyles, or, for instance, incomes of reference groups (household or community) or recent income changes may contribute to explain the variance in subjective minimum income, they would unlikely be considered relevant to policy choices (Ravallion and Lokshin, 2002). Depending on the authors’ judgements about the empirical, theoretical and/or political relevance of the explanatory variables to the poverty lines, the methods to calculate subjective poverty lines differ (Garner and Short, 2004). One way would be to calculate a single poverty line holding the explanatory variables at their national averages (or, more frequently, a set of lines differentiated by the variables defining subpopulations of interest, holding the values of other control variables at their national averages), while the other would employ all (relevant) explanatory variables to calculate household-specific lines. The latter approach is particularly useful when the key aim is distinguishing populations below and above the lines, rather than a definition of the line itself (Želinský et al., 2022). owever, the approach is different from simply calculating the number of households reporting actual household income that is less than the household expected minimum income or setting the average reported MIQ as the poverty line. See Box 13 for an example of the estimation of a SPL, Box 13. Subjective Poverty Line and the intersection method In practical applications, standard OLS regression model is applied to estimate the subjective minimum income as a function of actual income. Natural logarithms of both subjective and actual incomes are used instead of original values. The estimated function is: ln(�̂�) = 𝛼 + 𝛽 ln(𝑋), (1) where Y is the subjective minimum income, X represents the actual household income, and α and β are the estimated coefficients. At the intersection point, where Y = X = Z, rearranging the equation yields: ln⁡(𝑍∗) = 𝛼 1−𝛽 , with necessary conditions α > 0 and 0 < β < 1. (2) A household i is identified as subjectively poor if the following inequality holds: Xi < Z. (3) Employing control variables in Equation (1) we obtain: ln(�̂�) = 𝛼 + 𝛽 ln(𝑋) +⁡∑ 𝛾𝑘𝑉𝑘 𝐾 𝑘=1 , (4) where Vk k = 1 … K are control variables and γk are the corresponding estimated coefficients. The definition of SPL extends to: ln⁡(𝑍∗) = 𝛼+⁡∑ 𝛾𝑘𝑉𝑘 𝐾 𝑘=1 1−𝛽 . (5) The intersection method can also be used to estimate SPL based on Minimum Spending Question (MSQ) instead of MIQ. An example of a MSQ is provided in Box 15. Garner and Short (2003, 2004) found the MSQ-based poverty lines to be lower than the MIQ-based poverty lines, because the MSQ refers to a more narrowly defined set of needs than the MIQ (See Box 14). Compared to the MIQ-based poverty lines, the MSQ-based poverty lines were more like the absolute poverty lines applied in the U.S. (Garner and Short, 2003). Box 14. Minimum Spending Question in SIPP in 1995 33 In your opinion, how much would you have to spend each year in order to provide the basic necessities for your family? By basic necessities I mean barely adequate food, shelter, clothing, and other essential items required for daily living. SIPP – Survey of Income and Program Participation (Garner and Short, 2003) In addition, subjective poverty lines have been compared to population-based means and median incomes, and objective and relative poverty thresholds. For example, de os and Garner (1991), reported that for both the U.S. and the Netherlands, the SPLs lied in the range of 60–75% of incomes in most household size groups. In addition, with respect to the Netherlands, the subjective poverty line would have been higher than the objective and relative income poverty line currently applied in the EU (i.e., with the poverty line set at 60% of equivalised household income). With the same actual income compared to each threshold, the subjective poverty rate would have been highest. In addition, Saunders et al. (1994) found that the poverty rates resulting from the use of thresholds derived from subjective measures were markedly higher than those based on relative income poverty thresholds (i.e., with the poverty line defined as 50% of equivalised household income) for Australia and Sweden around the 1980/1990s. García-Carro and Sánchez-Sellero (2019), using the national EU-SILC data between 2008 and 2016, found the subjective poverty rate to be about 40% for Spain, as compared to the official relative income poverty (at risk-of- poverty rate, AROP) rate of roughly 20%. As opposed to country case-studies, the recent study by Želinský et al. (2022) compared the subjective poverty rates based on SPLs with the “at risk of poverty” (AROP) rates in all EU member states over the period of 2004–2019. It showed a substantially greater variation in subjective poverty rates than AROP rates across the EU countries: the subjective poverty rate substantially exceeded the AROP rate in some Eastern and Southern European countries, while it was lower in Scandinavian countries. Quasi Leyden Poverty Line Based on the Deleeck Question As the IEQ puts a burden on respondents, it is rarely integrated in statistical surveys. Piasecki and Bieńkuńska (2018) propose an alternative way to estimate a subjective poverty line using the intuition behind the LPL utilising the Deleeck-type of question (Box 7). In the first step, the approach assigns a utility level to each response option presented in the 6-categorical Deleeck question. In the second step, it is necessary to estimate parameters of a regression function modelling the level of actual income at which the household would find itself on the poverty threshold. The value of the poverty threshold at a (arbitrarily) given utility level (𝛼) depends on the size of the household and may also depend on additional characteristics of the household. See Box 15. Box 15. Quasi-Leyden Poverty Line The estimation procedure has several steps: (1) Assigning a value of utility to the evaluation of actual income for each household using the transformation 34 ui = (ji – 0.5)/m, (1) where ji is answer of household i to the Deleeck question, m is the number of categories (m = 6 for the Deleeck question integrated in EU-SILC survey). (2) Estimating parameters of an OLS regression function: ln(𝑦𝛼𝑖) = 𝛾0 + 𝛾1 ln(𝑠𝑖) + 𝛾2Φ −1(𝑢𝑖), (2) where 𝑦𝛼𝑖 is the actual income of household i, si is the household i size, 𝛼 is the utility level proxied by ui, and Φ−1(𝑢𝑖) is the value of the inverse function of standard normal distribution for ui. (3) The estimated regression coefficients then allow us to derive the subjective poverty lines for different values of household size (si). In formula (2), we employ α, which is an arbitrarily chosen parameter representing the level of utility from being at the poverty threshold. Piasecki and Bieńkuńska (2018) report estimations based on different values of α (0.25; 0.3; 0.33; 0.4; 0.5). Including further control variables also allows us to derive the poverty thresholds for other subgroups of households. Note that the estimated value of a subjective poverty line is also determined by the value of which corresponds to the assumed utility level (𝛼). The subjective poverty line estimated for a certain household size depends on an arbitrarily chosen welfare level below which households are considered poor. Nevertheless, individual poverty lines can be estimated for each household and aggregating poverty lines across households can help to address this concern. An Approach Based on Proportional Odds Logistic Regression Utilizing ordered categorical data (such as the Deleeck question, Box 7) allows us to employ proportional odds logistic regression, as recently suggested by Pittau and Zeli (2023). Adopting the alternative specification of ordered probit/logit model, as discussed by the authors, allows a direct interpretation of the estimated intercepts as thresholds on the scale of income. The poverty line is constructed as described in Box 16. Box 16. As the original (ordered) responses correspond to the self-declared status (e.g., the ability to make ends meet elicited on scale 1 – 6), the following parametrization of the model is required: (  (              = 5.5 5.55.4 5.25.1 5.1 if 6 , if 5 , if 2 if 1 cz ccz ccz cz y i i i i i  where ),0(N~ , 2 iiii xz += . Adopting this parametrization, intercepts c1.5, c2.5, …, c5.5 can be directly interpreted as thresholds on the scale of income. 35 Considering the proportional odds model: xc ky ky k +=        )(Prob )(Prob log , where ck are the intercepts, i.e. the cut-points that need to be estimated, x is income, 𝛽 is the regression coefficient that needs to be estimated; the estimated thresholds can be transformed in the scale of income using a simple re- parametrization: etc. ; ˆ ˆ ˆ ; ˆ ˆ ˆ 3\|2 5.2 2\|1 5.1  c c c c == , where 6\|52\|1 ˆ ,...,ˆ ,ˆ cc are the estimates of the standard parametrization provided within a statistical software output. For further details, refer to the study by Pittau and Zeli (2021). An Approach Based on Dichotomized Data An alternative way to estimate monetary subjective poverty line when having categorical variables has been produced by Želinský et al. (2020). This method was designed to apply a dichotomized variable. owever, the current most frequently applied question in the EU is a 6- point scale variable, the ability to make ends meet question (Box 7), integrated in the EU- SILC survey. A way to proceed is first dichotomize the question responses (e.g., households who report great difficulty to make ends meet are deemed poor and all other households are deemed as non-poor). This step is rather arbitrary, but it is necessary to assess the robustness of results by considering alternative dichotomizations. Once the responses are converted to a binary variable, we can utilize an approach proposed by Duclos and Araar (2006) allowing for the estimation of subjective poverty lines with discrete information. This approach relies on a binary variable (or a dichotomized multi-categorical variable) with 1 representing subjectively poor and 0 otherwise. The working assumption is that respondents compare their actual income to an unknown subjective poverty line Z which is unobserved and must be estimated. As shown by Figure II.2, with the binary classification of (non-)poor, some respondents can misclassify their own situation, i.e., individuals with high income classify themselves as poor (“false poor”), while individuals with low income classify themselves as non-poor (“false rich”). To estimate the subjective poverty line Z, it is necessary to minimize the numbers of “false poor” and “false rich”. Figure II.2 Estimating a subjective poverty line with binary categorical variable 36 Source: Želinský et al. (2020, p. 2); based on Duclos and Araar (2006, p. 125). Notes: Z represents the subjective poverty line. Following this intuition, Želinský et al. (2020) propose utilization of the Youden J index as an option to estimate the unknown subjective poverty line. The Youden Index estimates the poverty line by selecting the value of income at which the numbers of “false-poor” and “false- rich” individuals are minimized. As illustrated in Figure II.2, the cut-off point Z* (subjective poverty line) is defined as the income level that differentiates households which are subjectively poor from those who are not. The poverty line can be operationalized as in Box 17. One of the disadvantages of this approach is that it does not automatically allow for considering control variables, and subjective poverty lines need to be estimated separately for each subgroup of interest to account for household/individual characteristics. Box 17. Subjective poverty line based on dichotomized data Statistically, the Youden index, J, is a function of c which maximizes the sum of sensitivity (Se) and specificity (Sp) classification measures: 𝐽(𝑐) = max 𝑐 {𝑆𝑒(𝑐) + 𝑆𝑝(𝑐) − 1}. (1) At a given c, Se(c) and Sp(c) denote the probabilities of correctly identifying subjectively non-poor and poor households. Denoting X1, X2, . . . , Xm and Y1, Y2, . . . , Yn as the income levels of the non-poor and poor household groups, respectively, the Youden index is calculated as: 𝐽(𝑐) = max 𝑐 { ∑ 𝐼(𝑋𝑖≥𝑐) 𝑚 𝑖=1 𝑚 − ∑ 𝐼(𝑌𝑗>𝑐) 𝑛 𝑗=1 𝑛 }⁡, (2) where I(D) is an indicator function with I(D) = 1 if D is true, 0 otherwise. Subsequently, the optimal value of c is the one which maximizes the value of J, or equivalently, the number of correctly classified households. Statistically, the Youden J index is based on z* 0 1 Income F e e l p o o r? 'false poor' 'false rich' 37 maximising the sum of sensitivity and specificity classification measures. J = 1 represents a perfect classification while J < 1 indicates otherwise. The Youden (1950) index was initially introduced in medical literature to assess the ability of a biomarker test to classify individuals as either diseased or non-diseased, based on which side of a cut-off point, c, their biomarker values fell on along the distribution of possible values. The Youden index can be adapted to the poverty context by defining the cut-off point as the income level that differentiates households which are subjectively poor from those which are not. Nevertheless, the classification exercise is not limited to the adoption of the Youden index but can also be based on alternative metrics such as those based on a Receiver Operating Characteristics (ROC) curve. C. Cou try/ ter atio al or a zatio exa ple From the in-depth review of current country practices organized by the Bureau of the Conference of European Statisticians, only two countries reported using responses to monetary subjective poverty questions to produce such thresholds. The Italian National Institute of Statistics reports using the Subjective Poverty Line (SPL) method. The Brazilian Institute of Geography and Statistics reported periodically using the SPL method as well as exploring the possibility of using the Leyden Poverty Line (LPL) and the Center of Social Policy Poverty Line (CSP) methods. [A w S : S p p w p L L pp .] Chapter 4. STATCAN co tr butio Metho o ata collectio a u el e This section focuses on data collection methods for subjective poverty research, offering an overview of various approaches and guidelines, including their characteristics, benefits, and limitations. It underscores the importance of survey frame quality and sample selection in method selection, providing organizations with a comprehensive toolkit to choose the most suitable approach. Additionally, it hints at a forthcoming systematic review of questions conducted across the UNECE region by 15 countries in subjective poverty research, aiming to provide a comprehensive resource for organizations seeking to gather relevant data for their specific needs and priorities. The initial step in gathering and validating subjective poverty data involves understanding the range of collection methods in use. This section provides a description and comparison of common approaches, focusing on major methods and offering specific use cases. These approaches span from complex sampling surveys to simpler web panel data collected through crowdsourcing, summarized in Table 1. While this table does not serve as an exhaustive study comparing these methods, it offers an overview based on Statistics Canada's experience, considering factors such as data quality, sample control, duration, and cost. Notably, there is a trade-off between cheaper and quicker surveys with higher error rates and limited generalizability to population estimates, impacting the ability to study subpopulations as opposed to more expensive tradition surveys which are designed to produce higher quality 38 data. Therefore, aligning data collection methods with specific research needs is a critical initial step, and Table 1 serves as a helpful starting point for organizations engaged in subjective poverty research. In essence, this section outlines the importance of understanding various subjective poverty data collection methods and introduces a practical reference tool, as seen in Table 1, which organizations can use to make informed decisions based on their resource constraints and research objectives. Table 1 – Data collection methods Data collection type Description Control over sample Approximate Duration (planning to execution) Cost Country Use Cases Traditional Survey ‘Specialized need’ Very high control 1+ year Most expensive EU-SILC Opinion Poll Survey ‘Specialized need’ Some control 1+ year Medium expense United States – Gallup Poll Omnibus Survey ‘General Social Data’ High control 9 months Medium expense Canadian Social Survey Rapid Response ‘Quick and Stand alone’ Some control 7-8 months Medium expense Bureau of Labour Statistics Web panel31 ‘Rapid indicator’ Low control 4 months Low expense Statistics Canada Crowdsourcing ‘Pulse check’ Voluntary (low sample control) Shortest (4 month turn around) Low expense Statistics Canada Administrative data Used to improve sampling and calibration of surveys Often mandatory (tax data) n/a Varies Statistics Denmark (for EU- SILC) Source: Statistics Canada, 2022 Survey Fra e a a ple co eratio Prior to elaborating further on each of these survey designs it is worth mentioning two overarching considerations common to all approaches. One of them is the necessity of a high- quality survey frame, and the second is sample selection. Better descriptions of a survey frame can be found elsewhere as this chapter assumes a certain degree of prior knowledge of surveys by its audience. owever, a very broad review is helpful here to help understand the following descriptions. There are two types of frames used at Statistics Canada: a list frame and an area frame. Qualities of a good frame include: 31 Program and proceedings (statcan.gc.ca) https://www.statcan.gc.ca/en/wtc/conferences 39 • Relevance: the extent to which the survey frame corresponds and permits access to the target population. • Accuracy: includes evaluation of coverage errors to minimize and assess coverage and classification errors of the statistical units in the frame. • Timeliness: how up-to date is the frame with respect to the survey reference period and current affairs. • Cost: the total cost to develop the frame in comparison to the total cost of a survey. (Statistics Canada, 2010). The second consideration is sample selection when choosing a data collection method. Sample selection poses the following questions: (1) Is the survey mandatory or voluntary? (2) Is it a probability or non-probability sampling? (3) ow large is the sample size? Like the previous consideration, better references exist for more systematic review of survey design and sample considerations32. The following section is written in an accessible way such that, with the descriptions above, a more complex understanding of survey frames and samples is not needed. The details of each should be considered as secondary to the broad overview of approaches described below. The shift towards online surveys is increasing. Online surveys have gained popularity due to their cost-effectiveness, quick distribution, and utilization of multimedia elements. owever, online surveys often differ in terms of sampling principles. Many online surveys do not use probability sampling, which allows for unbiased estimates and accuracy calculations. Instead, they rely on self-selection of respondents (Bethlehem, J., 2008). This departure from probability sampling leads to biased results and prevents the application of probability theory. Self-selection surveys are not a viable solution. owever, web surveys conducted within the framework of probability sampling hold potential, either as standalone surveys or as part of mixed-mode approaches. In these cases, web surveys can contribute to addressing the dilemma of limited budgets and increased information demands. Tra tio al urvey The first approach is traditional surveys whose strength resides in standardization, generalizability33, and versatility. It is a method of gathering information from a set of people with the purpose of generalizing the results to a larger population. Surveys are used to understand the choices, preferences, and experiences respondents. They are longer and more detailed than polls and can be conducted in-person, over the phone, or online. When compared to non-survey-based data collection techniques such as focus groups traditional surveys are 32. References for developing samples including: Survey Methods and Practices (statcan.gc.ca) 1. American Association for Public Opinion Research (AAPOR): Survey Practice 2. The U.S. Census Bureau Our Surveys & Programs (census.gov) 3. The World Bank's Data Quality Assessment Framework (DQAF): Data Quality Assessment Framework (DQAF) for the International Comparison Program (ICP) : paper for session five (worldbank.org) 33 Generalizability is a measure of how representative your sample is to the target population, also known as external validity. https://www150.statcan.gc.ca/n1/en/pub/12-587-x/12-587-x2003001-eng.pdf?st=t5P36yl3 https://www.surveypractice.org/ https://www.census.gov/programs-surveys.html https://documents.worldbank.org/en/publication/documents-reports/documentdetail/142991468338434907/data-quality-assessment-framework-dqaf-for-the-international-comparison-program-icp-paper-for-session-five https://documents.worldbank.org/en/publication/documents-reports/documentdetail/142991468338434907/data-quality-assessment-framework-dqaf-for-the-international-comparison-program-icp-paper-for-session-five 40 more cost effective to capture data on a population but are the most expense data collection technique reviewed here. Strict control over the survey sample facilitates probability sampling and improves generalizability to the target populations. The European Statistics on Income and Living Conditions (EU-SILC) is an example of a traditional survey. It collects timely, cross-sectional, and longitudinal microdata from multiple European countries on income, social inclusion and living conditions cover objective and subjective aspects in monetary and non-monetary terms for households and individuals. Anchored in the European Statistical System (ESS), this survey was launched in 2003, replacing the European Community ousehold Panel (EC P), which expired in 2001. The data it collects is comparable between the member countries on: (a) income, (b) poverty, (c) social exclusion, (d) housing, (e) labour, (f) education, (g) health. They are used to monitor the Europe 2030 targets of the European Pillar of Social Rights Action Plan34, particularly its poverty reduction targets. The reference population includes all private households and their residents who were in the country at the time of data collection. All household members are considered, but only those aged 16 or older are interviewed. Persons living in collective households or institutions are excluded from the target population. Case Study 1: National Survey of Self-reported Well-being (ENBIARE) 2021 of Mexico The National Survey of Self-reported Well-being (ENBIARE) 352021 in Mexico aims to capture people's subjective well-being perceptions. This survey was conducted in two questionnaires, one for housing and households and another to collect data from adults aged 18 and older, covering various dimensions of well-being, life events, and financial difficulties, including perceptions of income sufficiency and future financial outlook. It employs a probabilistic, stratified, three-stage sampling method, resulting in a national sample of 37,000 housing units. ENBIARE uses a Master Sample provided by Mexico's National Statistical Office, INEGI, to select diverse clusters for data collection. The data are available five months after collection, and the survey is expected to be conducted biennially. Data collected from June 3rd to July 23rd, 2021, revealed that 64% of respondents faced difficulties paying household expenses in the past year, and 43% anticipated insufficient income for the following month. The survey provides valuable insights at both national and state levels into well-being and financial challenges among Mexico’s population. ENBIARE questions about the minimum income sufficient to pay for monthly home needs. Once the minimum sufficient income has been declared, ask if the person considers that their household will be able to reach e the minimum income sufficient. This question is applied to an adult person, 18 years or older, selected from each household who share a common expense and reside in the homes assigned for the survey. The selection of the appropriate informant begins with the identification of the usual members of the household who are within the 34 EU 2030 target on social protection aims that “out of 15 million people to lift out of poverty or social exclusion by 2030, at least 5 million should be children.”. The European Pillar of Social Rights Action Plan (europa.eu) 35 National Survey of Self-reported Well-being (ENBIARE) 2021 (inegi.org.mx) https://op.europa.eu/webpub/empl/european-pillar-of-social-rights/en/ https://en.www.inegi.org.mx/programas/enbiare/2021/ 41 established age range of 18 years of age or older, based on the information collected in the ousehold Questionnaire. Additionally, you meet the criteria of knowing how to read, write, and speak Spanish. Minimum income perception question: MINIMUM INCOME PERCEPTION OF MINIMUM INCOME In your opinion, how much income would be enough to meet all your household needs for a month? $\|___\| ,\|___\|___\|___\| , \|___\|___\|___\| PREFERS NO TO RESPOND 9 999 999 Do you consider that you or your home will reach this income level next month? Yes.........................................1 No .........................................2 Doesn´t know …....................9 In ENBIARE the definition of minimum income refers to the amount of income from various sources, defined by the person, sufficient to meet all their household needs in a month. Results: The population that considered they would not get the minimum income necessary to meet household needs next month was 43.4%, 11.3% did not know, and 45.4% declared they would get it. Figure 1. Share of households by perception of getting the minimum income level, 2021 Source: INEGI. National Survey of Self-reported Well-being (ENBIARE) 2021, Database. Encuesta Nacional de Bienestar Autorreportado (ENBIARE) 2021 (inegi.org.mx) 45.4 43.4 11.3 Will reach Won´t reach Doesn´t know 42 Regarding conceptual and statistical design, the ENBIARE target population is adults aged 18 years or over who are literate and Spanish-speaking. Observation units are the sample selected housing units, the households, the population residing in households, and the chosen people aged 18 years and over who can read, write, and speak Spanish. ENBIARE provides estimations with a geographical breakdown at the national and state levels. The indicator of subjective poverty in ENBIARE refers to the household where the adult population resides. The household income necessary to make ends meet is based on the personal perception of his household’s minimum needs. On the other hand, Mexico has an official, objective measurement of multidimensional poverty. This means that, in addition to considering the insufficiency of economic resources, it considers several additional dimensions on which social policy should focus. Under the General Law of Social Development, the guidelines and criteria to define, identify, and measure poverty are issued by the National Council for the Evaluation of Social Development Policy (CONE AL, by its Spanish acronym). CONE AL must use the information generated by INEGI through the National Survey of ousehold Income and Expenditure (ENIG ) to estimate poverty. The following graph compares the subjective poverty indicator (43.4%) with the population in poverty, those with income below the poverty line, and those below the extreme poverty line by income. The subjective indicator reports a similar level to the objective indicator that captures the population below the income poverty line (43.5 percent). Figure 2. Subjective poverty indicator and objectives poverty indicators, 2021 and 2022 Source: INEGI. National Survey of Self-reported Well-being (ENBIARE) 2021, Database. Encuesta Nacional de Bienestar Autorreportado (ENBIARE) 2021 (inegi.org.mx) National Council for the Evaluation of Social Development Policy (CONEVAL, by its Spanish acronym) https://www.coneval.org.mx/Medicion/MP/Paginas/AE_pobreza_2022.aspx Note: ENBIARE data refers to the year 2021. CONEVAL data refers to 2022. Figure 1. Percentage won´t be able to reach the next month's income, 2021 43.4 36.3 43.5 12.1 Won´t be able to reach the next month's income Population in poverty Population with income below the income poverty line Population with income below the extreme poverty line by income CONEVAL ENBIARE https://www.inegi.org.mx/programas/enbiare/2021/#microdatos https://www.coneval.org.mx/Medicion/MP/Paginas/AE_pobreza_2022.aspx 43 Source: INEGI. National Survey of Self-reported Well-being (ENBIARE) 2021, Database. Encuesta Nacional de Bienestar Autorreportado (ENBIARE) 2021 (inegi.org.mx) O bu Survey An omnibus survey is collects data on a wide variety of subjects in the same interview while sharing the common demographic data collected from each respondent. They provide a convenient and efficient way to collect data from a consistent group of respondents. They allow researchers to leverage the same sample over time, thereby improving the accuracy of their results, optimizing survey procedures, and potentially reducing costs associated with recruiting new samples for each individual survey. This approach is particularly valuable when there is a need for quick and frequent insights across different subjects within a population. Case Study 2 below elaborates on an omnibus survey methodology. 58.7 56.3 56.2 55.8 53.3 52.6 52.4 48.5 47.9 47.7 46.4 46.4 45.0 44.7 44.7 44.6 44.3 44.3 43.2 43.2 42.6 39.2 36.2 36.1 34.2 33.5 33.5 33.0 32.6 32.3 29.8 29.5 Yucatán Oaxaca Tabasco San Luis Potosí Campeche Puebla Guerrero Sinaloa Hidalgo Michoacán de Ocampo Durango Chiapas Zacatecas Veracruz de Ignacio de la Llave México Guanajuato Morelos Querétaro Ciudad de México Tlaxcala Quintana Roo Aguascalientes Chihuahua Jalisco Sonora Nuevo León Baja California Sur Nayarit Coahuila de Zaragoza Colima Baja California Tamaulipas 44 Case Study 2: The Quality of Life framework for Canada Canada's Quality of Life Framework, introduced in the 2021 budget alongside the report "Measuring What Matters," aims to move beyond GDP and incorporate social, economic, and environmental factors into Canada's assessment of quality of life. This framework acknowledges the multifaceted nature of well-being and incorporates both subjective and objective measures, some of which can be adapted to assess subjective poverty. It aligns with global trends seen in frameworks from countries like New Zealand, Scotland, Iceland, and the U 36, which blend subjective and objective indicators in response to recommendations from the 2009 Commission on the Measurement of Economic Performance and Social Progress. The Canadian Quality of Life Framework consists of 84 indicators organized into five domains: prosperity, health, environment, good governance, and society. Statistics Canada gathers data for many of these indicators through surveys and administrative sources, with 58 of them presently defined on the Quality of Life hub. Some indicators relevant to subjective poverty include job satisfaction, financial well-being, self-rated health, and trust. Data collection primarily relies on the Canadian Social Survey (CSS), a versatile survey that examines various social issues every three months and pools the data over a year to track changes in living conditions and well-being, showcasing Statistics Canada's approach to studying subjective well-being. Op o Poll Survey Opinion polls serve as a rapid means to gather public sentiment on specific topics and can be conducted through online, paper, in-person, or phone surveys. A poll is a method of collecting data by asking a single question with a limited number of answer options. Polls are generally used to make quick decisions and are conducted at various stages. These polls are particularly useful for gauging majority opinions and can be applied to assess perceived poverty levels or evaluate the validity of official poverty thresholds. With an adequate sample size and randomization, opinion polls offer reliable insights across various demographic groups and are generally cost-effective compared to traditional surveys. An illustrative example is a 1989 Gallup poll in the United States that revealed public opinion placed the Official Poverty Measure thresholds 19% higher than calculated using conventional objective methods. In Canada, government departments often collaborate with external organizations to conduct public opinion research, utilizing their expertise in questionnaire design and occasionally involving subject matter experts, such as psychologists or sociologists, to refine questionnaire wording and content. Rap re po e Rapid response surveys are ad-hoc surveys that provide snapshots of a population on specific issues and can obtain information directly on the most pressing data needs. While this shares many common features as typical surveys, when timeliness is of great importance, certain 36 Our Living Standards Framework \| The Treasury New Zealand, Quality of life in the UK - Office for National Statistics (ons.gov.uk), National Performance Framework \| Our Place, Iceland – Wellbeing Framework : Wellbeing Economy Alliance (weall.org) https://www.canada.ca/content/dam/fin/publications/mwmtqlsc-mqivsqvc/mwmtqlsc-mqivsqvc-en.pdf https://www.treasury.govt.nz/information-and-services/nz-economy/higher-living-standards/our-living-standards-framework#:~:text=The%20Living%20Standards%20Framework%20%28LSF%29%20captures%20many%20of,long-term%20and%20distributional%20issues%20and%20implications%20of%20policy. https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/bulletins/qualityoflifeintheuk/august2022 https://www.ourplace.scot/about-place/national-performance-framework https://www.ourplace.scot/about-place/national-performance-framework https://weall.org/resource/iceland-wellbeing-framework 45 parameters are loosened, such as randomization of the sample. This allows the survey to be developed and fielded faster than a typical survey. The benefit of this is that it can provide a pulse on a particular subject. These have been used widely during the pandemic, when the rapidly changing economic and political environment due to the ongoing health crisis necessitated more timely information for decision makers than had previously been built into official data collection strategies. The drawback to this speed is that often they are less representative of the target population and are considered of lower quality data. Case Study 3: The U.S. Census Bureau Household Pulse Survey Financial Well-being Question In response to the CO ID-19 pandemic, the U.S. Census Bureau launched the ousehold Pulse Survey ( PS)37 in collaboration with multiple federal agencies. This survey aimed to provide timely and efficient data compared to traditional surveys. The PS operates in two- week survey periods, with a one-week gap between them, and data releases about a week after each survey period ends38. Since, the beginning of SP in 2020, federal agencies contribute critical questionnaire items to inform their missions and understand the pandemic's impact on individuals, families, and households. The questions are periodically reviewed and updated to address evolving economic conditions and agency-specific needs. The PS sampling frame combines the Census Bureau's Master Address File with email addresses and mobile phone numbers. Participants receive email or text invitations to complete the online questionnaire, and follow-up reminders are sent if there's no response. Each survey period involves approximately one million households, resulting in about 80,000 respondents despite low response rates of around 8%. Weight adjustments ensure that responses are representative of the U.S. population. The PS collects a wide range of data, including both objective and subjective well-being dimensions. Objective questions cover household income, employment experiences, healthcare access, educational disruptions, and vaccination status. Subjective questions focus on perceptions of food and housing security, physical and mental health, and general financial well-being. Garner, Safir, and Schild (2020)39 40analyzed responses to the financial difficulty questions and in relationship to income using data collected from August 19 to 31, 2020. The data shows that financial difficulty is correlated with income, with 59.1% of those earning less than $25,000 reporting some financial difficulty compared to 7.5% among those earning $200,000 or more. Depending on how poverty is defined, it ranges from one-third of the population experiencing some difficulty to 8.3% facing both difficulty and lower income. 37 Additional details about the Household Pulse Survey and the public use data can be at the following link: https://www.census.gov/programs- surveys/household-pulse-survey.html 38 This schedule is how the survey is currently being conducted but is not how it has always been conducted. Additional information about how the survey was conducted during earlier cycles can be found in the technical documentation available on the Census Bureau’s ousehold Pulse Survey webpage. See Footnote 1 for link. 39 https://www.bls.gov/opub/mlr/2020/article/changes-in-consumer-behaviors-and-financial-well-being-during-the- coronavirus-pandemic.htm 46 Web-pa el Web panel surveys are a fast and cost-efficient method in market surveys thanks to the continued use of the internet and increasing nonresponse rates and prices. Per Bethlehem (2008), web-panels are just another mode of data collection. Questions are not asked face-to-face or by telephone, but over the Internet. The difference is the principles of probability sampling are not applied. By selecting random samples, probability theory can be applied, making it possible to compute unbiased, more accurate estimates. Web surveys often rely on self-selection of respondents instead of probability sampling having serious impact on the quality of survey. There are also risks of coverage and measurement errors. The absence of an inferential framework and of data quality indicators is an obstacle against using the web panel approach for high-quality statistics about general populations. Crow ource urvey Crowdsourcing involves collecting information by accessing a large community of online users on a given topic. Statistics Canada has conducted several crowdsourced surveys via means of a mobile application and engagement. This method lessens the burden for respondents and allows for quick responses on a variety of subjects. Case 4 below provides more information on Statistics Canada’s use of crowdsource surveys to collect subjective poverty data. Crowdsourcing is less costly than traditional surveys, quicker than other survey types, and can be a tool to improve how information is collected by filling data gaps. Its strengths, however, come with risks of population bias due to the lack of sampling control. Case Study 4: Using crowdsourced data Two Statistics Canada papers discussed the methodological issues that arise from integrating crowdsourced data into existing data sources. The goal is to use existing data sources to improve accuracy and remove bias in the crowdsourced data. The two approaches were the p (Poirier, 2021) and the q (Ding and Chatrchi, 2021). Both papers explored the Canadian Perspective Survey Series (CPSS) — an initiative that began during the pandemic to improve data timeliness. It collected data on just over 32,000 Canadians every month. The p combined the larger sample of the CPSS crowdsourced survey with an online web-panel survey, a quarter of its size. Only provincial estimates could be provided due to the smaller sample size. The web-panel survey used a probability sample of randomly selected respondents aged 15 years and older from the Labour Force Survey (LFS). The probability sample applied sample weights from the LFS to a portion of the CPSS respondents, thus reducing bias in the crowdsourced data, with the caveat that the bias reduction depended on the variable of interest. T q used a basic area-level model to evaluate the 47 effectiveness of a crowdsourced survey to reduce the variance in web-panel estimates. It adopted a similar methodology to the LFS. The small area estimate is based on two quantities: the direct estimate from the survey data and a predication-based model, also known as a synthetic estimate. The results from the first round of modeling were successful for the domains of province, age group, and sex. For the other domains of interest, such as the Census Metropolitan Area (CMA)41, the results were unsatisfactory. The area-level model may have improved the precision of estimates, yet achieving a suitable model remains a challenge. A trative a re try ata Administrative and registry data are valuable for enhancing survey data and reducing response burden, although they are not typically used directly to measure subjective poverty. These data sources, including demographics, income, wealth, labor market participation, and education, can improve data quality through methods like weight calibration after sampling. For instance, a census dataset linked to administrative data like income or education allows statisticians to oversample low-income households, enhancing the accuracy of subjective poverty surveys. In countries with low response rates and biases in voluntary household surveys, calibrating survey weights based on factors such as income and demographics can help mitigate these biases, provided there is a strong correlation between these factors and the measure of subjective poverty under investigation. owever, one limitation of administrative data is its timeliness, as income data may not align with survey collection periods, necessitating the use of preceding years' data or preliminary income information. Case Study 5: Use of administrative data for sampling and calibration of EU-SILC at Statistics Denmark In Denmark, the EU-SILC42 survey serves as the primary source for data on subjective poverty, with a voluntary participation rate of 52% in 2022, leading to biased responses where low-income households participate less frequently. To address this bias, Statistics Denmark employs administrative registers extensively for both sampling and post-calibration of survey weights. Using an anonymized version of the Danish Central Personal identifiers (CPR), Statistics Denmark links surveys and administrative data, obtaining comprehensive information on both respondents and non-respondents. The Danish census is continually updated, providing an up- to-date sampling frame for EU-SILC. To ensure adequate coverage of less populated regions, 41 A Census Metropolitan Area (CMA) is formed by one or more adjacent municipalities centered on a population center (known as the core). A CMA must have a total population of at least 100,000, based on data from the current Census of Population Program, of which 50,000 or more must live in the core based on adjusted data from the previous Census of Population Program. Source: Dictionary, Census of Population, 2021 – Census metropolitan area (CMA) and census agglomeration (CA) (statcan.gc.ca) 42 Documentation of statistics: Survey on Living Conditions (SILC) - Statistics Denmark (dst.dk) https://www12.statcan.gc.ca/census-recensement/2021/ref/dict/az/Definition-eng.cfm?ID=geo009 https://www12.statcan.gc.ca/census-recensement/2021/ref/dict/az/Definition-eng.cfm?ID=geo009 https://www.dst.dk/en/Statistik/dokumentation/documentationofstatistics/survey-on-living-conditions--silc- 48 the EU-SILC sample is stratified regionally (NUTS-2) and incorporates preliminary income data to oversample households likely to have incomes below 60% of the median. Following data collection, the survey undergoes calibration using administrative data on age- groups, household size, income groups, and socio-economic status for the entire population, ensuring more accurate and representative results. This comprehensive approach leveraging administrative data helps mitigate bias and improve the quality of subjective poverty data in Denmark's EU-SILC survey. Note: 1. Eurostat is the statistical office of the European Union. Who we are - Eurostat (europa.eu). 2. Nomenclature of Territorial Units for Statistics (NUTS-2). Source o error: co cer w th re po e a repre e tative e This section delves into sources of error and precision requirements related to EU-SILC (European Union Statistics on Income and Living Conditions), emphasizing the importance of studying error sources and standardizing quality measures across EU countries. In 2021, new legislation brought changes to EU-SILC data collection43, including precision requirements at national and regional levels for poverty and social exclusion indicators. The legislation, Regulation (EU) 2019/170044, establishes standards for geographical coverage, sample characteristics, data gathering periods, and data processing, striving to align with the EU's regulations. The section identifies six measures of error: standard errors, coverage errors, measurement and processing errors, non-response errors (both unit and item), sampling error, and representativeness error. Standard errors gauge data reliability and were considered during EU-SILC's design to ensure an absolute precision of about one point for the at-risk-of-poverty rate. Coverage errors relate to imperfections in the sampling frame and are influenced by the use of population registers or census databases, necessitating frequent updates. Measurement and processing errors can arise from questionnaire design and data collection complexity, impacting data accuracy. Non-response errors, including unit and item non-response, are inevitable and can introduce bias, particularly if specific survey patterns emerge such as a particular question being skipped by a significant number of respondents. Corrective measures, such as post-stratification or logistic regression models, are employed to address non-response. Sampling error is recognized as a challenge when measuring subjective phenomena due to susceptibility to non- 43 Legislation - Income and living conditions - Eurostat (europa.eu) 44 Regulation (EU) 2019/1700 establishing a common framework for European statistics relating to persons and households (IESS regulation). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv:OJ.LI.2019.261.01.0001.01.ENG https://ec.europa.eu/eurostat/web/main/about-us/who-we-are https://ec.europa.eu/eurostat/web/main/about-us/who-we-are https://ec.europa.eu/eurostat/web/income-and-living-conditions/legislation 49 sampling error, such as changes in respondent mood or external factors affecting perceptions. Representativeness error, particularly in the context of crowdsourced surveys where population bias can occur, may lack control over sample representativeness, potentially leading to biased outcomes. Val ty a relatio h p to other ea ure o poverty a eco o c well-be This section offers guidance on validity and reliability, beginning by examining the advantages and disadvantages of subjective measures in comparison to alternative measures. It also complements the decision regarding data collection methods and question design by summarizing typical errors related to responsiveness and representativeness, regardless of the chosen approach. Quality reports and validating data The national quality reports for EU-SILC45 are specified in EU regulation 2019/1700 on European statistics relating to persons and households, and regulated by EU regulations 2019/2180 46and 2019/224247, delves into the importance of validating collected data and its relation to other reliable sources. It emphasizes the need for countries to submit quality reports to Eurostat, following specific regulations, to ensure data accuracy. These reports cover various aspects, including sample design, data collection procedures, measurement errors, and data comparability. Regarding subjective well-being (SWB) assessment, the EU-SILC reports reveal an alignment between respondents’ hypothetical scenarios and their anticipated SWB rankings. Factors influencing this alignment include a sense of purpose, perceived control over life, family happiness, and social status. The research draws upon data from diverse sources, with an 83% alignment rate between SWB and choices. owever, systematic differences in some instances warrant investigation. Figure 1. Criterion and Construct Validity 45 See Quality - Income and living conditions - Eurostat (europa.eu) 46 EUR-Lex - 32019R2180 - EN - EUR-Lex (europa.eu) 47 EUR-Lex - 32019R2242 - EN - EUR-Lex (europa.eu) https://eur-lex.europa.eu/legal-content/en/TXT/?uri=CELEX%3A32019R1700 https://ec.europa.eu/eurostat/web/income-and-living-conditions/quality https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=uriserv:OJ.L_.2019.330.01.0008.01.ENG&toc=OJ:L:2019:330:TOC https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1580904011697&uri=CELEX:32019R2242 50 Source: Bureau of Labour Statistics (?) Each measure identifies about 20% of the population as poor. 33% of the population with at least one indicator and only 5.7% as experiencing all three. Furthermore, the report underscores the challenges in assessing various dimensions of poverty and social exclusion. It highlights the lack of overlap among measures such as deprivation, subjective poverty, and income poverty. The study explores overlapping poverties and different permutations, concluding that multiple measures are essential for reliable results. arious factors contribute to the lack of overlap, including transition, differing perceptions of poverty, and technical considerations like housing costs and income distribution within households. Ultimately, using multiple measures can lead to more accurate and nuanced insights into poverty. Advantages of subjective poverty measures Subjective poverty measures offer several advantages, including their multidimensionality, as respondents can consider various factors such as income, costs, living conditions, and societal norms in their assessments. Unlike one-dimensional income-based measures, subjective approaches reflect what individuals consider necessary to avoid poverty and meet their family's needs, considering socio-psychological factors that influence well-being. Disadvantages of subjective poverty measures Subjective poverty measures, despite their value in reflecting people's perceptions of their circumstances, come with certain drawbacks. They rely on individual opinions to identify deprivations, which can vary significantly based on location, culture, aspirations, age, and other factors, making it challenging to define adequate needs universally. Subjective measures of welfare, while valuable, come with several challenges. One major concern is the potential for response errors, variations in interpreting survey questions, mood fluctuations, and differences in personality and tastes among respondents (Ravallion and Lokshin, 2002, p. 1471). People may have diverse ideas about what it means to be "poor" or 51 "rich," leading them to interpret subjective welfare questions differently (Ravallion, 2014, p. 182–183). This subjectivity can lead to frame of reference bias, where individuals in vulnerable positions may adapt their preferences to their circumstances, resulting in an underestimation of their actual hardship (Graham, 2010). Conversely, those with objectively comfortable lives may express dissatisfaction, causing lower subjective welfare ratings than those who are objectively worse off (Ravallion, 2014, p. 160). Another challenge is the variability in responses over time, with studies showing fluctuations in reported subjective well-being for the same individuals when interviewed at different times Ravallion (2014, p. 153). Additionally, the framing and context of questions can impact responses, whether through interviewer-administered surveys or self-administered ones (Conti and Pudney, 2011, p. 1093). These challenges emphasize the complexity and subjectivity inherent in measuring welfare and well-being, making it crucial to consider multiple factors and sources when assessing individuals' economic and overall well-being. Differences in personal opinion Subjective indicators pose challenges when the cutoffs are set relative to the sampled population. This can complicate the interpretation of poverty trends because changes in poverty may result from changes in either the indicator thresholds or the relative threshold's adjustment. For example, if the subjective poverty threshold is recalibrated with each new dataset according to the sampled population, it can impact the axiomatic properties of measures, potentially rendering some axioms inapplicable (Alkire and Foster 2011). Most multidimensional measures typically set indicator thresholds based on consistent international or national standards, adjusting them transparently every decade or so. These standards often incorporate expert opinions, participatory exercises, international regulations, and development targets. aving fixed and given indicator thresholds simplifies policy interpretation and allows policymakers to track progress and allocate resources effectively based on observed disparities in poverty levels (Alkire, anagaratnam and Suppa 2018). owever, changes in the population's frame of reference and aspirations could lead to shifts in subjective poverty thresholds, making it challenging to interpret objective improvements alongside measured decreases in subjective poverty. T e ra e or ata collectio a relea e Subjective poverty is influenced by various factors and can be either a lasting or temporary condition. Yafit Alfandari (2020), states that when measuring temporary subjective poverty, determining the appropriate time frame is crucial. A one-year time frame is recommended because it is less susceptible to temporary fluctuations caused by short-term circumstances. This period provides a robust assessment of subjective poverty. Moreover, subjective poverty indicators should not be considered in isolation but should be compared to indicators from different domains. Using a one-year time frame for data collection allows for insights into both the present scope and nature of the phenomenon and estimates of assistance required. Lifetime experience data, collected over the years, provides an overall picture of the total number of individuals affected by subjective poverty, offering a 52 comprehensive perspective. This approach is consistent with measuring other complex social phenomena like violence against women. Cro - ectio al ver u lo tu al ata collectio In marketing research, there has been increasing concern about the validity of cross-sectional surveys by editors, reviewers, and authors. These validity concerns center on reducing common method variance bias and enhancing causal inferences. Longitudinal data collection is commonly offered as a solution to these problems. A study by Rindfleisch et al. (2008) looked at the role of longitudinal surveys in addressing these concerns and provided a comparison of the validity of cross-sectional versus longitudinal surveys using two data sets and a Monte Carlo simulation by reducing the threat of common method variance bias and enhancing causal inference. Under certain conditions, cross-sectional data exhibit validity comparable to the results obtained from longitudinal data. Though longitudinal surveys offer advantages in terms of reducing these two validity threats, is appropriate when the temporal nature of the phenomena is clear and unlikely that intervening events could confound a follow- up study, or alternative explanations are likely, a cross-sectional approach may be more adequate for studies that examine concrete and externally oriented constructs, sample highly educated respondents, employ diverse measurement scales, and are strongly rooted in theory (Rindfleisch et al. 2008). Marketing researchers recommended using longitudinal analysis and multilevel modeling to minimize the random measurement error and common method bias by measuring the study variables at multiple time points. A study by Shashanka et al. (2021) adopted the multilevel structural equation modeling (ML-SEM) to analyze the longitudinal data of the factors influencing the shoppers' Impulse purchase behavior (IPB). Structural equation modeling (SEM) was conducted to examine changes in the causal effects at each time point of data collection. The results of ML-SEM indicate significant fluctuations in the factors influencing IPB over time. Results from the SEM indicated that few factors (like store ambience and salesperson interactions) have had a significant influence on IPB initially, during the first store visits of shoppers, but lost significance over time. The findings suggest that the store crowd, secondary customers influence, and in-store promotions show a significant influence on the IPB. Therefore, the study results of both longitudinal and cross-sectional modeling of the research model at five-time points indicated that the model validity is not significant over a period. This study enhances the statistical validity of the research model by analyzing the fluctuations in the research model over a period of time (Shashanka et al., 2021). OECD ubjective well-be u el e The OECD Guidelines for Micro Statistics on ousehold Wealth publication introduces a set of internationally agreed guidelines for producing micro-level statistics on household wealth, addressing a crucial gap in existing global guidance for measuring different aspects of individuals' economic well-being. These guidelines aim to resolve common conceptual, definitional, and practical challenges that nations encounter when generating such statistics and to enhance the comparability of country-specific data. They are essential for integrating micro-level data on household wealth with information on other dimensions of economic well- being, such as income and consumption. Understanding the composition and distribution of 53 household wealth at the micro-level is valuable for policymakers as it provides insights into various aspects, including debt distribution, homeownership drivers, liquidity constraints, and the impact of economic shocks on wealth and indebtedness. To meet the increasing demand for micro-level wealth statistics and integrated economic well- being data, the OECD Committee on Statistics established an Expert Group in 201048. This group was tasked with developing guidelines for collecting and presenting household wealth statistics, resulting in a comprehensive report (2013). These guidelines complement the Framework for Statistics on the Distribution of ousehold Income, Consumption, and Wealth. While macro-level statistics are already well-established, focusing on economy-wide performance and institutional sectors, micro-level wealth statistics delve into the ownership and distribution of wealth among individual households, necessitating some conceptual and practical distinctions. These guidelines help address these differences and provide recommendations for conducting wealth surveys and addressing challenges in measuring asset and liability components. They emphasize the importance of a life-cycle perspective when analyzing wealth data, as wealth accumulation and usage vary across different life stages. The report also underscores the need for periodic reviews and refinement of these guidelines to stay aligned with evolving measurement methodologies and analytical requirements, encouraging countries to test and adapt them according to their specific contexts. Income, consumption, and wealth are three distinct dimensions of economic well-being, and this framework describes their central concepts, relationships, and additional elements that together form a self-contained system for assessing household economic well-being. The OECD framework49 recognizes that higher levels of income and wealth can contribute to higher economic well-being by enabling greater consumption and saving for future consumption. It also considers capital transfers, in-kind income, and expenditure payments as key elements in understanding household economic resources and transactions. While households are the primary unit for analysis, the report recommends reporting both household- and person-weighted statistics to provide a comprehensive view of economic well-being, considering factors like economies of scale in larger households. It suggests a one-year reference period for implementing the framework and discusses practical data collection methods, including the use of surveys, administrative sources, and statistical matching. Additionally, the report highlights tools for presenting and analyzing information on household economic well-being and suggests ongoing testing and refinement of the framework to adapt to evolving practices and emerging research needs. Hypothetical a e e t o ubjective poverty The following section focuses on hypothetical questions to assess subjective poverty. Researchers often employ hypothetical questions to ask respondents to consider the basic needs of a reference or hypothetical family, such as what would be required for a family of two adults and two children to make ends meet or not be considered poor. This approach allows researchers to maintain control over the survey context and reduces concerns about 48 OECD Guidelines for Micro Statistics on Household Wealth \| OECD iLibrary (oecd-ilibrary.org) 49 OECD Framework for Statistics on the Distribution of Household Income, Consumption and Wealth \| OECD iLibrary (oecd-ilibrary.org) https://www.oecd-ilibrary.org/economics/guidelines-for-micro-statistics-on-household-wealth_9789264194878-en https://www.oecd-ilibrary.org/economics/framework-for-statistics-on-the-distribution-of-household-income-consumption-and-wealth_9789264194830-en https://www.oecd-ilibrary.org/economics/framework-for-statistics-on-the-distribution-of-household-income-consumption-and-wealth_9789264194830-en 54 respondents' current situations. What the role o que tio wor ? The role of question wording and survey design in subjective questions is critical, impacting the data collected. Research suggests that respondents often prefer precise, straightforward language and questions categorized by components (e.g., shelter, transportation, food) (Morrissette and Poulin, 1991). While considering respondents' preferences can reduce response burden, it remains uncertain whether this enhances data accuracy due to the lack of consistent measures of external validity for subjective questions. Notable studies, such as Andrews and Withey's (1976) quality-of-life surveys, have explored effective scales like delighted/terrible (D/T) for measuring income-related feelings. apteyn et al. (1979) focused on income equation questions (IEQ) and D/T scales for assessing an individual's welfare function of income (WFI), with a preference for annual income reporting. Antonides et al. (1968) examined ten alternative methods for measuring welfare functions, emphasizing the need for further research. Garner's work (1991) compared data between the United States and the Netherlands, highlighting variations in responses attributed to question wording, survey design, and data collection instruments. These studies underscore the significance of question formulation and survey design in subjective data collection but also highlight the complexities in achieving consistency across responses. Statistics Canada A study conducted at Statistics Canada by Morrissette and Poulin (1991) found, using an Income Satisfaction Survey (IS), that question wording had a significant impact on the average minimum income reported by respondents. Using more restrictive language reduced the average minimum income by between 12% to 32% based on the 1987 and 1988 survey questions. The 1987 IS was split into two sample groups, each being asked a variation of the minimum income question, with the notable difference of using ‘considered necessary’ in one and ‘absolutely necessary’ in the second. The more restrictive language found in Figure 2 ersion 2 led to a 12% decrease in the amount of income reported. Figure 2 – More restrictive language lowers reported minimum income Version 1 (1987) To meet the expenses you consider necessary, what do you think is the minimum income a family like yours needs, on a yearly basis, to make ends meet (if you are not living with relatives, what are the minimum income needs of an individual like you)? Version 2 (1987) What do you think is the smallest yearly income a family the size of yours would need to meet absolutely necessary expenses (if you are not living with relatives, what is the smallest yearly income an individual like you would need?). 55 Source: Morrissette and Poulin (1991) As in the 1987 IS survey, the 1988 IS survey had two subsamples. It found an even larger impact due to question wording. Compared with using ‘consider necessary’ language and an additional qualifier of ‘before tax’ income, the more restrictive language referring to ‘basic needs’ in Figure 3 ersion 2 reduced respondents’ minimum income by 32%. Figure 3 – ‘Before tax’ in the question has a large impact on income reported Source: Morrissette and Poulin (1991) It is important to note that these surveys also contained unchanged questions, which helped ensure that the distributions of average minimum incomes were relatively stable over time. The data obtained from the original unchanged questions for 1983, 1986, and 1987 confirmed this (Morissette, 1991). It emphasizes the importance of consistency with question wording over time. Other examples, such as the General Social Survey (GSS) ran extensive cognitive testing on the new concepts of criminal victimization were to better understand the ways in which sensitive survey topics such as family violence required greater security. While it was determined that cognitive tests were needed to study sensitive topics, researchers started to run cognitive tests to evaluate subjective poverty question. Cognitive tests Bureau of Labor Statistics Stinson (1997 and 1998) ran a series of cognitive tests to evaluate the effectiveness of various subjective poverty questions and alternative approaches to asking questions. The questions that were tested in 1996 included the Minimum Income Question (MIQ), Minimum Satisfaction Question (MSQ), Income Evaluation Question (IEQ), and Delighted/Terrible (D/T) 7-points scales ranging from a deep frown to a broad smile. The 1997 cognitive test looked at alternative measures to test respondents’ feelings about the questions by using images such as faces, feeling thermometers, D/T, circles, economic attitudes, income balance, Version 2 (1988) In your opinion, how much do you have to spend each year in order to provide the basic needs for your family? By basic needs I mean barely adequate food, shelter, clothing and other essential items required for daily living. Version 1 (1988) To meet the expenses you consider necessary, what do you think is the minimum income, before tax, a family like yours needs, on a yearly basis, to make ends meet (if you are not living with relatives, what are the minimum needs, before tax, of an individual like you)? 56 and positive and negative lines scales50. Both tests revealed important lessons for subjective poverty questions, as demonstrated below in waves 1 and 2. Wave 1 findings showed that questions about feelings towards income and expenses were informative but complex and burdensome, with hidden internal questions increasing respondent burden. Language framing and response categories were also ambiguous, suggesting the need for clearer language to enhance response precision. In Wave 2, cognitive testing introduced new question wording and formats. Respondents preferred a segmented MIQ question, breaking it down into food, shelter, clothing, utilities, and work expenses, making it simpler and easier to understand. About 67% of respondents favored a shorter IEQ version. These findings emphasized the importance of question format in consistency of responses and revealed some inconsistencies between feelings expressed and objective assessments. Overall, respondents preferred simple, traditional survey question wording. Fra a o e effect Research has emphasized the significance of frame and mode effects in survey design and delivery, particularly when examining subjective phenomena. Frame effects, influenced by the survey's content or theme, have been observed to impact responses to subjective indicators. A study comparing the General Social Survey (GSS) and the Canadian Community ealth Survey (CC S) revealed that the GSS's changing theme led to variations in life satisfaction responses, mainly due to framing effects (Waverock et al., 2023). These effects were responsible for substantial year-over-year fluctuations in average self-reported life satisfaction. Mode effects, on the other hand, are influenced by the method of data collection, such as interviews, online surveys, or paper questionnaires. These effects have been found to create differences in self-reported life satisfaction, particularly across various socio-demographic backgrounds. Furthermore, the design and content of welcome screens in online surveys play a critical role in influencing response rates. Factors like the stated survey duration and the emphasis on explaining privacy rights on the welcome screen significantly impact participants' decisions to engage in web surveys. Both effects have the possibility of influencing a respondent, but the potential impact is greater for subjective questions. Individuals’ responses can be ‘primed’ by preceding questions. The mode effects respondents experience, leading to a social desirability bias (Atkeson, Adams and Alvarez 2014; Tourangeau and Yan 2007) by responding differently if they believe they will 50 Face: When used by Andrews and Withey, the faces formed a seven (7)-point scale ranging from a deep frown to a broad smile. In Stinson 1998, test was restricted the scale to five (5) faces. The “Feeling Thermometer” is a graphic device printed on a card that looks like a thermometer. It is, in fact, a nine (9) point scale ranging from 0 degrees (very cold or unfavorable feeling) up to 100 degrees (very warm or favorable feeling). The Delighted/Terrible (DT) Scale is a 7-point scale with a “mixed” category as the midpoint. In a previous test of this question, we found subjects generally unwilling to endorse extreme category as an expression of their feelings about their income. The Circles Scale is a series of seven circles that have each been divided into six segments. At the lowest end of the range, the six segments have all been labeled with minus signs; at the highest end of the range, there are plus signs placed within each segment. Of all the question formats that were tested, this series of five short-answer questions (dubbed as “economic attitude” questions), was the only section universally approved and applauded by all respondents. The Income Balance was single short-answer question asking respondents to compare the amounts of the income and expenses. The Line was a simple flat line with one end point labeled with a “+” and the other end point labeled with a “-.” In-between the poles were three equally spaced vertical marks. Respondents were instructed to place their feelings about their total family income at the appropriate place along the line. 57 be viewed negatively by the interviewer, resulting in differences depending on the method of data collection. Measurement errors in surveys like EU-SILC can stem from various sources, including the questionnaire, interview process, respondent, and data collection methods. To ensure data accuracy, it's crucial to construct questionnaires that facilitate accurate and efficient responses. This involves drawing insights from pilot surveys and past EU-SILC waves to identify and address potential issues. Pre-testing questionnaires helps anticipate problems and enhance the data collection process. Subjective poverty a the evolutio o ea ure Subjective poverty is a concept rooted in individuals' personal perceptions and assessments of their economic well-being, influenced by factors like income, personality, and societal perspectives. Unlike objective measures, which rely on externally set thresholds, subjective measures assess poverty based on personal evaluations and can encompass both monetary and non-monetary aspects. Monetary measures often center on respondents' perceptions of the income required for financial security, while non-monetary measures assess aspects like the ability to make ends meet or afford specific items. Subjective poverty can also be viewed through the lens of scarcity theory, which sees poverty as the gap between one's needs and available resources. Subjective income expectations play a significant role in this context, shaping how individuals perceive their welfare levels and make decisions regarding consumption and savings. While subjective and objective poverty assessments are related, they are often treated separately, with comprehensive measures considering both. This recommendation comes from the Stiglitz et al. report (2009) and has manifested in initiatives like the OECD Better Life Index (2023), which encompasses objective and subjective measures. This section explores various perspectives on developing subjective poverty measures, including consensual methods51 that define minimum needs or standards through responses about hypothetical situations and methods based on respondents' assessments of their own family or situation, which are more commonly used and theoretically grounded. These approaches aim to provide a holistic understanding of subjective poverty, offering valuable insights for policy development beyond income considerations. Case Study 5: Subjective assessments versus objective measures of poverty – discussion of the definitions of selected poverty measures based on the Polish edition of the EU-SILC survey Anna Bieńkuńska, Tomasz Piasecki Measuring poverty is essential for social policy planning and evaluation, but it is a complex concept with multiple definitions and measurement approaches, including objective and subjective ones. Subjective assessments complement objective measures, offering a different 51 Van den Bosch, 2001, p. xvi. 58 perspective on poverty and enabling a more comprehensive diagnosis of the phenomenon. These assessments can also verify and discuss the definitions of objective measures. An analysis based on 2019 micro-data from the Polish edition of the European Survey on Income and Living Conditions (EU-SILC) examines the relationship between objective poverty assessments and respondents' subjective evaluations of their material situation. It compares various objective poverty measures and demonstrates how subjective assessments can verify and interpret objective measures, including the discussion of poverty thresholds. The EU-SILC survey does not directly measure subjective poverty but provides variables for indirect methods of measurement. This analysis focuses on indirect methods and uses a question about the ability to make ends meet to calculate an indicator of subjective economic stress, serving as an indirect measure of subjective poverty. The indicator represents the percentage of people in households struggling to make ends meet. Additionally, the study considers both commonly used poverty measures like the 'at-risk-of-poverty rate' (AROP) and the 'severe material and social deprivation rate' (SMSD)52 for international comparisons and more specific indicators related to income poverty and deprivation. Figure 3. ‘False poverty’ rate by poverty threshold (restrictiveness of the poverty definition) – theoretical model 52 See Glossary: Severe material and social deprivation rate (SMSD) - Statistics Explained (europa.eu) ←extreme poverty poverty threshold moderate poverty→ https://ec.europa.eu/eurostat/statistics-explained/index.php?curid=99141&oldid=534257 59 Figure 4. ‘Undetected poverty’ rate by poverty threshold (restrictiveness of the poverty definition) – theoretical model Figures 3 and 4 illustrate the expected relationship between the restrictiveness of the poverty threshold and various poverty indicators. A more restrictive threshold indicates extreme poverty, suggesting that those considered poor under such conditions should have worse living conditions on average, making it less likely for people with positive assessments of their material situation to be classified as poor ('false poverty'). Conversely, less restrictive poverty thresholds may lead to more frequent cases of 'false poverty' among those experiencing less acute poverty. Additionally, cases where individuals with a negative assessment of their situation are not considered poor ('undetected poverty') are more likely with restrictive thresholds. As the threshold becomes less restrictive, the incidence of 'undetected poverty' should decrease. Any decrease in threshold restrictiveness accompanied by changes in the false poverty or undetected poverty rates would raise doubts about the relationship between the chosen poverty measure and economic hardship, potentially questioning the validity of the measure itself. ←extreme poverty poverty threshold moderate poverty→ 60 Figure 5. ‘False poverty’, ‘undetected poverty’ and overall misclassification – shares in the whole population (theoretical model) The relationship between 'false poverty' and 'undetected poverty' and the restrictiveness of the poverty threshold should follow the same pattern for the total population, leading to an overall misclassification. This overall misclassification reaches a minimum at a certain threshold value. This suggests that there exists an optimal threshold value for the objective poverty measurement method analyzed, where the classification of people into poor and non-poor aligns most closely with subjective assessments. This approach allows for the evaluation of poverty threshold values in terms of optimality and facilitates comparisons between various poverty measurement methods that use threshold values as parameters set at different levels. This in-depth analysis delves into the relationship between various objective poverty measures and individuals' subjective assessments of their economic well-being. It aims to understand the extent to which these different measurements align and examines the impact of poverty thresholds on these alignments. One key finding of the study is that the severe material and social deprivation indicator (SMSD) exhibits the highest consistency with subjective assessments among the objective poverty measures considered. In this regard, individuals classified as experiencing deprivation according to SMSD criteria tend to report greater economic stress and difficulties making ends meet. This suggests that SMSD effectively captures non-monetary aspects of poverty, providing a more comprehensive view of individuals' material conditions. Conversely, the study highlights some anomalies when considering extremely low-income thresholds to define poverty. Surprisingly, among those classified as extremely poor based on income criteria, a significant proportion still reports making ends meet easily or fairly easily. This raises questions about the accuracy of identifying extreme poverty solely through income-based measures, indicating that additional factors may influence individuals' perceptions of their material situation. ←extreme poverty poverty threshold moderate poverty→ ‘undetected poverty’ ‘false poverty’ ‘optimal’ threshold overall misclassification 61 The analysis emphasizes the complexity of poverty as a multifaceted phenomenon and underscores the importance of using a combination of both objective and subjective measures to comprehensively assess it. It argues that subjective assessments should complement objective measures, as they offer unique insights into individuals' experiences of poverty. owever, the study also highlights the need for clear communication about the strengths and limitations of each measure to avoid misinterpretation and ensure that policymakers and the public have a nuanced understanding of poverty. What the role o e u a e o e’ ubjective poverty po tio ? A decent lifestyle in socio-economical terms is the quality, quantity, and price of the goods and services required for a decent life, which should be sufficient to meet one's physiological, psychological and social needs and enable full participation in society. It comprises goods and services needed in everyday life so that people can ‘get by’ and their life goes smoothly while feeling oneself as part of the surrounding society. A decent minimum describes a consumption level that is necessary for all members of society in order to live a decent life but excludes commodities that are not necessary. A decent lifestyle necessary for preventing poverty is often defined in relation to the average consumption level without paying attention to the fact that the present average consumption in western welfare states is ecologically unsustainable (Lettenmeier et al 2014). An approach to defining minimums is a basic need one—having less than objectively defined. This method defines the absolute minimum in terms of “basic needs,” such as food, clothing, and housing. It requires the assessment of a minimum amount necessary to meet each of these needs. These amounts are added up to arrive at a poverty line in terms of income. In the Netherlands, budget experts from the Social Services Administration in Leeuwarden have calculated a poverty line based on this approach. The poverty line, while somewhat arbitrary, is differentiated according to household composition ( agenaars, A., & de os 1988). A simpler approach is defining the subjective minimum income, which is based on a survey question used to observe the income level that people consider to be ‘‘just sufficient” for their household. If their actual income level is less than the amount they consider to be ‘‘just sufficient,” they are considered poor. Comparison with the actual household income puts the household in the category poor or non-poor. This subjective poverty definition is based on the assumption that the expressions “sufficient” and “insufficient” are associated with the same welfare levels by everybody ( agenaars, A., & de os 1988). A third approach is the subjective minimum consumption definition which reconciles the subjective poverty and the basic needs definitions. Essentially it asks people what they consider to be basic needs and to specify how much they need to meet these necessities. The amount people consider to be minimally necessary for food is compared to the actual amount spent on food to the subjective minimum used to categorize the household as poor or non-poor ( agenaars, A., & de os 1988). In the Finnish welfare state, the minimum level of social benefit should guarantee a decent and 62 dignified lifestyle. People living on minimum income ought to have not only sufficient means for fulfilling basic needs (such as having a shelter or adequate nutrition) but also means for participation (such as having a phone, recreational activities and other forms of social participation). Thus, in Finland, reference budgets were compiled by using consumer panels to define which products and services are regarded necessary and parts of a decent lifestyle. The budget contains: food, clothing and footwear, household appliances, entertainment electronics, information and communication technology, health and personal care, leisure, participation, transport, and housing. The material footprint, measured by total material consumption which is based on the material requirement of an economy minus the export-based resource use, for a decent minimum based on the reference budget is approximately 20 tons per year. The households studied show that in the present Finnish society people living on minimum income is roughly between 15-20 tons per person per year. This affords them decent housing, adequate nutrition, means for participation and possibilities for recreational activities as well as some basic services. Below this amount, deprivation such as, homelessness or eating only leftover food would occur (Lettenmeier et al 2014). The rate of success of a reference budget depends on its accuracy in identifying the essential products, consumption quantities, prices, and the life span. The reference budgets should enable consumption that meets a decent minimum standard of living and allows participation in society, in the form of decent clothing, proper nutrition and eating out, and the opportunity to obtain and transmit information, based on today’s society. To determine quantities of products used, statistics, calculations, and the Finnish ousehold Budget Survey were used. Evaluation of the quantities and life spans of commodities was extracted from group discussion participants. The price and quality level chosen is the average, and items are expected to last a reasonable time. Low-quality or cheap products were not included in the study. Price information is available on the Internet, and price levels of food items and the differences in prices between various trade groups in different parts of Finland were gathered from a food price survey of the National Consumer Research Centre (Lehtinen et al 2011). What the role o eo raph c ffere ce pr ce ? While geographic differences in the cost of living are part of popular discourse, assessing these differences faces both data availability and conceptual challenges. Despite the obvious large gaps in prices that prevail in different areas, most studies take no account of geographic price differences or attempt to control for them (Carrillo et al. 2016). Since 1968, the Council for Community and Economic Research has produced the American Chamber of Commerce Researchers Association (ACCRA) price indices for six broad categories of goods and an overall consumer price index for many urban areas (Carrillo et al. 2016). One study attempted to construct an interarea housing price index for each metropolitan area and the non- metropolitan part of each state in 2000. It was based on a large data set with detailed information about the characteristics of dwelling units and their neighborhoods. For most areas, the price index for all goods—other than housing—is calculated from the ACCRA price indices, using a regression model explaining differences in the composite price index for non- housing goods for the areas where it is available, and used to predict a price of other goods for the uncovered areas. The price indices for housing services and other goods were combined with data from the Consumer Expenditure Survey to produce an overall consumer price index for all areas of the United States. The fit of the hedonic equation used to estimate price indexes 63 were consistent with popular views about differences in housing prices. The resulting overall consumer price index is not sensitive to the expenditure weights used and it differs little from a simple ideal consumer price index that accounts for how individuals alter their consumption in response to changes in relative prices (Carrillo et al. 2016). Since there is no national database that includes rural areas to assess the perception of these regions having lower prices, it may lead researchers to a faulty conclusion. Adjusting the poverty threshold for differences in the ‘cost of living’ based on perceptions of lower cost in rural areas superficially reduces poverty rates for rural areas, lowering federal funding and placing rural low-income families at greater risk. Rural residents commonly face higher prices for food and electricity than their urban counterparts due to the higher operating costs. Differences in the material conditions of rural living also lead to additional costs not typically found in urban areas. While interarea price comparisons assume that the material conditions of living are the same, Zimmerman et al. (2008) looked at the differences in rural versus urban living. They found that there were additional costs incurred for residents in the rural counties. For instance, in all eight U.S. rural counties studied, extended area phone service would have doubled the cost of having a phone compared to that in the urban areas. There were costs that price comparisons alone did not capture. In some cases, going to the grocery store to buy food meant on average driving 30 miles round trip. This would add additional cost to the price of the food purchased in order to cover transportation. Some median household income levels might be artificially inflated due to only parts of a rural area being more prosperous. For example, counties not part of a micropolitan area, yet adjacent to an interstate, may have a median household income level similar to the state as a whole, therefore increasing their home prices. owever, the higher income level may be influenced by a small area that in one case was dominated by high-income lake-based tourism with luxury boats and second homes, while the bulk of the county is sparsely populated with a limited number of businesses. Without a better understanding of the material conditions of rural life and local research there is a risk of exacerbating place-based inequities (Zimmerman et al 2008). Another study by Yilmazkuday (2017) focused on the determinants of the expected number of consumers searching for gas prices before making a purchase across zip codes. It was based on geographic, demographic and economic characteristics. Per the maximum likelihood estimation of a consumer search model, they recovered the distribution of search costs for each zip code in the U.S. by considering the gasoline purchasing behaviour of consumers. Consumers in zip codes suffering from poverty search for more gas stations before purchasing gasoline, while consumers at or above 150% of the poverty level do not search more than other consumers. Consumers double their expected number of stations searched when the average distance goes up, when the zip code area is tripled in size, and when the population density goes. Gasoline price spreads are higher in zip codes with spatially dispersed gas stations. Consumers would halve their expected number of searches when their income is quadrupled. This is obviously due to the opportunity cost of searching for lower gasoline prices where higher income consumers do not find it profitable enough to do so. The expected number of stations searched is halved when commuting time is quadrupled (Yilmazkuday 2017). 64 What the role o hou ehol co po tio a a u ptio re ar har ? The role of sharing was found to have an impact depending on the type of household composition. Based on the 2010 Luxembourg Income study data by Tai (2017), research examines cross-national patterns of rates of youth poverty using household composition. The increase in poverty following young adults' leaving the parental home indicates not only the tremendous impact of household composition, but also the marginalization of young adults in welfare states due to prolonged education and postponed entry into the labor market and marriage. School-leavers, first-time job seekers, and young adults cycling between education and work may cease to be eligible for unemployment benefits or social assistance. Thus, young adults are likely to meet economic needs by living with their parents, pooling their household income, and sharing living expenses. The prevalence of co-residence with parents is critical for the economic well-being of East Asian and Southern European young adults. If Taiwanese young adults had the same living arrangements as young adults in Scandinavian countries, the poverty level of Taiwanese young adults would increase by 5 to 9 percentage points. With 62% of respondents residing with their coupled parents, the household composition of Taiwan seems to be the most economically beneficial for young adults. In addition, many young people live in households with their grandparents, other relatives, or non-family household members. Young adults living with coupled parents or with their spouse are less likely to be poor. Scandinavian single parents are actually better off than single young adults without children due to Nordic welfare regimes providing generous social provisions for families with children. Single mothers are most vulnerable, with poverty rates ranging from 13.5% for Japan to 94.5% for Germany (Tai 2017). Snyder et al. (2006) looked at race and residential variation in the prevalence of female-headed households with children and how household composition is associated with several key economic well-being outcomes using data from the 2000 U.S. Census. ousehold poverty is highest for female-headed households with children that do not have other adult household earners. Earned income from other household members lifts many cohabiting and grandparental female-headed households out of poverty, as does retirement and Social Security income for grandmother headed households. Poverty was found to be at its highest among racial/ethnic minorities and for female-headed households with children in non- metropolitan areas compared to central cities and suburban areas. The presence of other earners in non-metro female-headed households with children is an important income source that lifts many out of poverty. The economic benefits of other household earners are important for white cohabiting households, and for black and ispanic grandmother-headed households. When the effect of another earner is added in the model, cohabiting female-headed households with children remain significantly less likely to be poor compared to single mother only families, indicating that this factor accounts for some of the association between household composition and household poverty. It was also found that an additional 100 hours worked by the household head in the prior year translates into a reduction in the odds of poverty by 14%. The earnings of a male partner are especially important for non-metro female-headed cohabiting households with children as it cuts poverty in half for these households for all ethnic groups considered. The presence of additional earners in the household is associated with a significant reduction in household poverty. This confirms the need to evaluate household composition, as it is an important determinant of household poverty due to the 65 economic resources that are available to specific household living arrangements (Snyder et al 2006). Tai (2009) reviewed data on individuals in households with older adults for 22 countries in the Luxembourg Income Survey. It looked at the risk of poverty to the type of state welfare regime and comparing it to the situation in Taiwan; the characteristics of the household head, number of earners, older adults, and children. It finds that persons in households with older adults are significantly less likely to be poor in countries with social democratic welfare regimes than in Taiwan, where there are limited social welfare programs. Living with fewer children, more older adults, and more earners lowers the risk of poverty, as does having a married and better educated household head. For persons residing in a household with an older adult, having a single man or a woman rather than a couple heading the household is linked to a greater likelihood of poverty. In households with more earners, people are less likely to be poor if only because stronger ties to the labor market bring greater income. An additional older adult in the household is associated with lower risks of being poor if only they are eligible for old- age benefits. The risk of poverty and the likelihood of older people living with others are more common where state provisions for dependents and families are limited. Family co-residence and welfare state provisions are alternative strategies that help older adults and their kin to cope when their market income shortfalls. Given the values of societies placed on families such as those in southern Europe and East Asia, it is not surprising that state welfare programs have been slow to develop in these regions, which is the opposite of what is observed in generous welfare such as Nordic countries (Tai 2009). What the role o Soc al Tra er K (STIK)? According to research conducted by Eurostat, social transfers in kind (STi s) 53are significant contributors to household income, particularly for those with lower incomes. These transfers, provided by governments or non-profit organizations, encompass various services and support for needs such as education, health, childcare, and long-term care. The analysis conducted by Alaminos and Geske specifically focuses on health related STi s received by households from governments. Understanding the impact of these social transfers is crucial for assessing material well-being, especially in Europe, both before and during economic crises. ousehold disposable income represents the income available to a household after taxes and can be spent or saved. It comprises both monetary and non-monetary components. Traditional monetary income indicators, derived from disposable income, are frequently used to analyze poverty and inequality. People are considered at risk of monetary poverty when their equivalized disposable income falls below the at-risk-of-poverty threshold, typically set at 60% of the national median disposable income after social transfers. owever, these indicators do not account for non-monetary income. Adjusted disposable income, which includes both monetary income and Social Transfers in ind (STi s), provides a more equitable measure of income distribution. International statistical guidelines recommend using adjusted disposable income to analyze the total redistributive impact of government interventions in the form of benefits and taxes on household income. 