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Example of use of paradata for the correct measurement of the progress of the field operation of the Agricultural Census of Mexico - Alan Martínez Hernández (INEGI, Mexico)

Languages and translations
English

12-14 June 2023

Susana Pérez Cadena

INEGI, México

2023 UNECE Expert Meeting on Statistical Data Collection 'Rethinking Data Collection'

Example of use of paradata for the correct measurement of the progress of the field operation of the Agricultural Census of Mexico

E

The resolution of these problems was found in the use of a paradata

The Agricultural Census (AC22) was carried out in Mexico in 2022, after 15 years of conducting the previous census, in 2007

During the collection of information,

problems arose that needed to be

resolved as operations in the field

progressed.

Because the Agricultural Census must reflect all the

land plots in the country, one of the main challenges

faced by AC22 was the identification and

georeferencing of the land that comprise the

Production Unit (PU)

The framework for the identification and

georeferencing activities was the Master

Framework for Agricultural Sector Statistics (MMESAGRO)

The role of MMESAGRO in the CA22

This implied generating "apparent additions" of land (i.e., land

previously owned by one producer and now managed by a

different producer).

Although the MMESAGRO was very important as a

framework, it was also necessary to update it and provide

feedback based on what was found in the field.

A land plot addition occurred when producers recognized new

land as part of their PU (however, many of the land identified

corresponded to land that previously belonged to other PUs).

Due to the changing dynamics of the agricultural sector, it

was necessary to reconform a significant number of PU’s in

the census framework.

The problem of apparent additions

Although it was expected that there would be a number of "apparent additions", this was higher than expected

Therefore, apparently, the number of land

plots found in the field was substantially

increasing, day by day, compared to the plots

included in the MMESAGRO, which would

generate a bias in the census universe

5 The problem of apparent additions of land

The results

obtained were

analyzed.

It was verified

whether these

additions of land

plots

corresponded to

existing

geostatistical

references or

whether they

corresponded to

new references.

We chose to

use a

paradata:

300'000 cases

were reviewed

tabularly.

Only about 20%

of them were

found to be

actual additions

(i.e., new land

with agricultural

or forestry

activity was

identified).

The use of paradata to solve the problem

6

The rest of the

land was

already

existing and

was only

changing

producers.

New land plots

(actual

additions)

~ 20%

Planned land

plots database

Data table analysis

Land found in the

planned database

(Change of

producer) ~80%

Land plots

“additions”

300,000

8 ,9

6 6

,7 1

8

9 ,8

1 0

,6 0

3

1 0

,2 2

3 ,0

3 2

1 0

,8 8

4 ,8

4 9

9 ,1

7 0

,1 5

9

9 ,2

0 6

,5 8

3

9 ,2

4 5

,4 7

1

8 ,8

4 3

,8 6

9

8 ,8

6 1 ,1

1 6

8 ,8

7 4

,9 6

9

8 ,9

0 2

,6 7

2

8 ,9

6 1

,4 9

2

8 ,9

7 8

,6 4

2

8 ,9

8 9

,1 0

2

8 ,9

9 2

,8 2

5

02OCT22 16OCT22 23OCT22 06NOV22 27NOV22 05DIC22 11DIC22 08ENE23 15ENE23 23ENE23 30ENE23 06FEB23 12FEB23 19FEB23 27FEB23

Adjusted Universe

(Initial Universe + Total

additions)

Re-adjusted Universe

(Initial Universe + Actual

additions)

8’310,033

Initial working universe

Percentage of actual additions, applied to total additions

26.0% 26.5% 27.6% 15.5% 16.0% 16.4% 17.2% 18.9% 19.4% 19.7% 19.8%

Using paradata allowed to detect the new real land plots and readjust the framework

Conclusions

From the analysis generated

by the paradata used, it was

possible to estimate the real

universe of plots, and the

progress in the field

operation based on that

number.

Although this estimate was

not exact, it was a good

parameter to be able to

move forward with greater

certainty during the AC22

data collection stage.

This is an example of one of the

many paradata used in the

Agricultural Census in Mexico: a

data obtained from the analysis of

what was happening in the field, in

order to be able to follow without the

large bias that was apparently being

generated.

  • Diapositiva 1
  • Diapositiva 2
  • Diapositiva 3
  • Diapositiva 4: The problem of apparent additions
  • Diapositiva 5
  • Diapositiva 6
  • Diapositiva 7
  • Diapositiva 8
  • Diapositiva 9
  • Diapositiva 10

Some Applications of Web Scraping in the CPI, The case of Gasoline, Mexico

Languages and translations
English

Some Applications of Web Scraping in the CPI, The case of Gasoline

June 2023

In Big Data we work with: Volume, Speed, Variety, Veracity and Value.

It provides sufficient infrastructure to generate consistent data series

It helps to maintain the continuity of the data collection flow, with basic quality standards

Leverage data based on robust analysis, for its implementation in the Price Index

1

3

2

3

4

Web Scraping

Scanner Data

Citizens´ Statistics

DATA VOLUMES EXTRACTION OF BIG

Since 2018, the appropriate methods and techniques for the recovery of products and prices have been investigated on the websites of different commercial chains.

The begin ning

4

Final stage

What's Next

At the door

The leading drill is gasoline. Data is available for all gas stations in the country since August 2018. Work is underway to incorporate this method into the official calculation.

RESEARCH AND DEVELOPMENT

WEB SCRAPING IN CPI

Learning

It started with household appliances and white goods. The extraction is carried out in department stores and supermarkets such as: Liverpool, Coppel, Famsa, Wal Mart, Soriana and La Comer.

In 2021, extractions of some services were incorporated: LP Gas • Air transport • Telecommunications In 2021, the extraction will be extended to all the generic foods in the basket offered in supermarkets

By the second half of 2021, extraction would have been extended to all generic food from the basket offered in the chains. New store chains will be incorporated, and data extraction will be distributed by entity.

SUBSYSTEM

GASOLINE

it consists of two generics: • High-octane gasoline (92 octane) • Low-octane gasoline (87 octane))

The sample of gas stations is directed (not probabilistic) consists of 570 service stations in in which prices are quoted once a week, by direct visit.

SUBSYSTEM

GASOLINE

6

Service stations Every day the prices of

12,545 existing gas stations are extracted.

Quoted prices On average, 24,245

gasoline prices are obtained in the extraction: • 12,542 low octane. • 11,703 high-octane.

Twice a day The extraction of the information is done automatically at 8:00 and 16 hrs, bringing about 48,490 daily prices.

7

WEB SCRAPING

GASOLINE PRICES

Matched model method.

The approach of the method is to estimate the price change between two periods using the products available in both time periods only, excluding the prices of new and missing products.

However, it is strengthened by the large surplus of the census of products represented in the complete set of data extracted from the web.

8

INTEGRATION METHODOLOGY

Products available in both periods

tt-1

New products

Missing products

REGIONS CPI

Geographic areas

55 Regions

7 Service stations

1. Northern Border 1,369 2. Northwest 1,101 3. Northeast 1,808 4. North Central 2,299 5. South Central 1,501 6. South 1,019 7. MCMA 1,001

10

GEOGRAPHICAL DISTRIBUTION

SCRAPING GASOLINE

South Central (13 AG) 16.2%

CPI REGIONS AND THEIR WEIGHTING

LOW-OCTANE GASOLINE The picture can't be displayed.

11

Northern Border (6 AG) 13.2%

1

2 3

4

5 6

Northwest (5 AG) 9.1%

Northeast (10 AG) 15.0%

MCMA (1 AG) 14.5%

North Central (10 AG) 23.3%

South (10 AG) 8.7%

South Central (13 AG) 15.7%

CPI REGIONS AND THEIR WEIGHTING

HIGH-OCTANE GASOLINE The picture can't be displayed.

12

Northern Border (6 AG) 15.5.%

1

2 3

4

5 6

Northwest (5 AG) 8.5%

Northeast (10 AG) 13.5%

MCMA (1 AG) 21.8%

North Central (10 AG) 15.5%

South(10 AG) 9.5%

13

PERFORMANCE: UNIVERSE OF GAS STATIONS ON THE NORTHERN BORDER

LOW-OCTANE GASOLINE

Scraping: • All gas stations in the cities of the region • Daily quote, twice daily • Matched Model Method • Regional weighted index

Mann-Whitney: Does not reject equal means, paired

sample (p-value 0.825)

Price index

Linear regression

Scraping = 5.30 + 0.95*CPIGasoline R2= 0.996

CPI Published Scraping CPI Sample

Linear regression Scraping = 0.93*CPIGasoline

R2= 0.974

Fortnightly variation (%)

Kolmogorov-Smirnov: Does not reject the same distribution (with p-value

of 0.604)

Levene: Does not reject equality of variance

(with p-value of 0.766)

Cucconi: Does not reject the same trend and

dispersion (with p-value of 0.911)

14

STATISTICAL TESTS LOW-OCTANE GASOLINE NORTHERN BORDER

CONSUMER PRICE INDEX

Scraping: • All gas stations in the cities of the region • Daily quote • Matched Model Method • Regional weighted index

Empirical cumulative distribution function for low-octane gasoline

Northern border index

Cu m

ul at

iv e

pr ob

ab ili

ty

Series

Empirical density function low octane

Northern border index

SeriesCPI CPI

15

STATISTICAL TESTS LOW-OCTANE GASOLINE NORTHERN BORDER

FORTNIGHTLY VARIATION

Scraping: • All gas stations in the cities of the region • Daily quote • Matched Model Method • Regional weighted index

Kolmogorov-Smirnov: Does not reject the same distribution (with p-value

of 0.999)

Levene: Does not reject equality of variance

(with p-value of 0.805)

Cucconi: Does not reject the same trend and

dispersion (with p-value of 0.778)

0.00

0.25

0.50

0.75

1.00

-10 -5 0 5 Variación Frontera Norte

Pr ob

ab ilid

ad a

cu m

ula da

Serie INPC Scraping

Función de distribución acumulada empírica gasolina bajo octanajeEmpirical cumulative distribution function for low-octane gasoline

Northern border variation

Cu m

ul at

iv e

pr ob

ab ili

ty

Series

Empirical density function, low octane gasoline

Northern border variation

SeriesCPI CPI

The picture can't be displayed.

16

LOW-OCTANE GASOLINE

STATISTICAL TESTS Indexes

For the seven regions of the CPI, equality tests were carried out for:

• Distribution • Variance • Trend and dispersion • In none of the cases can they be rejected.

Variations

For the seven regions of the CPI, equality tests were conducted for:

• Media • Distribution • Variance • Trend and dispersion • In none of the cases can they be rejected.

Both procedures describe the process that generates prices in the same way, indices describe overlapping curves. On the other hand, the dynamics behind the price developments come from the distribution itself.