53 Impact of health social transfers in kind on income distribution and inequality - Statistics Explained (europa.eu) https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Impact_of_health_social_transfers_in_kind_on_income_distribution_and_inequality 66 Non-monetary indicators complement traditional monetary measures and help explore aspects of inequality not covered by monetary indicators. In Eurostat's analysis, the EU-SILC survey microdata on disposable income is augmented by imputing health-related STi s to calculate health STi adjusted disposable income. These health related STi s align with government health expenditure profiles by age and gender, as reported in the National Accounts. The study examines the impact of health related STi s on income distribution and inequality measures like the Gini index. The findings demonstrate that health STi s contribute to a more equitable distribution of household income across income quintiles, reducing income shares in the highest quintiles and increasing them in the lowest. Without these health related STi s, income inequality would significantly worsen, especially for those needing to cover primary health expenditures from their own pockets. What the role o hou wealth a pute re t? Non-financial assets such as the principal residence represent the largest component of wealth for most households. Per Maestri (2015), imputed rent for owner-occupied accommodation is the most important form of non-cash income advantage. The difficult perception of this economic advantage is due to the dual nature of housing, representing at the same time consumption and investment. Living in social housing is another form of housing advantage. The rental equivalence approach consists of estimating the market rent that homeowners or below-market rate tenants should pay if they had to rent their places at full price. For homeowners, the capital market approach can be applied, which is the imputed rent that can be estimated as the rent that they would pay if the house were rented (net of costs such as mortgage interests). For tenants in social housing or under rent control, imputed rent is estimated as the difference between market and paid rent. The inclusion of tenants with below- market rent reduces relative poverty and inequality. On the other hand, the inclusion of homeowners only as beneficiaries of imputed rent leads to inequality and relative poverty tends to increase. If market rent is imputed for tenants with below-market rent as well, inequality and relative poverty decrease (Maestri 2015). There are three ways of estimating imputed rents. First is the rental equivalence approach, which calculates the value of housing from equivalent units in the private rental market. Rents are estimated per square metre and housing costs deducted and compared to owner-occupied housing to arrive at a market value. This method finds that imputed rents reduce income inequality as the distribution of imputed rents, while right skewed, is less unequal than the distribution of other income (Maestri 2015). The second estimation method is the capital market approach, which sees housing as capital income from an investment and assumes a return on its value in housing. Using the capital market approach reduces the dampening effect of imputed rent on income inequality. The third method is the self-assessment method, which uses subjective estimates provided by the owners on rent from their housing to measure the opportunity cost of renting out owner- occupied housing and is then used as a proxy for rent. This method leads to the smallest reduction in inequality (Maestri 2015). Using the 2010 EU-SILC data to provide an assessment of the impact of the housing situation 67 of households shows that relative income poverty and inequality decrease if imputed rent is taken into account, while they increase if housing expenses are considered. Therefore, the deduction of housing expenses provides a better measure of relative poverty. To add imputed rent, it can be estimated from rental equivalence and capital market methods. To deduct housing expenses from disposable income, it can be obtained from the out-of-pocket approach. The comparison of disposable income plus imputed rent, minus housing expenses and perception of housing costs provides useful hints on the distributional effects of housing in different housing systems and sheds some light on their possible future developments (Maestri 2015). In another study, the ousehold Finance and Consumption Survey ( FCS) conducted by the European System of Central Banks was used to estimate non-cash income from owner- occupied housing, subsidised rental housing, and free use of the main residence in Austria. The FCS provides detailed information on mortgages, debt of renters in cooperative housing and subjective information provided by interviewers on the dwellings and building quality. It enabled the evaluation of the impact of non-cash income from housing on the full unconditional household income distribution. Imputed rents have an equalising effect on the distribution of income, and we find similar evidence for non-cash income from subsidised rents. owever, imputed rents from owner-occupied housing equalise the upper part of the income distribution, and subsidised housing has an (albeit smaller) equalising effect for the lower part of the income distribution (Fessler et al 2016). What the role o ffere ce “culture” a rel o ? A study by Yurdakul (2016) on the role of religion discusses how religion may alter beliefs about the causes of poverty, helping the poor with coping mechanisms. These beliefs are classified as individualistic (poverty is related to the lack of ability or effort), structural (causes of poverty are the economic and social systems), and fatalistic (poverty is not caused by the individual or the system, but by forces such as chance, luck, and fate). Fatalistic beliefs in this case are closely related to religion. The discourses of informants from a Turkish panel reveal that religion helps them in resolving the tensions between reality (their poverty) and desire (especially the desire to consume). Religious beliefs can contribute to the different stances low-income consumers take towards their poverty, affecting the level of internalization and resistance to the poverty stigma, and how people respond to the marketing institution. When resistance is directed toward the desire to consume, arguments are often fueled by religious beliefs. The effects of religious beliefs differ when used for resistance versus non-resistance strategies stemming from different interpretations of Islam. Whereas resistant informants emphasize religious ethics regarding worldly issues, such as greed, sin, improperness of desire, non-resistant informants emphasize self-blame, fatalism, and the afterlife. Yurdakul’s findings indicate the empowering aspect of religious arguments in providing low- income consumers with the strength to cope by resisting consumer culture and re-creating meaning beyond consumption. Informants further disclose a form of subtle resistance when they intentionally stay away from consuming beyond the basic necessities for survival. Non- resistant informants, especially in the cases of fatalism and belief in the afterlife, disclose that internalized poverty stigma leads to negative feelings and contributes to perceived vulnerability. Religiosity is more prominent among non-resisters who are more fatalistic in 68 their beliefs. Participants with a more critical stance are more active in their efforts to improve their current situation, such as taking an active role in the workers’ unions, trying to break up the vicious cycle of persistent poverty, or engaging in subtle forms of resistance such as non- consumption (Yurdakul 2016). A study by Atkin (2016) on India’s National Sample Survey of 1983 and 1987–1988 asked households about their consumption of a broad set of foods as well as about their migration particulars to look at the relation between culture and deprivation. The surveys record household expenditures and quantities for each food item consumed in the last 30 days. The surveys also provided information on expenditures on non-food items as well as household demographics and characteristics. The findings suggest that interstate migrants consume fewer calories per rupee of food expenditure compared to their non-migrant neighbours, even for households on the edge of malnutrition. Migrants make calorically suboptimal food choices due to strong preferences for the favoured foods of their origin states. Migrants bring their origin-state food preferences with them when they migrate and that these preferences are stronger when there are more migrants in the household. The most adversely affected migrants would consume 7% more calories if they possessed the same preferences as their neighbours. These results provide insight into the value that households place on their culture. Even households on the edge of malnutrition are willing to substantially reduce their caloric intake to accommodate their cultural food preferences (Atkin 2016). Deprivation theory holds that poverty will be associated with high levels of religious identification for those who are already affiliated with a religion. overd (2013) used a large national probability sample to gather information about religious affiliation (state of having a commitment to a religion) and level of religious identification (strength of their religious commitment among those who stated having a religious affiliation). Results indicate that deprivation initially predicted religious affiliation, but only because deprivation tapped into variance also shared with ethnicity. When statistically adjusting for ethnicity, deprivation did not predict whether people affiliated with a religious group. To measure deprivation, the New Zealand Deprivation Index 2006 (NZDep2006) was used. This index allocates a deprivation score to each neighbourhood based on the proportion of adults receiving a government- supplied welfare benefit; household income; not owning their own home; single-parent families; unemployed; lacking qualifications; household crowding; no telephone access; and no car access. To examine whether deprivation was associated with levels of religious identification, a model including education and ethnicity among other factors was constructed. Results suggested that when controlling for deprivation, more educated participants were more likely to be strongly identified with their religious group. When ethnicity was added to the model, it revealed that cultural inheritance affected the strength of identification in connection with poverty ( overd 2013). In a 2002 unt study, three dependent variables were examined in a stratification survey that was conducted in southern California measuring the importance attributed to individualistic, structuralist, and fatalistic reasons for poverty. A series of statements representing possible explanations for why some people are poor were presented to respondents. Separate measures were constructed. Individualistic beliefs are composed of personal irresponsibility, lack of discipline, effort, thrift, ability, talent, money management among those who are poor. Structuralist beliefs are concentrated on low wages and lack of good jobs in some businesses 69 and industries, failure of society to provide good schools, discrimination. Fatalistic beliefs are measured simply with just bad luck as an explanation for poverty. Findings reveal that Protestants and Catholics are most likely to endorse the historically dominant individualistic interpretation. Minority religions are most likely to support structural challenges to poverty. Catholics and Jews are most likely to take the fatalistic view of poverty. Significant race/ethnic group differences are found between religious affiliation and structuralist and fatalistic beliefs. Among Whites, Protestants are significantly less likely than the other examined affiliations to endorse structuralist beliefs, while among Blacks and Latinos, Protestantism is significantly more positively aligned with structuralist beliefs. For racial and ethnic minorities in America, Protestantism is more collectivist in orientation. Catholics are similar to Protestants on individualistic beliefs but are significantly more likely than Protestants to “system blame” for poverty. Among Blacks and Latinos, unlike Whites, being Catholic is significantly more predictive of fatalism arising from the need for an alternative account of inequality to supplement the explanatory limits of individualism. It is important to intersect race/ethnicity and religion in research on stratification beliefs. Cultural differences between Protestants and Catholics in America in ideological beliefs about poverty differ among Blacks, Latinos, and Whites ( unt 2002). Co clu re ark o hypothetical que tio ypothetical assessments can be framed as second-order beliefs, where respondents are asked not to provide their opinion but to estimate what other respondents would answer on average. This approach helps assess social norms, which can shape individuals' first-order beliefs and influence what they find acceptable. Some argue that second-order beliefs are better predictors of behavior than personal beliefs and can be incentivized to reduce social desirability bias (Babin, 2019). owever, it is essential to recognize that hypothetical household questions represent a departure from the more common subjective approach, as they gauge respondents' perceptions of a hypothetical family's welfare rather than their own, resulting in different conceptualizations of poverty. Le o lear e ro COVID-19 In this section, we explore the dynamic landscape of subjective poverty research, driven by several key factors such as declining response rates in national surveys and the rapid adoption of online data collection methods, a trend notably accelerated by the CO ID-19 pandemic. As a response to the challenge of survey fatigue, statistical agencies have increasingly prioritized shorter surveys and concise questioning to maintain respondents' engagement (Statistics Canada, 201954). This shift in survey design has profound implications for the study of subjective phenomena, including subjective poverty. The section opens by providing a comprehensive overview of the OECD's ongoing research into subjective well-being indicators, which significantly overlaps with the broader subject of subjective poverty. It highlights the importance of understanding and measuring well-being from a subjective perspective, emphasizing the need for nuanced indicators that capture the multifaceted nature of poverty and well-being. Furthermore, the discussion pivots to the 54 Modernization: a key to Statistics Canada's efforts to reduce response burden (statcan.gc.ca) https://www.statcan.gc.ca/en/about/smr09/smr09_100 70 emergence of Socio-Economic Impact Assessments (SEIAs) conducted across 15 European and Central Asian countries during the onset of the CO ID-19 pandemic. These assessments play a vital role in enhancing our comprehension of subjective poverty by examining the socio-economic impacts of the pandemic on individuals and communities. Through SEIA questions and comparability analyses, we gain valuable insights into how subjective poverty evolves in the face of crises. This section underscores the transformative impact of the CO ID-19 pandemic on the landscape of subjective poverty research and the need to adapt research methodologies to effectively capture and understand subjective experiences, especially concerning poverty and well-being assessments. It also underscores the significance of international organizations like the OECD and UNDP in coordinating global efforts to advance subjective poverty research, shaping the future of this field. Subjective Poverty SEIA Que tio a re a Co parab l ty A aly In the context of Socio-Economic Impact Assessments (SEIA) conducted across the UNECE region by 15 countries, six of them incorporated subjective poverty measurements into their assessments: yrgyz Republic, Moldova, Serbia, Tajikistan, Ukraine, and Uzbekistan. Among these, five countries collected primary data to support these measurements, while Serbia utilized secondary data from its 2018 and 2019 annual surveys conducted by the Statistical Office of the Republic of Serbia (SORS). Data collection primarily focused on households and enterprises, with one exception being Mahalla-level 55administration in Uzbekistan. Subjective poverty was predominantly assessed through direct methods in SEIA questionnaires. ouseholds were queried about their perceptions of financial and material changes resulting from the CO ID-19 pandemic. These questions aimed to understand how the pandemic affected household income, their capacity to meet material and non-material needs, and timely household expenses. This approach allowed respondents to voice their experiences and opinions, offering insights into poverty criteria based on their pandemic- related experiences. In contrast, traditional poverty measurements evaluate household material resources and categorize households as poor if they fall below a certain threshold. The use of direct methods in socio-economic impact assessments is especially significant as it helps identify areas of economic hardship in the context of a global pandemic. The questionnaires employed in SEIA included inquiries using minimum income and economic ladder questions. Thirteen of the participating countries conducted primary data collection, primarily through quantitative surveys. While randomness and representativeness criteria were generally met, household-level data collection was less common, with nine countries conducting household surveys and one focusing on municipal-level data. Over and above the secondary data collection, high-frequency data, statistics, and desk reviews that were used, some countries employed adapted Post Disaster Needs Assessment56 methodologies and qualitative studies to complement quantitative and secondary data. Some countries even utilized Big Data sources like telecom and satellite data for a more 55 The smallest state administrative unit in Uzbekistan which consists of households. 56 https://www.undp.org/publications/pdna 71 comprehensive view of the pandemic's impact. Table 2 provides a summary of the countries and data collection methods on subjective poverty used in SEIA. Multiple subjective poverty approaches were adopted in SEIA questionnaires, which will be explored further below. Table 2 – Summary of data collections in SEIA Country Primary data collection HH Survey Other Surveys Use of digital survey Use of big and alternative data Armenia Yes 3550 households 2100 local governance service providers Yes, Kobo No Azerbaijan Yes No No No No Belarus Yes No No No No Bosnia and Herzegovina Yes 2182 respondents No No No Kazakhstan Yes 12024 households No No No Kosovo* Yes 1412 respondents No No No Kyrgyzstan Yes 2340 respondents (1371 women) based on random sampling No No No Moldova Yes UNDP analysis of the ad hoc module of the NBS Household budget survey 450 company respondents No Yes Telecom and Satellite. Micronarratives (300 collected) Montenegro Yes 1006 households No No No Tajikistan Yes 1250 Enterprises, individual entrepreneurs and dehkans (farmers) in-depth interviews (150 HHs, including 100 women and girls and 100 youth, and 50 MSMEs) No No Turkey Yes No No No No Ukraine Yes 1098 households No Yes, Kobo No Uzbekistan Yes No Mahalla survey 3670 mahallas surveyed No No 72 Poverty defined in a fully subjective way (direct self-identification as poor, feeling of poverty) Several countries, including yrgyz Republic, Moldova, Tajikistan, and Uzbekistan, adopted the method of self-identification to assess the feeling of poverty. Respondents were asked questions to determine if they had felt poor in the past, currently, or anticipated feeling at risk of poverty in the near future due to the pandemic's impacts. This approach was widely used in the surveys and adaptable, with questions often focusing on respondents' expectations regarding their household's financial well-being. Perceived financial difficulties Countries utilizing subjective poverty measures in their SEIA assessments often included questions aimed at assessing respondents' subjective economic stress. Questions inquired about the ability to meet expected/unexpected expenses, make ends meet, or satisfy basic needs. These questions considered not only traditional basic needs but also pandemic-related necessities, such as access to the internet for online schooling or personal protective equipment. Assessments mainly focused on changes in income-to-expense ratios and coping mechanisms. Subjective poverty line approach – perceived poverty line The yrgyz Republic employed this method, which involves questions about the income needed to secure a basic standard of living or meet necessities. Respondents were asked to estimate the amount of money required by a family with the same number of members to avoid poverty, considering prevailing price levels. Open-ended questions were also used to capture changes in respondents' lives related to the pandemic. Subjective poverty lines assessed with the use of statistical methods (so-called objectivised, quasi- subjective poverty lines) The yrgyz Republic and Ukraine directly employed this method in their SEIA assessments. Respondents were questioned about household assets or funds, which were used as indicators of deprivation. This approach focused on assessing financial restrictions resulting from cost- related inaccessibility of essential items. Perception of poverty as a social phenomenon yrgyz Republic included questions on respondents' views regarding poverty as a social phenomenon. These questions encompassed definitions of poverty, perceptions of poverty's extent in the country, its causes, and the role of the government in poverty reduction. They also examined opinions on the government's anti-crisis measures and the type of assistance needed personally. 73 Other Approaches In addition to the subjective poverty measures outlined above, various other approaches were adopted as well, including inquiries about the availability and access to food, estimations of fair expenses on basic needs, and negative coping mechanisms adopted by households due to pandemic-induced income reductions. Some questions assessed the dependence of individuals on their families during economic hardships. The approaches listed in this paragraph were used by countries such as the yrgyz Republic and Tajikistan. A overv ew o UNDP Soc o-Eco o c I pact A e e t (SEIA ) or hou ehol cou tr e o UNECE re o The SEIA assessments revealed varying impacts across countries, with differences in intensity based on economic structure, social protection systems, and other vulnerabilities. At the individual and household levels, the assessments highlighted the unwinding of development gains, increased poverty, and rising inequality. Regional and rural-urban disparities were observed, particularly affecting informal businesses in urban areas. The assessments also underscored the need to reconsider social protection systems to cover new classes of vulnerability, often referred to as the "missing middle." Challenges encountered during SEIA implementation included designing research methodologies, questionnaires, and sampling methods, as well as targeting vulnerable groups, dealing with fieldwork constraints, and ensuring data comparability. Coordination with various institutions, access to data, and data sharing by government and big data providers were additional hurdles. Nevertheless, some best practices emerged, including Digital SEIA, innovative use of Big Data, combining "thick" data57 (micronarratives58), high-frequency data, and other methods for sense-making during the pandemic. The process of sensemaking involved the integration of various pieces of evidence during the SEIA assessments, as seen in Table 2 – Summary of data collections in SEIA. This integration encompassed the use of qualitative studies to complement quantitative and secondary data. Additionally, certain countries leveraged Big Data sources, such as telecom and satellite data, to gain insights into the context of the CO ID-19 pandemic. Emerging issues from the SEIA assessments conducted reveal differentiated impacts. Income disparities are exacerbated by increasing unemployment, especially in urban areas, as well as reduced income, higher food and healthcare costs, and limited savings for many households. Gender disparities are notable, with women disproportionately affected in the labor market, while multi-dimensional consequences, including long-term education and health effects, contribute to rising inequalities among different groups. Entrepreneurs, migrant laborers, and informal workers face heightened vulnerabilities, with youth and women bearing the brunt of these impacts. School closures and ineffective remote learning exacerbate long-term challenges for children. 57 Thick data is a term that refers to qualitative data that reveals the contexts, emotions, and stories of the subjects being studied. 58 Micronarratives are a collection of short stories written by survey respondents 74 Macro-economic vulnerabilities have been exposed to varying degrees due to external and internal shocks, including declines in exports, remittances, and oil prices, as well as lockdowns. These vulnerabilities translate into micro-economic consequences affecting individuals, households, and small and medium-sized enterprises (SMEs). Demand-side shocks have led to falls in remittances and household incomes, reduced demand in sectors like tourism and hospitality, border closures disrupting supply chains, and increased household costs for essential goods and services. Supply-side challenges include temporary border closures affecting value chains and labor movement, plummeting commodity prices, currency depreciation, higher import costs, financial risks, and debt servicing burdens, as well as fixed costs and SME weaknesses. The impact of these macro-economic vulnerabilities varies among countries, with commodity-dependent nations facing a double shock from declining oil and gas prices. As the pandemic persists with multiple waves of infection, uncertainty rises, placing increased pressure on public policies and recovery efforts, particularly in terms of debt and fiscal space. Socio-economic impact assessments underscore the disproportionate effects on vulnerable groups, households, smaller enterprises, and disparities between urban and rural areas. Moreover, SEIAs reveal the need to reassess social protection systems to encompass new classes of vulnerability often referred to as the “missing middle.” This group includes formerly non-poor informal workers who lack basic security, occasional and gig workers who supplement their income with occasional work, long-term unemployed individuals who have lost eligibility for unemployment benefits, and labor migrants and seasonal workers who face challenges earning money abroad due to travel restrictions and increased costs. These findings emphasize the importance of adapting social protection systems to address evolving vulnerabilities in the wake of the pandemic. Figure 6. Missing Middle Source: Socio-Economic Impact Assessments, Statistics Canada (2022) Middle and upper middle class employed in the formal economy and covered with social security Missing middle Poor covered with targeted social assistance transfers 75 Case study 6: Self-assessed Financial Well-being: comparing objective and subjective measures This case study from Statistics Canada below examines the comparison between subjective and objective measures in the context of self-reported financial well-being and official poverty measures, such as the market basket measure. It contributes to the evolving understanding of subjective poverty measurement trends. Due to the impact of the CO ID-19 pandemic, Statistics Canada undertook the task of establishing a timelier approach to collecting data, enabling a monthly assessment of households' financial well-being. As a result, a supplementary question was introduced into the Labour Force Survey (LFS) from April 2020 onward. This question inquired about the ease or difficulty that households experienced in meeting their financial needs in various areas, including transportation, housing, food, clothing, and other essential expenses over the past month. This monthly incorporation of the question presents Canada with a distinctive opportunity to enrich its comprehension of official poverty measurements by adopting the perspective of subjective poverty. This approach offers advantages such as adaptability to evolving information demands, cost-effective data collection, and time-saving benefits for survey participants. Nevertheless, there are drawbacks, as the data must undergo an extensive validation process before being disseminated, which can introduce delays from approval to results. This is where the monthly LFS data proves advantageous, as it expedites data collection for swifter outcomes. owever, this comes with increased costs and potential data reliability concerns. Combining monthly and administrative data appears to bridge the gaps between subjective and objective poverty measures. This new incorporation thus offers the possibility to construct an indicator that amalgamates socio-demographic variables and income data from the Canadian Income Survey (CIS) to provide a more comprehensive analysis of subjective poverty. Research studies have been conducted to investigate sociodemographic characteristics in cases where individuals' financial well-being diverges from the anticipated official poverty line. Linking the CIS 2020 data with the financial difficulty data extracted from the supplemental LFS between January 2021 and July 2021 allows comparisons between subjective and objective poverty measures. The advantages derived from juxtaposing perceived financial well-being with official poverty measures can be observed in this case study by Statistics Canada. It delves into a comparison between employed and unemployed individuals, focusing on their Market Basket Measure (MBM) in relation to their financial well-being. The age group under examination was restricted to individuals aged 25 to 54. Results revealed that 43.7% of employed individuals reported financial comfort (Figure 7), in contrast to 21.0% of the unemployed cohort (Figure 8). A larger percentage of individuals above the poverty line, among the unemployment group, reported financial difficulty compared to the unemployed. This shows that one’s perception of poverty is not aligned with their objective poverty. Numerous avenues exist for understanding poverty, but the aim of this case study is to merge the subjective and objective dimensions to conceptualize and understand poverty more profoundly. Accordingly, Statistics Canada has been employing yearly data to calculate the 76 MBM, while the LFS relies on monthly data. By linking these two datasets, a deeper insight into subjective poverty and its nuances is achieved. The example offers just a glimpse of the potential when these two poverty conceptions converge. Yet, there remains a wealth of opportunities to explore further aspects, such as gauging the proximity to the poverty line and juxtaposing it with the ability to meet financial needs or examining the proportion of immigrants and visible minorities experiencing poverty at income levels exceeding the poverty threshold. These represent only a subset of the possibilities that could stimulate an array of future research endeavors. Source: Statistics Canada, Canadian Income Survey, 2020 and Labour Force Survey, September 2020 to September 2021. Source: Statistics Canada, Canadian Income Survey, 2020 and Labour Force Survey, September 2020 to September 2021. Overlaps in Dimensions of Poverty To further this, the article, O p D , explores the overlap among three dimensions of poverty and finds that there is minimal overlap in the group of individuals considered poor by these dimensions, largely due to differences in reliability and validity of 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Below Above Total Figure 7: Comparing MBM to subjective financial well-being in age group 25-54 - Employed persons Difficult Neither Ease 0 10 20 30 40 50 60 70 80 90 100 Below Above Total Figure 8: Comparing MBM to subjective financial well-being in age group 25-54, Unemployed persons Difficult Neither Ease 77 measures (Bradshaw and Finch, 2003). This lack of overlap implies that the policy response to poverty will vary depending on the chosen measure. For example, cumulatively poor individuals, those poor in multiple dimensions, exhibit different characteristics and social exclusion patterns compared to those poor in only one dimension. This suggests that cumulatively poor individuals might be a more reliable way to identify poverty and distinguish between different levels of poverty. The article recommends using a combination of measures in future poverty studies to provide a more robust basis for drawing conclusions, as relying on a single dimension has limitations in terms of reliability and validity. I pl catio re ar exper e ce w th COVID outbreak The Socio-Economic Impact Assessments (SEIAs) conducted during the CO ID-19 outbreak faced several challenges. One major challenge was ensuring the accuracy and suitability of primary data collection, including research methodology, questionnaire design, sampling methods, and reaching vulnerable groups. Designing questionnaires proved complex, particularly for household-level assessments, given the multidimensional nature of impacts and the need to avoid respondent fatigue during remote data collection. Sampling presented its own challenges, as some countries had to balance the rapid need for data with the potential for wider margins of error with smaller sample sizes. Others faced the trade-off of collecting larger samples, which required more time for data collection fieldwork. Remote data collection made it difficult to reach hard-to-reach and vulnerable groups like informal workers and migrants. Timing for data collection preparations varied across countries, influenced by factors such as country size, partnerships, and the pandemic's onset. Fieldwork constraints arose due to quarantine measures, lockdowns, and movement restrictions, further delaying data collection. Remote data collection methods, including telephone interviews and digital tools, became essential. Ensuring data comparability across SEIAs posed a significant challenge. Different countries employed context-specific approaches with varying questionnaires and sampling strategies, affecting cross-country comparisons and data aggregation. Maintaining questionnaire comparability for time-series comparisons with earlier surveys conducted in 2020 was also a concern. Data sharing by government partners and big data providers presented another hurdle. While some countries had open-source secondary data, others had lengthy processes to access data from partners. Additionally, primary data collected by various entities was often shared in forms for end-users, not as raw datasets, and some government counterparts were reluctant to share sensitive primary data. These challenges underscore the complexity of conducting SEIAs during a global crisis. In summary, subjective poverty measures in SEIA assessments demonstrated interconnections and provided valuable insights into the impacts of the CO ID-19 pandemic on households. These measures allowed affected households to establish poverty criteria and express their opinions about needed assistance. Gathering opinions about poverty and necessary support 78 proved invaluable in shaping government policies, including subsidies, direct cash transfers, and bill payment deferments. Regular adoption of subjective poverty measures, not only in SEIA but also at the country level, can inform government policymaking effectively. Co clu o Subjective poverty measures are gaining popularity, but their relationship with existing monetary and multidimensional poverty measures needs clarification. ey questions revolve around overlaps and discrepancies in identifying poverty and their relevance for public policy. It remains uncertain how much information subjective measures capture that monetary and multidimensional measures already encompass, what novel insights they offer, and whether they should stand alone or complement other measures. It is imperative that efforts to understand subjective poverty elucidate their utility for policymakers combating poverty. Future research should address the proportion of those reporting subjective poverty who also experience multidimensional or monetary poverty and explore what unique information subjective measures reveal for those not deemed poor by conventional standards. Additionally, it should discern when subjective measures provide value for those classified as poor by conventional criteria and when they reflect adaptive preferences. Whether subjective measures should replace or work alongside other poverty metrics is a critical consideration for guiding policymaking effectively. Caution is advised against portraying subjective questions as simplistic, as their adoption could potentially displace more robust multidimensional measures, necessitating a balanced approach to ensure a comprehensive understanding of poverty. It is recommended that subjective poverty questions always complement rather than replace multidimensional ones to avoid sacrificing valuable insights into poverty's multidimensional nature. Chapter 5. RECOMMENDATIONS Chapter 2 addresses the questions “what is subjective poverty”, “what is a subjective poverty measure” and “why should National Statistics Offices (NSOs) measure subjective poverty”? As its name suggests, subjective poverty is based on the personal perspective and evaluation of individuals. In subjective poverty, poverty is assigned in one of two ways. In the first way, individuals or households are asked to evaluate their life situation, thereby identifying themselves as “poor” or finding it “very difficult to make ends meet” through their response to a question. In the second, a household makes an evaluation of what resources are required to meet a standard such as “making ends meet”, which can in turn be converted into a “subjective poverty line”. Subjective poverty measures can capture aspects of poverty missed by traditional monetary poverty metrics. Subjective poverty incorporates the fundamental aspect of reflecting citizen’s perspectives on what constitutes poverty – an aspect which is, perhaps surprisingly, under-considered in policy development. Recommendation 1 Subjective measures of poverty should be included among the set of assessment tools used by countries. These do not replace objective measures or multidimensional 79 measures; rather, they are a complement. Countries with dashboards of poverty indicators should include subjective assessments among the poverty indicators. Chapters 2 and 3 relate non-monetary subjective poverty measures to the more common measures of subjective well-being, such as the Cantril ladder, and introduces the most common non-monetary subjective poverty question forms. They also introduce the most common monetary subjective poverty question forms including the “Daleeck” question and the “Minimum Income Question”. Examples of subjective poverty measures include some that ask respondents to self-identify as poor: (D consider p ?); evaluate their own situation as one of “making ends meet” (T k ’ k p xp ? W W W F E V ); or provide a subjective valuation of a poverty line (T k w w “ k ”?). The second of these questions is known as the “Deleeck” question and is found in the EU-SILC. The last of these questions is known as the Minimum Income Question (MIQ). The chapter then describes various ways that subjective questions can be used to create a subjective poverty line. The MIQ is one type of subjective poverty question that can be used to create a subjective poverty line, using a method known as the . Recommendation 2 Given their inclusion in EU-SILC, and their utility in identifying subjective poverty, the Deleeck and Minimum Income Question questions should be considered by NSOs as a standard for international comparison purposes. A . T k ' k p xp ? (W W W F E V ). E -SILC Q HS120. I opinion w w w k p xp ? w p w xp ( k ). EU-SILC variable S130. Recommendation 3 Utilize the Minimum Income Question and the intersection approach as the primary methods for estimating subjective poverty lines. Chapter 4 examines in depth good practises associated with surveys which can be used to determine subjective poverty. Several different survey types can be considered for subjective poverty content. While subjective poverty measures are not considered replacements for objective poverty measures, their inclusion on “pulse”, “omnibus”, “crowdsourced” and 80 opinion polls can provide timely information on individuals self-assessments of poverty status. Nevertheless, different survey models may have implications for results. Similarly, experimental results show that small differences in question wording or changes in question wording over time can have large effects on observed results. Chapter 4 also examines several efforts made by statistical agencies worldwide to rapidly pivot to provide rapid information during the CO ID-19 pandemic. For example, Socio- Economic Impact Assessments (SEIA) were conducted across the UNECE region by 15 countries. The example underscores the transformative impact of the CO ID-19 pandemic on the landscape of subjective research and the need to adapt research methodologies to effectively capture and understand subjective experiences, especially concerning poverty and well-being assessments. It also demonstrated challenges in applying rapid collection approaches, multi-nationally, in a quickly changing environment. In the conclusions, Chapter 4 underscores the need to continue to demonstrate, through empirical studies, the policy utility of subjective poverty measures. As with other measures of poverty. Subjective poverty is concentrated among particular groups. A similar breakdown of disaggregated groups suggested in the UNECE publication Poverty Measurement: Guide to Data Disaggregation should be used for disaggregation of subjective poverty. These would include age, sex, disability status, migratory status, ethnicity, household type, employment status, tenure status of the household, receipt of social transfers, educational attainment and degree of urbanisation. Recommendation 4 NSOs and analysts should consider the possible impacts of survey mode, context (framing) and sampling methods and wording differences when analysing subjective indicators such as subjective poverty. Recommendation 5 NSOs and analysts should continue to demonstrate the utility of subjective poverty measures, considering issues of overlap with objective poverty measures and policy applications. Recommendation 6 Subjective poverty measures should be disaggregated to at-risk groups, in a similar fashion as recommended in UNECE’s guide to disaggregation. https://unece.org/statistics/publications/poverty-measurement-guide-data-disaggregation#:~:text=This%20publication%20provides%20guidance%20on,international%20comparability%20of%20poverty%20statistics. 