17

HIGH OCTANE

GASOLINE

18

PERFORMANCE: SAMPLE OF GAS STATIONS ON THE NORTHERN BORDER

HIGH-OCTANE GASOLINE

Scraping: • Same gas stations of the sample of the region • Daily quote, twice daily • Matched Model Method • Regional weighted index

Mann-Whitney: Does not does not reject equal

means, paired sample (p- value 0.834)

Price index

Linear regression Scraping = 2.90 + 1.04*CPIGasoline

R2= 0.999 CPI Published Scraping CPI Sample

Fortnightly variation (%)

Linear regression

Scraping = 1.01*CPIGasoline R2= 0.963

19

PERFORMANCE : UNIVERSE OF GAS STATIONS ON THE NORTHERN BORDER

HIGH-OCTANE GASOLINE

Scraping: • All gas stations in the cities of the region • Daily quote, twice a day • Matched Model Method • Regional weighted index

Mann-Whitney: Does not reject equal means,

paired sample (p-value 0.376)

Price index

Linear regression

CPI Published Scraping CPI Sample

Scraping = 5.77 + 0.96*CPIGasoline R2= 0.993

Fortnightly variation (%)

Linear regression Scraping = 0.93*CPIGasoline

R2= 0.968

Kolmogorov-Smirnov: Rejects equal distribution

(with p-value of 0.001)

Levene: Does not reject equality of variance

(with p-value of 0.822)

Cucconi: Does not reject the same trend and

dispersion (with p-value of 0.120)

20

STATISTICAL TESTS GASOLINE HIGH OCTANE NORTHERN BORDER

PRICE INDEX

Scraping: • All gas stations in the cities of the

region • Daily quote • Matched Model Method

0.00

0.25

0.50

0.75

1.00

80 90 100 11 Índice Frontera Norte

Pr ob

ab ilid

ad a

cu m

ul ad

a

Serie INPC Scraping

Función de distribución acumulada empírica gasolina alto octanajeEmpirical cumulative distribution function for high-octane gasoline

Northern border variation

Cu m

ul at

iv e

pr ob

ab ili

ty

Series

Empirical density function, High-octane gasoline

Northern border variation

SeriesCPI CPI

21

STATISTICAL TESTS GASOLINE HIGH OCTANE NORTHERN BORDER

FORTNIGHTLY VARIATION

Scraping: • All gas stations in the cities of the region • Daily quote • Matched Model Method • Regional weighted index

Kolmogorov-Smirnov: Does not reject equal

distribution (with p-value of 0.959)

Levene: Does not reject equality of variance

(with p-value of 0.786)

Cucconi: Does not reject the same trend and

dispersion (with p-value of 0.925)

0.00

0.25

0.50

0.75

1.00

-10 -5 0 5 Variación Frontera Norte

Pr ob

ab ilid

ad a

cu m

ul ad

a

Serie INPC Scraping

Función de distribución acumulada empírica gasolina alto octanajeEmpirical cumulative distribution function for high-octane gasoline

Northern border variation

Cu m

ul at

iv e

pr ob

ab ili

ty

Series

Empirical density function, High-octane gasoline

Northern border variation

Series CPI

CPI

The picture can't be displayed.

22

HIGH-OCTANE GASOLINE

STATISTICAL TESTS Indexes

For the seven CPI regions, equality tests were carried out for:

• Distribution • Variance • Trend and dispersion • In Region 2, none can be rejected. In the regions:1, 4, 5, 6 and 7, the equality of distribution is rejected. In Region 3 the equality of distribution and trend are rejected.

Variations

For the seven CPI regions, equality tests were carried out for:

• Mean • Distribution • Variance • Trend and dispersion • In neither cases can they be rejected.

Both procedures describe the process that generates prices equivalently, indices describe parallel or overlapping curves (with the exception of region 3). On the other hand, the dynamics behind the evolution of prices come from the same distribution.

With the statistical tests, it is concluded that it is possible to incorporate the prices obtained by web scraping for generic gasoline into the CPI production calculation.

The prices that the Comision Reguladora de Energía (CRE) publishes represent the universe of gas stations in the 55 geographical areas, using them increases the accuracy in the measurement.

CONCLUSION 23

GRACIAS

25

Generic 1 Fresh Cheese 2 Avocado 3 Rice 4 Courgette 5 Shrimp 6 Pork 7 Beef 8 Onion 9 Chile Poblano

10 Dry Chile 11 Chile Serrano 12 Peach 13 Bean 14 Guava 15 Egg 16 Tomato 17 Milk powder 18 Lettuce and cabbage 19 Lemon 20 Apple

Generic 21 Melon 22 Orange 23 Nopales 24 Other canned fruits 25 Other fruits 26 Other dried legumes 27 Other vegetables and legumes 28 Other fresh chilies 29 Other seafood 30 Other Cheeses 31 Potato and other tubers 32 Papaya 33 Cucumber 34 Pear 35 Fish 36 Pineapple 37 Banana 38 Chicken 39 Yellow Cheese

Generic 40 Manchego and Chihuahua cheese 41 Oaxaca and asadero cheese 42 Watermelon 43 Grape 44 Yogurt 45 Carrot 46 Green tomato 47 Green beans 48 Chayote Squash 49 Chicken and salt concentrates 50 Pasteurized and fresh milk

List of products obtained with Web Scraping

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Distribution of household income, consumption, and savings MEDIDCAH: the case of Mexico

Languages and translations
English

Meeting of the Group of Experts on National Accounts (GENA)

Distribution of household income, consumption, and savings

MEDIDCAH: the case of Mexico

April 25-27,2023.

CONTENTS

Distributive

accounts

1

Imputation

methods Callenges

2 3

Main results

4

ObjetivoObjectives

• Present the imputation methods applied to transactions where it is not convenient to use

the microdata from the household survey (ENIGH) for distribution by a household group.

On some occasions, there is no good micro-macro conceptual alignment, or the survey

does not capture elements related to said transactions.

• The National Institute of Statistics and Geography (INEGI), in the framework of

complementing the economic indicators available in the country, is participating in this

initiative, presenting the experimental statistics on the "Measurement of the Distribution

of Available Income, Consumption, and Savings of Households (MEDIDCAH)"

developed up to the present.

Distributive accounts

MEDIDCAH

4

Structure of distributive accounts

In Mexico, the distributive accounts are

derived from the macro data of the

household sector measured in the

accounts by institutional sectors. These

accounts are adjusted to the criteria

suggested by the EG DNA guide for

distributive purposes.

6

Data sources

✓ National Accounts

✓ Administrative data

2003 – 2020

The National Accounts are updating their base year, 2018, with a

series of results for 2022.

The ENIGH will publish the results of the 2022

survey in the second semester of this year.

1. Private consumption and NPISH

The private consumption of households is extracted from total expenditure in the internal market, classified by purpose.

2. Non-resident households

The consumption of non- resident households in the economic territory is extracted with information from the Balance of Payments and from the measurements of receptive tourism consumption available in the INEGI tourism satellite account.

3. Rental services

Rental consumption is structured into:

a) effective rent

b) imputed rent

Based on measurements of the informal economy and output produced for own final use from the goods and services accounts.

National Accounts consumption adjust

1

2 3

Consumption adjustment example

NPISH Non residents

consumption

Household

consumption

Private

consumption

Concept 2018 2020 2018 2020 2018 2020 2018 2020 06 Health 573,134 581,974 65,149 68,762 3,176 1,540 504,809 511,672

06.1 Medical products, appliances and equipment 327,143 343,017 - - 2,473 1,199 324,670 341,818

06.2 Out-patient services 135,480 115,088 30,537 32,231 703 341 104,239 82,516 06.3 Hospital services 110,511 123,869 34,611 36,531 - - 75,900 87,338

Micro-Macro conceptual alignment

Conceptual homologation of ENIGH

variables with the SCN (aggregation or

reassignment of concepts).

Comparability between the levels of income and consumption

of the microdata with the totals of the National Accounts,

identifying the discrepancies and gaps between the two

sources of information or elements without counterparts in

the microdata.

Imputation methods evaluation.

Updating of expansion factors for

sociodemographic construction based on

the 2020 Population and Housing

Census.

Ingresos corrientes del hogarCurrent income

Micro-Macro conceptual

alignment and Imputation

methods evaluation

Item Name Imputation

method B2R1 Owner occupied dwellings A

B2R2 Leasing of dwellings A

D11R Wages and salaries A

D121R Employers' actual social contributions (counterpart in D611) B

D41R' Interest received (not adjusted for FISIM) A

D41R_FISIM Adjustment for FISIM (positive sign) B

D42R Distributed income of corporations A

D44R Investment income disbursements A

D441AR Investment income attributable to insurance policyholders A

D441BR Property income received attributed to life insurance policyholders B

D442R Investment income payable on pension entitlements (included in net social contributions paid)

B

D443R Investment income attributable to collective investment funds shareholders

A

D45R Rent received A

D41P Interest paid A

D41P' Interest paid (not adjusted for FISIM) A

D41P_FISIM Adjustment for FISIM (negative sign) B

D45P Rent paid B

D5P Less: Current taxes on income and wealth B

D611P Employers' actual social contributions paid A

D613P+D614P Households' social contributions (actual and supplements) A

D613P Households' actual social contributions B

D61xP Less: Social insurance scheme service charges B

D71P Non-life insurance premiums (including D441AR, see above) A

D75x Miscellaneous current transfers paid of which transfers between resident households

A

D63R1 Education A

D63R2 Health A

Consumption

Micro-Macro conceptual

approval and Imputation

methods evaluation

Item Name Método de

imputación CP010 Food and non-alcoholic beverages A

CP020 Alcoholic beverages, tobacco, and narcotics A

CP030 Clothing and footwear A

CP041 Actual rentals on housing A

CP042 Imputed rentals on housing A

CP043 Maintenance and repair of dwellings A

CP044 Water supply and miscellaneous A

CP045 Electricity, gas, and other fuels A

CP050

Furnishings, household equipment, and routine

maintenance of the house A

CP061 Medical products, appliances, and equipment A

CP062 Out-patient services A

CP063 Hospital services A

CP071 Purchases of vehicles A

CP072 Operation of personal transport equipment A

CP073 Transports services A

CP080 Communications A

CP090 Recreation and Culture A

CP100 Education A

CP110 Restaurants and hotels A

CP12x Miscellaneous (less FISIM, less insurance) A

CP1261 SIFMI B

CP125 Insurances expenditures (life and non-life) A

Imputation estimation

methods

MEDIDCAH

12

Simple calibration

Coefficient = (Total

macro / Total micro)

A

Imputation with

proxy variable

Information is imputed

with the distribution of

another variable

B

Imputation with

exogenous variable

Information is imputed

based on other surveys or

administrative data

C

13

Methods for scaling microdata

ENIGH provides information that allows

scaling with method “A” 44 of the 57

transactions that are not balances.

Code

SNA Description

D441BR Property income received attributed to life insurance policy holders

D442R Investment income payable on pension entitlements

D45P Rent paid

D613P Households' actual social contributions

D614P Households' social contributions supplements

D61xP Social insurance scheme service charges

D63R3 Other STiK

D8R Adjustment for the change in pension entitlements

P33 Final consumption expenditure of resident households abroad

Code

SNA Description

D121R Employers'’ actual social contributions

D5P Current taxes on income and wealth

D63R1 STiK Education

D63R2 STiK Health

Simple calibration

A Method

It consists in that the values ​​of

the micro source transactions

are scaled so that their totals

coincide with the

corresponding sums in the

national accounts.