81 82 Appe x Table A.1: Question Types Reported Being Asked by Country in UNECE (2021) Study Qualitative Categorical Money Metric Total # of Subjective Poverty Questions Other Country Identification Evaluation Prediction Evaluation Deprivation, Social Exclusion, Well-being Armenia 1 1 2 8 Austria* 1 1 2 3 Azerbaijan Belarus 5 1 2 7 2 Belgium* 1 1 2 Bosnia and Herzegovina 1 1 1 Brazil 1 1 4 Bulgaria* 1 1 1 Canada 8 1 1 9 Colombia 1 4 2 1 7 9 Costa Rica 1 1 2 Croatia* 1 1 1 6 Cyprus* 1 1 2 Czech Republic* Denmark* 2 1 2 Dominican Republic Estonia* 1 1 1 8 Finland* 2 1 2 17 Georgia Germany* 1 1 2 Hungary* 3 1 1 5 8 Ireland* 1 1 2 Israel 2 1 3 Italy* 1 1 2 Japan Kyrgyz Republic 1 2 1 83 Latvia* 1 1 1 Lithuania* 2 1 3 Luxembourg* 1 1 2 6 Malta* 1 1 2 Mexico 1 1 1 1 Mongolia Montenegro* 1 1 1 Netherlands* 3 1 2 4 12 New Zealand 1 1 1 10 Republic of North Macedonia* 1 1 2 10 Norway* 2 1 1 Portugal* 1 1 1 Republic of Moldova 2 Romania* 2 2 2 2 Russian Federation 2 1 3 4 Republic of Serbia* 1 1 1 Slovakia* 2 2 2 3 Slovenia* 1 1 1 Spain* 1 1 2 Sweden* 1 1 1 Switzerland* 2 1 3 1 Turkey 1 1 3 1 Ukraine 3 1 2 5 7 United States Uzbekistan 1 1 1 Viet Nam 1 1 1 Total # of Countries 4 42 6 40 45 22 National Experimental Wellbeing Statistics, Liana Fox (United States Census Bureau) Read more about National Experimental Wellbeing Statistics, Liana Fox (United States Census Bureau) This is the U.S. Census Bureau’s first release of the National Experimental Wellbeing Statistics (NEWS) project. The NEWS project aims to produce the best possible estimates of income and poverty given all available survey and administrative data. We link survey, decennial census, administrative, and commercial data to address measurement error in income and poverty statistics. We estimate improved (pre-tax money) income and poverty statistics for 2018 by addressing several possible sources of bias documented in prior research. Languages and translations English National Experimental Wellbeing Statistics Version 1, released February 14, 2023 SEHSD Working Paper Number 2023-02 CES Working Paper Number 23-04 Adam Bee, Joshua Mitchell, Nikolas Mittag, Jonathan Rothbaum, Carl Sanders, Lawrence Schmidt, and Matthew Unrath Abstract This is the U.S. Census Bureau’s first release of the National Experimental Wellbe- ing Statistics (NEWS) project. The NEWS project aims to produce the best possible estimates of income and poverty given all available survey and administrative data. We link survey, decennial census, administrative, and commercial data to address measure- ment error in income and poverty statistics. We estimate improved (pre-tax money) income and poverty statistics for 2018 by addressing several possible sources of bias documented in prior research. We address biases from (1) unit nonresponse through improved weights, (2) missing income information in both survey and administrative data through improved imputation, and (3) misreporting by combining or replacing survey responses with administrative information. Reducing survey error substantially affects key measures of wellbeing: We estimate median household income is 6.3 percent higher than in the survey estimate, and poverty is 1.1 percentage points lower. These changes are driven by subpopulations for which survey error is particularly relevant. For householders aged 65 and over, median household income is 27.3 percent higher than in the survey estimate and for people aged 65 and over, poverty is 3.3 percent- age points lower than the survey estimate. We do not find a significant impact on median household income for householders under 65 or on child poverty. Finally, we discuss plans for future releases: addressing other potential sources of bias, releasing additional years of statistics, extending the income concepts measured, and including smaller geographies such as state and county. ∗Send correspondence to [email protected] Bee: U.S. Census Bureau, [email protected]; Mitchell: U.S. Census Bureau, [email protected]; Mittag: CERGE-EI, [email protected]; Rothbaum: U.S. Census Bureau, [email protected]; Sanders: U.S. Census Bureau, [email protected]; Schmidt: MIT Sloan School of Management, [email protected]; and Unrath: U.S. Census Bureau, [email protected] Any opinions and conclusions expressed herein are those of the authors and do not reflect the views of the U.S. Census Bureau. The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data used to produce this product (Data Management System (DMS) number: P-7524052, Disclosure Review Board (DRB) approval number: CDRB-FY23-SEHSD003-025). 1 mailto:[email protected] 1 Introduction Accurately measuring household income and poverty is essential to understanding the na- tion’s overall economic wellbeing. Previous studies suggest that measurement error stem- ming from unit nonresponse, item nonresponse, and misreporting biases key official statistics such as mean or median income and the official poverty rate. The direction of bias differs among these sources of measurement error. Unit and item nonresponse have been found to bias income up and poverty down (Rothbaum et al., 2021; Rothbaum and Bee, 2022; Bollinger et al., 2019; Hokayem, Raghunathan and Rothbaum, 2022), while misreporting can bias income down and poverty up (Bee and Mitchell, 2017; Meyer et al., 2021b; Larri- more, Mortenson and Splinter, 2020). These previous papers document aspects of the overall problem of survey error in isolation, so the overall impact of these sources of error on the accuracy of survey estimates remains unclear.1 Important next steps are to study the joint impact of these error sources, and to develop a comprehensive solution that addresses all partial problems simultaneously. Doing so would provide survey users with the best possible measure of income. This paper summarizes the National Experimental Wellbeing Statistics (NEWS) Project, a project to create the most accurate estimates of household income and poverty. The NEWS project makes three unique contributions towards a more comprehensive solution to the problem of measuring income accurately. First, we address as many sources of bias as we can simultaneously, including unit and item nonresponse and underreporting in surveys as well as the various challenges in administrative data such as measurement error, conceptual misalignment, and incomplete coverage. Simultaneously addressing these error sources is crucial, since they have been found to bias key statistics in different directions. Second, we bring together all of the available survey and administrative data in order to overcome the shortcomings of individual data sources. For example, we use 5 different sources of wage and 1We discuss these existing approaches and how our methodology compares with them in section 2.4. 2 salary earnings, each of which capture earnings and jobs not on reported on others. Third, we propose a model to combine survey and administrative earnings data given measurement error in both sources, replacing ad hoc assumptions that have been used in prior work.2 To demonstrate the importance of more accurate data, we estimate pre-tax money income and poverty statistics for 2018, mirroring the Census Bureau’s annual income and poverty report (Semega et al., 2019). Under our approach, median household income is 6.3 percent higher than the survey-only estimate. The official poverty rate is 1.1 percentage points lower than the survey-only estimate, with 9.4 percent fewer people in poverty.3 However, these differences vary considerably across groups. Median household income is 27.3 percent higher for householders aged 65 and older, 5.0 percent higher for those aged 55-64, and not statistically different or lower for all other householder ages. Likewise, poverty is 3.3 percentage points lower for persons aged 65 and over (34.2 percent fewer people in poverty), compared to 0.7 percentage points lower for those aged 18-64 (6.7 percent fewer people in poverty), and not statistically different for children 17 and under.4 We find that combining survey responses and administrative records matters for the mea- sured income distribution, with different roles played by non-response and misreporting. At the bottom of the income distribution, we find that weighting and imputation augmented with administrative records decreases income at the lowest percentiles of the survey-response only income distribution. This negative shift of the income distribution is more than offset, however, by the additional income that administrative records report relative to surveys. We compare the household income distribution with and without the administrative data and find large effects across the distribution, from 17.1 percent more income at the 10th per- centile, to 10.3 percent more at the 25th, 6.8 percent more at the median, and 3.6 percent more at the 75th. As a result, while the survey estimate of household income at the 90th 2More detail on the earnings measurement error model will be provided in a forthcoming companion paper, Bee et al. (2023). 3All comparisons are statistically significant at the 5 percent level unless otherwise noted. 4Estimates are shown in for median household income by subgroup in Table 1 and Figure 1, for poverty by subgroup in Table 2 and Figure 2, and for inequality in Table 3. 3 percentile is 12.5 times as large as at the 10th percentile, with the NEWS estimates, the ratio is 11.5. In addition to the substantive differences summarized above, our analyses yield three key methodological takeaways. First, to obtain an improved income measure, it is indeed nec- essary to simultaneously address error sources such as nonresponse and misreporting. Our combined nonresponse bias corrections (weighting and improved income imputation) gen- erally adjust the point estimates of income down and poverty up.5 Including administra- tive wage and salary earnings to address underreporting, particularly when survey-reported earnings are zero, shifts income up and poverty down. Addressing retirement income under- reporting (defined benefit pensions and defined contribution withdrawals) has the biggest impact on household income across much of the distribution, echoing findings from Bee and Mitchell (2017). For householders under 55 whose income comes predominantly from wage and salary earnings, which is one of the best reported income sources in surveys, we find limited differences in income and poverty estimates. However, for those 55 and over and particularly for those 65 and over, who have more income in underreported sources (retire- ment, interest, dividends, etc.), the increase in income due to the underreporting adjustment is greater than the decline in income from the nonresponse bias correction. A second key takeaway is that each data source has its own strengths and shortcomings, making it difficult to produce accurate estimates of income and poverty when relying only on a single source. As is well-established in the literature, survey data have a number of limitations. For example, over 40 percent of all income is imputed in the CPS ASEC (Hokayem, Raghunathan and Rothbaum, 2022), including 46 percent of wage and salary earnings from a primary job. However, analyses which use administrative data alone are not a panacea. Administrative sources can miss income as well – 5 percent of adults report wage and salary earnings in the CPS ASEC but do not receive a W-2 (Bee, Mitchell and 5The differences in this paper are not generally statistically significant, however, as shown in Figure 3 Panel A. 4 Rothbaum, 2019). Likewise, 7 percent of occupied addresses in the 2018 CPS ASEC cannot be linked to any available source of administrative data. Program eligibility requirements often imply that certain regions or jobs are excluded from the administrative data. A third key takeaway is that it is critical to incorporate multiple survey and administrative data sources. Using multiple data sources allows us to combine their strengths and thereby reduce the shortcomings we point out above. On the positive side, we find that for some populations, a single data source can yield quite accurate estimates. Yet each single data source also misses or contains substantial error for categories of gross income that are of crucial importance to other subpopulations. Thus, improving measures of income for a wider population requires combining multiple data sources. Overall, we find that a comprehensive approach that leverages the strengths of each data source is required to construct the most accurate estimates of poverty and inequality. 2 Income Measurement Challenges The major challenge to estimating income is that we do not observe all the information that we would like for all individuals. 2.1 Survey Income With the survey data, there are several potential sources of missing data and measurement error, such as: 1. Survey unit nonresponse - not all individuals or households respond to the survey, which has been found to bias income up and poverty down (Rothbaum et al., 2021; Rothbaum and Bee, 2022). 2. Survey item nonresponse - individuals who do respond may choose not to respond to specific questions (a particular problem for income questions), which has been found 5 to bias income up and poverty down (Bollinger et al., 2019; Hokayem, Raghunathan and Rothbaum, 2022). 3. Survey mis- and underreporting - income is not always reported accurately on surveys and can be severely underreported for many income types, which has been found to bias income down and poverty up (Bee and Mitchell, 2017; Rothbaum, 2015). We refer to this as misreporting in the rest of the paper. As Meyer and Mittag (2021) showed in decomposing bias in estimates of means-tested pro- gram benefits, the various sources of measurement error can have biases of different signs and magnitudes across different programs and surveys. Also, correcting for one source of bias without addressing others does not necessarily reduce the overall bias in the estimates. We address all of these sources of measurement error simultaneously, building on prior work at the Census Bureau that addressed them separately. First, we create improved weights to address survey unit nonresponse (extending Rothbaum et al. 2021 and Rothbaum and Bee 2022). We use imputation to address survey item nonresponse (extending Hokayem, Raghunathan and Rothbaum 2022). We combine survey and administrative data (including replacing survey responses), which also helps address survey item nonresponse as well as survey misreporting (extending Bee and Mitchell 2017). 2.2 Administrative Income Replacing survey responses with administrative records does not fully address measurement error concerns. Many of the same types of issues in survey data are also present in admin- istrative data, including: 1. Selection into administrative data - not all individuals, households, or firms may be present in the administrative data due to how and why the administrative data is collected. For example, many low-income individuals are not required to file a tax return, meaning they may be not represented in tax data. And certain jobs are not 6 covered by unemployment insurance, meaning those jobholders are not included in commonly used earnings data. 2. Administrative data “nonresponse” - some records may be absent from the ad- ministrative data that should have been present. For example, although firms are required to file a W-2 for nearly all workers, some may not for a variety of reasons such as firm closure, or paying workers “under the table”, etc. 3. Administrative misreporting - even when an administrative record exists, it may not be accurate. For example, “under-the-table” earnings, such as unreported tips or underreported self-employment earnings, would result in underreporting in adminis- trative earnings. 4. Conceptual misalignment - in some cases the income concept measured by admin- istrative data does not match the concept we would like to measure. For example, the W-2s information received by the Census Bureau do not include information on employee pre-tax earnings used to pay health insurance premiums.6 For these workers, W-2 earnings are effectively an “underreport” of gross earnings. 5. Incomplete data coverage - we may not have access to the data for specific in- dividuals. For example, state-provided data on earnings and means-tested program participation are not available for all states. These make it inappropriate to rely on administrative data alone. For example, selection into administrative data can exclude subpopulations of interest, such as low-income households which may be underrepresented in tax data. Larrimore, Mortenson and Splinter (2020) created households using addresses from tax filings and information returns to estimate poverty over time, addressing income underreporting in surveys. However, they could not observe individuals and households that did not receive any information return or file taxes. 6Starting in 2012, Box DD on the W-2 reports the total cost of the employee’s health insurance premium, including the employer and employee contribution. Box DD is not currently available for this work. 7 Instead, they had to impute the presence and poverty status of an unknown number of individuals per year, which they estimated at 4 to 6 million. Through random sampling from the universe of residential addresses, surveys do not have the coverage gaps we see in administrative data.7 For example, in the 2019 Current Population Survey Annual Social and Economic Supplement (CPS ASEC), 7 percent of occupied housing units cannot be linked to any administrative or commercial data. But thanks to information from survey responses, we can generate improved weights, imputations, and income measures to better approximate our target universe of individuals and households, even in the absence of administrative data for some. 2.3 Addressing These Challenges The best estimates of income and poverty would rely on both survey and administrative data. Having different sources of information allows us to address shortcomings in each source. For example, we use 5 separate sources of wage and salary earnings. These are (1) W-2s, (2) the Detailed Earnings Record (DER) file from the Social Security Administration (SSA), (3) Longitudinal Employer-Household Dynamics (LEHD) data reported by firms to state unemployment insurance offices, (4) 1040 tax filings, and (5) survey responses. Survey earnings can help with “nonresponse” in administrative data, as 5 percent of adults report wage and salary earnings in the CPS ASEC but do not receive a W-2 (Bee, Mitchell and Rothbaum, 2019). Some individuals with no W-2s also report wage and salary earnings on their tax returns. Given the possibility of misreporting in administrative data, we develop a measurement error model for survey and administrative reports of wage and salary earnings. We use that model to replace ad hoc assumptions about when to use survey or administrative earnings given measurement error in both. We discuss that model in Section 4.3.1 and in more detail in a forthcoming companion paper (Bee et al., 2023). 7The Master Address File, from which housing units are sampled, is discussed in Section 3. 8 Likewise, conceptual misalignment in one source can be addressed using information from other sources. For example, while available W-2 data do not include employee pre-tax con- tributions to health insurance premiums, LEHD earnings for the same job should. Workers with survey-reported private health insurance coverage are 3 to 5 times more likely to have LEHD earnings that exceed the W-2 amounts by 1-3 percent, 3-5 percent, 5-10 percent, and 10+ percent, shown in Table A1.8 However, incomplete data coverage makes it more difficult to measure gross earnings in the administrative data. Many jobs are not covered by unemployment insurance, and are excluded from the LEHD – for 2018, there are nearly 20 million more W-2 jobs than LEHD jobs, shown in Table A2.9 LEHD and state-provided means-tested program data are also not available for some states. We use imputation to address this source of incomplete data coverage, to correct for underre- porting of means-tested program receipt in surveys, and to estimate missing gross earnings (given incomplete LEHD data), extending the work in Fox et al. (2022) and Hokayem, Raghunathan and Rothbaum (2022). An additional challenge in using linked survey and administrative data is selection into link- age. Linkage rates vary by group, which can bias income estimates that include only linked individuals (Bond et al., 2014), but if unlinked individuals are are also subject to survey measurement challenges above, then income estimates are biased if we measure unlinked 8Note that the CPS ASEC variable indicates private coverage, but not necessarily whether that job was the source of that coverage, rather than another job or another individual’s job, such as from a spouse, partner, or other family member. 9Workers not covered by unemployment insurance include federal employees and those in various pri- vate sector occupations For example, Maryland’s Department of Labor lists the following jobs as exempt from unemployment insurance: barbers and beauticians, taxicab drivers, owner-operated tractor drivers in certain E and F classifications, maritime employment, election workers, church employees, clergy, cer- tain governmental employees, railroad employment, newspaper delivery, insurance sales, real estate sales, messenger service, direct sellers, foreign employment, other state unemployment insurance programs, work- relief and work-training, family members, hospital patients, student nurses or interns, yacht salespersons who work for a licensed trader on solely a commission basis, services of aliens who are students, scholars, trainees, teachers, etc., who enter the U.S. solely to pursue a full course of study at certain vocational and other non-academic institutions, recreational sports officials, home workers, and casual labor. Refer to https://www.dllr.state.md.us/employment/empfaq.shtml accessed 11/1/2022. 9 https://www.dllr.state.md.us/employment/empfaq.shtml individuals’ incomes using survey responses only. We use weighting of households with all adults linked conditional on their survey responses to create a representative sample of linked individuals, extending Rothbaum et al. (2021) and Rothbaum and Bee (2022). 2.4 Relationship to Prior Research This is not the first project to attempt to address shortcomings in survey data to estimate improved income and poverty statistics.10 There have been several efforts to adjust sur- vey data for underreporting in the absence of linked administrative data, include from the Congressional Budget Office (CBO), Bureau of Economic Analysis (BEA), and the Transfer Income Model (TRIM) from the Urban Institute. In each case, researchers had to make assumptions about underreporting that could not be verified without linked data, such as whether underreporting is on the extensive or intensive margin, which households are more likely to misreport, etc. If those assumptions are not correct, which is likely in the absence of linked data, they risk imputing income and benefits to the wrong individuals and households, introducing biases of unclear direction and magnitude.11 10There has been considerable work on measurement error in income data, as well as comparing survey income to administrative data. As far back as the 1970’s, Kilss and Scheuren (1978) used CPS data linked to data from the Internal Revenue Service (IRS) and Social Security Administration (SSA) to evaluate survey income data. More recent examples include Abowd and Stinson (2013), Bee (2013), Benedetto, Stinson and Abowd (2013), Harris (2014), Bee, Gathright and Meyer (2015), Giefer et al. (2015), Hokayem, Bollinger and Ziliak (2015), Bhaskar et al. (2016), Chenevert, Klee and Wilkin (2016), Noon, Fernandez and Porter (2016), Bee and Mitchell (2017), Fox, Heggeness and Stevens (2017), O’Hara, Bee and Mitchell (2017), Abowd, McKinney and Zhao (2018), Benedetto, Stanley and Totty (2018), Bhaskar, Shattuck and Noon (2018), Brummet et al. (2018), Eggleston and Reeder (2018), Meyer and Wu (2018), Murray-Close and Heggeness (2018), Rothbaum (2018), Shantz and Fox (2018), Bee, Mitchell and Rothbaum (2019), Bollinger et al. (2019), Imboden, Voorheis and Weber (2019), Jones and Ziliak (2019), Eggleston and Westra (2020), Larrimore, Mortenson and Splinter (2020), Abraham et al. (2021), Eggleston (2021), Larrimore, Mortenson and Splinter (2021), Meyer and Mittag (2021), Rothbaum et al. (2021), Carr, Moffitt and Wiemers (2022), Fox et al. (2022), Hokayem, Raghunathan and Rothbaum (2022), Larrimore, Mortenson and Splinter (2022), McKinney and Abowd (2022), Moffitt et al. (2022), Moffitt and Zhang (2022), Rothbaum and Bee (2022), and others. For a more complete discussion of nonsampling error in income and poverty statistics, refer to Bee and Rothbaum (2019), which also discusses the challenges in addressing these issues and discussed the research agenda that led to this project. 11For example, BEA’s approach scales up income on the intensive margin in same cases, risking imputing income to accurate reporters rather than for extensive margin misreporting, which is common for retirement income (Bee and Mitchell, 2017) and means-tested program benefits (Shantz and Fox, 2018; Meyer and Mittag, 2019). The CBO model imputes missing income and benefits on the extensive margin conditional on survey characteristics, but underreporting is often not well captured by the observable survey information 10 Similar work has been pursued under a separate project at the Census Bureau, the Com- prehensive Income Database (CID, refer to Medalia et al. 2019), including Meyer and Wu (2018), Meyer et al. (2021b), Meyer et al. (2021a), and Corinth, Meyer and Wu (2022). A main focus of the CID project has been on addressing misreporting in income and means- tested program benefits. We additionally address nonresponse bias, missing administrative data, and model measurement error in survey and administrative earnings. 3 Data We would like to use any available data that can help inform estimates of income, resources, or wellbeing, broadly defined. This includes survey and decennial census data collected by the Census Bureau, administrative data, and commercial data. The data could be useful to directly measure resources, to model estimates of resources, to validate measures, to address nonresponse, etc. In this section, we discuss each source of data, also shown in Table 4. Figures 4 and 5 show how we put these data sources together to create the files we use to generate the income and poverty estimates, which we discuss in Section 3.7. 3.1 Survey Data Surveys collect information on many characteristics of individuals and households that are not available or well-measured in administrative data for all or subsets of the population. These include race, Hispanic origin, tenure (homeownership vs. renting), educational attain- ment, household composition, and much more. Surveys also include information on income, although we have considerable evidence on misreporting of income on surveys. (Mittag 2019 and Fox et al. 2022). The TRIM model uses unlinked auxiliary data and program rules to impute missing benefits on the extensive margin. However, Shantz and Fox (2018) and Mittag (2019) show that the underreported program benefits may not be missing from households that appear to qualify for them either through the rules-based imputations or from matching to auxiliary data, with the caveat that income item nonresponse means that household income and program receipt may be less correlated as the regular survey imputations do not condition on administrative program data. 11 Survey operations also provide information that can be crucial for these estimates. First, major surveys conducted by the Census Bureau are stratified random samples of addresses, in which the occupancy status of housing units (vacant/occupied) is assessed as part of the survey. This provides a sample of units in our target universe, occupied housing units, and their sampling probability. In administrative records, it can often be unclear or even impossible to identify the set of occupied units with no available data – i.e., households and individuals that received no W-2 or other information return and did not file taxes or units with no linked information because they are not the primary residence for a high-income or wealth household. The unobserved units in administrative data may be more likely to be at one end of the income distribution than the other – making their absence particularly problematic when measuring inequality or hardship, such as poverty. We use data from two household surveys. First, we use the Current Population Survey’s (CPS) Annual Social and Economic Supplement (ASEC). The CPS ASEC is an annual survey conducted from February to April each year as a supplement to the monthly CPS. Respondents are asked social and demographic questions, as well as questions about their income and resources in the prior calendar year. CPS ASEC data are available at the Census Bureau from 1967 to the present. In 2019, approximately 95,000 addresses were sampled for the CPS ASEC.12 It is the source of the official poverty measure produced by the Census Bureau as well as widely cited measures of the household income distribution (Semega et al., 2019). In Version 1, we estimate income and poverty statistics on the 2019 CPS ASEC sample for income in year 2018. Second, we use theAmerican Community Survey (ACS), which is available from 2005 to the present. The ACS is an ongoing survey of more than 2 million respondent households each year. Respondents are asked similar (although generally less detailed) questions than the CPS ASEC, particularly for income. Additionally, ACS respondents are asked about income 12Refer to the CPS ASEC technical documentation at https://www2.census.gov/programs-surveys/ cps/techdocs/cpsmar19.pdf. 12 https://www2.census.gov/programs-surveys/cps/techdocs/cpsmar19.pdf https://www2.census.gov/programs-surveys/cps/techdocs/cpsmar19.pdf in the prior 12 months, rather than the prior calendar year as in the CPS ASEC.13 For Version 1 of this project, the ACS provides summary information by geography and occupation that are used in our weighting model and earnings measurement error model. Both the CPS and ACS use field representatives to assess the occupancy status of housing units, the CPS as part of the Housing Vacancy Survey and ACS for estimates of vacancy rates.14 3.2 Other Census Bureau Data The Census Bureau has other data available on the nation’s people and households that we use. First, we use data from the decennial census. This includes information on each individual’s race, Hispanic origin, and age. We also use information from theMaster Address File File (MAF).15 The MAF contains continuously updated information of all known living quarters in the United States. The MAF is used to select housing units for inclusion in household surveys, including the CPS and ACS, as well as for decennial census operations. The MAF also includes housing unit characteristics, such as whether addresses are in single-family or multi-family units. We also use the Master Address File Auxiliary Reference File (MAF-ARF) which links addresses in the MAF to individuals who reside there in each year. The MAF-ARF is constructed from multiple administrative data sources, including from the IRS, Department of Housing and Urban Development (HUD), and the U.S. Postal Service, among others. Each of these other Census Bureau data sources provide information that can help us address nonresponse bias and better estimate income and poverty statistics on representative samples 13ACS technical documentation is available at https://www.census.gov/programs-surveys/acs/ technical-documentation.html and https://www.census.gov/content/dam/Census/library/ publications/2020/acs/acs_general_handbook_2020.pdf. 14Refer to https://www.census.gov/topics/housing/guidance/vacancy-fact-sheet.html for a dis- cussion of housing vacancy estimates in the Housing Vacancy Survey (from the CPS), ACS, and American Housing Survey. 15The specific file we use is the MAF extract file, or MAFx. 13 https://www.census.gov/programs-surveys/acs/technical-documentation.html https://www.census.gov/programs-surveys/acs/technical-documentation.html https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020.pdf https://www.census.gov/content/dam/Census/library/publications/2020/acs/acs_general_handbook_2020.pdf https://www.census.gov/topics/housing/guidance/vacancy-fact-sheet.html of individuals, families, and households. 3.3 Federal Administrative Data The federal government data we use are provided primarily by the IRS and Social Security Administration (SSA). The Census Bureau also has an agreement with the Department of Health and Human Services (HHS) for data on the Temporary Assistance for Needy Families (TANF) program from some states. That data will be discussed in Section 3.4, as TANF data are also shared with the Census Bureau by individual partner state agencies. 3.3.1 IRS Data From the IRS, we have the following data: 1. the Information Return Master File (IRMF) from 2005 to the present, 2. the universe of Form 1099-R returns on “Distributions From Pensions, Annuities, Retirement or Profit-Sharing Plans, IRAs, Insurance Contracts, etc.” from 1995 to the present, 3. the universe of Form W-2 returns on “Wage and Tax Statement” for all W-2 covered jobs from 2005 to the present, and 4. the universe of Form 1040 tax filings every five years from 1969 to 1994, 1995, and then each year from 1998 to the present. The IRMF includes an indicator for each individual that received one of several information returns in a given year as well as their address, including for Forms 1098, 1099-DIV, 1099-G, 1099-INT, 1099-MISC, 1099-R, 1099-S, SSA-1099, and W-2. The IRMF allows us to link individuals to their addresses and is used in constructing the MAF-ARF. The IRMF does not include any information on income amounts. The 1099-R extracts provided by the IRS include information on amounts of defined-benefit 14 pension payments (including survivor and disability pensions) and withdrawals from defined- contribution retirement plans. These extracts exclude 1099-R records corresponding to direct rollovers between accounts. The W-2 extracts provided by the IRS include select W-2 boxes, including wages and salary net of pre-tax deductions for health insurance premiums and deferred compensation, as well as the total amount of deferred compensation. This means that employee and employer pre-tax contributions to health insurance premiums are not available in the W-2 data. The 1040 extracts provided by the IRS include information on tax-unit wage and salary income, gross rental income, gross Social Security income, taxable and tax-exempt interest income, dividends, Adjusted Gross Income, and a constructed measure of Total Money Income (TMI). TMI is the sum of taxable wage and salary income, interest (taxable and tax-exempt), dividends, gross Social Security income, unemployment compensation, alimony received, business income or losses (including for partnerships and S-corps), farm income or losses, and net rent, royalty, and estate and trust income.16 The 1040 also includes information on marital status through filing status and filer information and identifies up to four dependents. We use IRS data to address nonresponse bias and measurement error. 3.3.2 Social Security Administration (SSA) Data From the SSA, we use the following data: 1. the Numerical Identification System (Numident) file, 2. extracts from the Detailed Earnings Records (DER). 3. several files from the Payment History Update System (PHUS), and 16Prior to tax year 2018, TMI also included total pensions and annuities. However, this was removed from TMI due to a change to income reporting on the Form 1040 and the regulations regarding data sharing between IRS and the Census Bureau. 15 4. several files from the Supplemental Security Records (SSR). The Numident contains information on any individual to ever receive a Social Security Num- ber (SSN), including their sex, date of birth, date of death, information on their citizenship status, and their location of birth. The DER contains job-level W-2 information that generally corresponds to the data provided by IRS, but with the potential for additional cleaning and error correction from SSA as part of their administration of the Social Security system. The DER also includes Social Security covered self-employment earnings reported on the Form 1040 SE (if at least $400). Like many SSA data sets, including some PHUS and SSR files, the DER is only available for linked respondents from specific surveys and years.17 The PHUS contains monthly Old Age, Survivors, and Disability Insurance (OASDI) program payment information from 1984 to the present. There are several PHUS files available to the Census Bureau. One set of PHUS files includes OASDI recipients in 2020 and 2021, with one record per address. There are also PHUS files for linked respondents from specific surveys and years. The SSR contains monthly Supplemental Security Income (SSI) payments for both federal SSI payments and state payments administered by the SSA, from 1984 to the present. One set of PHUS files includes SSI recipients in 2020 and 2021, with one record per address. There are also SSR files for linked respondents from specific surveys and years. We use the survey-linked SSA data (DER, PHUS, and SSR) to address item nonresponse bias and measurement error. The Numident and address-level SSA data (PHUS and SSR) are useful for weighting to address nonresponse bias. 17Specifically, the DER includes respondents with an assigned Protected Identification Key (discussed in Appendix A) who can be linked to the Numident from the CPS ASEC in 1973, 1979, 1981-1991, 1994, and 1996-present, the Survey of Income and Program Participation (SIPP) in 1984, 1990-83, 1996, 2001, 2004, 2008, 2014, and 2018-present, and the ACS in 2019. 16 3.4 State Administrative Data We use several data sets shared with the Census Bureau by state government agencies: 1. the Longitudinal Employer-Household Dynamics (LEHD) files, 2. data on Supplemental Nutrition Assistance Program (SNAP) participation, and 3. data on Temporary Assistance for Needy Families (TANF) program participa- tion. 3.4.1 LEHD Under the LEHD program, states provide data on wage and salary earnings reported by firms for the administration of the unemployment insurance (UI) program. Firms report gross earnings to UI offices, so the LEHD should include non-taxable earnings that are not reported on a Form W-2 for the same job such as pre-tax employee contributions for health insurance premiums. However, coverage in the LEHD data we use is not complete, as many government employees (such as federal civilian employees, postal workers, and Department of Defense employees) are not covered by state UI benefits. Furthermore, some private-sector employees, including those employed by religious organizations, are not covered by UI, and are therefore not present in the LEHD data. Finally, data sharing agreements between a state and the Census Bureau are not always available, resulting in LEHD earnings missing for all jobs in specific states and years.18 LEHD data are useful for addressing nonresponse bias and misreporting. 18More information on the LEHD program and data is available at http://lehd.ces.census.gov/data/ lehd-snapshot-doc/latest/, accessed 12/16/2022. While the LEHD program does receive data from the Office of Personnel Management (OPM) for many federal employees, those data are not part of the more recent years of data in the LEHD Interleave file used in this project. 17 http://lehd.ces.census.gov/data/lehd-snapshot-doc/latest/ http://lehd.ces.census.gov/data/lehd-snapshot-doc/latest/ 3.4.2 SNAP The Census Bureau has agreements with many states to receive data on SNAP participation, although the available states vary by year.19 The SNAP data includes benefits received for each case as well as the individual members recorded in that SNAP case. SNAP data are useful for addressing misreporting of other income items. SNAP is not included in money income, but these data will be used to address misreporting of in-kind benefits in future releases. 3.4.3 TANF The Census Bureau also has agreements with many states to receive data on TANF partici- pation. In addition to the state agency data, the Census Bureau also has data on TANF cash assistance receipt from HHS. As with SNAP, the available states vary by year.20 TANF data are also available by case (benefit amounts) with individuals in each TANF case recorded as well. TANF data are useful for addressing misreporting. 3.5 Commercial Data We use information on home values from Black Knight, a third party aggregator of prop- erty tax records, which can be useful in correcting for selection into nonresponse on sur- veys.21 These data are useful for weighting to address nonresponse bias. 19For example, SNAP data are available for 17 states in 2018, 20 states in 2014, 16 states in 2010, and 6 states in 2006. In 2018, the states with available SNAP data are Arizona, Connecticut, Florida, Hawaii, Idaho, Indiana, Kentucky, Maryland, Mississippi, Montana, Nevada, New Jersey, New York, North Dakota, Tennessee, Utah, and Wyoming. 20TANF data are available for 36 states in 2018, 37 states in 2014, 36 states in 2010, and one state in 2006. 21Chapin et al. (2018) evaluated the use of similar data from CoreLogic in ACS production and discuss some strengths and limitations of this kind of data. One limitation is that the coverage varies by location. 18 3.6 Firm Data We also use data on firm characteristics from the Longitudinal Business Database (LBD), which is described in Chow et al. (2021). The LBD contains establishment-level information on firm employment and payroll. The LBD is constructed from other data sources at the Census Bureau, including the Business Register (BR), that are constructed using data from the IRS and surveys of businesses, including the Economic Census. Firm data are useful for addressing nonresponse bias, because they help predict survey responses. They can also be used to address misreporting when there is measurement error in both survey and administrative data, since firm information might help us diagnose error in both data sources. 3.7 Linkage and File Construction To make use of all of this data, we link them to create two main files: (1) the Address File and (2) the Person File, with linkages made at the following levels: • Individual - using Protected Identification Keys (PIKs), • Address - using Master Address File identifiers (MAFIDs), • Job - using PIKs and Employer Identification Numbers (EINs) and by the job matching procedure described below, • Firm - using the LBD firm identifiers (LBDFID) and Employer Identification Numbers (EINs), and • Geography - by state, county, and census tract. The data linkage process for the individuals and addresses is straightforward. We match observations using unique identifiers attached to each person (PIK) and address (MAFID) in each file. The assignment of these identifiers is discussed in Appendix A. To link a 19 survey respondent to any administrative data, we must assign that respondent a PIK using the personally identifiable information (PII) on the survey. If a survey respondent is not assigned a PIK, they cannot be linked to any administrative data. As discussed in Section 2, we have many sources of wage and salary earnings information. Three of them are available at the job level – W-2s, the DER, and LEHD. However, linking LEHD and W-2 jobs is not trivial.22 In the simplest case, a firm files a W-2 and reports the job to the UI office with the same EIN. We can link these “direct matches” by PIK and EIN. However, some firms do not file their W-2s and UI reports under the same EIN. We use individual and job-level information from the universe of W-2 and LEHD jobs to create indirect matches of firm identifiers across datasets. We discuss this process in detail in Appendix A.3 with an example in Figure A1. After direct and indirect linkage, of the 264 million jobs, we find 82 percent of jobs matched directly by PIK-EIN, 6 percent matched indirectly, 10 percent unmatched from W-2s, and 3 percent unmatched from the LEHD (shown in Table A2). We use this linked job infor- mation to better estimate gross earnings at the job and person level for use in our income estimates. Because firms do not necessarily correspond to unique EINs, we use information from the redesigned Longitudinal Business Database (LBD) to link workers (through EINs in the job data) to unique firms (Joint Committee on Taxation, 2022; Chow et al., 2021), which we discuss in Appendix A.4. We create the Address File by linking the sample of occupied (non-vacant) housing units in the survey to the aforementioned sources of administrative, survey, census, and commercial data, as shown in Figure 4. By starting with addresses, we have information from all occupied units, including respondents and nonrespondents. In the address file, we do not use any information from survey responses other than whether the unit responded. This file is used 22As the DER is sourced from W-2s, linking DER and W-2 jobs is generally simple. 20 with the Person File to construct the weights that address selection into our sample and selection into linkage, issues discussed in Section 2. We then create the Person File by linking survey respondents to administrative data, as shown in Figure 5. In combination with the weights created using this and the Address File, the Person File is used for all of the subsequent steps in generating the income and poverty estimates. The Address and Person Files are discussed in more detail in Appendix B. 4 Methodology In this section, we describe the steps needed to take the data described in Section 3 through to estimating income and poverty statistics, shown in Table 5. We have categorized the steps into three groups: (1) weighting, (2) imputation, and (3) estimation. 4.1 Weighting Our analysis sample is the set of households that respond to the CPS ASEC with all survey- adults assigned a PIK.23 We use weighting to address several measurement challenges dis- cussed in Section 2, particularly survey unit nonresponse and selection into linkage. Weight- ing is particularly useful when all of the information is missing for a subset of units – in our case we have no survey information for nonrespondents and no administrative information for individuals that cannot be assigned a PIK. To address survey unit nonresponse, we use information from the linked administrative and decennial census data which is not observed in the survey. This information is available for all linkable households regardless of whether they responded, as is the geographic sum- mary information. We weight respondent households so that the weighted estimates for these 23We define survey-adults as those 15 and over as the survey income questions are asked for all individuals 15 and over in a household. 21 linked characteristics match the estimates obtained using all occupied households given their sampling probability in the CPS while the person-level weights also match to external pop- ulation controls by state. This should address survey unit nonresponse, following prior work in the ACS (Rothbaum et al., 2021) and the CPS ASEC (Rothbaum and Bee, 2022). To address selection into linkage, we extend that work by estimating statistics from survey responses in the respondent sample and reweight households with all adults linked (our analysis sample) so that the weighted estimates from analysis sample simultaneously match: (1) the linked administrative characteristics from the sample of occupied units, (2) the survey- response estimates from the respondent sample, and (3) the external population controls by state. This step should address selection into linkage, extending the prior work that was focused only on survey estimates and survey unit nonresponse. Weighting also helps address selection into administrative data and administrative data nonresponse. The survey frame contains geographic summary information at the address level for each occupied household and survey responses for respondent households that we cannot link to administrative data, whether at the individual or address level. For a more complete discussion of weighting, including the underlying assumptions, imple- mentation details, and statistics validating the model, refer to Appendix C. 4.2 Imputation Many of our measurement challenges are not the result of blocks of information missing completely for defined subsets of observations. For example, an individual that does not respond to the survey earnings question (46 percent of all workers) or has a missing LEHD job may have all the other information (e.g., other survey responses, W-2 job earnings, etc.) that we need to estimate income and poverty. For these measurement challenges, imputation is a better approach to fully utilize the information that is available (Raghunathan et al., 2001). 22 There are four sets of variables that we impute: 1. Survey earnings, 2. LEHD job-level gross earnings, 3. Means-tested program benefits (TANF and SNAP), and 4. Administrative income for tax nonfilers in certain categories (unemployment compen- sation, interest, and dividends) In the 2019 CPS ASEC, 46 percent of individuals with earnings in the survey had their primary job earnings imputed.24 We impute earnings for these individuals (and the individ- uals with missing earnings from other jobs/employers) conditional on the survey and linked administrative data. These imputed values reflect the distribution of differences between survey and administrative earnings, conditional on the observed information. This allows us to address potential measurement error in administrative earnings for survey nonrespon- dents. Likewise, we are missing LEHD job-level gross earnings for 8 percent of individuals’ highest earning job.25 There are additional jobs where W-2 earnings exceed LEHD earnings or the disagreement between them is sufficiently large that we impute gross earnings out of concerns about data quality. As discussed in Section, 2, we would like gross earnings from all jobs because of the conceptual misalignment between available W-2 earnings and the gross earnings we would like to measure. However, gross earnings is not available because of incomplete data coverage (some states missing from the LEHD), selection into administrative data (some jobs not covered by unemployment insurance and thus missing from the LEHD), administrative data “nonresponse” (missing jobs in the LEHD that should be present), and administrative data misreporting. Following Fox et al. (2022), we also use imputation for missing means-tested program benefits 24Refer to Table 6 for rates of missing data for imputed income items. 25If we order jobs from highest to lowest earning in the job-level administrative data. 23 due to incomplete data coverage. Finally, we impute specific administrative income items for individuals that do not file taxes using parameters estimated on more detailed data by Rothbaum (2023). 85 percent of survey-adults can be linked to a 1040 tax filing (refer to Table A4). For those individuals, the Total Money Income measure includes many income items that are underreported on surveys such as unemployment insurance compensation, interest, and dividends, even if not all items are available separately. However, we observe only whether non-filers received several information returns, including Forms 1099-G, 1099-INT, and 1099-DIV in the IRMF. From these we have information on whether they received UI compensation, interest income, and dividends, respectively. Each of these income sources are significantly underreported on surveys (Rothbaum, 2015). Rothbaum (2023) worked with more detailed data available under a separate agreement between the Census Bureau and IRS, for limited use. In that data, the 1099-G, 1099-INT, and 1099-DIV data are available, including income amounts. Rothbaum (2023) released coefficients that can be used to impute these amounts for nonfilers conditional on survey responses and the administrative data used in this project. We use that information to impute these underreported income items for nonfilers. This imputation addresses selection into administrative data (tax filing) and survey misreporting of these specific income types. For a more complete discussion of imputation, including the underlying assumptions, imple- mentation details, and statistics on the imputed values, refer to Appendix D. 4.3 Estimation With the Person File, weights, and imputations, we have complete data for all the inputs used in the NEWS estimates. The final step in processing is putting that data together to estimate income and poverty. 24 4.3.1 Earnings Measurement Error Model Earnings represent 80 percent of all income (Rothbaum, 2015). Measurement error in survey and administrative earnings, therefore, merits particular attention.26 Although survey wage and salary earnings are relatively well reported when compared to external benchmark aggregates (Rothbaum, 2015), work with linked microdata has identified systematic differences between administrative records and survey responses.27 This work has generally found survey wage and salary earnings are “mean-reverting” relative to adminis- trative reports; i.e., low earners in the administrative data tend to report higher earnings on surveys, and high earners in the administrative data tend to report lower earnings in surveys. There is also extensive margin disagreement between survey and administrative records – about 10 percent of working-age individuals have earnings in one data source but not the other (Bee, Mitchell and Rothbaum 2019). Some papers in the survey misreporting literature assumed the administrative records were free of error (Bound and Krueger 1991, Bound et al. 1994, Pischke 1995, for example).28 However, more recent work considers the possibility that administrative data also contain measurement error, such as unreported earnings. Abowd and Stinson (2013) consider a model in which both survey and administrative reports for a given job may contain error. Under their approach, “true” earnings are a weighted average of the two reports, but they leave the selection of the proper weight to future work. Using Danish administrative data, Bingley and Martinello (2017) cannot rule out that survey income reports have only classical measurement error given the presence of measurement error in administrative records. We 26Some of the discussion in this section follows Bee and Rothbaum (2019) closely. 27Alvey and Cobleigh (1975), Duncan and Hill (1985), Bound and Krueger (1991), Bound et al. (1994), Pischke (1995), Bollinger (1998), Bound, Brown and Mathiowetz (2001), Roemer (2002), Kapteyn and Ypma (2007), Gottschalk and Huynh (2010), Meijer, Rohwedder and Wansbeek (2012), Abowd and Stinson (2013), Murray-Close and Heggeness (2018), Bee, Mitchell and Rothbaum (2019), Imboden, Voorheis and Weber (2019), Jenkins and Rios Avila (Forthcoming), and many others have studied wage and salary earnings. 28In some cases, the authors restrict their analysis to a subset of workers for which the assumption is more likely to be valid. For example, Pischke (1995) compares surveys of employees of a particular firm against firm reports of the same workers’ earnings. Bound and Krueger (1991) specifically remove occupations they suspect may have under-the-table earnings. 25 do not assume that measurement error is only present in surveys. Under-the-table earnings are, by definition, not reported to the IRS, which can bias income estimates for particular subgroups of the population (such as by occupation). In the absence of a “truth set” of data, it is an open question how much of this disagreement is due to misreporting on surveys or measurement error in the administrative data.29 We have several separate reports of administrative earnings. In Table 7, we show summary statistics on the number of individuals assigned a PIK with any wage and salary earnings reported from all possible combinations of W-2s, the DER, and the LEHD. We also show the probability that survey respondents report non-zero survey earnings for each combination of administrative wage and salary sources. The vast majority of individuals with earnings in one source have earnings in all three.30 From the three separate administrative job-level wage and salary earnings sources (including gross earnings imputed as discussed in Section 4.2), we construct our job-level estimate of gross earnings. We aggregate these job-level earnings to estimate total administrative wage and salary earnings for each individual. This gives a measure of total administrative wage and salary earnings (ya), which we then use in the model with our final post-imputation total survey wage and salary earnings (ys) discussed in Appendix D. 29Compounding the challenge, it is not always the case that different sources of administrative data agree. Bee, Mitchell and Rothbaum (2019) found a 0.4 percentage point difference in the estimated poverty rate if survey earnings are replaced using administrative earnings data from SSA compared to data from IRS, both of which are based on the same W-2s. 30Table 7 also has information on how the W-2 earnings information available in the DER differs from the IRS W-2 information. In Panel B, we focus on individuals we can and cannot link to the Numident (a proxy for having a valid SSN). If individuals have W-2 and DER earnings, they are basically always present in the Numident and are very likely to report wage and salary earnings in the survey (87 percent). However, if individuals are in the Numident and have W-2 earnings, but no DER earnings, then they are very likely not to report wage and salary earnings in the survey. This suggests that there is measurement error in the W-2 file for these cases that is not in the cleaned, SSA-provided DER data. We therefore default to the DER information in these cases of no job-level administrative earnings. However, if individuals are not in the Numident and have W-2 earnings, but no DER earnings, they are very likely to report wage and salary earnings on the survey (85 percent). In these cases, we conclude the DER is missing earnings for those without SSNs that are correctly present in W-2s. For these individuals, we default to the W-2 information of positive job-level earnings. This is a clear example of how administrative data are not necessarily free of error and different sources of administrative data covering the same concept (wage and salary earnings) from the same tax information do not necessarily agree. 26 The survey and administrative earnings can differ on the extensive or intensive margin. With extensive margin disagreement, where earnings are present in one but not both sources, we default to the earnings report that is non-zero. In other words, we assume that any survey report in the absence of administrative earnings reflects under-the-table income or a reporting or linkage issue in the administrative data. We also assume that any administrative earnings without a corresponding survey earnings report reflect under-/misreporting on the survey. These are both assumptions that we plan to examine in future work. The other difference we observe is intensive margin differences in reporting, where the re- ported values are not equal. Figure 6 shows a scatterplot of survey versus administrative reports of wage and salary earnings.31 Several important features of the data are visible in the figure. First, survey and administrative earnings generally agree, reflected in the clus- tering around the 45◦ line. However, regressing survey on W-2 wage and salary earnings (in logs) yields a slope of 0.8, which is consistent with mean reversion in survey earnings reports.32 In our forthcoming companion paper, Bee et al. (2023) define a model that parameterizes the measurement error in ya and ys relative to the unobserved true earnings (y) for intensive margin disagreement. We provide a concise summary of the model here. Since there can be measurement error in both survey and administrative earnings reports and we do not have data on “true” earnings for anyone, we must impose assumptions on the data that are untestable or can only be tested indirectly. For example, we believe that administrative earnings could be underreported either because some income is missing (such as some portion of tips) or some jobs may be missing. Likewise, we do not assume that administrative earnings are free of classical measurement error, or noise, even if we believe that noise may be of lower variance than the noise in survey earnings reports. 31The figure is reproduced from O’Hara, Bee and Mitchell (2017) as more recent disclosure rules limit the possibility of releasing such detailed information of individual survey and administrative earnings values. 32For example, if we assumed no measurement error in W-2 earnings, then a slope that is less than one could indicate mean-reverting error non-classical measurement error in survey responses. 27 These assumptions provide some structure to our earnings measurement error model. The model setup consists of two earnings measures: (a) survey earnings, which are condition- ally unbiased but have potentially downward-biased conditional variances, and (b) admin- istrative earnings records, which can be conditionally biased but have accurate conditional variances.33 While these assumptions on survey versus administrative records are not directly testable, they were chosen to be both consistent with prior literature on measurement error in earnings and to be consistent with previous measurements of average income. Under our assumptions, the survey would be unbiased for average income measures but may have trouble accurately assessing income in the tails of the distributions. On the other hand, relying only on admin- istrative records may generate significant biases in the estimation of income for populations with income typically not captured by those data. Combining these two sources allows us to mitigate both these problems simultaneously. With our assumptions on survey and administrative earnings from above, Bee et al. (2023) define a model in a Mean Squared Error (MSE) framework with a set of parameters on the random noise and relative mean reversion in survey report, ys, and administrative record, ya, conditional on other observed characteritsics, x. The model also defines a “survey confidence” (SC) measure that is a function of two sets of terms. The first is a measure of the estimated bias in the administrative data by comparing E(ys\|x) to E(ya\|x). The second set of terms compares the relative variance of the random noise in the two reports conditional on x. We 33To further motivate the relevance of these assumptions, consider estimating earnings for auto mechanics as a group. Assumption (a) would imply that if you asked auto mechanics to report what they earned on a survey, some would over-report and some would under-report, but you would still recover an unbiased estimate of average earnings. On the other hand, at the individual-level these mechanics might not remember their exact earnings and so might report their earnings from an average of prior years, such that variation across survey reports would not reflect true variation in earnings for that year. On the other hand, assumption (b) implies that administrative records would fail to generate a correct average for auto mechanic earnings, presumably due to the prevalence of under-the-table payments. Under assumption (b), administrative data better capture variation across individual-level earnings, such that a mechanic whose W2 earnings were twice as large as another mechanic would be expected to have actually earned twice as much in that year. This would be satisfied if, for example, all auto mechanics reported 50 percent (or any fixed percent) of their income to the IRS. 28 select the survey report if the squared bias term exceeds the difference in the variance terms, or if in the MSE framework, the estimated administrative bias is exceeded by its relatively lower noise. The model is only identified and possible to estimate with an assumption about the degree of mean reversion in survey reports relative to administrative reports. This mean reversion parameter, κ (or “kappa” in tables and figures in this paper), cannot be estimated, and must be assumed because true earnings, y, are never observed. If κ = 1, there is no mean reversion in the survey relative to the administrative data. We assume greater mean reversion as κ decreases from 1. With a given κ, we can estimate the SC measure for each individual conditional on his or her x characteristics, which would reflect the model’s “confidence” by comparing the bias and variance terms in an MSE framework. We use this SC measure in our decision rule to select the survey or administrative wage and salary earnings report — if SC > 0, we select the survey report.34 We select the “best” wage and salary earnings report for individuals based on their observable characteristics x, but not conditional on their actual survey or administrative reports. This is in contrast with Meyer et al. (2021b), which takes the maximum of survey-reported and administrative earnings in at least some cases. In other words, we take survey reports for people whose characteristics suggest that their survey reports are better according to the SC measure than their administrative reports. Bee et al. (2023) discuss potential limitations and extensions of this approach to incorporate the actual earnings reports and additional information, such as longitudinal earnings histories, to improve our estimates of earnings given survey and administrative reports. Misclassification of wages versus self-employment earnings further complicates efforts to rec- oncile multiple earnings reports. If individuals report wage and salary earnings on the 34Bee et al. (2023) discuss the implementation details of the estimation and additional features of our decision rule in the case when we determine that E(ys\|x) < E(ya\|x) with some confidence for a given individual. 29 survey but self-employment earnings on their tax returns, it’s not clear whether those rep- resent two separate sources of income or the same income reported in different categories. Misclassification appears to be a common issue. Only 35 percent of individuals with pos- itive administrative self-employment earnings report any self-employment earnings on the survey and less than 50 percent of the survey self-employed have positive self-employment earnings in the administrative data (Abraham et al., 2021). At this time, we generally defer to the administrative data when there is disagreement about the source of earnings (wage and salary vs. self-employment) or if self-employment is reported in both survey and admin- istrative data. In the future, addressing misclassification of earnings and self-employment earnings misreporting is an important avenue of research and improvement of our income estimates. In Table A8, we summarize the possible combinations of survey and administrative reports of wage and salary and self-employment earnings and show which we use in our income estimates. The measurement error model discussed in this section is used for 53 percent of adults35 and for 74 percent of individuals with any reported earnings in either source. Another 39 percent of adults had no survey or administrative earnings or reported earnings in one source, but not the other. Given that we default to the source with reported earnings under extensive margin disagreement, that leaves above 8 percent of adults or 12 percent of individuals with earnings in either source for whom we ignore survey reported wage and salary earnings and use only administrative data due to potential misclassification or other data issues. In Table A9, we show the share of individuals whose survey earnings would be used for various κ mean-reversion parameter values (from the set of people listed as using the measurement error model in Table A8). The share varies from 6 percent (κ = 0.7) to 31 percent (κ = 1, no survey-report mean reversion). For the NEWS estimates, we select κ = 0.9 as it implies 35In this context, we define adult as people aged 15 and above who are asked the CPS ASEC earnings questions. 30 a relatively modest level of mean reversion and selects the survey wage and salary earnings report 21 percent of the time. However, we assess robustness to alternative values of κ in Section 5.2. Given our chosen survey mean reversion parameter, Table 8 reports the share of individuals whose survey earnings were used as part of our measurement error model (as a share of workers from Table A8 for whom the measurement error model was used). Overall, we use survey earnings for 21 percent of workers. The rate at which survey earnings are used varies by age, race, occupation, and industry. For example, survey earnings are used less often for Black workers and younger (18-24) and older (55+) workers. However, survey earnings are used for 59 percent of workers in the construction industry. 4.3.2 Income Replacement In this section, we discuss the final step – combining the survey and administrative data and replacing particular survey income components with their counterparts in the administrative data in order to estimate each survey respondent’s money income. We use separate processes for filers and nonfilers. There is more income information available for tax filers, but some of it is only available at the tax unit, but not the individual, level. Table A10 summarizes the income information available for filers and nonfilers. For tax filers, we start with Total Money Income (TMI) constructed from their 1040s, which is the sum of taxable wage and salary income, interest (taxable and tax-exempt), dividends, alimony received, business income or losses (including from partnerships and S-corps), farm income or losses, net rent, royalty, and estate and trust income, unemployment compensation and gross Social Security benefits (as noted in Section 3.3.1). For wage and salary earnings, TMI includes taxable wage and salary earnings reported on the 1040. This amount will understate true earnings if gross earnings are greater than taxable earnings, for example, if individuals have deferred compensation or use pre-tax earnings to 31 pay health insurance premiums. It will also understate earnings if filers underreport their true earnings to the IRS. Therefore, we replace the wage and salary earnings component of TMI with our survey or job-level administrative earnings according to the rules shown in Table A8 and discussed in Section 4.3.1. We also replace 1040-reported Social Security income, as we are more confident in the data quality of the SSA data than in the gross 1040 amounts, which may not be well-reported in tax returns (particularly for non-taxable Social Security income). For retirement income, we cannot distinguish defined contribution (DC) plan withdrawals from defined benefit (DB) pensions in the 1099-R data.36 In the CPS ASEC, DC withdrawals are only counted as income for people aged 59 and above. We therefore follow that convention and include 1099-R retirement income for all individuals aged 59 and older. For those under 59, we include the 1099-R income if they reported pension or annuity income on the survey. We add this retirement income to TMI. Finally, we add several income components that are not taxable. From administrative sources, we add SSI and TANF and from the survey, we add educational assistance, fi- nancial assistance, workers’ compensation, and veterans benefit payments. For filers, that gives us our adjusted TMI, which we use in the income and poverty estimates. For nonfilers, we must add up the available components individually, since we do not have a 1040 TMI amount. To get the nonfiler equivalent of adjusted TMI, we start with wage and salary and self-employment earnings as indicated in Table A8. From administrative data sources, we add Social Security income (PHUS), retirement income (from the 1099-R following the same rules for filers as noted above by age), SSI (SSR), and TANF (state data). We add UI compensation, interest, and dividends imputed using the parameters estimated on the complete 1099-G, 1099-DIV, and 1099-INT data (Rothbaum, 2023). From the survey, we add rent and royalty income, educational assistance, financial assistance, 36We will apply and extend the work in Bee and Mitchell (2017) to characterize individual withdrawals as defined benefit or defined contribution in future work. 32 workers’ compensation, and veterans benefit payments. The sum of these amounts represents our best estimate of adjusted TMI for nonfilers, which we use in the income and poverty estimates in the next section. 5 Results 5.1 NEWS Estimates Table 1 and Figure 1 compare the NEWS estimates for median household income in 2018 to the survey estimates released in Semega et al. (2019).37 Across all households, the NEWS estimate for median household income was 6.3 percent higher ($67,170 vs. $63,180). Median household incomes were also higher for nearly all subgroups shown. The main exceptions were by age of householder. Pooled together, median household income for households under age 65 was not statistically different (-0.1 percent lower point estimate) whereas households 65 and older had 27.3 percent greater median household income ($55,610 vs. $43,700). For households aged 55-64, the difference was 5.0 percent ($72,430 vs. $68,950). For all age groups below 55, the point estimates were not statistically different from zero or negative. Figure 7 shows estimates from the 10th to 95th percentiles of the household income distribu- tion overall and by race and Hispanic origin, age of householder, and educational attainment. Overall, income increased more in proportional terms at the bottom of the distribution than at the top. This is particularly true for age 65 and over households, for which NEWS house- hold income was 31 percent higher at the 25th percentile, 20 percent higher at the 75th percentile, and 15 percent higher at the 90th percentile. Comparisons between NEWS and survey estimates for poverty are shown in Table 2 and Figure 2. Overall, poverty was 1.1 percentage points lower than in the survey estimate, equivalent to 9.4 percent fewer people in poverty. As with income, poverty was much lower 37All estimates are in 2018 dollars. To adjust to 2021 dollars using the R-CPI-U-RS as in official Census Bureau publications, multiply each income estimate by 399.0/369.8 = 1.079. 33 for the 65 and older population. We estimate a 3.3 percentage-point lower poverty rate and 34.1 percent fewer people in poverty. There were no groups for which poverty was statistically higher with the NEWS estimates. However, we did not find a statistically significant difference in poverty for Black individuals, children, residents of the Midwest, those outside of Metropolitan Statistical Areas, those with a disability, and those with some college education. Finally, in Table 3, we compare NEWS estimates for inequality statistics to the survey estimates, including for income shares, the Gini index, and various percentile ratios.38 For shares of income, we find a decrease in the share of income in the 2nd to 4th quintile and an increase in the share of income in the top quintile and particularly the top 5 percent. We estimate an increase in the Gini coefficient from 0.459 to 0.476. This is likely coming from no top coding and higher extreme income values in the administrative data relative to the survey, despite the larger increase in income at lower percentiles of the income distribution shown in Figure 7, Panel A.39 However, consistent with that figure, we find declines in the percentile ratio estimates (90/10, 90/50, and 50/10). For example, in the survey responses, household income at the 90th percentile is 12.5 times as large as at the 10th percentile. With the NEWS estimates, the ratio is 11.5. 5.2 Robustness to Alternative Uses of Earnings Data Figure A5 compares NEWS estimates of household income to estimates using alternative combinations of survey and administrative wage and salary earnings. In Panel A, we show how income varies under different rules for using earnings when the survey and administrative 38One important area of future research is how to address potential data issues that affect inequality, including how well our sample captures income at the far right tail of the distribution and how to address administrative data issues (like implausible extreme values) that might bias inequality statistics. We note this when discussing our future plans in Section 6. This will affect statistics such as income shares and the Gini coefficient that condition on the entire income distribution, but have less of an impact on statistics such as percentile ratios. 39Survey income top codes vary by income item, but generally do not exceed $1.1 million dollars for a given income source. 34 data disagree at the extensive margin, whether any earnings are present. We compare four scenarios to the NEWS estimates (with ya for administrative earnings and ys for survey earnings: (1) use ya unless ya = 0 and ys ̸= 0, (2) use ya, even if ya = 0 and ys ̸= 0, (3) use ys unless ys = 0 and ya ̸= 0, and (4) use ys, even if ys = 0 and ya ̸= 0. Scenarios (1) and (2) give priority to administrative earnings and (3) and (4) give priority to survey earnings. If we use either source of earnings when the other is zero, income declines substantially ((2) and (4)), particularly at lower income levels. If we use administrative earnings if ̸= 0 , scenario (1), the household income point estimates are generally lower than the NEWS estimates, although most of the differences are not statistically significant. If we use survey earnings if ̸= 0, scenario (3), the household income point estimates are lower everywhere, but the differences are only statistically significant in the tails of the distribution. To summarize, how we handle extensive margin disagreement substantially affects our income estimates, as does whether we prioritize survey or administrative earnings. Compared to just using administrative earnings (if ̸= 0), the measurement error earnings model does not have a substantial impact on household income overall, despite using survey earnings for 21 percent of the individuals the model was used on. In Figure A5 Panel B, we estimate the household income distribution for alternative κ/survey mean-reversion parameters in the earnings measurement error model. As κ varies from 1 to 0.7, the share of individuals whose survey earnings are used changes from 6 to 31 percent. Despite this, and while there are statistically significant differences between the NEWS estimates (κ = 0.9) and estimates with other κ, there are few economically meaningful differences in the household income estimates. For example, none of the alternative κs estimates a statistically significant difference in median household income and the range on the point estimates is from -0.05 percent to 0.03 percent different from NEWS estimate. At the 95th percentile, the estimates range from -0.46 percent to 0.89 percent different from the NEWS estimate (with only 0.89 percent different for κ = 0.7 statistically different from the NEWS estimate). 35 However, the choice of how to combine survey and administrative earnings could matter considerably more, shown in Panel C of Figure A5. We add another possible decision rule, which is to take the maximum of the two reports. This approach might be reasonable if one thinks all misreporting in both survey and administrative data is underreporting, although that does not seem consistent with the noise in survey reports around administrative wage and salary earnings we observe in Figure 6.40 Taking the maximum of reported wage and salary earnings would vastly increase measured household income across the distribution. Across the percentiles plotted in Figure A5, the income estimate using the maximum rule would be 13.5 percent greater than the NEWS estimate, on average. 5.3 Impact of Different Processing Steps on Income and Poverty Estimates The NEWS estimates reflect several bias correction steps, including reweighting for non- response, reweighting for linkage to administrative data, imputing to address nonrandom nonresponse, replacement of survey responses with administrative income information (in- cluding observed and imputed TANF and gross earnings), and the earnings measurement error model to select survey or administrative earnings. In Figure 3, we decompose the ad- justments to show the impact of each of these steps on the distribution of household income. In Panel A, we show the weighting and survey imputation steps compared to the survey es- timates, as these steps use administrative data to adjust for bias in survey-only information (the weights and imputed earnings). In Panel B, we show the impact of using administrative data (as discussed in Section 4.3.2) and the earnings measurement error model compared to the adjusted survey estimates from Panel A. In other words, Panel A illustrates the effect the survey-only adjustments and Panel B shows the effect of the final two steps after accounting 40Meyer et al. (2021b) take the maximum of survey and administrative earnings (total earnings, not just wage and salary) at least in some cases. However, they argue their estimates of extreme poverty are not affected by this because in most cases both the survey and the administrative earnings measure exceeds their extreme poverty thresholds when they disagree on the intensive margin. 36 for the survey-only adjustments. The weighting steps lower income across most of the distribution by 1 to 2 percent.41 Re- placing the survey earnings imputations (and accounting for uncertainty through multiple imputation) lowers the point estimates at the bottom of the distribution, consistent with the selection into response observed by Bollinger et al. (2019) in the tails and results in confidence intervals that are wider on average. In Figure 3 Panel B, we show the impact of the final two steps, income replacement and the earnings measurement error model, compared to the estimate after survey earnings impu- tation from Panel A. We compare the household income distribution with and without the administrative data and find large effects across the distribution, from 17.1 percent at the 10th percentile, to 10.3 percent at the 25th, 6.8 percent at the median, and 3.6 percent at the 75th. Panel B also shows the impact of the earnings measurement error model and the use of survey earnings, which has a minimal impact on household income.42 Panel C shows the overall comparison between the NEWS and survey estimates.43 Figure A6 shows the same decomposition by survey adjustments (Panel A) and adminis- trative income replacement and measurement error model (Panel B) for the subgroups in Table 1. Figure A7 does the same for poverty. In both, it is generally the case that the survey adjustments move point estimates for median household income down and poverty up, but generally the differences are not statistically significant. The administrative income replacements move income up and poverty down for most subgroups as well. 41This is slightly different than Rothbaum and Bee (2022), which found no statistically significant differ- ences across the distribution with an average point estimate of -0.23. However, we use more data, particularly contemporaneous rather than lagged 1040 income in the NEWS project, which may reflect selection into response that was not captured in that paper using data available during the regular CPS ASEC production schedule. 42We discuss how alternative uses of survey earnings could have had a large impact in the next section. 43The same information by age of householder (under 65 and 65 and over) is available in the Appendix in Figure A2. 37 5.4 Impact of Different Income Types on Income and Poverty Estimates Finally, we assess how specific administrative income components affect the household income distribution and poverty. To do so, we start with the NEWS income estimates and replace each administrative income item one by one (not sequentially or cumulatively) with its survey counterpart and compare each statistic after the replacement to the NEWS estimate. The results are shown for income in Figure 8 and poverty in Figure 9. For income, we make several replacements: (1) interest and dividends, (2) retirement income, including DC withdrawals and retirement, survivor, and disability pensions, (3) Social Se- curity and SSI, and (4) wage and salary earnings. For interest and dividends, we make three replacements: 1) replace administrative interest income with survey interest income, including the survey measure of interest (and other re- turns) on retirement accounts, 2) replace administrative income with survey interest income, excluding the retirement account interest, and 3) replace administrative dividends with sur- vey dividends, with detail shown in Figure A3 Panel A. If we include interest on retirement accounts (as is the case in the survey income estimate), we get more income across the dis- tribution than using administrative income (which does not include this interest). Because we already count withdrawals from these same retirement accounts as income, this risks double counting the same income, which is why we exclude it from the NEWS estimate. If we replace interest or dividends excluding this interest from retirement accounts, we see slightly lower income across the distribution.Together, interest and dividend replacement with survey responses lowers income by 1.3 percent at the 25th percentile and 0.5 percent at the 75 percentile, shown in Figure 8. Next, we look at transfer income, including Social Security (OASDI), SSI, and TANF income, shown in detail in Figure A3 Panel B. If we just replace SSI income with survey responses, we observe increases in income at the bottom of the distribution, primarily because of misclassi- 38 fication of Social Security and SSI, effectively double counting Social Security for individuals that reported Social Security income as SSI. If we replace Social Security only, we observe big declines in income at the bottom and smaller declines higher in the income distribution. If we replace both together, we observe slightly smaller declines at the bottom because we are preserving the misclassified income (SSI reported as Social Security on the survey, for example). Replacing TANF with survey responses results in small declines in income that are only significantly different at a handful of points.Replacing both Social Security and SSI together lowers income by 1.0 perent at the 25th percentile, but the difference is not statistically significant at the 75th percentile, shown in Figure 8. Figure 8 also shows the the impact of replacing retirement, survivor, disability, and pension income (retirement income, from Form 1099-R) with the corresponding survey items. Even for overall income, the retirement income replacement has the biggest impact across much of the income distribution, including 8.7 percent at the 25th percentile and 4.1 percent at the 75th percentile. As shown in Figure 9, overall poverty is higher when using survey reports for interest and dividends. It is much higher if we use survey-reported retirement income. Likewise, replacing administrative with survey earnings has a large effect on poverty, particularly if we ignore positive administrative earnings when the survey reports are zero. 6 Release and Future Research 6.1 Transparency and Data Availability An integral goal of the NEWS project is to be as transparent and open about the data we use, how we clean them, and how we combine them to generate the NEWS income, poverty, and resource estimates. Clarity and transparency are especially important in this context, as there are many decisions about how to clean, process, and combine survey and administrative 39 data that can have major effects on the results. These choices can be relatively opaque and “in-the-weeds” for even a well-informed outsider. For example, using the maximum of survey and administrative income reports, as shown in Figure A5 Panel C, would drastically bias our income and poverty estimates in a way that is not consistent with the survey reporting noise in Figure 6. Transparency about our methods, code, and estimates is required for readers to understand the implications of those kind of detailed data choices. As such, we commit to making all of the code and as much of the data as we are permitted available to researchers through the Federal Research Data Center (FSRDC) system.44 We also commit to making the code publicly available, with as few edits as possible as required by the rules on the disclosure of code to abide by Titles 13 and 26 and our agreements with data providers. With each run of the NEWS code, we also plan to log any changes to input extracts so we can track any changes to input data (such as data provided by the IRS or an updated version of a survey file) that may affect our estimates. We also use git, a software version control system, to ensure that the code that generated the results in this paper (or any future paper with updated data, code, and methods) can be replicated.45 We also have written documentation for nearly all the files and functions involved in loading and cleaning the data, creating the address and person extracts, implementing the reweight- ing, imputation, and earnings measurement error model, generating the final person and tax unit income variables, and estimating income and poverty. While no documentation is perfect, we have endeavored to be as detailed as possible in this documentation, detailing what each section of code is doing, including references to particular line numbers. This is 44Subject to the constraints of our data agreements with the various state and federal agencies and commercial data providers. 45Up to the limit of what is possible in the software we use. Unfortunately, there are functions we currently use, such as Stata’s rmcoll function to remove collinear variables from a regression that do not necessarily remove the same variables even when run with the same random seed. The exact set of variables kept can then affect the results from subsequent steps, such as LASSO regression feature selection. A goal for future releases is to remove our dependence on any function that has this property as we would like to ensure that a rerun of the code with the same data and initial seeds generates exactly the same estimates. 40 in addition to the regular commenting provided within the code itself. 6.2 Future Plans This release represents version 1.0 of the NEWS project. There are many aspects of this work that we were not able to include in this release and have left for future work. In this section we discuss our goals for version 2.0 and beyond. First, we have estimated income and poverty in a single year, 2018, as a proof of concept and first step in this work. We plan to expand this to include more years, both earlier years and years up to the present. This will introduce additional challenges. Some administrative data are not available before a specific year. For example, the Census Bureau currently only has access to the universe of W-2 earnings starting in 2005. Likewise, not all administrative data are available in time for estimates of income in the prior year. For example, we might get data from SSA or state agencies with a lag of a year or more. Creating historical or preliminary estimates in the absence of complete data is an important direction for future research. Second, we have only estimated income and poverty statistics at the national level. In the future we plan to extend the estimates to smaller geographic units, including states, counties, and possibly census tracts. However, to do so would require changes to how the estimates are generated. First, we would likely move to the ACS as the main source of survey information for subnational estimates. However, the ACS has less detailed income information, which makes this work more challenging and would require our using a different approach to estimating various income sources. For example, we do not have separate survey reports of interest, dividends, rental income, unemployment compensation, workers’ compensation, etc., because these items are reported as part of questions that ask about several income items simultaneously. Therefore, it will be difficult to know whether the respondent was also reporting another type of income that is not well-covered by available 41 administrative data. In the long term, we may even move beyond the survey sample (while using survey information in the process) to better estimate statistics for small areas using the available administrative, decennial census, and commercial data. Third, we have generated estimates only for pre-tax money income, as measured in the Cen- sus Bureau’s annual income and poverty release (Semega et al., 2019). However, there is considerable interest in how in-kind benefits, taxes, and credits affect measures of material wellbeing. We plan on expanding the notions of resources we measure and as well as the set of wellbeing and deprivation statistics we report. For example, we could measure the distri- bution of disposable income, disposable income plus the cash value of some (or all) in-kind transfers, improved measures of compensation that include employer matches to retirement contributions and employer contributions to health insurance premiums, the Supplemental Poverty Measure (SPM), etc. This will entail estimating taxes and credits and/or addressing household roster disagreement between administrative and survey data (Unrath, 2022; Meyer et al., 2022), incorporating additional data on housing assistance from the Department of Housing and Urban Development and from states on the Special Supplemental Nutrition Assistance Program for Women, Infants, and Children (WIC), and potentially improved im- putation and misreporting corrections for other programs such as the National School Lunch program, etc. Finally, there are dimensions of misreporting and measurement error that we were not able to address in this version. For example, we have discussed how self-employment earnings are underreported in both survey and administrative data (Hurst, Li and Pugsley, 2014; Internal Revenue Service, Research, Analysis & Statistics., 2016) and how much survey and administrative reports disagree on the extensive margin (Abraham et al., 2021). It is not settled in the literature how to adjust for this underreporting (Auten and Splinter, 2018; Piketty, Saez and Zucman, 2017), much less how one would do so and get unbiased estimates by subgroup. We plan to extend our measurement error model to self-employment earnings 42 for which different assumptions about misreporting would be necessary. Likewise, it may be the case that survey samples, even those as large as the ACS, do not adequately capture the incomes of the top individuals and households. Imputation, combination, or reweighting may be insufficient to address this issue to estimate unbiased inequality statistics from a survey sample. We plan on also researching methods to better estimate inequality statistics that account for the far-right tail of the income distribution. We would also like to further investigate how our adjustments affect estimates for subgroups that may be challenging to reach or be unlikely to be present in the administrative data, such as non-citizens. Weighting and imputation, in particular, assume that the data is missing at random conditional on the observable information. However, there may be limited observable information in the address-linked administrative records to identify and adjust for selection into response by citizenship status. Likewise, our weighting adjustment for linkage uses survey response information to reweight individuals and households that can be linked to administrative data to be representative of the full sample. However, it may be that conditional on the observable survey information (and the address-linked administrative data), the data are not missing at random and that our final estimates for this group are biased. Similarly, there are difficult to reach subgroups that are not in sample for the CPS ASEC that we would like to estimate wellbeing statistics for, such as individuals in group quarters and the homeless or unhoused. 7 Conclusion This release under the NEWS project is a first step toward integrating what we know about bias and measurement error in survey and administrative data into a set of “best possible” estimates of income, poverty, and resource statistics. We have attempted to address as many of the sources of bias as possible, including nonresponse bias (unit and item), selection into linkage to administrative data, misreporting of survey and administrative income, and 43 incomplete data. However, much work remains to be done to address additional potential sources of error. As we and other researchers advance our understanding of how to address these measurement challenges, we will revise these estimates. This work also suggests several additional avenues of possible research at the Census Bureau. For example, estimating income and poverty from linked survey and administrative data could impact the information we depend on surveys to provide. Surveys could focus less on items that are well captured in administrative data (such as Social Security payments) and more on items that improve linkage and those that are less well captured by administrative data (self-employment income, etc.). The Census Bureau could also increase efforts to collect survey responses from hard-to-reach groups who may be less well covered by administrative data. The focus of this project is on improving our estimates of income and poverty. However, much of our planned future work entails trying to understand the quality of various data sources. 