Item Name 2018 2020

B2R1 Owner occupied dwellings 2.1049 1.9228

B2R2 Leasing of dwellings 1.4842 1.4689

B3R1 Own account production 2.8735 2.5023

B3R2 Underground production 3.6682 3.5637

B3R3 Mixed-income excluding underground and own

account production 79.9441 53.6149

D11R Wages and salaries 1.3050 1.4101

D41R Interest received 39.1968 41.9352

D41R' Interest received (not adjusted for FISIM) 30.1187 33.3061

D42R Distributed income of corporations 10.3657 9.8760

D44R Investment income disbursements 1.8224 4.2847

D443R Investment income attributable to collective

investment funds shareholders 0.4834 0.3816

D45R Rent received 4.0595 1.3705

D611P Employers" actual social contributions paid (see

corresponding item above) 1.4085 1.5570

D612P Employers imputed social contributions paid

(see corresponding item above) 13.5816 7.3031

D71P Non-life insurance premiums (including D441AR

(see above)) 4.0033 2.1129

D72R Non-life insurance claims 243.2974 68.8063

D75R Miscellaneous current transfers received 2.9061 3.3329

D75P Miscellaneous current transfers paid 2.2334 2.0894

D75x of which transfers between resident households

(2008 SNA 8.133) 11.6193 14.6720

D63R1 Education 1.2045 1.1851

D63R2 Health 1.0984 1.2165

The ENIGH provides information that

allows escalation with method "A" in 44

of the 57 transactions that are not

balanced.

15

Identify variables

without

components in the

survey

Can it be

imputed with

administrative

records?

Review definitions

of the SNA and

suggestions of the

group of experts to

impute based on

another variable of

group A

(Method C)

Build variable from

external sources

(Method C)

NO

YES

Imputation with proxy variable, Method B

Receiver Source

Receiver

item Name

Source

item Name

D41R_ FISIM Adjustment for FISIM (positive sign) D41R Interest received

D442R Investment income payable on

pension entitlements D443R

Investment income attributable to

collective investment funds shareholders

D441BR Property income received attributed

to life insurance policyholders D41R Interest received

D41P_FISIM Adjustment for FISIM (negative sign) D41P Interest paid (not adjusted for FISIM)

D45P Rent paid D45R Rent received

D613P Households' actual social

contributions D443R

Investment income is attributable to

collective investment funds shareholders.

D614P Households' social contributions

supplements D443R

Investment income is attributable to

collective investment funds shareholders.

D61xp Social insurance scheme service

charges D612P

Employers imputed social contributions

paid

D63R3 Other social transfers in kind D63R1 Social transfers in kind: Education

P33 Resident households’ expenditure

abroad CP125 Insurances expenditures (life and non-life)

D8R Adjustment for change in pension

entitlements D121R Employers' actual social contributions

Method C Imputation with exogenous variable uses exogenous resources to the primary

sources from the ENIGH, establishing the criterion that the microdata does not exceed the

value of the macroeconomic aggregates calculated in the national accounts.

Imputation to:

Imputation with exogenous variable

Wages and

salaries, for the

distribution of

imputed social

contributions.

Current taxes on income, wealth,

etc.

Social transfers in kind (Stik).

Education Health

17

Hypothetical case of imputation T𝒂𝒙𝒆𝒔 = 𝑻𝒂𝒙𝒂𝒃𝒍𝒆 𝒊𝒏𝒄𝒐𝒎𝒆 − 𝒍𝒐𝒘𝒆𝒓 𝒍𝒊𝒎𝒊𝒕𝒓𝒊𝒐𝒓 𝑰𝑺𝑹 ∗ 𝒓𝒂𝒕𝒆𝑰𝑺𝑹 + 𝒇𝒊𝒙𝒆𝒅 𝒇𝒆𝒆𝑰𝑺𝑹

Method C

Taxable Income 11,605.00 Estimated gross income 11,290.18 Adjusted gross income 11,605.47

­ ISR lower limit 10,031.08 ­ ISR lower limit 10,031.08 ­ ISR lower limit 10,031.08

= Difference 1,573.92 = Difference 1,259.10 = Difference 1,574.39

x ISR rate 17.92 x ISR rate 17.92 x ISR rate 17.92

= Marginal Tax 282.05 = Marginal Tax 225.63 = Marginal Tax 282.13

+ ISR fixed fee 917.26 + ISR fixed fee 917.26 + ISR fixed fee 917.26

= Tax to withhold 1,199.31 = Tax to withhold 1,142.89 = Tax to withhold 1,199.39

Effective perception 10,405.69 Effective perception 10,147.29 Effective perception 10,406.08

Residue -258.41 Residue 0.93

Tax calculation Iteration number 850 Iteration number 1,153

Theoretical calculation Gross income of ENIGH

18

Social transfers in kind (Education) The adjustment process in the microdata uses as exogenous variable administrative records on the enrollment of students

who receive public education by educational level.

In the case of social transfers in kind related to education, the information reported by the Education Minestry was used,

precisely, the records of public spending on education and student enrollment according to their educational level (pre-

primary, primary, secondary, baccalaureate, bachelor's degree, and postgraduates), allowing to have the level of educational

expenditure per student.

Educational expenditure per student by level = 𝑬𝒅𝒖𝒄𝒂𝒕𝒊𝒐𝒏𝒂𝒍 𝒔𝒑𝒆𝒏𝒅𝒊𝒏𝒈

𝑺𝒕𝒖𝒅𝒆𝒏𝒕 𝒆𝒏𝒓𝒐𝒍𝒍𝒎𝒆𝒏𝒕

When having information from the ENIGH regarding the degree of studies that the household members who said they were

students studying, the measurement of social transfers in kind was determined by multiplying the Educational expenditure

per student by level of education by the number of people studying reported by ENIGH.

TSE(Edu) = 𝑬𝒅𝒖𝒄𝒂𝒕𝒊𝒐𝒏𝒂𝒍 𝒆𝒙𝒑𝒆𝒏𝒅𝒊𝒕𝒖𝒓𝒆 𝒑𝒆𝒓 𝒔𝒕𝒖𝒅𝒆𝒏𝒕 ∗ 𝒏𝒖𝒎𝒃𝒆𝒓 𝒐𝒇 𝒑𝒆𝒐𝒑𝒍𝒆 𝒔𝒕𝒖𝒅𝒚𝒊𝒏𝒈 (𝑬𝑵𝑰𝑮𝑯)

Where:

TSE(Edu) = Social transfers in kind in educational services

19

Social transfers in kind (Health)

ISFLSH : Individual consumption expenditure on health

Government : Individual consumption expenditure on

health by institution

SCNM

Social transfers in-kind: health

ENIGH 2020

Number of affiliates

48,714,614

9,037,472

35,824,834

1,026,100

Annual per capita expenditure by institution and federal entity

$4,530.39

$5,175.01

$5,515.03

$19,761.05

MAIN

RESULTS 2020

INCOME

Income composition by quintile, 2020

The Mexican society has

mainly income from:

• Employee compensation

31.6 %

• Mixed-income 22.8%

• Property income 20.6%

Composition of households by the main source of income, 2020

• Households with mixed

income and property income

have a preponderance of

primary income representing

83% and 85.1% of income,

respectively.

• Wage and salary dependent

households have a diversity

of income from different

sources.

• Households that mostly

receive transfers have many

sources of income to

supplement total income

Composition of household income by sociodemographic characteristic, 2020

GI: Single under 65; GII: Single over 65; GIII: Single with children at home; GIV: 2 adults under 65 with no children in the Home; GV: 2 adults at least one older than 65 without children in the Home; GVI: 2 adults with less than

three children in the Home; GVII: 2 adults with at least three children in the Home; GVIII: More than two adults with at least one older than 65; GIX: More than two adults without over 65 with at least one child in the

household; GX: Others

• Income patterns are observed that

respond mainly to the age of the

inhabitants of the dwelling, such as

group II, where they live single with

less than 65 years of age, and their

primary source of income is current

transfers with 38.9% participation,

in contrast to the group VI with two

adults and less than three children

have labor income as their primary

source of income.

• In groups IV and IX, the inhabitants

are under 65 years of age and with

no more than two minors or none.

In these groups, labor income and

mixed-income

D63R1 Social transfers in kind of educational

services

• The primary beneficiaries of social transfers in

kind of educational services are members of

households from quintile 1 to 4, who receive

essential education services (preschool and

primary). In contrast, quintile 5 are students of

professional education.

Access to educational services by quintile

CENDI (T.E.)

CBTIS (T.E.)

Normal (T.E.)

Master´s /

Doctorate

CETIS (T.E.)

Preschool

High school

Preschool

Middle school

Elementary school

D63R2 Social transfers in kind of health

• The drop-in health services provided to

households, mainly in quintiles 1 and 2 is from

the adjustment of the change in the health

system that occurred in 2019 and is reflected

in the results of 2020.

Behavior of the records of insured persons by quintile of

adjusted income.

Consumption

Consumption patterns by quintiles, 2020

• Consumption patterns

tend to be more

homogeneous between

groups but follow the

principle of income

level and preferences

• The destination of the

income of low-income

households is food,

miscellaneous, and

household services

(fuel and electricity),

which could be called

their autonomous

consumption.

Household consumption patterns by the main source of income, 2020

• There is not so

much discrepancy in

its intra-household

percentage

composition in the

household services,

clothing and

footwear

consumption groups.

• The contributions of

the consumption

patterns with the

greatest discrepancy

between the 5 main

sources of income is

that of food and non-

alcoholic beverages.

SAVING

Saving as a percentage of equivalent disposable income, 2008-2020

The reduction in dissaving to -18% of households located in the lowest distribution level in 2020 is explained by

implementing 19 priority social programs to support the sectors most affected by the pandemic in the short term.

-18%

-26%

-60%

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

2020201820162014201220102008

Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5

32

Disposable Income (B6). Proportion concerning the

household average, 2008-2020.

Quintile 2018 2020

Q1 0.3 0.3

Q2 0.4 0.5

Q3 0.6 0.7

Q4 0.9 1.0

Q5 3.1 2.9

3.4

2.9

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

Q1 Q2 Q3 Q4 Q5

2008 2010 2012 2014 2016 2018 2020

Ratio reduction

(Q5) compared to

the average from

2008 to 2020

Ratio values ​​for the last

two years

Effective Final Consumption (P4). The proportion

concerning the household average, 2008-2020.

33

Quintile 2018 2020

Q1 0.5 0.5

Q2 0.7 0.7

Q3 0.8 0.8

Q4 1.1 1.1

Q5 2.2 2.0

Ratio reduction

(Q5) compared to

the average from

2008 to 2020

Ratio values ​​for the last

two years

2.2

2.0

0.0

0.5

1.0

1.5

2.0

2.5

Q1 Q2 Q3 Q4 Q5

2008 2010 2012 2014 2016 2018 2020

34

Disposable Income per capita and average consumption benchmarks, 2020

2,802

4,174 5,032

6,202 7,263

8,581 10,500

13,288

18,774

56,902

783

1,414 1,930

2,419

3,069

3,796 4,786

6,276

8,648

19,623

9,472

4,349

2,407

600

6,000

60,000

I II III IV V VI VII VIII IX X

P e

s o

s (

e s

c a

la l o

g a

rí tm

ic a

)

Decil

Ingreso disponible per cápita mensual (B6-MEDIDCAH) Ingreso corriente per cápita mensual (ENIGH)

Gastos de consumo (media nacional) Gastos de consumo indispensables (media nacional)

Gastos de consumo alimentos (media nacional) Linea de pobreza por ingresos

Note: This is considering that the income poverty line remains unchanged by CONEVAL.

6,0325,032

per capita monthly disposable income (B6-MEDIDCAH) consumer spending (national average) food consumption expenditure (national average)

monthly per capita current income (ENIGH) essential consumer spending (national average) income poverty line

SOCIODEMOGRAPHIC CHARACTERISTICS

Sociodemographic characteristics, 2020

Largest group

Adults predominate from 45 to 65 years old

Minors

predominate

Challenges 37

38

Pareto tail adjustment

The household survey microdata presents an underestimation in the tails of the distribution,

particularly in the upper end. Therefore, it is evaluated to use Pareto distributions to add households

that are not being captured in the survey, mainly due to the lack of data—responses from higher-

income respondents.

It worked on the problem of the lack of response in the distribution at the upper end of the Available

Income (B6) for the year 2020, making an adjustment and verifying the existence of Pareto

distributions

Steps to follow:

2

3

the goodness-of-fit

tests

the adjustment of the microdata is

made with the missing observations.

the shape of

the tail1

ObjetivoChallenges

Continue developing the following issues that will allow addressing the actions indicated in

the workplan of the new Data Gaps Initiative (DGI3-G20), related to the issue of

information gaps on the distribution of households.

✓ Correct the biases in the upper part of the distribution with Pareto distributions for all

income and consumption variables.

✓ Include distributions by decile in recurring calculations.

✓ Address the issue of wealth distribution.

✓ Start with microsimulation models to improve data relevance.

THANK YOU

Statistics derived from the NA of Mexico that contribute to wellbeing and sustainability approach

Languages and translations
English

Wellbeing and Sustainability Session

Statistics derived from the NA of Mexico that contribute to well- being and sustainability approach

Francisco Guillén Martín April, 2023

INSTITUTIONALIZATION OF THE MEASUREMENT OF

WELL-BEING INFORMATION

Promote the use and

exploitation of

information

Guarantee the recurrence,

consistency and timeliness

of the information

Economic, social and

environmental

information on well-

being

Inter-institutional

coordination: economic,

social and environmental

sector in Collegiate

Working Groups

System of National Accounts of Mexico: Satellite

Accounts: Information offered to users regarding well-being

Satellite Account Information available

Macroeconomic variables Other information

Unpaid work • Value of unpaid work in domestic and care work (Current and constant values)

• Hours per week • Information by sex • Production of self-consumption goods • Cost per hour

Housing • Production accounts (current and constant values) • Supply and use (Current and constant values) • Housing Financing

• Job positions

Environmental Accounting • Environmentally Adjusted Net Domestic Product (PINE, by its Spanish acronym)

• Total Costs for Depletion and Environmental Degradation

• Global supply and demand of goods and services net environmentally adjusted

• Spending on environmental protection

• Balance of economic and environmental assets

• Hybrid table of activities and products related to water

• Material flow account • Balance of available timber forest area • Balance of produced and non-produced

economic assets

Satellite

Account

Information available

Macroeconomic variables Other information

Culture • Production accounts (current and constant values)

• Supply and use of cultural goods and services (Current and constant values)

• Expenditure and financing (Current and constant values)

• Job positions • Non-monetary data (cultural infrastructure,

attendance at cultural events, employed persons)

Health • Production accounts (current and constant values)

• Supply and use of the health sector • Current and constant values

• Traditional medicine production accounts • Job positions • Hours per week of unpaid work in health care

Non profit Institutions • Production accounts (current and constant values)

• Paid staff • Voluntary work

System of National Accounts of Mexico: Satellite

Accounts: Information offered to users regarding well-being

National accounting has evolved through satellite accounts to generate information to

help measure well-being and sustainable development

For over 10 years, The Mexican unpaid household work satellite

account has provided key information key for evaluating progress in

equality between women and men.

Traditional National

Accounting

NEW PARADIGM Incorporates the

measurement of well- being through satellite

accounts

Traditional Approach Towards the Wellness Approach

and

Percentage share

of GDP 2020

Environmental accounts

Some of the main results of the Satellite Accounts of Mexico

Unpaid work in

households

Tourism HousingHealth Environmental

cost (depletion and degradation)

Culture Non-profit institutions

Environmental Protection

Expenditures

26.3%

7.5% 6.2% 5.7% 4.6% 3.0% 2.9% 0.5%

➢ GDP of the study sector as share to GDP of Total economy

(Percentage 2021)

Tourism Non-profit institutions

Health Unpaid work in households

Quarterly indicators of tourism activity

Culture Housing

8

2023

• Update of Househ old Sector Accoun ts, Base 2018.

2022

•4th financial year 2008- 2020 with demogra phic indicators

2021

• Publica tion of results on the OECD page

2020

• 3rd

exercise 2008-2018

2019

• Study of the new recomm endatio ns

•Methodol ogy EG DNA.

•2nd exercise (2008- 2016)

2018

• Househ olds databa se

• 1st exercis e (2008+ 2012)

Mexican Experience on DNA

• 6 years timeline: 2018

• Participation in the OECD-EGDNA Group. • Analysis of distributive statistical sources (field: Micro - Macro). • Development first exercise, Series 2008-2012, without

adjustments to the ENIGH concepts. 2019

• Study and implementation of the progress of the Manual on the distribution of Household Income, Consumption, and Savings in line with the National Accounts.

• Development of the 2nd. Exercise, Series 2008-2016 2020

• Elaboration of the 3rd. exercise applying the final methodological guidelines embodied in the "Methodology and results of the 2020 collection round" of the OECD. Series 2008-2018.

2021 • Publication of results on the OECD website, Series 2008-2018. • Analysis of results and trends for the study of bias in the last

quintile. 2022

• Update of the series, which has added the 2020 financial year and the information record of the "main source of income" and "type of household" tables for the 2008-2020 biennial series.

2023 • Update of the macroeconomic aggregates of the Household

Sector from the change in the base year of the national accounts, series 2008-2022.

• Review of the R script developed by the OECD of the centralized approach in compiling distribution results.

• Continue with the study of the Pareto method.

These results have been possible thanks to the assistance and feedback from EG DNA.

** The series are biennial, given the periodicity of the ENIGH, a source of information for the distribution of transactions.

Distributional National Accounts

9

Disposable Income per capita and average consumption benchmarks, 2020

2,802

4,174 5,032

6,202 7,263

8,581 10,500

13,288

18,774

56,902

783

1,414 1,930

2,419 3,069

3,796 4,786 6,276

8,648

19,623

9,472

4,349

2,407

600

6,000

60,000

I II III IV V VI VII VIII IX X

P e

s o

s (

e s

c a

la l o

g a

rí tm

ic a

)

Decil

Ingreso disponible per cápita mensual (B6-MEDIDCAH) Ingreso corriente per cápita mensual (ENIGH) Gastos de consumo (media nacional) Gastos de consumo indispensables (media nacional) Gastos de consumo alimentos (media nacional) Linea de pobreza por ingresos

Note: This is considering that the income poverty line remains unchanged by CONEVAL.

6,0325,032

per capita monthly disposable income (B6-MEDIDCAH) consumer spending (national average) food consumption expenditure (national average)

monthly per capita current income (ENIGH) essential consumer spending (national average) income poverty line

Socio-demographic Q1 Q2 Q3 Q4 Q5 Total

Households 7,149,992 7,150,716 7,149,554 7,149,783 7,149,614 35,749,659

Consumption units 16,414,922 15,757,780 15,173,412 14,486,527 13,409,753 75,242,395

Highest level of education achieved (persons) Q1 Q2 Q3 Q4 Q5 Total

Low 24,209,131 20,347,334 17,262,981 13,061,479 7,703,828 82,584,753

Middle 4,083,536 5,156,096 5,586,694 6,187,712 4,819,561 25,833,599

High 1,052,420 1,823,272 2,693,519 4,375,073 8,475,831 18,420,115 Sex (persons)

Q1 Q2 Q3 Q4 Q5 Total

Females 15,366,841 14,226,382 13,202,469 12,025,767 10,727,323 65,548,782

Males 13,978,246 13,100,320 12,340,725 11,598,497 10,271,897 61,289,685

Main activity (persons) Q1 Q2 Q3 Q4 Q5 Total

Employee1 6,414,609 7,861,223 8,631,012 9,176,892 8,641,087 40,724,823

Employer1 1,591,054 1,131,907 993,377 973,719 1,118,205 5,808,262

own- account worker 1 2,384,971 1,862,723 1,593,117 1,400,287 985,209 8,226,307

Unpaid family worker1 1,134,673 580,851 441,222 337,755 254,983 2,749,484

Student 2 6,800,542 5,637,093 4,517,611 3,553,858 2,653,320 23,162,424

Retired2 174,729 515,839 801,275 1,074,258 1,818,428 4,384,529

Unemployed1 906,800 749,835 722,284 538,096 339,746 3,256,761

Not classifiable2 9,937,709 8,987,231 7,843,296 6,569,399 5,188,242 38,525,877

Socio-demographic information , 2020

1: segmento LFPR; 2: segmento NLFPR

System of National Accounts of Mexico Model of the Statistical and Geographical Process (MPEG, by its

Spanish acronym)

INPUT Structured need for information

Documentation of needs

Design Construction

Detailed methodology to satisfy the structured need for information and technological infrastructure

INPUT Primary data and

other data sources Data capture

Captured data set

Processing

Processing data set

Information set

OUTPUT Output

presentations

INPUT Inputs for the Assessment

Processing assessment

OUTPUT Improvement proposal for

the next cycle

Production analysis

Information dissemination

Recurring work with users of information

12

Texto editable

✓ The way to communicate the topics and results to the data User depends on the type and level in question.

✓ For User with a decision-making profile, simple language is used with an emphasis on the main results of the product and its possible applications.

✓ For User of technical level, the approach is carried out with the use of the methodology.

✓ For general User the language used is based on examples and dynamics close to their fields of study.

Collegiate Working Groups 13

Texto editable

✓ We make recurrent presentations of the projects, through workshops and seminars

to disseminate the knowledge and use of

the products of the National Accounts of

Mexico to accompany users in a better

use.

✓ User confidence, depth of knowledge,

and feedback are fostered.

✓ We apply a questionnaire to know if the communication is adequate for the better understanding of the projects and to identify what other topics or details are required to complement the knowledge.