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Zhao, Qingyuan, and Daniel Percival. 2017. “Entropy Balancing is Doubly Robust.” Journal of Causal Inference, 5(1). 52 http://usa.ipums.org/usa/resources/repwt/Use_of_the_Public_Use_Replicate_Weight_File_final_PR.doc http://usa.ipums.org/usa/resources/repwt/Use_of_the_Public_Use_Replicate_Weight_File_final_PR.doc Figure 1: NEWS Estimate of Median Household Income Relative to Survey in 2018 All Households Family households Married-couple Female householder, no husband present Male householder, no wife present Nonfamily households Female householder Male householder White White, not Hispanic Black Asian Hispanic (any race) Under 65 years 15 to 24 years 25 to 34 years 35 to 44 years 45 to 54 years 55 to 64 years 65 years and older Native born Foreign born Naturalized citizen Not a citizen Northeast Midwest South West Age 25 and older householder No high school diploma High school, no college Some college Bachelor's degree or higher Inside metropolitan statistical areas Inside principal cities Outside principal cities Outside metropolitan statistical areas Type of Household Race and Hispanic Origin of Householder Age of Householder Nativity of Householder Region Education Residence -30 percent -20 percent -10 percent 0 10 percent 20 percent 30 percent Percent Difference from Survey Notes: This figure shows the percent difference between the NEWS estimates of median household income compared to the survey estimates in 2018, also shown in Table 1. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 53 Figure 2: NEWS Estimate of Poverty Relative to Survey in 2018 All White White, not Hispanic Black Asian Hispanic (any race) Male Female Under age 18 Age 18 to 64 Aged 65 and older Native-born Foreign-born Naturalized citizen Not a citizen Northeast Midwest South West With a disability with no disability Aged 25 and older No high school diploma High school, no college Some college Bachelor's degree or higher Inside metropolitan statistical areas .Inside principal cities .Outside principal cities Outside metropolitan statistical areas Race and Hispanic Origin Sex Age Nativity Region Disability Status Educational Attainment Residence -5 -4 -3 -2 -1 0 1 2 Percentage Point Difference Notes: This figure shows the percentage point difference between the NEWS estimates of poverty compared to the survey estimate in 2018, also shown in Table 2. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 54 Figure 3: Decomposition of NEWS Processing Steps: Household Income A. Survey Steps: Weighting B. Administrative Income Replacement and Earnings Imputation and Survey Earnings Choice Modeling -10 -5 0 5 10 15 20 25 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile Reweighted (Nonresponse) + Reweighted for Linkage + Imputed Earnings -10 -5 0 5 10 15 20 25 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile + Administrative Income NEWS (+ Earnings Choice Model) C. Overall -10 -5 0 5 10 15 20 25 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile Notes: This figure decomposes the impact of the NEWS processing steps on household income. In Panel A, the figure shows the adjustments made to the survey data, including reweighting and improved earnings imputation comparing household income after the adjustment to the survey estimate. In Panel B, the figure shows impact of replacing survey income responses with administrative income, comparing the estimates after each step to the estimates after reweighting and earnings imputation. The full impact of all adjustments is shown in Panel C. The 95 percent confidence interval for the last step is shown in each: for Panel A comparing the estimate after earnings imputation to the survey estimate and for Panel B comparing the final NEWS estimate to the estimate after earnings imputation. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 55 Figure 4: Linkage Diagram for Address File Survey Housing Units (Occupied) Master Address File Black Knight IRMF Link Addresses to People (MAFID→PIK) MAFARF 1040 Tax Returns MAFID Linked Individuals at Occupied Units W-2s 1040 Tax Returns Information Returns (IRMF) IRS Data SSA Data Social Security/OASDI Payments (PHUS) SSI Payments (SSR) State Data (from partner states) LEHDPIK Firm Data (LBD) EIN Job-Level Match Decennial Censuses Geographic Summaries of Characteristics ACS 5-Year Files IRMF MAFARF NumidentPIK MAFID Housing Unit Information EIN EIN EIN EIN Numident PIK W-2s PIK 1040 Tax Returns Geographic ID (State, County, Tract) 1099-Rs PIK Notes: This diagram shows the linkage used to create the address-based extract file used for weighting. The file starts with the set of occupied addresses in the survey. That file is linked to three sets of files: (1) Geographic summaries of characteristics (by state, county, and tract identifiers), (2) housing unit information from the Master Address File and Black Knight data, and (3) files to link the addresses to people living in them (MAFID → PIK). From the third set of files, we create a roster of all individuals found in the occupied surveyed units and link them to the files shown to the right. 56 Figure 5: Linkage Diagram for Person File Survey Respondents Linked Survey Respondents Information Returns (IRMF) 1040 Tax Returns W-2s Assign PIKs PIK PIK SSA Data IRS Data Social Security/OASDI Payments (PHUS) Detailed Earnings Record (DER) PIK PIK SSI Payments (SSR) PIK State Data (from partner states) SNAP PIK TANF (states + HHS) PIK LEHD PIK Firm Data (LBD) EIN EIN Job-Level Match EIN EIN EIN EIN 1099-Rs PIK PIK Notes: This diagram shows the linkage used to create the person-level extract file. The file starts with the set of respondents in the survey. For those respondents that can be linked to their Social Security Numbers and therefore assigned a Protected Identification Key (PIK), we link them to the administrative records shown. 57 Figure 6: Intensive Margin Disagreement in Wage and Salary Earnings Lo g AC S W ag e an d Sa la ry E ar ni ng s Log W-2 Earnings Regression Fit (𝛽 = 0.8) 45∘ Line Notes: This figure was published in O’Hara, Bee and Mitchell (2017) and is replicated here with permission, as it is no longer possible to disclose scatter plots of individual earnings reports. The figure compares individual survey wage and salary earnings reports to W-2 earnings from the 2011 ACS. The regression fit line is shown and the 45◦ is visible in the clustering of points below the regression line on the left side of the figure and above the regression fit on the right. While the survey reports cluster around the 45◦ line, there is considerable noise in the survey relative to the administrative reports, and the figure is consistent with mean-reversion of survey relative to administrative reports (both in the location of points relative to the diagonal and the fact that β̂ < 1). The axes are unlabeled as a condition of the original release. Source: O’Hara, Bee and Mitchell (2017) using 2011 American Community Survey data linked to 2010 W-2s. 58 Figure 7: NEWS Estimate of Household Income Relative to Survey by Subgroup in 2018 A. Race and Hispanic Origin B. Age of Householder -10 -5 0 5 10 15 20 25 30 Pe rc en t D iff er en ce fr om S ur ve y 0 20 40 60 80 100 Household Income Percentile All White, Non-Hispanic Black Asian Hispanic -10 -5 0 5 10 15 20 25 30 Pe rc en t D iff er en ce fr om S ur ve y 0 20 40 60 80 100 Household Income Percentile 25-34 35-44 45-54 55-64 65+ C. Educational Attainment -10 -5 0 5 10 15 20 25 30 Pe rc en t D iff er en ce fr om S ur ve y 0 20 40 60 80 100 Household Income Percentile No HS HS Some College Bachelor's and Above' Notes: This figure shows the percent difference between the NEWS estimates of household income compared to the survey estimate at the 10th, 25th, 50th, 75th, and 90th percentiles in 2018. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 59 Figure 8: Effect of Removing Individual Administrative Income Items on Household Income -10 -8 -6 -4 -2 0 2 4 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Household Income Percentile Interest & Dividends Retirement Social Security & SSI WS Earnings Notes: In this figure, we replace individual income items from the NEWS estimates with the corresponding survey information and compare the estimate after replacement with the NEWS estimate. An estimate below the zero line indicates that administrative item increases income at that percentile. We replace: (1) interest and dividends, (2) retirement income, including withdrawals from Defined Contribution plans and retirement, survivor, and disability pensions. For interest and dividends, we exclude survey-reported interest earned in Defined Contribution retirement plans. For wage and salary earnings, we replace administrative wage and salary earnings with survey responses in all cases where the individual does not have administrative self-employment earnings, even if the individual reported no earnings on the survey. More detailed decompositions are available in Figure A3. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 60 Figure 9: Effect of Removing Individual Administrative Income Items on Poverty Interest (including from Retirement Plans) Interest Dividends Retirement Social Security & SSI TANF Survey WS Earnings Survey WS Earnings (Adrecs if Survey == 0) -1.5 -1 -.5 0 .5 1 1.5 Percentage Point Difference Notes: In this figure, we replace individual income items from the NEWS estimates with the corresponding survey information, including for interest, dividends, retirement income, Social Security, SSI, TANF, and survey wage and salary earnings. An estimate above the zero line indicates that administrative item decreases overall poverty. For survey interest, we show two measures, including and excluding the interest earned in Defined Contribution retirement plans such as 401(k)s. We replace Social Security and SSI together to address misclassification across programs, as discussed in Bee and Mitchell (2017). We replace administrative wage and salary earnings with two survey-based earnings measures. In the first, we use survey responses in all cases where the individual does not have administrative self-employment earnings, even if the individual reported no earnings on the survey. In the second, we only replace administrative wage and salary earnings if the survey report was positive. Retirement includes Defined Contribution plan withdrawals, pensions, and survivor and disability pensions. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 61 Table 1: NEWS Median Household Income Estimates Compared to Survey in 2018 Survey NEWS Median Income Median Income Percent Difference Number (dollars) Number (dollars) (NEWS - Survey) Characteristic (thousands) Estimate 95 percent CI (thousands) Estimate 95 percent CI Estimate 95 percent CI HOUSEHOLDS All Households 128,600 63,180 823 133,700 67,170 962 6.3* 1.4 Type of Household Family households 83,480 80,660 791 85,840 85,210 1,221 5.6* 1.3 .Married-couple 61,960 93,650 1,340 63,950 98,100 1,402 4.7* 1.4 .Female householder, no husband present 15,040 45,130 1,329 15,250 47,490 1,754 5.2* 3.6 .Male householder, no wife present 6,480 61,520 1,485 6,644 63,550 2,798 3.3 4.4 Nonfamily households 45,100 38,120 983 47,890 41,800 846 9.6* 2.7 .Female householder 23,510 32,010 794 24,860 38,010 1,201 18.7* 3.6 .Male householder 21,580 45,750 1,034 23,030 46,230 1,212 1.0 2.5 Race and Hispanic Origin of Householder White 100,500 66,940 769 104,000 71,390 984 6.6* 1.3 ..White, not Hispanic 84,730 70,640 777 87,370 74,210 1,166 5.1* 1.4 Black 17,170 41,360 1,079 18,290 43,100 2,058 4.2* 4.3 Asian 6,981 87,190 3,342 7,019 89,270 5,614 2.4 5.6 Hispanic (any race) 17,760 51,450 876 18,400 57,710 2,314 12.2*** 4.2 Age of Householder Under 65 years 94,420 71,660 683 99,370 71,580 1,001 -0.1 1.2 ..15 to 24 years 6,199 43,530 3,204 6,961 41,350 2,245 -5.0 6.4 ..25 to 34 years 20,610 65,890 1,281 22,080 65,110 1,764 -1.2 2.3 ..35 to 44 years 21,370 80,740 1,276 22,490 78,600 2,390 -2.7* 2.7 ..45 to 54 years 22,070 84,460 2,198 23,000 84,940 2,017 0.6 2.7 ..55 to 64 years 24,170 68,950 1,720 24,840 72,430 1,975 5.0* 2.9 65 years and older 34,160 43,700 972 34,360 55,610 1,370 27.3* 3.0 Nativity of Householder Native born 108,600 64,240 848 114,100 67,680 981 5.3* 1.3 Foreign born 20,020 58,780 1,891 19,670 64,140 2,322 9.1* 3.9 ..Naturalized citizen 11,040 65,520 2,682 10,480 72,290 2,877 10.3*** 4.6 ..Not a citizen 8,976 51,940 1,254 9,193 55,670 4,458 7.2* 8.3 Region Northeast 22,050 70,110 2,247 22,840 76,810 2,876 9.6* 3.4 Midwest 27,690 64,070 1,722 28,730 66,460 1,726 3.7* 2.5 South 49,740 57,300 978 52,470 58,890 1,418 2.8 2.2 West 29,100 69,520 1,900 29,700 77,560 2,366 11.6* 3.1 Residence Inside metropolitan statistical areas 110,800 66,160 725 112,600 71,010 1,049 7.3* 1.4 ..Inside principal cities 42,980 59,360 1,457 43,040 63,210 1,653 6.5* 2.4 ..Outside principal cities 67,810 70,930 902 69,520 75,780 1,522 6.8* 1.8 Outside metropolitan statistical areas 17,790 49,870 1,941 21,170 50,040 1,722 0.3 3.2 Education Age 25 and Above 122,400 64,760 806 126,800 69,200 963 6.8* 1.4 No HS 11,230 28,330 1,260 11,850 32,400 1,599 14.4* 5.3 HS 31,810 46,070 870 33,270 50,630 999 9.9* 2.3 Some College 33,940 60,940 918 35,090 64,620 1,432 6.0* 2.0 Bachelor’s and Above 45,410 101,800 1,135 46,550 105,400 1,940 3.5* 1.6 Notes: This table compares the NEWS median household income estimates to the survey estimates by subgroup in 2018. *, , and * indicate significance at the 1, 5, and 10 percent levels and are only shown for percent differences. Federal surveys give respondents the option of reporting more than one race. Therefore, two basic ways of defining a race group are possible. A group, such as Asian, may be defined as those who reported Asian and no other race (the race-alone or single-race concept) or as those who reported Asian regardless of whether they also reported another race (the race-alone-or-in-combination concept). This table shows data using the first approach (race alone). The use of the single-race population does not imply that it is the preferred method of presenting or analyzing data. The Census Bureau uses a variety of approaches. About 2.9 percent of people reported more than one race in the 2010 Census. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. separately. 62 Table 2: NEWS Poverty Estimates Compared to Survey in 2018 Change in poverty Survey NEWS (NEWS - Survey) Characteristic Percent 95 percent CI Percent 95 percent CI Difference 95 percent CI PEOPLE ....Total 11.78 0.29 10.67 0.39 -1.11* 0.37 Race and Hispanic Origin White 10.07 0.30 8.91 0.40 -1.16* 0.41 ...White, not Hispanic 8.07 0.28 7.48 0.35 -0.59* 0.35 Black 20.77 1.16 20.10 1.46 -0.67 1.31 Asian 10.10 0.94 8.52 1.41 -1.57 1.38 Hispanic (any race) 17.56 0.80 14.61 1.14 -2.95* 1.19 Sex Male 10.57 0.32 9.71 0.40 -0.86* 0.40 Female 12.94 0.33 11.59 0.48 -1.34* 0.45 Age Under 18 years 16.20 0.67 15.62 0.86 -0.57 0.83 18 to 64 years 10.68 0.29 9.97 0.37 -0.71* 0.35 65 years and older 9.75 0.46 6.42 0.45 -3.33* 0.56 Nativity Native-born 11.45 0.31 10.48 0.40 -0.97* 0.38 Foreign-born 13.79 0.67 11.86 0.97 -1.93*** 1.01 ...Naturalized citizen 9.93 0.75 9.07 0.99 -0.86* 1.01 ...Not a citizen 17.46 1.01 14.40 1.59 -3.06* 1.63 Region Northeast 10.28 0.66 9.14 0.86 -1.14 0.87 Midwest 10.37 0.66 10.51 0.83 0.14 0.75 South 13.57 0.55 12.24 0.66 -1.33* 0.66 West 11.22 0.64 9.41 0.83 -1.80* 0.83 Residence Inside metropolitan statistical areas 11.34 0.32 10.11 0.43 -1.23* 0.39 ...Inside principal cities 14.59 0.65 13.47 0.74 -1.12* 0.70 ...Outside principal cities 9.42 0.40 8.18 0.47 -1.24* 0.45 Outside metropolitan statistical areas 14.68 0.99 13.93 1.14 -0.75 0.98 Disability Status ....Total, aged 18 to 64 10.68 0.29 9.97 0.37 -0.71* 0.35 With a disability 25.72 1.32 26.64 1.66 0.92 1.58 With no disability 9.46 0.25 8.68 0.36 -0.78* 0.35 Educational Attainment ....Total, aged 25 and older 9.90 0.24 8.62 0.32 -1.27* 0.30 No high school diploma 25.90 1.05 21.96 1.36 -3.94* 1.41 High school, no college 12.73 0.47 10.83 0.56 -1.90* 0.56 Some college 8.38 0.38 8.05 0.51 -0.33 0.52 Bachelor’s degree or higher 4.37 0.32 3.65 0.33 -0.72* 0.38 Notes: This table compares the NEWS poverty estimates to the survey estimates by subgroup in 2018. , , and indicate significance at the 1, 5, and 10 percent levels and are only shown for differences. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 63 Table 3: NEWS Inequality Estimates Compared to Survey in 2018 Percent Difference Survey NEWS (NEWS - Survey) Measure Estimate 95 percent CI Estimate 95 percent CI Estimate 95 percent CI Shares of Aggregate Income 1st Quintile 0.036 0.001 0.037 0.001 0.001 0.001 2nd Quintile 0.091 0.001 0.089 0.002 -0.002* 0.002 3rd Quintile 0.148 0.001 0.142 0.003 -0.005* 0.003 4th Quintile 0.227 0.002 0.215 0.004 -0.012* 0.004 5th Quintile 0.498 0.004 0.516 0.009 0.018* 0.008 Top 5 Percent 0.218 0.005 0.252 0.012 0.034* 0.012 Summary Measures Gini Index 0.459 0.004 0.476 0.009 0.017* 0.009 90/10 percentile ratio 12.52 0.34 11.52 0.36 -1.00* 0.35 90/50 percentile ratio 2.92 0.04 2.82 0.04 -0.10* 0.05 50/10 percentile ratio 4.29 0.10 4.09 0.10 -0.20* 0.11 Notes: This table compares NEWS inequality statistics to the survey estimates in 2018. *, , and * indicate significance at the 1, 5, and 10 percent levels and are only shown for percent differences. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 64 Table 4: Data Sources File Data Source Description Current Population Survey Annual Social and Economic Supplement (CPS ASEC) Census Annual survey fielded in February to April with household structure and characteristics at the time of interview and income from the prior calendar year. About 95,000 housing units sampled each year. American Community Survey (ACS) Census Rolling survey fielded throughout the year about income from prior 12 months. About 3.5 million housing units sample each year. Short Form Decennial Census Census Complete count decennial census data from 2000 and 2010. Master Address File (MAF) Census File of residential addresses used to support census survey and decennial operations. Survey samples are drawn from this file for both the CPS ASEC and ACS. Master Address File Auxiliary Reference File (MAFARF) Census Comingled file constructed from administrative records, including the IRMF, postal ser- vice change of address information, program data, etc. that links individuals (identified by Protected Identification Keys) to addresses in the Master Address File (identified by MAFIDs). Longitudinal Business Database (LBD) Census Database of private non-farm establishments with employees from 1976 forward. For each establishment the LBD has information on industry, payroll, employment, and a firm iden- tifier to group establishments into firms. Information Returns Master File (IRMF) IRS Universe file with flags for whether an individual received each of the following information returns forms: 1098, 1099-DIV, 1099-INT, 1099-G, 1099-MISC, 1099-R, 1099-S, SSA-1099, and W-2. No income information is available. Also contains address information which has matched to the MAF to get a MAFID for each form. Form 1040 Tax Returns (1040s) IRS Universe tax filings with a subset of the information on the complete Form 1040. The extracts provided by the IRS include information on tax-unit wage and salary income, gross rental income, taxable social security income, taxable and tax-exempt interest income, interest income, dividends, Adjusted Gross Income, and a constructed measure of Total Money Income (TMI). TMI is the sum of taxable wage and salary income, interest (taxable and tax-exempt), dividends, gross social security income, unemployment compensation, alimony received, business income or losses (including for partnerships and S-corps), farm income or losses, and net rent, royalty, and estate and trust income. Self-employment income is not available (except as a component of TMI), but flags exist for the filing of different 1040 schedules (such as C, D, E, F, SE). Form W-2 (W-2s) IRS Universe data with a subset of information from the Form W-2. The extracts provided by the IRS include select boxes from the form, including wages and salary net of pre-tax deductions for health insurance premiums and deferred compensation (boxes 1 and 5), as well as the total amount of deferred compensation (summed values from Box 12 Codes D-H). Employee and employer pre-tax contributions to health insurance premiums are not available in the W-2 data. Form 1099-R (1099-Rs) IRS Universe data with a subset of information from the Form 1099-R. The extracts provided by the IRS include information on amounts of defined-benefit pension payments (including for survivor and disability pensions) and withdrawals from defined-contribution retirement plans. Numerical Identification System (Numident) SSA The Numident contains information for anyone ever to have received a Social Security Number. It includes information on date and place of birth, date of death, sex, and some information on citizenship. Payment History Update System (PHUS) SSA Monthly Old Age, Survivors, and Disability Insurance (OASDI) payments from 1984 to the present. The PHUS exists for several subsamples of individuals including 1) those receiving payments in 2020 and 2021, 2) CPS ASEC respondents in linked years, and 3) ACS respondents in linked years (currently only 2019). Supplemental Security Record (SSR) SSA Monthly Supplemental Security Income (SSI) payments from 1984 to the present for fed- erally SSI and federally administered state SSI. The SSR exists for several subsamples of individuals including 1) those receiving payments in 2020 and 2021, 2) CPS ASEC respon- dents in linked years, and 3) ACS respondents in linked years (currently only 2019). Detailed Earnings Record (DER) SSA Annual job-level income (by Employer Identification Number, EIN) from Form W-2s and annual positive self-employment income (from Form 1040 Schedule SE). The DER exists for several subsamples: 1) CPS ASEC respondents in linked years and 2) ACS respondents in linked years (currently only 2019) Longitudinal Employer Household Dynamics (LEHD) States Quarterly job earnings reports from firms to state Unemployment Insurance offices for participating states. For covered jobs, the LEHD includes gross earnings - this includes employee contributions for health insurance premiums not available on the W-2 extracts. Coverage in the LEHD is not complete as many government employees, such as federal civilian employees, postal workers, and Department of Defense employees are not covered by state UI benefits. Some private-sector employees, including those employed by religious organizations, are not covered by UI, and are therefore not present in the LEHD data. Supplemental Nutrition Assistance Program States SNAP participant data from partner states. In 2018, SNAP data is available for 17 states. Temporary Assistance for Needy Families (TANF) States + HHS TANF participant data from partner states as well as from the Department of Health and Human Services (HHS) for additional states. In 2018, TANF data is available for 36 states. Black Knight Home Value (Black Knight) Black Knight Third party data on home values and housing unit characteristics. Notes: This table describes the data used in this project, including the source of the data and a short description. The name for the data used in Figures 4 and 5 is in parenthesis. 65 Table 5: Measurement and Estimation Steps Section Step Inputs Category Measurement Challenge Description Related Work A. Weighting 1. Weight respondents Address and Per- son Files Survey Survey unit nonresponse Selection into administrative data Administrative data “nonresponse” Use linked information on all occu- pied housing units and population controls to weight respondent sam- ple to be representative of the target universe of households Rothbaum et al. (2021); Rothbaum and Bee (2022) 2. Weight respondents with all adults as- signed a PIK Address and Per- son Files Survey Survey unit nonresponse Selection into administrative data Administrative data “nonresponse” Selection into Linkage Use information from A1 and reweight households with all adults assigned a PIK to be representative of the target universe of households B. Imputation 1. Impute survey earn- ings Person File Survey Survey item nonreponse Impute survey earnings conditional on survey and administrative infor- mation Hokayem, Raghunathan and Roth- baum (2022) 2. Impute LEHD gross earnings Person File Admin Administrative data “nonresponse” Conceptual misalignment Incomplete data coverage Impute LEHD earnings when miss- ing or there is large disagreement be- tween W-2s and LEHD 3. Impute missing means-tested pro- gram benefits Person File Admin Incomplete data coverage Impute means-tested program data (TANF and SNAP) for states for which administrative data is not available Fox et al. (2022) 4. Impute adminis- trative income for nonfilers Person File and nonfiler income pa- rameters Admin Selection into administrative data Incomplete data coverage Impute unemployment insurance compensation, interest, and divi- dends for nonfilers Rothbaum (2023) C. Estimation 1. Earnings Measure- ment Error Model Person File (for CPS ASEC and ACS) Admin Survey misreporting Administrative misreporting Combine survey and administrative wage and salary earnings according to the earnings measurement error model Bee et al. (2023) 2. Income replacement Person File Admin Survey misreporting Administrative misreporting Use survey and administrative data, imputed income, and earnings from the measurement error model to con- struct household and family income Bee and Mitchell (2017) 3. Estimate income and poverty statistics Person File Admin Notes: This table describes the processing steps used to address measurement error and estimate income and poverty. For each step, we include the Category (Survey or Administrative) matching the breakdown used in the decomposition used in Figure 3. Each step also references the relevant measurement challenges discussed in Section 2 and related work done at the Census Bureau that is being integrated into the NEWS project and extended. 66 Table 6: Rates of Missing Data for Imputed Income Items Missingness Rate Survey Earnings from Primary Job 0.456 (0.003) Earnings from Other Employers Wage and Salary 0.367 (0.007) Self Employment 0.445 (0.014) Farm Self Employment 0.574 (0.020) Usual Hours Worked Per Week 0.260 (0.003) Weeks Worked Last Year 0.250 (0.003) Administrative Job 1 LEHD (gross earnings) missing \| W-2 or DER not missing 0.080 (0.001) or large disagreement between LEHD and W-2 0.178 (0.002) Job 2 LEHD (gross earnings) missing \| W-2 or DER not missing 0.120 (0.002) or large disagreement between LEHD and W-2 0.184 (0.003) SNAP administrative data unavailable 0.695 (0.001) TANF administrative data unavailable 0.474 (0.001) Notes: This table shows the share of the 2019 CPS ASEC sample that is missing information for the various items imputed in this work, as discussed in Section 4.2. Standard errors in parenthesis. Jobs are ordered in the administrative data (Job 1, Job 2, etc.) from highest to lowest earnings across the three sources of job-level earnings (W-2, DER, and LEHD). Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 67 Table 7: Sources of Administrative and Survey Earnings A. All Individuals Administrative Earnings Sources Share with Unimputed Survey: W-2 DER LEHD N Wage and Salary Earnings Self-Employment Earnings X X X 72,000 0.887 0.029 (0.002) (0.001) X X 5,900 0.704 0.033 (0.010) (0.003) X X 400 0.105 0.034 (0.018) (0.011) X 300 0.804 0.024 (0.036) (0.011) X X 30 1.000 Z Z Z X <15 Z Z Z Z X 500 0.244 0.058 (0.026) (0.016) 75,000 0.045 0.027 (0.001) (0.001) B. Citizenship and DER Earnings N Share Reporting Administrative Earnings Sources (Survey Earnings Respondents Only) Wage and Salary Earnings Self-Employment Earnings W-2 DER LEHD In Numident Not In Numident In Numident Not In Numident In Numident Not In Numident X X Yes or No 47,000 <15 0.874 Z 0.029 Z (0.002) Z (0.001) Z X Yes or No 350 200 0.093 0.847 0.035 0.023 (0.018) (0.033) (0.011) (0.011) Notes: This table shows the counts and share of adults with each possible administrative earnings data source (W-2, DER, and LEHD) as well as the share in each group that reported survey earnings (among those that responded to the survey earnings questions). Panel A shows the estimates for all individuals in the CPS ASEC. Panel B shows how the presence or absence of DER earnings given W-2 earnings is related to differential probability of reporting survey earnings for individuals who can be assigned PIKs that have SSNs (In Numident) and do not (Not In Numident). Z indicates an estimate rounds to zero. Standard errors in parenthesis. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 68 Table 8: Combining Administrative and Survey Earnings: Use of Survey Earnings by Group A. Race and Hispanic Origin B. Age Share Survey Earnings Race/Hispanic Origin Overall Relative to Average All 20.6 Z (2.7) (0.2) Black 13.8 -6.8* (2.9) (3.1) Hispanic 22.1 1.5 (2.9) (1.2) White Non-Hispanic 22.6 2.0 (3.0) (1.2) Share Survey Earnings Age Overall Relative to Average 18-24 6.3 -14.3 (1.4) (3.5) 25-34 29.0 8.4 (4.4) (2.5) 34-44 26.8 6.3** (3.5) (1.8) 45-54 20.5 -0.1 (4.1) (2.1) 55-64 16.2 -4.3* (3.3) (2.1) 65+ 8.7 -11.9* (2.6) (2.4) Notes: This table shows the share of individuals in each subgroup where survey earnings are used from the measurement error model for choosing survey or administrative earnings discussed in Section 4.3.1 and in more detail in Bee et al. (2023) Standard errors in parenthesis. , , and indicate significance at the 1, 5, and 10 percent levels and are only shown for differences relative to average. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 69 Table 8 Combining Administrative and Survey Earnings: Use of Survey Earnings by Group, Continued C. Occupation D. Industry Share Survey Earnings Occupation (Last Week) Overall Relative to Average Unemployed 14.2 -6.4 (5.3) (6.0) Management 30.3 9.7 (5.6) (3.0) Business and Financial Operations 25.2 4.6 (2.8) (3.2) Computer and Mathematical 41.5 20.9 (7.2) (6.9) Architecture and Engineering 52.3 31.7* (4.0) (2.9) Life, Physical, and Social Science 9.1 -11.5* (2.1) (2.2) Community and Social Services 3.1 -17.5* (1.8) (3.3) Legal 11.0 -9.6 (11.0) (8.5) Education, Training, and Library 8.8 -11.8* (4.2) (2.5) Arts, Design, Entertainment, Sports, and Media 7.5 -13.1 (2.7) (3.5) Healthcare Practitioners and Technical 21.9 1.3 (3.8) (2.0) Healthcare Support 4.1 -16.4* (1.6) (3.8) Protective Service 15.4 -5.2 (3.5) (5.8) Food Preparation and Serving Related 10.2 -10.4 (9.8) (7.7) Building and Grounds Cleaning and Maintenance 15.1 -5.5 (6.1) (3.9) Personal Care and Service 8.8 -11.8* (4.0) (4.8) Sales and Related 11.9 -8.7* (1.2) (1.8) Office and Administrative Support 16.9 -3.7 (1.9) (1.9) Farming, Fishing, and Forestry 61.1 40.5 (24.3) (22.3) Construction Trades and Extraction Workers 42.2 21.6 (11.3) (10.6) Installation, Maintenance, and Repair Workers 38.4 17.8 (4.6) (5.9) Production Occupations 20.5 -0.1 (5.1) (3.7) Transportation 11.9 -8.7** (2.6) (2.7) Material Moving 29.9 9.3* (5.4) (4.2) Share Survey Earnings Industry (Last Week) Overall Relative to Average Unemployed 14.2 -6.4 (5.3) (6.0) Agriculture, Forestry, Fishing, and Hunting 64.1 43.5 (30.7) (28.7) Mining 29.2 8.6 (11.2) (8.8) Construction 58.6 38.0 (12.1) (11.5) Manufacturing 18.9 -1.7 (6.6) (5.1) Wholesale Trade 13.5 -7.1 (7.6) (8.4) Retail Trade 4.2 -16.4* (1.5) (2.8) Transportation and Warehousing 17.2 -3.4 (6.6) (5.8) Utilities 6.8 -13.8* (5.9) (6.4) Information 23.9 3.3 (8.4) (8.2) Finance and Insurance 43.8 23.2* (8.1) (10.2) Real Estate and Rental and Leasing 79.0 58.4* (11.3) (11.7) Professional, Scientific, and Technical Services 36.2 15.7 (11.6) (11.1) Management of companies and enterprises 2.0 -18.6* (3.6) (4.5) Administrative and support and waste management services 22.8 2.2 (11.2) (9.2) Educational Services 9.8 -10.8* (3.7) (2.1) Health Care and Social Assistance 10.9 -9.7* (2.3) (1.7) Arts, Entertainment, and Recreation 39.3 18.7 (24.6) (23.7) Accommodation and Food Service 14.4 -6.2 (14.5) (12.4) Other Services 27.0 6.4 (9.3) (10.4) Public Administration 7.4 -13.2 (4.7) (7.0) Notes: This table shows the share of individuals in each subgroup where survey earnings are used from the measurement error model for choosing survey or administrative earnings discussed in Section 4.3.1 and in more detail in Bee et al. (2023) Standard errors in parenthesis. *, , and * indicate significance at the 1, 5, and 10 percent levels and are only shown for differences relative to average. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 70 Figure A1: Simple Job Linkage Example W-2 Jobs PIK EIN Earnings 1 100 10,000 2 100 20,000 2 400 12,000 3 100 5,000 3 500 200 3 600 2,600 LEHD Jobs PIK EIN Earnings 1 200 11,000 2 200 20,005 2 400 12,000 3 200 5,200 3 500 225 Direct Matches PIK W-2 LEHD EIN Earnings EIN Earnings 2 400 12,000 400 12,000 3 500 200 500 225 Indirect Matches PIK W-2 LEHD EIN Earnings EIN Earnings 1 100 10,000 200 11,000 2 100 20,000 200 20,005 3 100 5,000 200 5,200 Unmatched PIK W-2 LEHD EIN Earnings EIN Earnings 3 600 2,600 Notes: This is an example of how jobs are linked between W-2s and the LEHD (all PIKS, earnings, and EINs in the example are made up and do not correspond to actual individuals or firms). First and easiest are the jobs that match on PIK and EIN (same person, same firm identifier), which we call direct matches. Next, we find the indirect matches, where each person has one EIN on the W-2s and another on the LEHD (same person, but different firm identifiers on the two files). In this example, everyone with W-2 EIN = 100 has a job with similar earnings on the LEHD, but with EIN = 200. Finally, there are jobs that remain unmatched and only exist on one file or the other. 71 Figure A2: Decomposition of NEWS Processing Steps By Age: Distribution of Household Income A. Under 65 Survey Steps Administrative Income + Earnings Measurement Error Overall -20 -15 -10 -5 0 5 10 15 20 25 30 35 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile Reweighted (Nonresponse) + Reweighted for Linkage + Imputed Earnings -20 -15 -10 -5 0 5 10 15 20 25 30 35 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile + Administrative Income NEWS (+ Earnings Choice Model) -20 -15 -10 -5 0 5 10 15 20 25 30 35 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile NEWS (+ Earnings Choice Model) B. 65 and Over Survey Steps Administrative Income + Earnings Measurement Error Overall -20 -15 -10 -5 0 5 10 15 20 25 30 35 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile Reweighted (Nonresponse) + Reweighted for Linkage + Imputed Earnings -20 -15 -10 -5 0 5 10 15 20 25 30 35 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile + Administrative Income NEWS (+ Earnings Choice Model) -20 -15 -10 -5 0 5 10 15 20 25 30 35 Pe rc en t D iff er en ce 0 20 40 60 80 100 Household Income Percentile NEWS (+ Earnings Choice Model) Notes: This figure decomposes the impact of the NEWS processing steps on household income. In the first column, the figures show the adjustments made to the survey data, including reweighting and improved earnings imputation comparing household income after the adjustment to the survey estimate. In the second column, the figures show impact of replacing survey income responses with administrative income, comparing the estimates after each step to the estimates after reweighting and earnings imputation. The full impact of all adjustments is shown in the third column. The 95 percent confidence interval for the last step is shown in each: for A comparing the estimate after earnings imputation to the survey estimate and for B comparing the final NEWS estimate to the estimate after earnings imputation. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 72 Figure A3: Effect of Removing Individual Administrative Income Items on Household Income, Additional Detail A. Interest and Dividends B. Transfers -10 -8 -6 -4 -2 0 2 4 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Household Income Percentile Interest (including from Retirement Plans) Interest Dividends -10 -8 -6 -4 -2 0 2 4 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Household Income Percentile Social Security SSI Social Security & SSI TANF C. Wage and Salary Earnings -10 -8 -6 -4 -2 0 2 4 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Household Income Percentile WS Earnings WS Earnings (Adrecs if Survey == 0) Notes: In this figure, we replace individual income items from the NEWS estimates with the corresponding survey information and compare the estimate after replacement with the NEWS estimate. An estimate below the zero line indicates that administrative item increases income at that percentile. In Panel A, we replace interest and dividend income with survey responses. For survey interest, we show two measures, including and excluding the survey-reported interest earned in Defined Contribution retirement plans such as 401(k)s. In Panel B, we replace Social Security and SSI separately and together (to address misclassification across programs, as discussed in Bee and Mitchell (2017)) and TANF with survey-reported public assistance income. In Panel C, we replace administrative wage and salary earnings with two survey-based earnings measures. In the first, we use survey responses in all cases where the individual does not have administrative self-employment earnings, even if the individual reported no earnings on the survey. In the second, we only replace administrative wage and salary earnings if the survey report was positive. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 73 Figure A4: Effect of Removing Individual Administrative Income Items on Household Income by Householder Age A. Under 65 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Household Income Percentile Interest (including from Retirement Plans) Interest & Dividends Retirement Social Security & SSI WS Earnings B. 65 and Over -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Household Income Percentile Interest (including from Retirement Plans) Interest & Dividends Retirement Social Security & SSI WS Earnings Notes: In this figure, we replace individual income items from the NEWS estimates with the corresponding survey information and compare the estimate after replacement with the NEWS estimate. An estimate below the zero line indicates that administrative item increases income at that percentile. We show each of the major administrative income items, including (1) interest (including and excluding the interest earned in Defined Contribution, DC, retirement plans such as 401(k)s), (2) interest (without DC plan interest) and dividends, (3) DC plan withdrawals, pensions, and survivor and disability pensions (Retirement), (4) Social Security and SSI, and (5) wage and salary earnings. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 74 Figure A5: Alternative Uses of Survey and Administrative Earnings A. Extensive Margin Disagreement B. Alternative Kappa Parameters -10 -5 0 5 10 15 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Percentile Administrative (if != 0) Administrative (even if == 0) Survey Earnings (if != 0) Survey (even if == 0) -10 -5 0 5 10 15 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Percentile kappa = 0.70 kappa = 0.75 kappa = 0.80 kappa = 0.85 kappa = 0.95 kappa = 1.00 Administrative (if != 0) C. Maximum of Survey and Administrative -10 -5 0 5 10 15 Pe rc en t D iff er en ce fr om N EW S 0 20 40 60 80 100 Percentile Administrative (if != 0) Survey Earnings (if != 0) Max Notes: This figure shows the impact on household income (relative to the baseline NEWS estimates) of alternative uses of survey and administrative earnings in the income estimates. In Panel A, we show how income estimates vary when survey or administrative wage and salary earnings were used for individuals indicated as “Measurement error model” in Table A8. The four options in Panel A include: (1) Administrative earnings if they are not equal to 0, (2) administrative earnings even if they are equal to 0 and survey earnings are positive, (3) survey earnings if they are not equal to 0, and (4) survey earnings even if they are equal to zero and administrative earnings are positive. Panel B shows the impact on household earnings of alternative mean-reversion kappa parameters in the measurement error model (with the share of individual’s whose survey earnings are used under each shown in Table A9). Panel B also includes (1) from Panel A, with administrative earnings if they are not equal to 0. Panel C compares the NEWS estimates to simpler uses of survey and administrative earnings, including (1) and (3) from Panel A and using the maximum of administrative and survey earnings. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 75 Figure A6: Decomposition of NEWS Processing Steps By Subgroup: Median Household Income A. Survey Steps: Weighting and Earnings Imputation All Households Family households .Married-couple .Female householder, no husband present .Male householder, no wife present Nonfamily households .Female householder .Male householder White .White, not Hispanic Black Asian Hispanic (any race) Under 65 years .15 to 24 years .25 to 34 years .35 to 44 years .45 to 54 years .55 to 64 years 65 years and older Native born Foreign born .Naturalized citizen .Not a citizen Northeast Midwest South West Age 25 and older householder No high school diploma High school, no college Some college Bachelor's degree or higher Inside metropolitan statistical areas .Inside principal cities .Outside principal cities Outside metropolitan statistical areas Type of Household Race and Hispanic Origin of Householder Age of Householder Nativity of Householder Region Education Residence -30 percent -20 percent -10 percent 0 10 percent 20 percent 30 percent Percent Difference Reweighted (Nonresponse) + Reweighted for Linkage + Imputed Earnings Notes: This figure decomposes the impact of the NEWS processing steps on median household income. In Panel A, the figure shows the adjustments made to the survey data, including reweighting and improved earnings imputation comparing median household income for each group after the adjustment to the survey estimate. In Panel B, the figure shows impact of replacing survey income responses with administrative income, comparing the estimates after each step to the estimates after reweighting and earnings imputation. The 95 percent confidence interval for the last step is shown in each: for Panel A comparing the estimate after earnings imputation to the survey estimate and for Panel B comparing the final NEWS estimate to the estimate after earnings imputation. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 76 Figure A6: Decomposition of NEWS Processing Steps By Subgroup: Median Household Income, Continued B. Administrative Income Replacement and Survey Earnings Choice Modeling All Households Family households .Married-couple .Female householder, no husband present .Male householder, no wife present Nonfamily households .Female householder .Male householder White .White, not Hispanic Black Asian Hispanic (any race) Under 65 years .15 to 24 years .25 to 34 years .35 to 44 years .45 to 54 years .55 to 64 years 65 years and older Native born Foreign born .Naturalized citizen .Not a citizen Northeast Midwest South West Age 25 and older householder No high school diploma High school, no college Some college Bachelor's degree or higher Inside metropolitan statistical areas .Inside principal cities .Outside principal cities Outside metropolitan statistical areas Type of Household Race and Hispanic Origin of Householder Age of Householder Nativity of Householder Region Education Residence -30 percent -20 percent -10 percent 0 10 percent 20 percent 30 percent Percent Difference + Administrative Income NEWS (+ Earnings Choice Model) Notes: This figure decomposes the impact of the NEWS processing steps on median household income. In Panel A, the figure shows the adjustments made to the survey data, including reweighting and improved earnings imputation comparing median household income for each group after the adjustment to the survey estimate. In Panel B, the figure shows impact of replacing survey income responses with administrative income, comparing the estimates after each step to the estimates after reweighting and earnings imputation. The 95 percent confidence interval for the last step is shown in each: for Panel A comparing the estimate after earnings imputation to the survey estimate and for Panel B comparing the final NEWS estimate to the estimate after earnings imputation. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 77 Figure A7: Decomposition of NEWS Processing Steps By Subgroup: Poverty A. Survey Steps: Weighting and Earnings Imputation All White White, not Hispanic Black Asian Hispanic (any race) Male Female Under age 18 Age 18 to 64 Aged 65 and older Native-born Foreign-born Naturalized citizen Not a citizen Northeast Midwest South West With a disability with no disability Aged 25 and older No high school diploma High school, no college Some college Bachelor's degree or higher Inside metropolitan statistical areas .Inside principal cities .Outside principal cities Outside metropolitan statistical areas Race and Hispanic Origin Sex Age Nativity Region Disability Status Educational Attainment Residence -5 -4 -3 -2 -1 0 1 2 Percentage Point Difference Reweighted (Nonresponse) + Reweighted for Linkage + Imputed Earnings Notes: This figure decomposes the impact of the NEWS processing steps on poverty. In Panel A, the figure shows the adjustments made to the survey data, including reweighting and improved earnings imputation comparing poverty for each group after the adjustment to the survey estimate. In Panel B, the figure shows impact of replacing survey income responses with administrative income, comparing the estimates after each step to the estimates after reweighting and earnings imputation. The 95 percent confidence interval for the last step is shown in each: for Panel A comparing the estimate after earnings imputation to the survey estimate and for Panel B comparing the final NEWS estimate to the estimate after earnings imputation. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 78 Figure A7: Decomposition of NEWS Processing Steps By Subgroup: Poverty, Continued B. Administrative Income Replacement and Survey Earnings Choice Modeling All White White, not Hispanic Black Asian Hispanic (any race) Male Female Under age 18 Age 18 to 64 Aged 65 and older Native-born Foreign-born Naturalized citizen Not a citizen Northeast Midwest South West With a disability with no disability Aged 25 and older No high school diploma High school, no college Some college Bachelor's degree or higher Inside metropolitan statistical areas .Inside principal cities .Outside principal cities Outside metropolitan statistical areas Race and Hispanic Origin Sex Age Nativity Region Disability Status Educational Attainment Residence -5 -4 -3 -2 -1 0 1 2 Percentage Point Difference + Administrative Income NEWS (+ Earnings Choice Model) Notes: This figure decomposes the impact of the NEWS processing steps on poverty. In Panel A, the figure shows the adjustments made to the survey data, including reweighting and improved earnings imputation comparing poverty for each group after the adjustment to the survey estimate. In Panel B, the figure shows impact of replacing survey income responses with administrative income, comparing the estimates after each step to the estimates after reweighting and earnings imputation. The 95 percent confidence interval for the last step is shown in each: for Panel A comparing the estimate after earnings imputation to the survey estimate and for Panel B comparing the final NEWS estimate to the estimate after earnings imputation. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 79 Figure A8: Comparing Bias in Linked Administrative Characteristics with Different Weights Any Linkage PHUS SSR Numident IRMF 1099-R W-2 or LEHD Any 1040 1040 (2018) 1040 (2019) Decennial MAFARF Black Knight SSA Data IRS Data Census Bureau Data 3rd Party Data -3 -2 -1 0 1 2 3 4 5 6 Percentage Point From Target A. Linkage Rates by Adrec Data 0-17 18-24 25-34 35-44 45-54 55-64 65+ Black White Hispanic Citizen Foreign Born Age Race/Hispanic Origin Citizen/Foreign-Born -3 -2 -1 0 1 2 3 4 5 6 Percentage Point From Target B. Address-Linked Demographics 10th 25th 50th 75th 90th 10th 25th 50th 75th 90th W-2 Earnings AGI -10000 -5000 0 5000 10000 Difference From Target Respondents Survey HH EBW EBW EBW + PIKed C. Address-Linked Income Notes: This figure shows various statistics of address-linked administrative, decennial census, and commercial data (refer to Section B.1) using different weights compared to the weighting targets (discussed in Appendix C and shown in Table A5). “Respondents” uses the base weights which adjust only for probability of selection into the sample. “Survey” uses the survey weights. “HH EBW” are the Stage 1 weights that adjust for selection into response at the household level. “EBW” are the Stage 2 weights that further adjust to population controls and “EBW + PIKed” are the Stage 3 weights that further adjust for selection into linkage. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 80 Figure A9: Comparing Survey Characteristics with Different Weights 0-17 18-24 25-34 35-44 45-54 55-64 65+ Black White Hispanic Native-Born Citizen Foreign-Born Citizen Non-Citizen High School Some College Bachelors Masters Professional Poverty Homeowner Age Race/Hispanic Origin Citizen/Foreign-Born Education -3 -2 -1 0 1 2 3 Percentage Point From Survey-Weighted A. Respondent Demographics 10th 25th 50th 75th 90th 10th 25th 50th 75th 90th Person Household -10000 -5000 0 5000 10000 Difference From Survey-Weighted EBW EBW + PIKed B. Respondent Survey Income Notes: This figure shows various statistics of survey demographics and survey-reported income using the entropy balance weights (discussed Appendix C) relative to the survey-weighted estimates. “EBW” are the Stage 2 weights that further adjust to population controls and “EBW + PIKed” are the Stage 3 weights that further adjust for selection into linkage. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 81 Table A1: Comparing Job-Level LEHD and W-2 Earnings Health Insurance LEHD-W-2 Comparison All Yes No Yes - No LEHD < W-2 8.7 9.7 3.9 5.85* (0.2) (0.2) (0.3) (0.30) LEHD ≥ W-2 0-1 percent greater 66.9 61.8 89.3 -27.52* (0.3) (0.3) (0.4) (0.54) 1-3 percent greater 6.4 7.5 2.0 5.51* (0.1) (0.2) (0.2) (0.26) 3-5 percent greater 4.9 5.8 1.3 4.50* (0.1) (0.2) (0.1) (0.20) 5-10 percent greater 6.8 8.0 1.6 6.32* (0.1) (0.2) (0.2) (0.24) 10+ percent greater 6.3 7.3 2.0 5.34* (0.1) (0.2) (0.2) (0.25) Observations 47,000 39,000 8,100 Notes: This table shows basic summary statistics on job-level comparisons of LEHD earnings to W-2 earnings (including deferred compensation) for the highest earning job. Jobs are classified by the ratio of LEHD to W-2 earnings. The first category, W-2 > LEHD, indicates that W-2 earnings exceed LEHD earnings by more than a trivial amount ($100). The other categories indicate that LEHD gross earnings exceeded W-2 earnings + deferred compensation by specific percent ranges. Because LEHD gross earnings should exceed W-2 taxable earnings + deferred compensation primarily due to employee pre-tax contributions to health insurance premiums, the sample in this table includes only individuals that responded to the health insurance question in the CPS ASEC, i.e., whose health insurance status was not imputed. The first column shows the share in each LEHD-W-2 bin for all workers with a job in both data sources. The next two columns show estimates for those that reported having and not having private health insurance, respectively. The last column shows the difference between the share in each bin between those having and not having private health insurance. Standard errors in parenthesis. *, , and * indicate significance at the 1, 5, and 10 percent levels and are only shown for differences. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 82 Table A2: Direct and Indirect Job Linkage Statistics EIN Matches Only EIN and Indirect Matches All Jobs Unmatched Jobs Share of Implied Total Unmatched Jobs Share of Implied Total Total Jobs W-2 256,800,000 40,720,000 0.146 25,680,000 0.097 LEHD 237,900,000 21,780,000 0.078 6,744,000 0.026 EIN Matches 216,100,000 0.776 0.820 Indirect Matches 15,040,000 0.057 Implied Total Jobs 278,600,000 263,600,000 Notes: This table shows the count of jobs that could be directly linked by Employer Identification Number (EIN) and indirectly linked as discussed in Section A.3. Source: 2018 W-2 and Longitudinal Employer-Household Dynamics data. 83 Table A3: Weighted Linkage Rates by Administrative Data Source in the Address Data Target Estimate Difference from Target Base-Weighted Base-Weighted Survey Weighted EBW-Weighted Occupied Units Respondent Units Respondent Units Respondent Units Respondent + All Adults PIKed Units Any Linkage 0.932* 0.0037* 0.0047* -0.0006 -0.0012* (0.002) (0.0006) (0.0010) (0.0006) (0.0005) SSA Data PHUS 0.402* 0.0584* 0.0427* Z 0.0001 (0.002) (0.0010) (0.0019) (0.0024) (0.0043) SSR 0.050* 0.0050* 0.0003 Z Z (0.001) (0.0004) (0.0007) (0.0010) (0.0015) Numident 0.921* 0.0046* 0.0058* Z Z (0.002) (0.0006) (0.0012) (0.0007) (0.0004) IRS Data IRMF 0.837* 0.0085* 0.0067* -0.0005 -0.0018 (0.002) (0.0008) (0.0014) (0.0013) (0.0014) 1099-R 0.436* 0.0127* 0.0070* Z Z (0.002) (0.0010) (0.0018) (0.0006) (0.0019) Any 1040 0.856* 0.0018* 0.0055* Z 0.0001 (0.002) (0.0007) (0.0013) (0.0009) (0.0006) 1040 (2018) 0.828* 0.0027* 0.0068* Z 0.0001 (0.002) (0.0008) (0.0014) (0.0005) (0.0008) 1040 (2019) 0.835* 0.0021* 0.0055* Z 0.0001 (0.002) (0.0008) (0.0014) (0.0009) (0.0007) W-2 or LEHD 0.751* -0.0060* 0.0037 Z 0.0001 (0.002) (0.0008) (0.0017) (0.0010) (0.0010) Census Bureau Data Decennial 0.867* 0.0084* 0.0083* Z 0.0001 (0.002) (0.0008) (0.0013) (0.0013) (0.0015) MAFARF 0.822* 0.0092* 0.0065* Z -0.0014 (0.002) (0.0009) (0.0014) (0.0022) (0.0031) 3rd Party Data Black Knight 0.644* 0.0119* 0.0071* Z Z (0.003) (0.0011) (0.0020) (0.0019) (0.0034) Notes: This table shows statistics on selection into response at the household level by data source that can be linked to occupied housing units, as discussed in Section B.1. The target estimate is calculated on the base- weighted set of all occupied housing units in the March monthly CPS. The other estimates show differences from the target (evidence of selection into the sample unaddressed by weighting if ̸= 0) for the indicated samples of respondents and weights. Standard errors in parenthesis. , , and indicate significance at the 1, 5, and 10 percent levels and are only shown for differences. Z indicates an estimate rounds to zero. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 84 Table A4: Linkage Rates by Administrative Data Source in the Person Data NEWS Sample (All Survey-Adults in Full Sample HH Assigned PIK) Survey-Adults (15+) Survey-Children (<15) Survey-Adults Survey-Children Assigned PIK 85.8 79.4 100.0 89.4 (0.18) (0.33) (0.30) Any Adrec Linked to Address If Assigned PIK 94.7 95.6 93.9 95.0 (0.15) (0.22) (0.16) (0.26) If Not Assigned PIK 89.9 92.6 92.3 (0.40) (0.48) (0.88) Present In \| Assigned PIK Any Administrative Record 98.1 85.2 98.0 87.4 (0.05) (0.30) (0.07) (0.33) IRS Data Tax Filing (1040) 84.6 83.2 84.4 85.6 (0.17) (0.30) (0.19) (0.34) IRMF 89.4 7.8 88.2 7.5 (0.10) (0.22) (0.12) (0.24) W-2 64.3 1.0 63.9 1.0 (0.16) (0.07) (0.17) (0.08) 1099-R 21.1 0.1 20.1 Z (0.14) (0.02) (0.13) (0.02) SSA Data DER 67.6 0.3 67.2 0.3 (0.16) (0.04) (0.17) (0.05) PHUS 37.8 3.9 35.2 3.5 (0.16) (0.16) (0.16) (0.16) SSR 3.6 1.3 3.4 1.2 (0.09) (0.10) (0.09) (0.10) State Data LEHD 64.3 1.0 63.9 1.0 (0.16) (0.07) (0.17) (0.08) Notes: This table shows statistics on the individuals that can be assigned a PIK as well as the households in which those 15 and over (survey-adults) can be assigned a PIK. For all households and the 82 percent of households with all survey-adults assigned a PIK (the NEWS analysis sample), we show the share of survey- adults and survey-children that can be linked to various data sets. Estimates and standard errors that are 0 by construction are omitted. Z indicates an estimate rounds to zero. Standard errors in parenthesis. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 85 Table A5: Entropy Balance Reweighting Procedure Stage/Step Moment Variables Moment Sample Reweighted Sample 1. Housing-unit level Linked survey, administrative, and census variables Non-vacant housing units in March Basic CPS (respondents and nonre- spondents) Respondent housing units 2. Person level A. Preserve distribution of hous- ing unit characteristics Linked survey, administrative, and census variables Householders and householder- partners, using the housing-unit level weights from Stage 1 Householders and house- holder partners B. Spousal equivalence Linked survey, administrative, and census variables Married couples and cohabiting partners Married couples and cohabit- ing partners C. External population targets State-level population estimates by race, Hispanic-origin, gender, and age External population estimates All individuals D. Match distribution of house- hold characteristics in March Ba- sic Sample Subset of linked survey, adminis- trative, and census variables and state-level population controls Householders and householder part- ners in the March Basic File Householders and house- holder partners in the full CPS ASEC sample 3. Address Selection into PIK assignment (for all adults in HH) A. Preserve distribution of re- spondent and housing unit char- acteristics Linked survey, administrative, and census variables. Additional moments for survey-only and linked survey- administrative characteristics from full respondent sample Respondent sample with weights from step 2. Households where all individ- uals asked income questions (age 15+) are linked to a PIK. B. External population targets State-level population estimates by race, Hispanic-origin, gender, and age External population estimates Notes: This table describes the entropy balance reweighting procedure. In the first stage, respondent housing units are reweighted to control for selection into response. This is done by reweighting them to match the characteristics of the target population – all nonvacant housing units in sample. In the second stage, we estimate individual weights that preserve the distribution of housing-unit characteristics from the first stage, while also matching external population totals and approximating the spousal equivalence of weights that are a part of the existing CPS ASEC weights, as in Rothbaum and Bee (2022). To address selection into PIK assignment (and the availability of administrative data), we add a third-stage weighting adjustment. 86 Table A6: Imputation Summary Statistics: Survey Earnings Imputed Estimate SRMI - Survey W-2 Earnings Respondents Survey SRMI (Percent difference for dollar values) Has Survey Earnings = 0 0.181 0.282 0.230 -0.052* (0.007) != 0 0.908 0.860 0.907 0.046* (0.005) q = 1 0.676 0.623 0.706 0.083* (0.014) q = 2 0.924 0.842 0.921 0.079* (0.009) q = 3 0.967 0.928 0.961 0.033* (0.008) q = 4 0.984 0.960 0.978 0.018* (0.006) q = 5 0.985 0.960 0.973 0.013** (0.006) Average Wage and Salary Earnings = 0 45,760 43,550 40,440 -0.071 (from main job) (0.061) != 0 55,520 52,470 53,330 0.016 (0.047) q = 1 11,960 22,010 20,840 -0.053 (0.084) q = 2 23,540 29,810 26,300 -0.118* (0.055) q = 3 37,750 43,950 37,910 -0.137 (0.045) q = 4 57,340 62,050 56,790 -0.085 (0.058) q = 5 120,300 100,000 124,900 0.248* (0.061) Median Wage and Salary Earnings = 0 25,900 30,210 31,360 0.038 (from main job) (0.092) != 0 41,200 37,690 37,090 -0.016 (0.047) q = 1 6,747 12,400 13,780 0.111 (0.158) q = 2 20,720 24,660 22,160 -0.102 (0.055) q = 3 35,630 36,250 33,570 -0.074 (0.055) q = 4 55,350 51,490 52,060 0.011 (0.045) q = 5 100,300 78,690 97,460 0.238 (0.073) Notes: This table shows basic summary statistics of survey wage and salary earnings conditional on W- 2 earnings (having a W-2 and by W-2 earnings quintile for q = 1,2,3,4,5). Each row shows the relevant survey wage and salary earnings statistic for survey earnings respondents, imputed as part of regular survey production and by SRMI, as discussed in Appendix D. Standard errors in parenthesis. , , and indicate significance at the 1, 5, and 10 percent levels and are only shown for differences. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 87 Table A7: Imputation Summary Statistics: Means-Tested Benefits Administrative Data Available? Difference Diff in Diff Yes No No - Yes (Adrec - Survey) and (No - Yes) TANF Survey Receipt 1.03 1.05 0.02 0.17 (0.08) (0.08) (0.11) (0.20) Amount 3,054 3,937 882 -975 (205) (331) (391) (471) Administrative Receipt 0.78 0.97 0.19 (0.06) (0.16) (0.18) Amount 2,604 2,511 -93 (168) (244) (293) SNAP Survey Receipt 9.85 9.28 -0.57* -0.42 (0.32) (0.22) (0.38) (0.51) Amount 2,363 2,345 -18 73 (70) (51) (87) (120) Administrative Receipt 16.11 15.12 -0.99* (0.44) (0.39) (0.58) Amount 2,807 2,862 55 (60) (80) (100) Notes: This table shows basic summary statistics of means-tested benefits imputed for incomplete state-level administrative data. For both TANF and SNAP, the first rows show how survey responses vary across states with and without administrative records and the next set of rows show the administrative and imputed estimates. For each, we then compare the states without administrative data (No) to the states with (Yes) and take the difference in difference by comparing the administrative (No - Yes) to the survey (No - Yes). The means-tested benefit imputation is discussed in Appendix D. Standard errors in parenthesis. *, , and * indicate significance at the 1, 5, and 10 percent levels and are only shown for differences. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 88 Table A8: Combining Survey and Administrative Earnings A. By Reported Earnings Type and Source Survey Administrative Rule Percent of Sample Wage and Salary Self Employment Wage and Salary Self Employment Wage and Salary Self Employment All Adults Any Earnings X X X X Job-level administrative 1040 (from TMI) 0.4 0.6 X X X Job-level administrative 1040 (from TMI) 0.4 0.6 X X X Job-level administrative 1040 (from TMI) 4.1 5.7 X X Job-level administrative 1040 (from TMI) 0.4 0.5 X X X None (administrative) 1040 (from TMI) 0.7 1.0 X X None 1040 (from TMI) 1.5 2.1 X X None (administrative) 1040 (from TMI) 1.3 1.7 X None 1040 (from TMI) 1.2 1.7 X X X Measurement error model Survey 1.8 2.4 X X Measurement error model 0.8 1.1 X X Measurement error model None 50.5 70.1 X Job-level administrative None 5.6 7.7 X X Survey Survey 0.8 1.1 X None Survey 1.0 1.4 X Survey None 1.6 2.3 None None 28.0 B. By Combination Rule Percent of Sample Combination Rule All Adults Any Earnings Simple - no earnings or only earnings in one source 38.6 14.7 Earnings Choice 53.0 73.6 Default to administrative data due to data issues (potential misclassification, missing self-employment, etc.) 8.4 11.7 Notes: This table describes the possible combinations of survey and administrative reports of wage and salary and self-employment earnings as well as our rules for when we use survey and administrative reports for each. If the administrative wage and salary earnings on the 1040 is positive but there are no reported job-level administrative earnings, then we use the 1040 value when the rule indicates use of the job-level data. “All adults” includes anyone 15 or over as they are asked survey earnings questions. The sample only includes individuals in the NEWS sample. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 89 Table A9: Combining Administrative and Survey Earnings: Share with Survey Earnings by Mean Reversion Parameter Kappa Share Kappa Survey Earnings 0.7 5.8 (1.1) 0.75 8.4 (1.5) 0.8 11.8 (2.0) 0.85 16.0 (2.3) 0.9 20.6 (NEWS) (2.7) 0.95 25.8 (3.4) 1 30.9 (3.8) Notes: This table shows how variation in the mean-reversion kappa parameter in the measurement error model affect the share of individuals whose survey wage and salary earnings are used. Figure A5 shows how the household income distribution differs under these alternatives. Standard errors in parenthesis. Source: 2019 Current Population Survey Annual Social and Economic Supplement linked to administrative, decennial census, and commercial data. 90 Table A10: Income Type by Source for Filers and Nonfilers Source Income Type Filers Nonfilers Notes Wage and Salary Earnings W-2 DER LEHD 1040 W-2 DER LEHD Administrative data may miss unreported ”under-the-table” earnings. Current W-2s and DER do not include pre-tax employee contributions to health insurance premiums. LEHD does not have complete coverage. Survey has potential for misreporting and underreporting. Self-Employment Earnings 1040 DER Survey only Under-reported substantially on surveys and in administrative records. Considerable disagreement between extensive margin reporting on surveys and administrative data (Abraham et al., 2021). Social Security 1040 PHUS PHUS Supplemental Security SSR SSR Unemployment Insurance 1040 Survey only Included in 1040 Total Money Income. Imputed for nonfilers using disclosed results from more detailed 1099-G data. Worker’s Compensation Survey only Survey only Not available federal administrative data. Public Assistance TANF TANF Current data only covers some states. TANF data does not cover all possible cash assistance programs. Veteran’s Benefits Survey only Survey only Potential for VA data use in the future Disability, Survivor, and Retirement Income 1099-R 1099-R Interest 1040 Survey only Imputed for nonfilers using disclosed results from more detailed 1099-INT data. Dividends 1040 Survey only Imputed for nonfilers using disclosed results from more detailed 1099-DIV data. Rent and Royalty Income 1040 Survey only Net rent and royalty income included in 1040 Total Money Income. Gross rent and royalty income available as a separate variable. Educational Assistance Survey only Survey only Financial Assistance Survey only Survey only Alimony 1040 Survey only Included in 1040 Total Money Income Gambling Winnings 1040 Survey only Included in 1040 Total Money Income. Potentially available on survey as ”other in- come.” Notes: This table describes the available data sources for the various types of income, including notes about the limitations of various sources. The availability of income varies between filers and nonfilers, with more income sources available in the currently available administrative records for filers. 91 Appendices A Data Linkage A.1 Person Linkage46 The Census Bureau developed the Person Identification Validation System (PVS) to probabilis- tically match individuals’ records in survey and other data to their SSN or Individual Taxpayer Identification Number (ITIN) using personally identifying information (PII), such as name, date of birth, and residential address (Wagner and Layne, 2014). Linked records are assigned a Pro- tected Identification Key (PIK) and the PII and SSN or ITIN are removed. The PIK serves as the anonymized linkage key to match individuals across data sets. As a result, if PVS is unable to assign a PIK to a given survey respondent, no administrative data are available for that respondent. Bollinger et al. (2019) found a linkage rate in their CPS ASEC sample (2006-2011) of 86 percent, which matches our estimate for the 2019 CPS ASEC. Because observable characteristics, such as race, ethnicity, citizenship status, etc., are correlated with PIK assignment (Bond et al., 2014), we must account for this selection into linkage in our estimates, which we discuss in Section C. A.2 Address Linkage Brummet (2014) describes the development and performance of the system used to link household records, via residential address fields, to the Master Address File (MAF), called the “MAF Match.” Information such as house number (and suffix, such as apartment number), street name (and prefix/suffix, such as rural routes or state highway identifiers), city, state, ZIP code, etc. is used to link addresses in each data set to the MAF, to assign them MAFIDs. As with PIKs, this means that if the MAF Match process is unable to assign a MAFID to an address, the information associated with that address in that data source cannot be linked to other address-level data. For recent years of surveys such as the ACS, CPS ASEC, and SIPP, every 46The discussion in this section follows Bee and Rothbaum (2019) closely. 92 housing unit has a MAFID because the sample was drawn directly from the MAF. A.3 Job Linkage The W-2, DER, and LEHD files all have information on individual jobs. However, unlike the LEHD, the W-2s and DER do not capture gross earnings. The Census Bureau receives W-2 extracts from the IRS that include Box 1 “Wages, tips, and other compensation,” Box 3 “Social Security wages,” and the sum of deferred compensation in Box 12 codes D-H.47 We only observe taxable earnings and deferred compensation, but not other non-taxable earnings. We therefore do not have information on pre-tax employee payments for health insurance and other forms of pre-tax compensation not available in the extract provided by the IRS, such as contributions to Health Savings Accounts. In most of this section, we will primarily discuss W-2s and not the DER, as the two are identical for most workers for whom the DER is available. Not all jobs are covered by unemployment insurance, and thus some jobs are out of universe for the LEHD. This includes all federal government employees and some private sector employees.48 In the earnings question on the CPS ASEC and ACS, respondents are asked to report “money income”, which includes gross wage and salary earnings. To match this concept, we would like gross earnings for each individual job, which we could then use to estimate person-level gross earnings. However, we have gross earnings for only a subset of jobs (from the LEHD) and taxable earnings + deferred compensation from the universe of jobs (from W-2s). Because the LEHD includes a subset of jobs we should observe in W-2s, it is possible for an individual to have one job 47These codes include elective deferrals to plans under Box 12 codes D: 401(k), E: 403(b), F: 408(k)(6), G: 457(b), and H: 501(c)(18)(D). These boxes cover 96.3 percent of all elective re- tirement contributions on W-2s, calculated from IRS Statistics of Income Tax States for Indi- vidual Information Return Form W-2 Statistics, Table 7.A at https://www.irs.gov/statistics/ soi-tax-stats-individual-information-return-form-w2-statistics, accessed 11/17/2021. 48For example, Maryland’s Department of Labor lists the following jobs as exempt: barbers and beauti- cians, taxicab drivers, owner-operated tractor drivers in certain E and F classifications, maritime employ- ment, election workers, church employees, clergy, certain governmental employees, railroad employment, newspaper delivery, insurance sales, real estate sales, messenger service, direct sellers, foreign employment, other state unemployment insurance programs, work-relief and work-training, family members, hospital pa- tients, student nurses or interns, yacht salespersons who work for a licensed trader on solely a commission basis, services of aliens who are students, scholars, trainees, teachers, etc., who enter the U.S. solely to pursue a full course of study at certain vocational and other non-academic institutions, recreational sports officials, home workers, and casual labor. Refer to https://www.dllr.state.md.us/employment/empfaq.shtml accessed 11/1/2022. 93 https://www.irs.gov/statistics/soi-tax-stats-individual-information-return-form-w2-statistics https://www.irs.gov/statistics/soi-tax-stats-individual-information-return-form-w2-statistics https://www.dllr.state.md.us/employment/empfaq.shtml in the LEHD and two in the W-2s. Therefore, we cannot just sum the earnings from both sources and take the maximum, because the one with the higher value (in this case, W-2 earnings from two jobs) may understate this individual’s true gross earnings. Therefore, we would like to combine the LEHD and W-2 records at the job level. For an individual with one LEHD job and two W-2 jobs, we would then observe gross earnings for one job and taxable earnings plus deferred compensation for the other. For the second job, we could impute gross earnings conditional on the other information observed about them (discussed in Appendix D) and then sum the job-level gross earnings to estimate their administrative gross earnings. However, linking LEHD and W-2 jobs is not trivial. In the simplest case, a firm files a W-2 and reports the job to the UI office with the same EIN. We can link these “direct matches” by PIK and EIN. However, some firms do not file their W-2s and UI reports under the same EIN, and some firms use multiple EINs in one source but a single EIN in the other (i.e., a separate EIN for each state’s employment in the LEHD but one EIN in the W-2s). Other firms use other identifiers, such as state EINs, when they report jobs to UI offices. Therefore, we cannot directly link many jobs between the LEHD and W-2 files using PIK/EIN combinations. Since nearly all jobs in both files include a PIK, we can create a set of possible matches that match on PIK but not EIN. We can then identify the W-2 EINs that correspond to a different EIN or state EIN in the LEHD by looking across all workers with unmatched jobs. We create a W-2 EIN to LEHD EIN crosswalk of these “indirect match” jobs. An example of how we find direct and indirect matches is shown in Figure A1. In the example, we have three workers (PIK = 1, 2, 3) and their W-2 and LEHD jobs. For EIN = 400 and 500, the jobs match at the PIK-EIN level. However, EINs 100 and 600 in the W-2s and 200 in the LEHD do not match. Each worker with EIN = 100 in the W-2s also has a job with EIN = 200 in the LEHD and each of those jobs has similar earnings on the two files. We use this information to infer that W-2 EIN 100 is the same firm as LEHD EIN 200. We would then be left with the W-2 job at EIN = 600 that does not match to any job in the LEHD, perhaps representing a job that is not covered by unemployment insurance. To create a crosswalk of all indirect matches between W-2 and LEHD EINs, we develop an iterative 94 algorithm using three pieces of information: 1. The diference in earnings reported on the W-2 and LEHD for the possible job match, 2. The share of jobs in the W-2 EIN that match to the same LEHD EIN and the share of jobs from the LEHD EIN match to the same W-2 EIN, and 3. The number of likely matches between a W-2 EIN and an LEHD EIN For the first rule, we can identify matches as likely if the W-2 and LEHD earnings are within some percent of each other. For the second, we can only keep matches in the crosswalk if many or most of the jobs in a W-2 or LEHD EIN are identified as likely matches to a single EIN on the other file. For the third, we may be more confident of a possible match if 100 jobs are all flagged as likely matches than if two are. We create an iterative process to create our indirect matches where we set the thresholds for each of these three possible rules to identify likely matches. We identify the W-2 EIN-LEHD EIN combinations that match under these thresholds, add those combinations to our crosswalk and then remove the matched jobs from our possible match dataset. The removed jobs include all jobs with those pairs of EINs, not just the ones flagged as likely matches by our percent difference cutoff. We then repeat the process with the remaining jobs after adjusting the thresholds used to identify possible matches. The goal of the iterative process is to first add the matches we are sure of from the set of unmatched jobs (large firms, for example) before we match jobs from smaller firms or with larger differences in earnings across the files. For example, in the first pass at identifying indirect matches, we flag jobs as likely matches if the W-2 and LEHD earnings are within 10 percent of each other. We then keep the W2 EIN-LEHD EIN combinations where 50 percent or more of them match in one direction or the other - i.e., 50 percent of jobs at a W-2 EIN match to the same LEHD EIN or 50 percent of jobs at the LEHD EIN match to the same W-2 EIN. Finally, we only keep EIN matches for the crosswalk if at least 5 jobs match. In the example in Figure A1, there are three jobs at W-2 EIN = 100 and LEHD EIN = 200 that are 95 within 10 percent of each other and flagged as likely matches. All jobs in W-2 EIN = 100 match to LEHD EIN = 200 (and vice versa). This combination meets the first two conditions. However, the number of matches is 3, which is less than the threshold of 5 so this combination of EINs would not be flagged as a match. These jobs would be kept in the set of unmatched jobs for the next round of the process. In subsequent rounds, we can (1) increase the tolerance on likely matches (i.e., from 10 to 20 percent difference in earnings), (2) reduce the share matched needed within W-2 or LEHD EINs (i.e., from 50 percent to 25 percent), or (3) lower the threshold of likely matches needed to confirm a match (i.e., from 5 to 3). From Figure A1, if we lowered the number of likely matches to 3, then we would count W-2 EIN = 100, LEHD EIN = 200 as an indirect match, add that match to our crosswalk, and remove the matches under Indirect Matches from the set of unmatched jobs.49 Finally, we implement a series of additional steps to match the remaining set of jobs. First, we try to find jobs that have multiple EINs in the LEHD but one EIN in the W-2s, for example if a firm changed EIN mid-year for any reason (restructuring, acquisition, etc.). In that case, the LEHD might have multiple EINs during the year as the firm filed its quarterly reports, but only one EIN for the workers’ W-2s. We then flag remaining unmatched jobs as ad hoc likely matches if their earnings are within a certain percent of each other, but they were not matched by the iterative process. In Table A2, we show summary statistics from the linkage process. In the W-2s, there are 257 million unique jobs in 2018, with 238 million in the LEHD. Of those, 216 million are direct matches by PIK-EIN combination. This leaves 41 million unmatched W-2 jobs and 22 million unmatched LEHD jobs. However, we find an additional 15 million indirect matches through our matching algorithm, covering 70 percent of the unmatched LEHD jobs and 37 percent of the unmatched W-2 jobs. We then have 82 percent of jobs matched directly by PIK-EIN, 6 percent matched indirectly, 10 percent unmatched from W-2s, and 3 percent unmatched from the LEHD. We use this linked 49In practice, we first increase the earnings percent difference threshold for likely matches from 10 percent to 20 percent to 25 percent. We also decrease the share of matches within an EIN that must match from 50 percent to 25 percent to 10. Finally, we also decrease the minimum number of matches from 5 to 2 to 1. We make each of these changes separately from the initial thresholds and then change them simultaneously. 96 job information to better estimate gross earnings at the job and person level for use in our income estimates. Since LEHD earnings should exceed W-2 taxable earnings + deferred compensation in large part due to employee pre-tax payments for health insurance premiums, we compare them in our CPS ASEC sample for individuals who reported whether they have private health insurance coverage.50 As shown in Table A1, individuals with private coverage are less likely to have LEHD earnings that are approximately the same as their W-2 earnings + deferred compensation (LEHD ≥ W-2 by 0-1 percent), and covered individuals are 3 to 5 times more likely to have LEHD values that exceed the W-2 amounts by 1-3 percent, 3-5 percent, 5-10 percent, and 10+ percent. This likely reflects the missing gross earnings for employee pre-tax contributions to health insurance premiums on W-2s. However, Table A1 also shows that there is a substantial number of jobs whose W-2 taxable earnings + deferred compensation exceeds LEHD gross earnings. At present, we treat these jobs as having measurement issues in the LEHD and default to the taxable earnings + deferred compensation from the W-2 and impute gross earnings for those jobs as discussed in Appendix D. We plan to investigate this issue further in future NEWS releases. A.4 Firm Linkage Our firm identifier in the employment data is the EIN. However, as we noted when crosswalking the job-level data between the W-2 and LEHD, an EIN does not necessarily correspond to a firm. Some firms have multiple EINs, for example in each state of operation, which can make matching individual workers to their firm (rather than subunits of the firm) difficult. This is a challenge for all users of EIN-based administrative data (Joint Committee on Taxation, 2022; Chow et al., 2021). Chow et al. (2021) redesigned the Longitudinal Business Database (LBD) in part to help bridge this gap and to make linkages between various worker- and firm-level datasets easier. We use this redesigned LBD to map EINs to LBD firm identifiers (LBDFID). In the LBD, 50Note that the CPS ASEC variable we use indicates receipt of private coverage, but not necessarily that the individual’s job (rather than a spouse, partner, or other family member) was the source of the coverage. 97 each establishment is associated with one or more EINs and also to a LBDFID. We create a crosswalk of all EIN to LBDFID combinations by year. If a firm restructures during a given year, it is possible for the same EIN to map to different LBDFIDs in the same year. When that happens, we assign the EIN to the associated LBDFID in the subsequent year. From that, we create a year-by-year EIN-LBDFID crosswalk for all firms in our data. We can then merge the job-level data by EIN to an LBDFID to match each worker to a firm. At the firm level (by LBDFID), we can then use LBD data or create our own summary statistics on firm employment and payroll from the linked job-level data. At present, we use this firm information for modeling, imputation, and weighting. B File Construction B.1 Address File The first file we create from the data in Sections 3.1-3.6 is the Address File. We link the sample of occupied (non-vacant) housing units in the survey to the aforementioned sources of administrative, survey, census, and commercial data, as shown in Figure 4. By starting with addresses, we have information from all occupied units, including respondents and nonrespondents. In the address file, we do not use any information from survey responses other than whether the unit responded. This file is used to construct the weights that address selection into our sample, discussed in Section C. First, we link the MAFIDs of occupied housing units to the MAF and Black Knight data to get information on the housing units, such as home value and type (single vs. multi-unit). We then link the same MAFIDs to several files that have both MAFIDs and PIKs, including the IRMF, MAF- ARF, and 1040 tax returns, giving us information on the information returns (W-2, 1099-G, etc.) sent to that address, their income (from tax returns), and PIKs for individuals who are associated with that address. We create a roster of PIKs for the linked individuals in each occupied unit. We then link this roster to various files, including the universe PHUS and SSR files, the Numident, W- 98 2s, LEHD, and the IRMF and 1040 tax returns.51 We then link the LEHD and W-2 jobs together using the job crosswalk discussed in Section A.3. We also link those jobs to the characteristics of the employer firm in the LBD using the EIN-firm ID crosswalk discussed in Appendix A.4. Finally, we create geographic summary files at different levels of aggregation (state, county, and tract) that summarize the characteristics of residents of those locations from different files. These include (1) a summary of demographic characteristics from the 2010 decennial census, (2) de- mographic and socioeconomic characteristics from 5-year ACS files, (3) earnings and information return receipt from the IRMF and W-2 files, (4) citizenship information from the MAF-ARF linked to the Numident, and (5) income and marital status information from 1040 tax returns. This gives us information on the income, earnings, industry, race, Hispanic origin, marital status, presence of children, home value, housing unit type, etc., as well as information about the neigh- borhoods in which each household lives. However, data coverage is not perfect. As shown in Table A3, we can link 93 percent of occupied CPS ASEC addresses to at least one data set (exclud- ing the MAF, from which the addresses were sampled). That leaves 7 percent of addresses that we cannot link to any data other than the MAF. For these, we have no additional address-level information, and we cannot link the address to possible residents, which means that we cannot observe any address-level demographic or socioeconomic characteristics for these households (apart from the survey responses). For them, we only have information about their communities from the geographic summary files and about their housing unit from the MAF. Furthermore, we do not directly observe some characteristics that may be related to wellbeing and survey response, such as educational attainment, health insurance status, disability status (except if receiving SSI or OASDI), etc.52 51For the IRMF and tax return link, we do this in case an individual associated with the address received an information return at a different address or was on a 1040 tax return filed from a different address. 52Rothbaum and Bee (2022) evaluate how well weighting can control for differences between respondents and nonrespondents by one of the dimensions unobserved in our linked data, educational attainment, by linking the subset of housing units to prior ACS responses. They find that most, but not all, of the selection into response by educational attainment is addressed by weights created using similar linked data. 99 B.2 Person File The second file we create from the data in Sections 3.1-3.6 is the Person File. We create this file by linking survey respondents to administrative data, as shown in Figure 5. In combination with the weights created using the Address File, the Person File is used to create our income and poverty estimates. The Person File contains survey responses, including demographics, socioeconomic characteristics, income, etc. as well as administrative information on income on the following files: 1040s, W-2s, DER, LEHD, 1099-Rs, PHUS, SSR, and TANF. Table A10 shows the data sources with information by income type (wage and salary earnings, Social Security, etc.) for tax filers and nonfilers. For tax filers, most income types are available in the administrative data, either as separate variables or as part of 1040 Total Money Income. For nonfilers, we observe wages and salary earnings (W-2s, DER, and LEHD), OASDI benefits (PHUS), SSI (SSR), retirement, survivor and disability income (1099-R), and TANF income (state data), as well as flags for the potential presence (but not amount) of interest income (1099-INT), dividends (1099-DIV), and unemployment compensation (1099-G). Several types of income are only available on the survey, regardless of tax filing status, including workers’ compensation, veterans benefits, educational assistance, and inter-household financial assistance. Table A4 shows the share of the sample that can be assigned a PIK and the share of individuals with a PIK that can linked to each of the administrative data sources. C Weighting Weighting is one method for addressing missing data, where variables are completely unobserved for a subset of the sample.53 Let R be an indicator for whether the information is available for an individual or unit (i.e., response to a survey). Given a set of k variables X = {x1, x2, . . . , xk} for n units (individuals, households, firms). These covariates are observed for some units, but not others, X = {XO, XM}, where O indicates observed (R = 1) and M indicates missingness (R = 0). There are several possible relationships between missing data and the individual and household 53The discussion in this section follows Rothbaum and Bee (2022) closely. 100 characteristics we are interested in estimating. The simplest possible pattern of missingness (for the analyst) is if the data are missing completely at random (MCAR). In this case, nonresponse is completely random and not related to XO or XU , or R ⊥ (XO, XM ). For example, if a unit flips a coin when deciding whether to respond to the survey, nonresponse would be MCAR. If the data are MCAR, then the solution is easy – we do not need any adjustment to the data to get an unbiased estimated. We can just drop missing observations. Only precision is affected by MCAR data, as the sample is smaller than if all individuals were observed. Another possibility is that the data are missing at random (MAR), conditional on the observable information. Given a distribution f(·), data are MAR if f(R\|X) = f(R\|XO), which means that missingness is conditionally independent of the unobserved information (XU ). This is the underlying assumption of most nonresponse bias adjustments, such as survey weights. However, another possibility is that the data are not missing at random (NMAR), where f(R\|X) ̸= f(R\|XO). This is much more challenging to address. Suppose the probability of information availability varies with income, which is in X. Then f(R\|X) ̸= f(R\|XO), and we cannot easily recover the true underlying income distribution from the observed data in XO without strong, generally difficult to verify assumptions about f(R\|X). However, MAR is an independence assumption conditional on X. Suppose there is another set of variables A that are observed for the full sample, independent of response. In that case it is possible that the data are NMAR with respect to X, but MAR with respect to A, or more formally f(R\|X) ̸= f(R\|XO) but f(R\|X,A) = f(R\|XO, A). Rothbaum and Bee (2022) found that from 2020 to 2022, nonresponse in the CPS ASEC was NMAR with respect to X and that income statistics were biased by 2-3 percent as a result. They used additional information from administrative data linked at the address level to the addresses of respondent and nonrespondent households to adjust the weights for nonresponse.54 There are several aspects of our data that lend themselves to weighting to address missing informa- tion — where a subset of variables is completely missing for some units. For survey nonresponse, none of the survey information is observable for the nonresponding units. For incomplete linkage, 54Rothbaum et al. (2021) did the same to address nonresponse bias in the 2020 ACS. 101 none of the administrative data is available for the unlinkable individuals. If survey nonresponse or linkage are MAR, we can address the bias through weighting. To include additional characteristics in the weighting model, we use entropy balancing (Hainmueller, 2012). Entropy balancing is an application of exponential empirical calibration. Empirical calibra- tion has a long history of use in survey weighting (Deming and Stephan, 1940; Deville and Särndal, 1992) – the existing weighting models (using raking) in the ACS and CPS ASEC are applications of empirical calibration.55 We use the unobservable information (in the survey) from the linked administrative and decennial census data, which are available for all linkable households regardless of whether they responded as well as the geographic summary information. Entropy balancing estimates weights that match a specified set of moment constraints (i.e., to adjust the weights according to f(R\|XO, A)) while keeping the final weights as close as possible to the initial weights. Entropy balancing has several appealing features for this application. The first is flexibility. Inverse probability weighting (or any simple regression-based reweighting technique) is only amenable to matching characteristics of the distribution in the sample, but not external targets. Empirical calibration will adjust the weights to match any properly specified target moment, whether that moment was estimated on the sample or with external data. The second is statistical efficiency, which is achieved by keeping the final weights as close as possible to the initial probabilities of se- lection.56 Third, entropy balancing directly adjusts the weights to the moment conditions, like with raking but unlike single-index propensity score weighting approaches (such as inverse probability weights). In propensity score approaches, the adjustment is made to the single index generally estimated from a regression. The resulting balance must be assessed to evaluate the success and quality of the propensity score model. In some cases, a misspecified propensity score model can make balance worse on a given set of dimensions. As entropy balancing directly targets those moments, balance is assured. Fourth, unlike raking, or cell-based empirical calibration methods, 55Raking, also called iterative proportional fitting, adjusts the weights for each group to match the population total for that group. It is solved by iterating across groups to match the different population targets in stages. 56Through the minimization in equation C.1. 