General Housing Law

General Law on Climate

Change

National Biodiversity Strategy

of Mexico and action plan

2016-2030

Among other examples

Uses in public policy

The results of the Satellite Accounts are used for planning or regulation in different areas related to Well-being

6

9

10

National Development Plan

2019-2024

Tourism Sector Program 2019-

2024

General Law of Ecological Balance

and Environmental Protection

Querétaro State Volunteer Law

Initiative

1

2

3

5

Initiative to reform the Federal Law

for the Promotion of the Activities

of Civil Society Organizations4

National Program for Equality

between Women and Men

2020-2024

8

National Housing Program

2019-20247

THANK YOU

Towards the Digital Economy: The Case of Mexico

Towards the Digital Economy: The Case of Mexico

Languages and translations
English

Towards the Digital Economy: The Case of Mexico

Francisco Guillén Martín Deputy Director General of National Accounts Meeting of the National Accounts Expert Group 25-27, 2023, Geneva

• Body Level One

• Body Level Two

• Body Level Three

• Body Level Four

• Body Level Five

Title Text

Title Text Topics

1

2 Advancements in measuring the Digital Economy

Background

3 Mexico's Case Towards the Digital Economy

25-27 Abril 2023, Ginebra

Background Towards the digital economy 4

https://www.inegi.org.mx/temas/vabcoel/

February 10, 2023

Gross Value Added (GVA) of e-commerce 2013 - 2021p

Base year 2013

In 2018, for the first time, INEGI presented an estimate of the Gross Value Added (GVA) of E-commerce as an approximation to estimate the Digital Economy

Towards a more detail approach the digital economy: Gross Value Added (GVA) of e-

commerce

Coverage: National

Frequency: Annual

Publication

Background

Base year 2013

Gross Value Added (GVA) of E-commerce

1

EC

Economic Census 2014

2

SUT

Supply and Use Tables 2013

3

ATS

Annual Trade Survey

4

GSA

Goods and Services

Account, period

For estimating the base year 2013 For the estimation of the series 2014 -

2021

Sources of information

Background

PROCEDIMIENTOS APLICADOS

E-commerce census factor

Trade margins of Supply and

Use Tables (SUT)

Estimation of e-commerce

for goods

Ratios GVA SUT / P

E-commerce census factor Use of services

SUT

Estimation of electronic

commerce for services

Ratios GVA SUT / P

SUT.- Supply and Use Tables GVA.- Gross Value Added P.- Production

Base year 2013 Gross Value Added (GVA) of E-commerce

Background

Share in GDP

2013

3.0 %

2014

3.4 %

2015

3.6 %

2016

4.1 %

2017

4.6 %

2018

4.9 %

2019

5.8 % 5.8 %

2020r

R: Revised P: preliminary

2021p

5.8 %

Base year 2013 Gross Value Added (GVA) of E-commerce

Serie 2013 - 2021p

Background

R: Revised P: preliminary

Base year 2013 Gross Value Added (GVA) of E-commerce

2013 2014 2015 2016 2017 2018 2019 2020r 2021p

Retail tradeOther services Wholesale trade

% Structure by type of commerce

Serie 2013 – 2021p

Other services include activities that made sales through electronic means, other than wholesale and retail trade.

Advances in Measuring the Digital Economy

25-27 April 2023, Geneva

Advances in Measuring the Digital Economy

GVA Digital Products Base year 2013

In 2021 INEGI and the IMF collaborated to estimate an experimental statistic of the Gross Value Added of Digital Products to reinforce efforts to measure the Digital Economy in Mexico.

Balance of Payments (BoP)

System of National Accounts (SNA) 2008 to 2025

International recommendations proposed for updating are being considered

1

2

Advances in Measuring the Digital Economy

GVA Digital Products

1. Catalogue of Products

(CE14) 2. Guidance for the Digital Economy

Supply and Use Tables (OECD, 2018 and 2020)

3. Identify digital products: CPC 2.1.

correspondences to CPC 2.0 and NAICS. 4. digital products (%)

in Production for 2013SUT 2013

Classification CPC 2.1

5. Calculate the GVA/P ratios by

activity type in the series

6. EstimatesThe GVA of digital and non-

digital products 2013- 2018.

7. The GVA of e- commerce is

included. 8. Digital products GDP shares

Goods and Services Account

(Series 2014- 2018)

Sources of information / Procedure applied

Base year 2013

P Production CE Economic Census 2014

Advances in Measuring the Digital Economy

VAB E-Commerce and Digital Products

Experimental Statistics of the Digital Economy.

% GDP 6.40% 7.00% 7.30% 7.60% 8.10% 8.50%

ICT Products 3.00% 3.10% 3.30% 3.10% 3.10% 3.10%

Content and media Products 0.40% 0.40% 0.40% 0.40% 0.40% 0.40%

E-commerce 3.00% 3.40% 3.60% 4.10% 4.60% 5.00%

Advances in Measuring the Digital Economy

VAB E-Commerce and Digital Products

Base year 2013

Share of GDP

2013 2014 2015 2016 2017 2018

6.4%

7.0%

7.3%

7.6% 8.1%

8.5%

Towards the Digital Economy: The Case of Mexico

April 25-27, 2023, Geneva.

Towards the Digital Economy: The Case of Mexico

Digital Economy

Estimates based on the basic and derived information from 2018 to face the challenge of making the Digital Economy more visible in the macroeconomic statistics of the National System of Statistical and Geographical Information.

Base year 2013

1 Derived Information

2

 Census  Surveys  RA’s

 SUT  GSA

Reprocessing

Innovations from the Change of Base Year 2013 to 2018.

Towards the Digital Economy: The Case of Mexico Base year 2013

Inovation in the Update of the SCNM

The estimates for Digital Economy framework will include the following information:

E-commerce GVA 2013-2021

Digital SUT

Towards the Digital Economy: The Case of Mexico

Publication date

Calendar of Dissemination of Statistical, Geographical, and National Interest Information

Experimental statistics

GVA E- commerce

Digital Economy,

2018

Series Annual

National

2013-2021p Quinquennial

National

Supply

UseInnovation Products, Base Year 2018

https://www.inegi.org.mx/app/saladeprensa/ calendario/default.html.

  • Towards the Digital Economy: The Case of Mexico
  • Slide Number 2
  • Background
  • Slide Number 4
  • Slide Number 5
  • Slide Number 6
  • Slide Number 7
  • Slide Number 8
  • Advances in Measuring the Digital Economy
  • Slide Number 10
  • Slide Number 11
  • Slide Number 12
  • Slide Number 13
  • Towards the Digital Economy: The Case of Mexico
  • Slide Number 15
  • Slide Number 16
  • Slide Number 17
  • Slide Number 18

National Survey of Household Income and Expenditure, Edgar Vielma, Mexico

Languages and translations
English

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS Group of Experts on Measuring Poverty and Inequality 8-9 December 2022 Workshop on Harmonization of Poverty Statistics to Measure SDG 1 and 10 7 December 2022

Title of contribution National Survey of Household Income and Expenditure (ENIGH) Author Name Edgar Vielma Author Organization Instituto Nacional de Estadística y Geografía (INEGI) Topic Impact of global shocks on poverty and inequality Summary: The National Survey of Household Income and Expenditure (ENIGH, by its acronym in Spanish) gives a statistical overview of the behavior of household income and expenses in terms of amount, origin, and distribution, including current monetary expenditure. The ENIGH measures consumption expenditure considering the purchase value of goods and services and whether they were paid in full in the reference period. The expenditure items captured by the survey are food, drinks, and tobacco; clothes and footwear; housing and conservation services; electricity and fuel; dwelling maintenance care; health, transportation, education; and personal care. Regarding mobility by household cars, the survey inquiries about spending on gasoline and the main petroleum-derived inputs (oils and tires); for public transportation users, it collects the amounts spent on this service. Regarding direct household inputs, it asks about spending on electricity, gas, coal, or other fuel for heating. Thanks to its biennial periodicity, ENIGH permits quantifying the change in spending on energy goods from 2018 to 2020. This statistical information allows decision-makers to quantify households' energy expenditure, in addition to knowing whether the increase in prices implied that the proportion of spending on these items is higher, which would impact the consumption of other essential goods. It is possible to identify the groups with the most significant increase in their current expenditure before and during the first COVID impact, emphasizing the lowest income deciles or vulnerable groups. Please select your preferred contribution (you may select both options): X Presentation ☐ Paper

Russian

ОРГАНИЗАЦИЯ ОБЪЕДИНЕННЫХ НАЦИЙ ЕВРОПЕЙСКАЯ ЭКОНОМИЧЕСКАЯ КОМИССИЯ КОНФЕРЕНЦИЯ ЕВРОПЕЙСКИХ СТАТИСТИКОВ Группа экспертов по измерению бедности и неравенства 8-9 декабря 2022 года Семинар по гармонизации статистики бедности для измерения прогресса в достижении ЦУР 1 и 10 7 декабря 2022 года

Название доклада Национальное обследование доходов и расходов домохозяйств (ENIGH)

Имя автора Эдгар Виелма Организация автора Instituto Nacional de Estadística y Geografía (INEGI) Тема Влияние глобальных потрясений на бедность и неравенство Резюме: Национальное обследование доходов и расходов домохозяйств (ENIGH, сокращение на испанском языке) дает статистический обзор характера изменения доходов и расходов домохозяйств в контексте суммы, происхождения и распределения, включая текущие денежные расходы. ENIGH измеряет расходы на потребление с учетом покупной стоимости товаров и услуг, а также с учетом того, были ли они оплачены полностью в учетный период. Потребительские расходы, фиксируемые в рамках обследования, - это расходы на продукты питания, напитки и табачные изделия; одежду и обувь; жилье и услуги по энергосбережению; электроэнергию и топливо; техническое обслуживание жилья; здравоохранение; транспорт; образование; а также предметы личной гигиены. Что касается передвижения на автомобилях, принадлежащих домохозяйству, в рамках обследования задаются вопросы о расходах на бензин и основные расходные материалы на основе нефти (масла и шины). Для тех, кто пользуется общественным транспортом, собирается информация о расходах на эти услуги. Что касается прямых вложений в домохозяйство, задаются вопросы о расходах на электроэнергию, газ, уголь или иное топливо для отопления. Благодаря тому, что обследование ENIGH проводится один раз в два года, оно позволяет численно оценить изменения в расходах на энергоносители с 2018 по 2020 год. Эта статистическая информация позволяет лицам, принимающим решения, количественно оценить расходы домохозяйств на энергию, а также узнать, означает ли повышение цен увеличение доли расходов на эти товары, что повлияет на потребление других товаров первой необходимости. Можно определить группы с наиболее значительным увеличением их текущих расходов до и во время первого воздействия COVID, уделяя особое внимание децилям с самым низким доходом или уязвимым группам. Выберите предпочтительный способ участия (можно выбрать оба варианта): X Презентация ☐ Статья

-- Presentation

Languages and translations
English

8-9 December

Impact of global shocks on poverty and inequality

National Survey of Household Income and Expenditure

(ENIGH, by its acronym in Spanish)

United Nations Economic Commission for Europe

Conference of European Statisticians

Group of Experts on Measuring Poverty and Inequality

MEc. Edgar Vielma Orozco General Director of Sociodemographic Statistics

Objective

1. The recent COVID-19 crisis showed that the lockdown, social distance

measures, and the closure of the economy in Mexico brought adverse

effects that impacted the household economy, directly affecting income,

employment, and expenses. Based on the above, the behavior of current

monetary expenditure will be analyzed by income deciles and each of the

major expenditure items in rural and urban areas.

2. Observe the change in household spending in Mexico, starting with the

COVID-19 pandemic, identifying the increase in healthcare spending in

2020 compared to 2018.

Objective

3. In times of uncertainty due to health, economic and financial crises, it is

essential to have information that allows decision-makers to implement the

necessary actions to reduce the adverse effects on the household

economy. The National Survey of Household Income and Expenditure

(ENIGH, in Spanish) has cross-sectional measurements that allow

identifying the impacts of the price increases of 2020, as well as the

allocations that households make to their expenses.

4. The results indicate that households with lower incomes are the most

affected due to the loss of purchasing power.

Methodological aspects

The data source was the 2018 and 2020

National Survey of Household Income and

Expenditure editions. Both were

conducted from August 21 to November

28.

It aims to present an overview of the

behavior of income and expenditure at

the household level in terms of amount,

origin, and distribution.

Additionally, ENIGH provides

information on labor participation and

the socio-demographic characteristics

of household members. It also includes

information on dwelling characteristics

and equipment.

This data source, by its periodicity and

the level of geographical

disaggregation, was designed to

generate a rigorous diagnosis.

1

2

3

4

Initially, the design of the ENIGH

focused on the need to provide

information to update the National

Consumer Price Index weights.

However, the data collected by the

survey has had many other uses in the

last few years, mainly to measure

poverty.

In 2018 and 2020 the ENIGH continued to generate

the improved indicators of 2016, which implies:

1. The sample size is the largest in the

country's history for a survey of income

and expenditures.

2. Allows representativeness at the state

level with estimates for urban and rural

domains.

3. Measures both income and

expenditure.

4. Operationally, it remains the most

supervised project that has been carried

out in this area so far.

5. Strength in training permits interviewers

to be more attached to methodological

definitions.

6. It has an automated tracking report

system that supports supervision and

control.

5

Characteristic Description

Target population The national and foreign households within the country's boundaries.

Observation unit The household, the dwelling, and the householders.

Geographical

coverage

The survey is designed to provide results at the following levels:

• National

• Urban-Rural

• At the state level, with estimates for urban and rural domains.

Sample Design

• Probabilistic: The sampling units have a known probability different from zero of being

selected.

• Stratified: The sampling units with similar characteristics and that belong to localities of the

same size are grouped to form strata.

• Conglomerates: The sample units with distinct characteristics that belong to localities of the

same size are grouped to form the primary sampling units (PSU).

• Double stage: Dwellings are selected in two steps: primary sampling units (groups of blocks)

and housings.

Sample size 105,483 households represent a population of 126,760,856.

Survey date From August 21 to November 28, 2020.

Main methodological characteristics

Questionnaire of

households and housing

Household expenses

questionnaire

Questionnaire for people aged 12 and over

Questionnaire for home

businesses

Questionnaire for people under 12 years

old

The questionnaires and booklet used in the ENIGH

2020

Booklet of daily expenses

In 2020, it was considered to carry out a

survey that complemented the ENIGH,

called Seasonal ENIGH (ENIGH E).

1. The Survey captures seasonal and temporal

events that affect household income and

expenditure outcomes.

2. Its objective is to provide a statistical

overview of household income and

expenditure's seasonal and temporal

behavior in terms of their amount, origin, and

distribution over a year.

3. ENIGH E presents the phenomenon's

evolution concerning its predecessor year,

ENIGH 2018, and provides an economic

overview of Mexico before the health

contingency caused by COVID-19.

4. The results of the ENIGH E also allowed us

to compare the income and expenditures of

households captured between January and

March 2020.

Questionnaire of

households and housing

Household expenses

questionnaire

Questionnaire for people aged 12 and over

Questionnaire for home

businesses

Questionnaire for people under 12 years

old

The questionnaires and booklet used in the ENIGH

2020

Booklet of daily expenses

In 2020, it was considered to carry out a

survey that complemented the ENIGH,

called Seasonal ENIGH (ENIGH E).

5. ENIGH E also allows to measure the change

with the results of the ENIGH 2020,

motivated by the measures of confinement of

the population and the closure of economic

activity that led to changes in income and

expenditure in Mexican households.

6. ENIGH E and ENIGH have the same

conceptual and methodological elements,

thus preserving consistency.

7. A difference between the programs is that

each contains sample sizes of different

magnitudes. in the case of ENIGH E, its size

only allows for generating results at the

national level.

ENIGH

Reference period

Sources of income and

Large items of expenditure

February March April May June July August September October November

Income from work

Property Rent

Transfers

Estimation of the rent of the house

Other current income

Spending on food, beverages, and tobacco

Spending on clothing and footwear

Spending on housing and conservation services; Electric power, and

fuels

Spending on items and services for cleaning and care of the house;

glassware, whites and household utensils; household goods and furniture

Health Care Spending

Transportation expenditure; acquisition, maintenance, accessories, and

services for vehicles; and communications

Expenditure on education and leisure items and services; Tour and

Party Packages, Lodging and Accommodation

Spending on personal care; accessories and personal effects; and

other miscellaneous expenses

Expenditure transfers

Diagram 1. Reference period of the income and expenditure of ENIGH 2020, according to concept or item

Seasonal ENIGH

Reference period

Sources of income and

Large items of expenditure

2019 2020

July August September October November December January February March

Income from work

Property Rent

Transfers

Estimation of the rent of the house

Other current income

Spending on food, beverages, and tobacco

Spending on clothing and footwear

Spending on housing and conservation services; Electric power, and

fuels

Spending on items and services for cleaning and care of the house;

glassware, whites and household utensils; household goods and furniture

Health Care Spending

Transportation expenditure; acquisition, maintenance, accessories, and

services for vehicles; and communications

Expenditure on education and leisure items and services; Tour and

Party Packages, Lodging and Accommodation

Spending on personal care; accessories and personal effects; and

other miscellaneous expenses

Expenditure transfers

Diagram 2. According to concept or item, the reference period of income and expenditure of ENIGH E 1-2020.

Conceptual design

WEALTHREGULARITY

Conceptual

Design

Income sources must be

regular and subject to an

uninterrupted flow in each

period.

AVAILABILITY

Income sources must

contribute to economic

well-being; that is, they

must be available to

purchase goods and

services that the

household can use.

These income sources

should not include the flows

that modify the net wealth or

the net value of the assets

and debts of the household.

INCLUSION CRITERIA EXCLUSION CRITERIA

Current expenditures:

A household's expenditures are the expenses that

satisfy its needs and commitments.

A

Methods to measure consumption expenditure:

ENIGH measures consumption expenditure considering

the purchase value of goods and services and whether

they were paid in full in the reference period.

B

Given the transactional role of money, the total current income

splits into current monetary and non-monetary income

1. Food, beverages, and tobacco;

2. Clothing and footwear;

3. Housing and conservation services; Electric power and fuels;

4. Items and services for cleaning and care of the house;

glassware, whites, and household utensils; household goods

and furniture;

5. Healthcare Spending;

6. Transportation expenditure; acquisition, maintenance,

accessories, vehicles services; and communications;

7. Expenditure on education, leisure items, and services; Tour

and Party Packages, Lodging, and Accommodation;

8. Personal care; accessories, personal effects, and other

miscellaneous expenditures;

9. Expenditure transfers.

By composition, the

current monetary

expenditure of

households is grouped

into nine items:

Results

Average quarterly current monetary expenditure by income deciles, by survey year

Dollars, at constant 2020 prices

Decile ENIGH 2018 ENIGH 2020 Percentage

Change

I 503 553 9.81%

II 715 690 -3.54%

III 867 826 -4.70%

IV 1,021 954 -6.50%

V 1,206 1,086 -9.98%

VI 1,370 1,234 -9.91%

VII 1,601 1,424 -11.06%

VIII 1,902 1,650 -13.24%

IX 2,411 2,072 -14.07%

X 4,372 3,424 -21.68%

Total 1,597 1,391 -12.87 %

The average quarterly current monetary expenditure per household

of the ENIGH 2020 was 1,391 dollars, in contrast to 1,597 dollars in

the 2018 edition, which represents a decrease of about 13%.

Source: ENIGH 2018 and ENIGH 2020

Households and their current monetary expenditure in selected items

Percentage of current monetary expenditure

Item ENIGH 2018 ENIGH 2020 Percentage

Change

Foods and

beverages 35.22% 38.05% 2.83%

Clothing and

footwear 4.48% 2.99% -1.50%

Housing and

services 9.54% 10.98% 1.45%

Cleaning and

care of the house 5.87% 6.55% 0.68%

Transport and

communications 19.95% 18.56% -1.39%

Education and

leisure 12.11% 7.68% -4.43%

Personal care 7.40% 8.01% 0.60%

Healthcare 2.62% 4.23% 1.61%

Expenditure

transfers 2.80% 2.96% 0.16%

Source: ENIGH 2018 and ENIGH 2020 Source: ENIGH 2018 and ENIGH 2020

Foods and

beverages

Clothing and

footwear

Housing and

services

Cleaning and

care of the

house

Transport and

communications

Education and

leisure

Personal care Health care Expenditure

transfers

Thousands of dollars

D e

c ile

s

2020

URBAN

2018

URBAN

2018

RURAL

2020

RURAL

Source: ENIGH 2018 and ENIGH 2020

Households and their

total quarterly current

monetary expenditure,

by decile and by area

Source: ENIGH 2018 and ENIGH 2020

It e

m

Thousands of dollars

2020

RURAL

2020

URBAN

2018

URBAN

2018

RURAL

Households and their

total quarterly current

monetary expenditure,

by item and by area

Conclusions

+40% -19% -45%

Health

Care

Transportation Education and

Leisure

Destination of Expenditure

Households in Mexico, on average, during 2020, modified their consumption habits in:

Households in Mexico in 2020 adapted their consumption habits in the face of the global health crisis. First, they decreased

total average current monetary expenditure by almost 13%; The expenditure item that, on average, increased the most,

compared to 2018, was health care with 40.5%. On the other hand, the average expenditure on education decreased by

almost 45% and on transportation by nearly 19%. Finally, electricity power increased by 2%; however, it increased in all

income deciles.

+2%

Electricity

Power

Household expenditure

45%

FOODS

42%CLOTHING AND

FOOTWEAR

40%HEALTHCARE

PERSONAL

6%

EDUCATION AND

LEISURE

19%TRANSPORTATION

CLEANING 3%6%

EXPENDITURE

TRANSFERS 8%

Lower-income

22 %

10 %

13% GENERAL

Overall, households in Mexico reduced

their spending by 13%; households in the

first decile alone increased their average

current monetary expenditure by almost

10%.

Higher-income

Households with the lowest incomes increased

their quarterly average monetary expenditure by

almost 10%, as was the case of households in the

first income decile. The ENIGH 2020 reflects that

lower-income households increased their

expenses while higher incomes reduced them

considerably. Households in the highest income

decile reduced their monetary expenditure by

almost 22%.

• In summary, in 2020, household monetary expenditure fell significantly. However, lower-income

households saw increased spending, while high-income households decreased their monetary

disbursements.

• Faced with widespread healthcare needs, households sacrificed spending on education,

clothing, footwear, and transportation. On the other hand, spending on housing and domestic

services was an item of expenditure that increased across the board compared to 2018.

• Transportation spending increased in lower-income households but decreased significantly in

high-income households; Reductions explain this in fuel consumption, foreign transport, and, to

a lesser extent, by the fall in the acquisition of vehicles for private use.

• Expenditure on electricity consumption had a general increase in all deciles and urban and rural

areas.

• Gini Coefficient allows appreciation of income inequality. The value of this coefficient for income

with transfers by deciles of households was 0.415 in 2020. Besides, the Gini Coefficient will

enable us to appreciate the positive effects of transfers on income distribution. If 2020 there

were no income transfers, the concentration of these would have been higher because the Gini

coefficient would have had a value of 0.468 against 0.415 with transfers.

THANK YOU

Russian

8-9 декабря

Влияние глобальных потрясений на бедность и неравенство Национальное обследование доходов и расходов домохозяйств (ENIGH, сокращение на испанском языке)

Европейская экономическая комиссия Организации Объединенных Наций Конференция европейских статистиков

Группа экспертов по измерению бедности и неравенства

Эдгар Виелма Ороско Генеральный директор по вопросам демографической и социальной статистики

Цель

1. Недавний кризис, вызванный COVID-19, показал, что карантин, меры по соблюдению социальной дистанции и сокращение экономической активности в Мексике негативно повлияли на экономику домохозяйств, напрямую затронув доход, занятость и расходы. Исходя из вышесказанного, будут проанализированы характер текущих денежных расходов по доходным децилям, а также каждая из основных статей расходов в сельских и городских районах.