102 entropy balancing allows for the inclusion of continuous variables in the weighting model. The fifth is computational efficiency – entropy balancing allows matching to a high-dimensional vector of moment constraints. In terms of our MAR assumption, if A or X is high dimensional, then the computational efficiency makes it feasible to include all of A and X in the weighting model. As in Rothbaum and Bee (2022), we use state-level population controls that include estimates of the share of the population in 20 separate groups in each of the 50 states and the District of Columbia. That yields 1,020 separate target population moments before even considering information from the linked administrative data. The computational efficiency of the entropy balancing optimization algorithm allows us to match to both the linked administrative and population control targets simultaneously. This eliminates the need for an additional population control raking step that can undo the balance from the nonresponse adjustment.57 Next we discuss entropy balancing in detail. Suppose we have n observations, where i = 1, 2, . . . , n with base weights based on sampling probabilities of q = {q1, q2, . . . , qn}. Entropy balancing esti- mates weights w = {w1, w2, . . . , wn} that solve the following minimization problem: min w n∑ i=1 wi log( wi qi ) (C.1) subject to several sets of constraints. First, we have p moment conditions. Let X = {X1, . . . , Xp} be a matrix of observable characteristics. For characteristic j, the moment conditions are defined 57Several studies have implemented first-stage nonresponse adjustments followed by second-stage raking to population controls that do not condition on the first-stage adjustment. Slud and Bailey (2010) found that for some metrics of weight quality, the benefits of the first-stage adjustment disappeared after the application of the second-stage raking to population controls. Eggleston and Westra (2020) found that for some measures used in the first-stage adjustment, the bias is not improved or can be greater using the final weights after raking to population controls, although most statistics show reduced bias after the second-stage raking. Rothbaum et al. (2021) found something similar in follow-up work on the ACS when applied to the 5-year release. Without including very detailed population controls in the 2020 1-year ACS weights (down to tract-level population), when the 2016-2020 files were combined and raked to the 5-year population controls, the 2020 nonresponse adjustment had little impact on the 5-year estimates. Only when the 2020 file was simultaneously reweighted to detailed population controls and the linked administrative targets, limiting the need for additional raking adjustments, did the nonresponse bias adjustment persist on the final 5-year file. 103 to match a vector of pre-specified constants c̄j , where: n∑ i=1 wicj(Xi,j) = c̄j . (C.2) cj(·) can be any arbitrary function. Second, we have constraints on the weights themselves: n∑ i=1 wi = w̄ wi ≥ 0, i = 1, . . . , n (C.3) which ensure that the weights sum to some pre-specified total weight w̄, which can be the population count or 1. The value of w̄ does not affect the relative weights of each observation. As such the weights can be adjusted to match pre-specified moments such as population means, variances, higher-order moments, moments of any transformed distribution of X(i, j), etc. In summary, entropy balancing adjusts the weights according to (C.1), subject to the constraints in (C.2) and (C.3).58 Entropy balancing was developed as an application of empirical calibration to balance treatment and control groups when estimating causal treatment effects in observational studies. Zhao and Percival (2017) show that, in that context, entropy balancing is equivalent to estimating a logistic model for the propensity score and a linear regression model for the outcome, conditional on the covariates used in the moment conditions. They find that entropy balancing is doubly robust - if at least one of the two models is correctly specified, the estimated population average treatment effect on the treated (PATT) is consistent.59 Using the notation of that literature, let γ be the PATT, Y be an outcome of interest where Y (1) is the outcome if treated and Y (0) is the outcome if untreated, then: 58In practice, as is not necessarily possible to satisfy all constraints simultaneously through weighting adjustment, the analyst sets a tolerance level for the moment constraints. The weighting algorithm adjusts the weights iteratively until all constraints are satisfied subject to the specified tolerance. 59Double robustness is not a panacea. Kang and Schafer (2007) show via simulation that doubly robust models for missingness can perform poorly when neither model is correctly specified, or as they write, “in at least some settings, two wrong models are not better than one.” 104 γ = E[Y (1)\|T = 1]− E[Y (0)\|T = 1]. (C.4) In the causal inference literature, the challenge is that E[Y (0)\|T = 1] is not observed. Under entropy balancing, given ∑n i=1 qi = q̄, the PATT is estimated as: γ̂ebw = 1 q̄ ∑ Ti=1 qiYi − 1 w̄ ∑ Ti=0 wiYi. (C.5) In the case of survey weights, the “treatment” is nonresponse, and the double robustness result applies. Entropy balancing reweights the sample so that the estimate of Y for the weighted respon- dents is equal to the estimate of Y for the population,60 or: E[Y ] = 1 w̄ n∑ i=1 wiY. (C.6) We would like to reweight the respondent sample so that its distribution of characteristics matches the target population from which the sample was drawn. However, some characteristics are not observable for all housing units with the available linked census, survey, and administrative data. For example, we do not observe any demographic information for housing units that are not linked to an information return in the IRMF file, as the IRMF provides the identifier needed (PIK) to link individuals to all other data sources. Therefore, we use a second source of data for our reweighting – the aforementioned external estimates of population by geography. For both the linked data and the external population estimates, we can specify a set of moment conditions, which are intended to capture the distribution of characteristics in the target population. In the language of our MAR assumption, we are concerned that f(R\|A) ̸= f(R\|X) and that we need XO (the demographic information) in the weighting model as well, such that f(R\|A,XO) = f(R\|X). Our data have one additional complication – the target moments are at separate levels of aggre- gation. Estimates from the linked administrative and census data are at the housing unit level 60Conditional on strong ignorability (Y (0), Y (1) ⊥ T \|X) and overlap (0 < P (T = 1\|X) < 1), from Rosenbaum and Rubin (1983), as well as the proper specification of the moment conditions required for the Zhao and Percival (2017) double robustness result. 105 whereas the external state-level population moments are at the individual level. Entropy balancing is not amenable to matching moments at different levels of aggregation. Therefore, we proceed with a multi-stage reweighting procedure, which we discuss below and summarize in Table A5. This is analogous to two-step calibration, as discussed in Estevao and Säarndal (2006). In the first stage, we adjust the household base weights for nonresponse, controlling to moments estimated from the linked administrative and census data. The target distribution is estimated using the nonvacant housing units in the March Basic CPS Sample, which includes both respon- dent and nonrespondent housing units. Given the known probability of inclusion in the sample (from the base weights), these are estimates of the underlying population moments for each of the included characteristics. The moments include housing-unit-level summary statistics on race, His- panic origin, age, marital status, income, sources of income (through information return dummies), citizenship, and nativity. Entropy balancing adjusts the housing unit weights so that the weighted estimates from respondent units match the moments estimated from all nonvacant households. Let us designate the housing- unit moment constraint variables as XL i,j , where L indicates linked data. Let w1 i be the output weights of the first-stage reweighting. Given n respondent households, and a set of nonvacant (occupied) households NV , where i = 1, 2, . . . , nNV with survey base weights qi, the moment conditions are of the form: n∑ i=1 w1 i cj(X L i,j) = nNV∑ i=1 q1cj(X L i,j). (C.7) With these moment conditions, we estimate w1 i for each household using entropy balancing. In the second stage, we would like to create weights (denoted w2 m,i) for each individual m and household i, where m = 1, 2, . . . ,M , that adjust to external population controls while maintaining the household weighting adjustment from the first stage. We do so by simultaneously matching to three sets of target moments (2A-C in in Table A5): A Preserve the distribution of housing unit characteristics B Spousal equivalence 106 C External population targets In the first set of constraints (A), we calculate person-weighted moments from the stage-1 weights. Given the number of people in household i, nHH i , we define the moment conditions using the stage-1 weights as follows: M∑ m=1 w2 m,i 1 nHH i cj(X L i,j) = n∑ i=1 w1 i cj(X L i,j). (C.8) This ensures that if we take the average weight of household members in household i (HHi) as w̄2 i = 1/nHH i ∑ p∈HHi w2 m,i , the following condition will be satisfied: n∑ i=1 w̄2 i cj(X L i,j) = n∑ i=1 w1 i cj(X L i,j). (C.9) This does not require that w̄2 i is equal to w1 i for any household i, but rather that the specified constraints from stage one hold in the final entropy-balance weights, when the final weights are averaged across all household members. This procedure of dividing the household moments equally among the family members helps ensure that each person contributes to satisfying the moments from the linked administrative and decennial census data, which should reduce the variability of weights among household members. It is particularly important for person-level statistics, such as poverty or health insurance status, that are functions of household or family characteristics. For example, poverty status (poor/non-poor) is defined at an aggregated level (the family), but the share in poverty is estimated from individual weights. By having each household member be part of the moment conditions for the linked data, administrative income affects each member’s weight, which affects the poverty estimate. For the second set of moments in the second-stage reweighting (2.B. in Table A5), we approximate the spousal equalization that is part of existing CPS ASEC weights. We include this set of conditions because household- and family-level statistics should also be invariant to which spouse’s weight is used as the family or household weight. Let S = {0, 1, 2}, where S = 0 if an individual is unmarried, 1 if the individual is the first spouse or cohabiting partner on the file, and 2 if the individual is the second spouse or partner on the file. Given an indicator function I(·), the spousal equivalence 107 moment condition for a given characteristic in the linked data is: M∑ i=m [ I(S = 1)w2 i,mcj(X L i,j)− I(S = 2)w2 i,mcj(X L i,j) ] = 0. (C.10) This does not require that each individual’s weight be equal to their partner’s, as that would require a separate moment condition for each couple. Instead, it requires that the characteristics of the households of partners in the linked data be balanced. The third set of moment conditions (2.C. in Table A5) reweight the individual observations to match the age by race/Hispanic-origin/gender cells for each state and the District of Columbia, as noted above. These conditions have the simple form of equation (C.2). With these three sets of conditions, we reweight the March Basic CPS sample to simultaneously match the household-level linked administrative data and the individual-level state population targets. For each individual, the initial weights for the stage 2 reweighting are the household weights from the stage 1 reweighting (w1 i ), so that the minimization from (C.1) becomes: min w2 n∑ i=1 w2 i log( w2 i w1 i ). (C.11) However, for the full CPS ASEC sample, there is one more complication. The full sample includes groups that were oversampled based on characteristics reported in earlier survey responses, includ- ing Hispanic origin and the presence of children. Therefore, in the full sample, the weights for these oversampled individuals and households need to be adjusted to reflect their prevalence in the population and characteristics. To do this, we add a fourth set of moment conditions (2.D. in Table A5). We create these conditions from the entropy-balance weighted March Basic sample, because it is a stratified random sample that is not affected by oversampling based on observable characteristics from prior survey responses. Let w2,March i,m be the second-stage weights from the March Basic Sample, w2,Full i,m be the second-stage weights from the full CPS ASEC sample, and MFull and MMarch be the number of individuals in the full and March Basic CPS samples. This fourth set of conditions has the form: 108 mFull∑ m=1 w2,Full i,m cj(Xi,k) = mMarch∑ m=1 w2,March i,m cj(Xi,k). (C.12) This fourth set of moments includes information on race, Hispanic origin, income (from the linked administrative data), and the number of adults and children in the household. Without this set of conditions, estimates of the number of households by type (especially for oversampled groups) differ between the full and March Basic CPS ASEC samples. Additionally, without these constraints, observables-based oversampling in the full CPS ASEC biases estimates for oversampled subgroups relative to estimates from the March Basic sample. Although we focus on the estimates from the full CPS ASEC sample in this paper, we present the results from the Basic March sample in the Appendix as well, because it is a stratified random sample with no oversampling based on observable characteristics from earlier survey responses. At this point, the weights would adjust for selection into response. However, because we are using administrative data to address survey misreporting, inclusion in our sample is also conditional on linkage to a PIK as that is the key to linking each individual to every source of administrative data. We therefore include in our sample only those households in which all those old enough to receive survey income questions (15+) are assigned a PIK. To address this selection, we add a third stage to the entropy balancing weight procedure used in Rothbaum and Bee (2022), as shown in Table A5, Stage 3. Stages 3A and 3B have the same form as 2A and 2C, but add additional moments to the already specified ones from the linked data and external population controls. In adjusting for selection into linkage, we include moments on survey-reported income, administrative income, and survey poverty status by survey reported demographics such as race, Hispanic-origin, citizenship, and age. The weights after this third-stage adjustment should adjust the sample for both selection into survey response and selection into linkage, to the extent possible given the observable survey and linked administrative data. For valid inference, we repeat the above two-stage reweighting procedure 160 additional times using the baseline successive difference replicate factors created during the sampling process, which are 109 available for all households regardless of response status. These replicate factors account for the sampling design of the monthly Basic CPS and CPS ASEC. Also, the first-stage target moments from the March Basic CPS sample are estimates and thus subject to sampling error. By repeating the procedure with the base weights and replicate factors, the target moments for each replicate will vary, and variation in the final weights across the replicates will reflect the uncertainty in our linked data estimates. All standard errors reported using EBW are calculated with these 160 replicate-factor EBW.61 As noted in Rothbaum et al. (2021), in addition to changing point estimates, improved weights can also affect standard errors. It is generally understood that increased variability among the survey weights can increase the standard errors, so weighting adjustments aimed at reducing bias are often done at the expense of increasing variance. However, Little and Vartivarian (2005) show that this may not hold true if variables used to adjust for nonresponse are correlated with survey variables of interest, a property they call “super-efficiency.” This also has implications for how weighting models should be constructed, as including variables that are not strongly predictive of response, but are correlated with outcomes of interest can reduce variance of an estimate even if they do not affect its bias. The full reweighting procedure is described in Table A5 . Stage 1 adjusts for nonresponse at the housing unit level by reweighting respondent households to match the characteristics of occupied households estimated from the linked administrative, decennial, and commercial data. Stage 2 creates individual weights that maintain the adjustment from Stage 1, but additionally adjust the person weights to match the external population controls. As in Rothbaum and Bee (2022), the Stage-2 weights adjust the sample for selection into survey response. However, because we are using administrative data to address survey misreporting, inclusion in our sample is also conditional on linkage to a PIK, as that is the key to linking each individual to every source of administrative data. Our final sample includes only those households where all 61Refer to “Estimating ASEC Variances with Replicate Weights” (U.S. Census Bureau, 2009) for a dis- cussion of successive difference replication in the CPS ASEC. Note also that at present we do not include uncertainty in the external population targets, but we hope to explore how best to account for that uncer- tainty in the weights as well in future research. 110 those old enough to receive survey income questions (15+) are assigned a PIK. To address this selection, we add a third stage to the entropy balancing weighting procedure used in Rothbaum and Bee (2022), as shown in Table A5, Stage 3. The Stage-3 weights maintain the adjustments of the Stage-2 weights, but also control for selection into linkage, to the extent possible given the observable survey and linked administrative data. For valid inference, we repeat the above two-stage reweighting procedure 160 additional times using the baseline successive difference replicate factors created during the sampling process, which are available for all households regardless of response status. These replicate factors account for the sampling design of the monthly Basic CPS and CPS ASEC. Also, the first-stage target moments from the March Basic CPS sample are estimates and thus subject to sampling error. By repeating the procedure with the base weights and replicate factors, the target moments for each replicate will vary and variation in the final weights across the replicates will reflect the uncertainty in our linked data estimates. All standard errors reported using EBW are calculated with these 160 replicate-factor EBW. As noted in Rothbaum et al. (2021), in addition to changing point estimates, improved weights can also affect standard errors. It is generally understood that increased variability among the survey weights can increase the standard errors, so weighting adjustments aimed at reducing bias are often done at the expense of increasing variance. However, Little and Vartivarian (2005) showed that this may not hold if variables used to adjust for nonresponse are correlated with survey variables of interest, a property they call “super-efficiency.” This also has implications for how weighting models should be constructed, as including variables that are not strongly predictive of response, but are correlated with outcomes of interest, can reduce variance of an estimate even if they do not affect its bias. Figure A8 shows the bias in estimates of address-linked characteristics using the various weights. In each panel, we compare the five separate weights to the target moments estimated on the set of all occupied housing units. They are: 1. Respondents — the weights only adjust for the probability the housing unit is selected into 111 the sample 2. Survey — the final survey weights 3. HH EBW — the Stage 1 weights that adjust for response at the household level only 4. EBW — the Stage 2 weights that adjust for response at the household level and to the external population controls 5. EBW + PIKed — the Stage 3 weights that adjust for response at the household level, to external population control, and for selection into linkage. From Figure A8, we can see that OASDI recipients (linked to the PHUS) are overrepresented with the respondent and survey weights (Panel A), as are housing units with residents that are 65 and over (Panel B). The EBW bias estimates in Panels A and B (those that can be directly targeted in the weighting) are all very close to zero, with few statistically significant differences.62 Figure A9 compares statistics estimated on survey responses using the survey weights to those estimated using the Stage 2 (EBW) and Stage 3 (EBW + PIKed) weights. In this case, the survey- weighted and EBW estimates by race, Hispanic origin, and age should match the survey estimates by construction (as they are each weighting to external population controls). However, differences for other statistics for the EBW relative to the survey-weighted estimates reflect potential bias in the survey estimates, which we see, for example, for household income. D Imputation Suppose we have two variables Yi and Yj with missing values indicated by Ri = 0 or Rj = 0.63 Missingness is monotone if Rj = 0 in all cases where Ri = 0. The pattern of missingness discussed above for weighting is one case of monotone missingness.64 Missingness is non-monotone if Ri = 0 62Percentiles cannot be directly matched by entropy balancing. Instead, the weighting model weights respondents to match the share of units in different income bins (i.e., the share of households with address- level W-2 earnings ≤ $25,000. 63The discussion in this section follows Hokayem, Raghunathan and Rothbaum (2022) and Fox et al. (2022) closely. 64In that case, we are assuming that for all variables in X, Ri = R, where i = 1, . . . , k. 112 does not imply that Rj = 0. While weighting can address missing data for the monotone missingness discussed in the prior section, it is not optimal as a general missing data correction when missingness is non-monotone. For non-monotone missingness, imputation is a better approach as it fully utilizes the available information (Raghunathan et al., 2001). In this section, we discuss imputations models generally followed by our implementation. Suppose O is a collection of observable variables with no missing values, with O = (O1, O2, . . . , Oq) and Y1, Y2, . . . , Yp are variables with missing values, with Y = (Y1, Y2, . . . , Yp). Further, let U be a set of unobserved characteristics. Let f(Y \|O,U, θ) be the conditional joint density, with θ = (θ1, θ2, . . . , θp) and where θj is a vector of parameters in the conditional distribution for Yj such as regression coefficients and dispersion parameters. An imputation model imposes some assumptions on f and θ to assign plausible values to Y where data are missing. In this case, Y is MAR if missingness can be accounted for by observable characteristics, which can be written as f(Y \|O, θ) = f(Y \|O,U, θ) (Rubin, 1976).65 Another way to view imputation is through the lens of a researcher or data user. Consider a statistic Q, which could be a distributional statistic (such as a mean or median), a regression coefficient, or any other statistic or parameter of interest to the researcher. An imputation model is congenial or proper and results in unbiased estimates of Q if E(Q̂\|O, θ) = E(Q̂\|O,U, θ) = Q and has valid confidence intervals for Q̂ (Meng, 1994; Rubin, 1996). This is only true when the imputation model is congenial and proper for the analysis being con- ducted. There are many examples in the literature where this congeniality condition fails for a given statistic or set of statistics. An example is match bias in the CPS. Bollinger and Hirsch (2006) showed that because the imputation model in the CPS does not include union status, estimates of the relationship between union status and earnings are attenuated in the imputed data. Even in this case, the issue is not that their earnings are misclassified (as very rarely will imputed earnings match the true value for a given individual), but that they are drawn from the wrong distribu- tion – one that does not condition on union status. However, uncongeniality for one statistic does 65It is NMAR if f(Y \|O, θ) ̸= f(Y \|O,U, θ). 113 not indicate bias for other related statistics. For example, match bias on union status does not necessarily mean that the CPS imputation model will bias statistics of the unconditional earnings distribution. It is impossible for congeniality to hold for all possible statistics Q, unless the model perfectly predicts the missing values, i.e., there is no misclassification.66 However, we could assess the quality of an imputation model by comparing a set of the resulting Q̂ estimates against known Q values. Fox et al. (2022) took this approach, using a variety of statistics, including regression coefficients and conditional and unconditional distributional statistics to evaluate their imputation model. Hokayem, Raghunathan and Rothbaum (2022) addressed survey nonresponse in the CPS ASEC in 2009-2013 by including more covariates in the imputation model than the current CPS ASEC hot deck approach and comparing models with and without administrative data on earnings and income in the model. They find further evidence of match bias. However, with sufficient information in the model, they do not find evidence of nonignorable nonresponse (NMAR) when they compare the estimates of imputes that condition on administrative income to those that do not. This non-monotone missingness is present in several variables in our data. Income items are partic- ularly prone to survey nonresponse - over 40 percent of earnings (and all income) is imputed in the CPS ASEC due to nonresponse in recent years (Hokayem, Raghunathan and Rothbaum, 2022). We also do not observe gross wage and salary earnings (in the LEHD) for all jobs because not all jobs are covered by unemployment insurance and non-covered jobs are not reported to state UI offices. Gross earnings are also missing for jobs that are not available in the LEHD for other reasons, such as firms that erroneously fail to report jobs and states with no data-sharing agreement in a given year. For the missing survey responses and missing gross earnings, we observe a lot of information (variables in O) that can help us predict the missing values, such as W-2 job-level earnings, survey- reported occupation, hours and weeks worked, educational attainment, private health insurance coverage, etc. 66In this sense, misclassification can be important. If the imputed value equals true value for all cases, the data are not truly “imputed.” However, in practice, imputations are unlikely to have extremely low misclassification rates, and we must evaluate the potential bias of each Q̂ with the available information. 114 We use Sequential Regression Multivariate Imputation (SRMI) to impute plausible values for the missing data (Raghunathan et al., 2001).67 SRMI is an iterative resampling technique to estimate f(Y \|O, θ) while imposing fewer strong parametric assumptions on the joint conditional distribution f . Under SRMI imputation, We estimate the model for each Yj iteratively as follows. In the first iteration, Y1 is regressed on O and the missing values are imputed. Any imputation model can be used to impute values for each Yj , such as a regression model, a hot deck, or predictive mean matching, with their attendant assumptions about f(Y \|O, θ). Let Y (1) 1 denote the filled-in version of the variable Y1 from the first iteration. Now Y2 is imputed using (O, Y (1) 1 as covariates to generate Y (1) 2 , the filled in version of Y2 from the first iteration. This process continues until the missing values in Yp are imputed using (O, Y (1) 1 , Y (1) 2 , . . . , Y (1) p−1) as predictors. We cannot stop at iteration 1 because the imputation of Y (1) 1 , for example, fails to exploit the observed information from (Y2, Y3, . . . , Yp). Iterations t = 2, 3, . . . proceed in the same manner except that all other variables (with some filled at the current and the rest in the previous it- erations) are used in imputing each variable. Specifically, at iteration 2, Y1 is re-imputed using (O, Y (1) 2 , Y (1) 3 , . . . , Y (1) p ) as predictors; Y2 is re-imputed using (O, Y (1) 1 , Y (1) 3 , . . . , Y (1) p ) as predictors, etc. In each iteration, we are updating our predictions of θ as well as Y . In general, at iteration t > 1, Yj is re-imputed using (O, Y (t) 1 , Y (t) 2 , . . . , Y (t) j−1, Y (t−1) j+1 , . . . , Y (t−1) p ) as predictors. The iterations are continued several times in order to fully use the predictive power of the rest of the variables when imputing each variable. Empirical analysis has shown that fewer than 20 and generally as few as 5 to 10 iterations are sufficient to condition the imputed values in any variable on all other variables (Ambler, Omar and Royston, 2007; Van Buuren, 2007; He et al., 2010). By repeating the imputation process in each iteration, SRMI is akin to a Gibbs or MCMC resampling technique that should iteratively converge to the true conditional joint density (if the model is properly specified). We impute survey earnings, job-level administrative gross earnings (or LEHD-equivalent earnings), and missing state-level means-tested program data. For survey earnings, we impute extensive 67SRMI has also been called Fully Conditional Specification and Flexible Conditional Models in the literature. 115 margin earnings receipt and intensive margin earnings amounts for all earnings variables. In the CPS ASEC this includes the variables ern yn (earnings receipt), ern srce (primary job earnings source - wage and salary, self employment, or farm self employment), ern val (earnings amount from primary job), ws yn, se yn, and frm yn (secondary wage and salary, self employment, for farm self employment earnings?), and ws val, se val, and frm val (amount of secondary earnings in each category). We also impute upstream variables that are highly predictive of earnings, including weeks worked last year (wkswork) and hours worked per week last year (hrswork). For gross earnings by job (for the two highest earning jobs for each worker), we impute several variables to simplify the imputations and capture important features in the data. First, we impute a dummy variable for whether gross earnings ≈ taxable earnings + deferred compensation, which is true for a large share of workers. For those where gross earnings > taxable earnings + deferred compensation, we then impute a series of dummies for whether gross earnings/(taxable earnings + deferred compensation) falls in several bins, including 1.1 and above, [1.05, 1.1), [1.03, 1.05), [1.02, 1.03), [1.01, 1.02), and (1, 1.01). After assigning each job to a gross earnings/(taxable earnings + deferred compensation) bins, we then impute the amount of gross earnings for each job. We chose this approach because many variables (such as survey-reported private health insurance coverage) are good predictors of whether gross earnings/(taxable earnings + deferred) compensation exceeds specific thresholds while not necessarily being good predictors of the exact value of gross earnings/(taxable earnings + deferred). For each earning variable, we have separate imputation models by spouse (by sex if an opposite-sex couple, by order on the file if a same-sex couple). This allows for a more flexible imputation model and allows us to condition on spousal income in the SRMI. For state-level means-tested program data, we impute program receipt ({Program} yn) and, con- ditional on receipt, the amount received ({Program} val) for each program at the household level. As discussed in Hokayem, Raghunathan and Rothbaum (2022), there are a number of challenges to implementing SRMI in this context. First, many income types do not follow a normal distribution. Second, we must select predictors for the modelling of each income variable from a very large set 116 of possible covariates. Third, we must properly account for uncertainty in our estimates of the parameters in θ. Included in this uncertainty is the selection of variables for our imputation models because when we select predictors for our models, we are imposing the assumption that there is no relationship between the excluded variables and the variable being imputed conditional on the included variables. Next, we discuss how we address each of these issues. To address non-normality, we transform each continuous variable using the inverse hyperbolic sine, which allows us to include negative values, as in Fox et al. (2022).68. As the inverse hyperbolic sine is nearly perfectly correlated with the natural log over most of the defined range of the natural log, one can interpret the regression coefficients of continuous variables as elasticities (for continuous dependent variables) or semi-elasticities (for binary dependent variables). As a practical matter, there are too many potential variables in O to be used in our model. We reduce the set of variables to be used to impute each Yj in two stages, both using the Least Absolute Shrinkage Operator (LASSO, Friedman, Hastie and Tibshirani (2010)). In the first stage, we take all of the possible interaction terms we specify in O and use LASSO to prune the list to Ôj that predict Yj (including all non-interacted terms in Ôj). The set of variables in Ôj will generally be large (hundreds of variables and interactions, if the regression sample size is large). In terms of the general notation f(Y \|O, θ), this process places constraints on θ.69. During the imputation process, we have a second-stage of regularization when we estimate the values in θ̂. As θ̂ is a set of unknown parameters, we also must incorporate the uncertainty in θ̂ into the imputation process – the third challenge noted above. We do this as follows. In each implicate c (independent run of the imputation model), we start by taking a Bayesian Bootstrap of the sample, we then do a second-stage variable selection process to further reduce the number of variables in Ôj to Ôj,c, again using LASSO regularization.70 From the regression of Yj on Ôj,c, we estimate θ̂j,c. 68Hokayem, Raghunathan and Rothbaum (2022) tested alternative transformations, such as Tukey’s gh transformation (He and Raghunathan, 2006) and an empirical normal transformation (Woodcock and Benedetto, 2009). However, as in Fox et al. (2022), they found the inverse hyperbolic sine performed well, and we use that transformation here. 69This is primarily done for practical speed considerations. Reducing the number of candidate variables upfront considerably speeds up the process of imputation for each variable in each implicate. 70The Bayesian Bootstrap (Rubin, 1981) is the Bayesian analogue of the bootstrap. Each observation is drawn (with replacement) with an expected probability of 1/n, but with variability. The probabilities of being drawn are defined by taking n − 1 draws from the uniform distribution (0,1), ordering draws from 117 Doing this on a Bayesian Bootstrap sample enables us to account for the uncertainty present in each step of this process, including which variables are used as model predictors (Ôj,c) and to draw from the distribution of parameters values θ̂j,c. This resampling approach to estimating uncertainty in regression-based imputation has been taken in other data products and research, including SIPP topic flag imputation (Benedetto, Motro and Stinson, 2016), the SIPP Gold Standard and SIPP Synthetic Beta (Benedetto, Stinson and Abowd, 2013), and imputation research on missing income in the CPS ASEC (Hokayem, Raghunathan and Rothbaum, 2022). With the transformed continuous variables, regularization, and Bayesian Bootstrap-based estima- tion of the uncertainty of θ̂, we are almost ready to impute missing values. We must also specify the functional form of our imputation models (parametrizing f(Y \|O, θ)). Unless otherwise indi- cated, we use predictive means matching (PMM) to impute both binary and continuous dependent variables. For binary dependent variables, we use a Linear Probability Model (LPM), regressing the dependent variable on the model selected using the LASSO on the Bayesian Bootstrap sample. We then predict the vector p̂j(Y = 1\|X, θ̂j), which includes the estimated probability for all individuals in sample whether Rj = 0 or Rj = 1. We then take a random draw for each unit i where Ri,j = 0 from the ten nearest units k where Rk,j = 1 to assign Yi,j values. We use LPM rather than a logit or probit model as the LPM model more predictor variables. Although LPM does not impose 0 ≤ p̂i,j ≤ 1, the Yi,j draws must equal 0 or 1. Fox et al. (2022) used the same approach for imputing SNAP receipt and showed that this PMM model performed well for several conditional and unconditional statistics (Q’s such as SNAP receipt, SNAP receipt conditional on earnings and demographics, for example). For continuous dependent variables, we use Ordinary Least Squares (OLS), regressing the dependent variable on the model selected using the LASSO on the Bayesian Bootstrap sample. We then predict lowest to highest, where u = u0, u1, u2, . . . , un given u0 = 0 and un = 1. The probability of being drawn for each observation i is based on the gaps between each adjacent value in u, so that for observation i the probability of being drawn is gi = ui − ui−1. As noted in Benedetto, Stinson and Abowd (2013), using the Bayesian Boostrap adds additional variability to the imputation process to account for the fact that the sample distribution may not be the same as the population distribution. Without the use of the Bayesian Bootstrap, the confidence intervals would not be proper. 118 the vector Ŷj(Y−j , X, θ̂j) where Y−j is the matrix Y excluding Yj , again for all individuals in sample whether Rj = 0 or Rj = 1. We then take a random draw for each unit i where Ri,j = 0 from the ten nearest units k where Rk,j = 1 to assign Yi,j values. For survey wage and salary earnings from the longest job (ern val if ern srce == 1), rather than using PMM, we use a two-stage model that incorporates OLS and quantile regressions. As before, we first use OLS to predict Ŷj(Y−j , X, θ̂j) after LASSO regularization. We then use quantile regression to regress Yj on binned Ŷj and several variables from O, including race and Hispanic origin, age, education, and hours worked. We do this for each 5th percentile from the 5th to the 95th. This gives us an estimate for ˆYj,i,q for each individual i at each quantile q.71. From the values of Ŷj,i,q, we have a posterior predictive distribution (PPD) of Yj,i for each individual i (after interpolation using Schmidt et al. (2022)). For each individual, we then draw a percentile value from 0 to 1 to impute Yj,i from the PPD. 72 Using quantile regression to estimate the PPD is useful if there is potential heterogeneity in the relationship between specific variables in O and Yj . For example, suppose the average relationship between education and earnings reflects a bigger right tail for college graduates (more very high earners), the PMM-based estimate would not necessarily reflect that in the resulting imputes. However, the quantile regression-based PPD would. However, more data (a large sample) is required to use quantile regressions to reliably estimate the PPD. Because of the possibility of heterogeneity and the greater data needs, we implement this approach from survey wage and salary earnings from the primary job (the largest single source of survey income, covering almost 70 percent of total income). For the means-tested program variables imputed at the household level, we recode the data to summarize the information of household members (such as presence of members by race, total 71The regressions do not impose monotonicity, i.e., it does not ensure that for two quantiles q and r where r > q, Ŷj,i,r > Ŷj,i,q (the quantile crossing problem). Following Chernozhukov, Fernández-Val and Galichon (2010), we rearrange the curve by sorting the Ŷj,i,q values from lowest to highest and assigning them to the corresponding position’s q value. As Chernozhukov, Fernández-Val and Galichon (2010) show, the rearranged curve is closer to the true quantile curve than the original curve in finite samples. 72If any part of this process fails (such as from nonconvergence in a quantile regression estimate), we impute using PMM. This is unusual, but possible, in an automated process like SRMI that runs many regressions per iteration repeated across implicates. 119 household earnings, etc.) and household head variables (such as education, race, etc.) to use as predictors and then impute receipt and amounts using PMM as discussed above. For nonfilers, we observe whether they received several information returns, including Forms 1099- G, 1099-INT, and 1099-DIV in the IRMF. From these we have information on whether they received UI compensation, interest income, and dividends, respectively. Each of these are vastly underre- ported on surveys (Rothbaum, 2015). Rothbaum (2023) has been working with more detailed data available under a separate agreement between the Census Bureau and IRS, for limited use. In that work, the 1099-G, 1099-INT, and 1099-DIV data is available, including income amounts. Rothbaum (2023) released coefficients that can be used to impute these amounts for nonfilers conditional on survey responses and the administrative data used in this project. To release this statistics, Rothbaum (2023) estimated models for the synthesis of four variables: 1. UI compensation receipt conditional on receipt of a Form 1099-G 2. UI compensation amount conditional on receipt of UI compensation 3. Interest income amount conditional on receipt of a Form 1099-INT 4. Dividend income amount conditional on receipt of a Form 1099-DIV In order to allow the creation of synthetic data to correct for survey underreporting, Rothbaum (2023) released three sets of results for each variable. For UI compensation receipt, they estimate a Linear Probability Model (LPM) of UI compensation receipt conditional on receiving a Form 1099-G. Individuals receive a 1099-G for various government payments, including (1) UI compensation, (2) state or local income tax refunds, credits, or offsets, (3) reemployment trade adjustment assistance payments, (4) taxable grants, and (5) agricultural payments. This model is estimated as described above using the two-stage LASSO feature selection, with the second stage estimated on a Bayesian Bootstrap. As such, the released parameters are effectively a draw from the distribution of possible parameter estimates that could be used to predict nonfiler UI receipt. 120 With these regression coefficients, we can estimate the expected probability of UI receipt for each nonfiler (p̂j(Y = 1\|X, θ̂j)) on a separate sample (or the data without access to the more detailed 1099-G data). However, as they were estimated using a LPM, we cannot directly use them to synthesize UI receipt data (as the p̂j(Y = 1\|X, θ̂j) can be < 0 or > 1, which PMM addresses by taking a random draw from individuals with similar p̂j(Y = 1\|X, θ̂j), but with observed values for Yj . Instead, Rothbaum (2023) then separate the expected probability space into bins and released the boundaries between those bins and the empirical probability that an observation received UI compensation in each bin. For example, the top quintile of observations has an expected probability of receipt of 0.87 or higher (the boundary). Within that bin of observations with an expected probability of 0.87 or higher that received UI compensation was 0.98 (the empirical probability in the bin), then we can impute UI receipt for this group by drawing a random number between 0 and 1 and assigning receipt if it is ≤ 0.98. By releasing regression coefficients, bin boundaries, and empirical probabilities, Rothbaum (2023) implement a semiparametric imputation technique that is similar to the binned imputation proposed by Bondarenko and Raghunathan (2007). For the income variables – UI compensation, interest income, and dividends – the approach is slightly different. The first step is the same as above for continuous variables – estimate an OLS model to predict expected income amounts conditional on the available information. Again, the models are estimated using the two-stage LASSO feature selection, with the second stage estimated on a Bayesian Bootstrap. The coefficients from this model are released so that the expected income amount can be estimated on a separate sample (ŷi,j). To allow the synthesis of continuous variables, Rothbaum (2023) release two set of variables. First, they partition ŷi,j into bins. Then, using quantile regression at various percentiles, the regress income amounts on bin dummies. As with ern val above, these regression coefficients can be used to estimate a PPD for each individual. By drawing a value from 0 to 1, we can impute income amounts from these PPDs. In summary, for each income amount synthesized, Rothbaum (2023) release three sets of statistics, regression coefficients, bin boundaries and quantile regression coefficients to enable relatively low dimensional data to be used to synthesize or impute UI compensation amounts, interest income, 121 and dividends. Finally, we repeat this process five times, to create the five independent implicates. In each impli- cate, we use SRMI to impute the survey and gross earnings variables, followed, in a separate step, by the imputation of means-tested program variables. For any statistic or parameter estimate, we can account for the uncertainty in the imputation process (Rubin, 1976). To do so, we calculate the total variance by combining the within-implicate variation (for example, the standard error of an estimate in one implicate) with the between-implicate variation (the variance of the estimates for that parameter across the five implicates). In Table 6, we show the rates of missing data for survey earnings, state program data, and LEHD job-level gross earnings. In the 2019 CPS ASEC, 46 percent of individuals with earnings had their primary job earnings imputed. We do not have state-level administrative TANF data for 47 percent of households. Finally, we impute gross earnings for 18 percent of jobs, either because there is no LEHD information for them (8 percent of highest earning jobs) or because the LEHD and W-2 values disagree substantially (i.e., the LEHD < W-2, about 10 percent of highest earning jobs). As the imputation models are applications from prior work (Hokayem, Raghunathan and Rothbaum 2022 for earnings, Fox et al. 2022 for means-tested benefits, and Rothbaum 2023 for nonfiler UI, interest, and dividends), we provide limited statistics on the imputation outputs. Table A6 shows summary statistics for survey earnings imputation, comparing the CPS ASEC imputations to the NEWS SRMI imputations conditional on W-2 earnings. The SRMI estimates fewer individuals with zero survey earnings conditional on having zero W-2 earnings. Also, the SRMI estimates higher survey earnings conditional on having higher W-2 earnings (such as in the 5th quintile of W-2 earnings). Table A7 provides some summary statistics for means-tested program imputation. 122 Introduction Income Measurement Challenges Survey Income Administrative Income Addressing These Challenges Relationship to Prior Research Data Survey Data Other Census Bureau Data Federal Administrative Data IRS Data Social Security Administration (SSA) Data State Administrative Data LEHD SNAP TANF Commercial Data Firm Data Linkage and File Construction Methodology Weighting Imputation Estimation Earnings Measurement Error Model Income Replacement Results NEWS Estimates Robustness to Alternative Uses of Earnings Data Impact of Different Processing Steps on Income and Poverty Estimates Impact of Different Income Types on Income and Poverty Estimates Release and Future Research Transparency and Data Availability Future Plans Conclusion Data Linkage Person Linkage Address Linkage Job Linkage Firm Linkage File Construction Address File Person File Weighting Imputation Pagination Previous page Page 2 Next page ABOUT UNECE Mission Executive Secretary Organizational Structure OPPORTUNITIES Vacancies Internship Programme RESOURCES Statistical Database Evaluations Follow UNECE Facebook Twitter Flickr LinkedIn RSS Youtube Instagram Soundcloud © United Nations Economic Commission for Europe \| Terms and Conditions of Use \| Privacy Notice Contact us

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Manual

conversion factors

JQ1 Production

JQ2 Trade

JQ3 Secondary PP Trade

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Annex1 | JQ1-Corres.

Annex2 | JQ2-Corres.

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