2. С начала пандемии COVID-19 наблюдается изменение расходов домохозяйств в Мексике и рост расходов на медицинское обслуживание в 2020 году по сравнению с 2018 годом.

Цель

3. Во времена неопределенности в связи с кризисом системы здравоохранения, экономическим и финансовым кризисом важно располагать информацией, которая позволит лицам, принимающим решения, предпринимать необходимые шаги, чтобы снизить это негативное влияние на экономику домохозяйств. В рамках Национального обследования доходов и расходов домохозяйств (ENIGH - от названия на испанском языке) проводятся сквозные измерения, которые позволяют определить влияние роста цен 2020 года, а также суммы, выделяемые домохозяйствами на расходы.

4. Результаты обследования указывают на то, что домохозяйства с более низкими доходами пострадали сильнее всего из-за утраты покупательской способности.

Методические аспекты

Источником данных послужили Национальные обследования доходов и расходов домохозяйств 2018 и 2020 годов. Оба обследования проводились с 21 августа по 28 ноября.

Обследование проводится с целью представить статистический обзор характера изменения доходов и расходов домохозяйств в контексте суммы, происхождения и распределения.

Кроме того, обследование ENIGH предоставляет информацию о доле экономически активного населения и социально-демографических характеристиках членов домохозяйств. Кроме того, оно включает информацию о характеристиках жилья и оборудования.

Этот источник данных в соответствии со своей периодичностью и уровнем географической разбивки был разработан с целью постановки четкого диагноза.

1

2

3

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Поначалу обследование ENIGH проводилось с целью предоставить информацию для обновления весов Национального индекса потребительских цен. Однако данные, собранные при проведении обследования, нашли разнообразное применение в последние годы, главным образом для измерения бедности.

В 2018 и 2020 годах обследование ENIGH продолжило улучшать показатели 2016 года, что подразумевает:

1. Размер выборки - самый большой в истории страны для обследования доходов и расходов.

2. Обеспечивает репрезентативность на уровне штата с оценками для города и сельских территорий.

3. Измеряет как доходы, так и расходы.

4. С операционной точки зрения оно остается наиболее контролируемым проектом, реализованным в этой области на сегодняшний день.

5. Качественное обучение позволяет интервьюерам более точно придерживаться методологических определений.

6. В рамках обследования существует автоматизированная система отчетности, которая обеспечивает надзор и контроль.

5

Характеристика Описание

Целевое население Национальные и иностранные домохозяйства в пределах страны Единица

наблюдения Домохозяйство, жилище и домовладельцы.

Географический охват

Обследование должно предоставить результаты на следующих уровнях: • Национальный • Город-село • На уровне штата, с оценками для города и сельских территорий

Дизайн выборки

• Вероятностная: Единицы выборки имеют известную вероятность быть отобранными, отличную от нуля.

• Послойная: Единицы выборки со схожими характеристиками и принадлежащие к населенным пунктам одинакового размера группируются в слои.

• Группированная: Единицы выборки с различными характеристиками, принадлежащие населенным пунктам одинакового размера, группируются для формирования первичных единиц выборки (ПЕВ).

• Двухступенчатая: Жилища отбираются на двух ступенях: первичные единицы выборки (группы многоэтажных зданий) и жилища.

Размер выборки 105 483 домохозяйства представляют 126 760 856 человек Дата проведения

обследования С 21 августа по 28 ноября 2020 года

Основные методологические характеристики

Анкета домохозяйств и жилья

Анкета о домашних расходах

Анкета для лиц в возрасте 12 лет и старше

Анкета для домашнего бизнеса

Анкета для лиц младше 12 лет

Анкеты и буклет, использованные при проведении обследования ENIGH 2020

Буклет ежедневных расходов

В 2020 году было принято решение провести обследование, дополняющее ENIGH, под названием Сезонное обследование ENIGH (ENIGH E).

1. Обследование фиксирует сезонные и временные события, влияющие на доходы и расходы домохозяйств.

2. Его задача - дать статистический обзор характера сезонных и временных изменений доходов и расходов домохозяйств в контексте суммы, происхождения и распределения на протяжении года.

3. Обследование ENIGH E показывает развитие ситуации по сравнению с предшествующим годом (ENIGH 2018) и дает экономический обзор Мексики до появления непредвиденных обстоятельств в области здравоохранения, вызванных COVID-19.

4. Результаты обследования ENIGH E также позволили нам сравнить доходы и расходы домохозяйств, зафиксированные в период с января по март 2020 года.

Анкета домохозяйств и жилья

Анкета о домашних расходах

Анкета для лиц в возрасте 12 лет и старше

Анкета для домашнего бизнеса

Анкета для лиц младше 12 лет

Анкеты и буклет, использованные при проведении обследования ENIGH 2020

Буклет ежедневных расходов

В 2020 году было принято решение провести обследование, дополняющее ENIGH, под названием Сезонное обследование ENIGH (ENIGH E).

5. Помимо этого, ENIGH E позволяет оценить

изменение с учетом итогов ENIGH 2020,

вызванное карантинными мерами и

прекращением экономической деятельности,

которые привели к изменениям в доходах и

расходах мексиканских домохозяйств.

6. ENIGH E и ENIGH имеют одинаковые

концепцию и методологические элементы, тем

самым обеспечивается преемственность в

методах учета.

7. Отличие между программами состоит в том, что

выборки каждой из этих программ разной

величины. В случае обследования ENIGH E ее

размер позволяет получать результаты только

на национальном уровне.

ENIGH

Учетный период Источники дохода и Крупные статьи расходов

февраль март апрел ь май июнь июл

ь август сентябрь октябрь ноябрь

Доход от работы Аренда недвижимости Социальные выплаты Оценка аренды дома Прочие текущие доходы Расходы на продукты питания, напитки и табачные изделия Расходы на одежду и обувь Расходы на жилищно-эксплуатационные услуги; Электроэнергия и топливо

Расходы на предметы и услуги по уборке и уходу за домом; стеклянная посуда, фарфор и домашняя утварь; товары для дома и мебель

Расходы на медицинское обслуживание Транспортные расходы; приобретение, техническое обслуживание, аксессуары и услуги для транспортных средств; и связь

Расходы на образование и товары и услуги для досуга; Турпакеты и пакеты для вечеринок, проживание и размещение

Расходы на предметы личной гигиены; аксессуары и личные вещи; и другие разные расходы

Перераспределение расходов

Диаграмма 1. Учетный период доходов и расходов обследования ENIGH 2020, по концепции или статьям

Сезонное обследование ENIGH

Учетный период Источники дохода и Крупные статьи расходов

2019 2020

июл ь август сентябрь октябр

ь ноябрь декабрь январь феврал ь март

Доход от работы

Аренда недвижимости

Социальные выплаты

Оценка аренды дома

Прочие текущие доходы

Расходы на продукты питания, напитки и табачные изделия

Расходы на одежду и обувь

Расходы на жилищно-эксплуатационные услуги; Электроэнергия и топливо

Расходы на жилищно-эксплуатационные услуги; Электроэнергия и топливо

Расходы на медицинское обслуживание

Транспортные расходы; приобретение, техническое обслуживание, аксессуары и услуги для транспортных средств; и связь Расходы на образование и товары и услуги для досуга; Турпакеты и пакеты для вечеринок, проживание и размещение

Расходы на предметы личной гигиены; аксессуары и личные вещи; и другие разные расходы

Перераспределение расходов

Диаграмма 2. По концепции или статьям, учетный период доходов и расходов согласно ENIGH E 1-2020.

Концепция

БОГАТСТВОРЕГУЛЯРНОСТЬ

Концепция

Источники дохода должны быть регулярными и непрерывно поступать в течение каждого периода времени.

ДОСТУПНОСТЬ

Источники дохода должны способствовать экономическому благосостоянию; то есть они должны быть доступны для покупки товаров и услуг, которыми может пользоваться домохозяйство.

Эти источники дохода не должны включать потоки, которые изменяют чистое богатство или чистую стоимость активов и долгов домохозяйства.

КРИТЕРИИ ВКЛЮЧЕНИЯ КРИТЕРИИ ИСКЛЮЧЕНИЯ

Текущие расходы: Расходы домохозяйства – это расходы для

удовлетворения своих потребностей и выполнения обязательств.

A

Методы измерения потребительских расходов: ENIGH измеряет расходы на потребление с учетом покупной стоимости товаров и услуг и того, были ли

они полностью оплачены в учетном периоде.

B

Учитывая роль денег в транзакциях, общий текущий доход делится на текущий денежный и неденежный доход

1. Продукты питания, напитки и табак;

2. Одежда и обувь;

3. Жилищно-эксплуатационные услуги; Электроэнергия и топливо;

4. Предметы и услуги для уборки и ухода за домом; стеклянная посуда, фарфор и домашняя утварь; товары для дома и мебель;

5. Расходы на медицинское обслуживание;

6. Транспортные расходы; приобретение, техническое обслуживание, аксессуары, автомобильные услуги и связь;

7. Расходы на образование и товары и услуги для досуга; Турпакеты и пакеты для вечеринок, проживание и размещение;

8. Расходы на предметы личной гигиены; аксессуары и личные вещи; и другие разные расходы;

9. Перераспределение расходов.

По составу текущие денежные расходы домашних хозяйств группируются по 9 статьям расходов:

Результаты

Среднеквартальные текущие денежные расходы по доходным децилям, по годам исследования долларов в постоянных ценах 2020 года

Дециль ENIGH 2018 ENIGH 2020 Изменение

доли в процентах

I 503 553 9,81% II 715 690 -3,54% III 867 826 -4,70% IV 1 021 954 -6,50% V 1 206 1 086 -9,98% VI 1 370 1 234 -9,91% VII 1 601 1 424 -11,06% VIII 1 902 1 650 -13,24% IX 2 411 2 072 -14,07% X 4 372 3 424 -21,68%

Итого 1 597 1 391 -12,87 %

Среднеквартальные текущие денежные расходы одного домохозяйства согласно ENIGH 2020 составили 1391 доллар по сравнению с 1597 долларами по данным обследования 2018 года, что означает сокращение примерно на 13%.

Источник: ENIGH 2018 и ENIGH 2020

0

5

10

15

20

25

30

35

40

ENIGH 2020

ENIGH 2018

Expenditure transfers

Health carePersonalEducation and recreation

TransportCleaningHousingClothing and footwear

Foods 2.83% -1.50% 1.45% 0.68% -1.39% -4.43% 0.60% 1.61% 0.16%

Домохозяйства и их текущие денежные расходы по выбранным статьям

Статья расходов ENIGH 2018 ENIGH 2020 Изменение

доли в процентах

Продукты питания и напитки 35,22% 38,05% 2,83%

Одежда и обувь 4,48% 2,99% -1,50% Жилье и обслуживание 9,54% 10,98% 1,45% Уборка и обслуживание дома 5,87% 6,55% 0,68% Транспорт и коммуникации 19,95% 18,56% -1,39% Образование и досуг 12,11% 7,68% -4,43%

Уход за собой 7,40% 8,01% 0,60%

Здравоохранение 2,62% 4,23% 1,61% Перераспределени е расходов 2,80% 2,96% 0,16%

Источник: ENIGH 2018 и ENIGH 2020 Источник: ENIGH 2018 и ENIGH 2020

Продукты питания и напитки

Одежда и обувь Жилье и обслуживание

Уборка и обслуживание

дома

Транспорт и коммуникации

Образование и досуг

Уход за собой Медицинское

обслуживание

Перераспределе

ние расходов

Процент текущих денежных расходов

0 3,000,000 6,000,000 9,000,000 12,000,000 15,000,000

I

II

III

IV

V

VI

VII

VIII

IX

X 1,657,150

10,296,340 1,861,547

12,694,529

1,089,515 6,175,784

1,182,379 6,914,846

904,942 5,014,400

956,729 5,548,573

789,016 4,269,793

821,618 4,632,577

676,998 3,768,455

719,327 4,103,315

597,289 3,305,192

622,366 3,532,880

536,984 2,932,555

540,359 3,125,878

457,797 2,559,890

471,346 2,633,673

390,888 2,195,789

397,942 2,232,748

324,810 1,795,979

298,284 1,642,391

Тысяч долларов

Де ци

ли

2020 ГОРОД

2018 ГОРОД

2018 СЕЛО

2020 СЕЛО

Источник: ENIGH 2018 и ENIGH 2020

Домохозяйства и их общие квартальные текущие денежные расходы, по децилям и по месту проживания

0 5,000,000 10,000,000 15,000,000 20,000,000

Expenditure transfers

Health care

Personal

Education and recreation

Transport

Cleaning

Housing

Clothing and footwear

Foods 3,188,488

15,736,063 3,205,029

16,141,817

266,939 1,217,996

384,786 2,077,161

500,639 4,962,323

484,485 4,754,383

532,193 2,724,552

491,485 2,732,446

1,322,730 7,909,784

1,465,873 9,494,589

414,114 3,405,463

743,376 5,910,308

611,331 3,371,366

610,751 3,456,442

397,893 1,706,807

293,409 1,148,454

191,063 1,279,822

192,703

Источник: ENIGH 2018 и ENIGH 2020

С та

ть я

ра сх

од ов

Тысяч долларов

2020 СЕЛО

2020 ГОРОД

2018 ГОРОД

2018 СЕЛО

Домохозяйства и их общие квартальные текущие денежные расходы, по статьям расходов и по месту проживания

Продукты питания

Одежда и обувь

Жилье

Уборка

Транспорт

Образован ие и отдых

Личные

Медицинс кое обслужива ние

Перераспр еделение расходов

Выводы

+40% -19% -45%

Медицинское обслуживание Транспорт Образование и досуг

Назначение расходов

Домохозяйства в Мексике в среднем в течение 2020 года изменили свои привычки потребления:

Домохозяйства Мексики в 2020 году адаптировали свои привычки потребления перед лицом глобального кризиса в сфере здравоохранения. Во-первых, они сократили общие средние текущие денежные расходы почти на 13%. Статья расходов, которая в среднем увеличилась больше всего по сравнению с 2018 годом, - это медицинское обслуживание (+40,5%). С другой стороны, средние расходы на образование сократились почти на 45%, а на транспорт - почти на 19%. Наконец, расходы на электроэнергию возросли на 2%, однако они возросли во всех доходных децилях.

+2%

Электроэнергия

Расходы домохозяйства

45% ПРОДУКТЫ ПИТАНИЯ

42%ОДЕЖДА И ОБУВЬ

40%МЕДИЦИНСКОЕ ОБСЛУЖИВАНИЕ

ЛИЧНАЯ ГИГИЕНА

6%

ОБРАЗОВАНИЕ И ДОСУГ

19%ТРАНСПОРТ

УБОРКА 3%6% ПЕРЕРАСПРЕД

ЕЛЕНИЕ РАСХОДОВ

8%

Более низкий уровень доходов

22 %

10 %

13% ВСЕГО

В целом, домохозяйства в Мексике сократили свои расходы на 13%,

домохозяйства только в первом дециле увеличили свои средние текущие денежные расходы почти на 10%.

Более высокий уровень доходов

Домохозяйства с самыми низкими доходами увеличили свои среднеквартальные денежные расходы почти на 10%, как и домохозяйства в первом доходном дециле. Обследование ENIGH 2020 года показывает, что домохозяйства с более низкими доходами увеличили свои расходы, в то время как домохозяйства с более высокими доходами значительно сократили их. Домохозяйства в дециле с самым высоким доходом сократили свои денежные расходы почти на 22%.

• Таким образом, в 2020 году денежные расходы домохозяйств значительно сократились. Однако домохозяйства с более низкими доходами увеличили расходы, а домохозяйства с высокими доходами сократили расходование своих средств.

• Столкнувшись с необходимостью больших расходов на медицинское обслуживание, домохозяйства пожертвовали расходами на образование, одежду, обувь и транспорт. С другой стороны, расходы на жилье и бытовые услуги были статьей расходов, которая увеличилась повсеместно по сравнению с 2018 годом.

• Расходы на транспорт увеличились в домохозяйствах с более низкими доходами, но значительно снизились в домохозяйствах с высокими доходами. Сокращения объясняются сниженным расходом топлива, иностранным транспортом и, в меньшей степени, падением числа автомобилей, приобретаемых в личное пользование.

• Расходы на потребление электроэнергии возросли во всех децилях в городских и сельских районах.

• Коэффициент Джини позволяет оценить неравенство доходов. Значение этого коэффициента для дохода путем социальных выплат по децилям домохозяйств в 2020 году составило 0,415. Кроме того, коэффициент Джини позволит нам оценить положительное влияние социальных выплат на распределение доходов. Если бы в 2020 году не было социальных выплат, концентрация доходов была бы выше, поскольку коэффициент Джини имел бы значение 0,468 по сравнению с 0,415 при наличии социальных выплат.

СПАСИБО

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-- Paper

Languages and translations
English

NOTE: The designations employed in this document do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Economic Commission for Europe

Conference of European Statisticians Group of Experts on Measuring Poverty and Inequality Geneva, Switzerland, 8-9 December 2022 Workshop on Harmonization of Poverty Statistics to Measure SDG 1 and 10 Geneva, Switzerland, 7 December 2022

Agenda item: Impact of global shocks on poverty and inequality

National Survey of Household Income and Expenditure (ENIGH)

Note by National Institute of Statistics and Geography (INEGI)

Abstract

The National Survey of Household Income and Expenditure (ENIGH) gives a statistical overview of the behavior of household income and expenses in terms of amount, origin, and distribution, including current monetary expenditure. The ENIGH measures consumption expenditure considering the purchase value of goods and services and whether they were paid in full in the reference period.

The survey also inquiries about mobility by cars, including spending on gasoline and main petroleum-derived inputs (oils and tires); and on the use of public transportation. It also asks about spending on electricity, gas, coal, or other fuel for heating.

Thanks to its biennial periodicity, ENIGH permits quantifying the change in spending on energy goods from 2018 to 2020. This statistical information allows decision-makers to quantify households' energy expenditure and know whether the increase in prices on these items has an impact on the consumption of other essential goods. Identifying the groups with the most significant increase in their current expenditure before and during the first COVID impact is possible, emphasizing the lowest income deciles or vulnerable groups.

Working paper 1

Distr.: General 10 November 2022

English

2

I. Objective

1. The recent COVID-19 crisis showed that the lockdown, social distance measures, and the closure of the economy in Mexico brought a series of adverse effects that impacted the household economy, directly affecting income, employment, and expenses. Based on the above, the behavior of current monetary expenditure by income deciles will be analyzed, and each of the major expenditure items in its rural and urban areas.

2. Observe the change in household spending in Mexico, starting with the COVID-19 pandemic, identifying, in particular, the increase in healthcare spending in 2020 compared to 2018.

3. In times of uncertainty due to health, economic and financial crises, it is essential to have information that allows decision-makers to implement the necessary actions to reduce these adverse effects on the household economy. The National Survey of Household Income and Expenditure (ENIGH, by its Spanish acronym) has cross- sectional measurements that allow identifying the impacts of the price increases of 2020, as well as the allocations that households make to their expenses.

4. The results indicate that households with lower incomes are the most affected due to the loss of purchasing power.

II. Methodological aspects

5. The data source for this paper was the National Survey of Household Income and Expenditure in the 2018 and 2020 editions.

6. Both were conducted from August 21 to November 28, 2018, and 2020.

7. ENIGH aims to present an overview of the behavior of income and expenditure at the household level in terms of amount, origin, and distribution. It also includes information on dwelling characteristics and equipment. Additionally, ENIGH provides information on labor participation and the socio-demographic characteristics of household members.

8. Initially, the design of the ENIGH focused on the need to provide information to update the National Consumer Price Index weights. However, the data collected by the survey has had many other uses in the last few years, mainly to measure poverty.

9. The periodicity and geographical disaggregation of ENIGH have been improving. The survey provides decision-makers with the elements to diagnose material life conditions and design public policies to improve the population's quality of life.

10. In 2016, INEGI presented the ENIGH Nueva Serie, which showed improvement in its indicators; in 2018 and 2020, it continued with the survey of this series, observing the following characteristics:

3

• The sample size is the largest in the country's history for a survey of income and expenses.

• Allows representativeness by the federative entity (state level) with estimates for urban and rural domains.

• Measures both income and expenditure. • Operationally, it continues to be the most supervised event carried out in this area

so far. • Strength in training permits interviewers to be more attached to methodological

definitions. • It has an automated tracking report system that supports supervision and control.

11. The main methodological characteristics are:

Target population

The target population consists of all national and foreign households that live within the country's boundaries.

Unit observation The units of analysis for ENIGH are the household, the dwelling, and the householders.

Geographical coverage

The survey is designed to provide results at the following levels: • National • Urban-Rural

At the state level, with estimates for urban and rural domains. Sample Design • Probabilistic: The sampling units have a known probability that is

different from zero of being selected.

4

• Stratified: The sampling units with similar characteristics and that belong to localities of the same size are grouped to form strata. • Conglomerates. The sample units with distinct characteristics that belong to localities of the same size are grouped to form the primary sampling units (PSU). • Double stage. Dwellings are selected in two steps: primary sampling units (groups of blocks) and housings.

Sample size 105,483 households represent 126,760,856 population. Date of uprising From August 21 to November 28, 2020.

12. Users are informed that as of 2020, the survey data conform to a population estimate prepared by INEGI based on the update in the population estimates generated by the Housing Sampling Framework.

13. The results presented are constructed from the new population estimate for 2018 and 2020.1

14. The results of the survey of the 2020 edition allow us to measure the changes in household income and expenses as a result of the health contingency period caused by COVID-19, where the confinement measures of the population and the closure of economic activity motivated changes in income and expenses in households in Mexico.

15. ENIGH collects various information, including current monetary expenditure. It measures consumption expenditure considering the purchase value of goods and services and whether they were paid in full in the reference period. The expenditure items captured by the survey are food, drinks, and tobacco; clothes and footwear; housing and conservation services; electricity and fuel; dwelling maintenance care; health, transportation, education; and personal care.

16. Regarding mobility by household cars, the survey inquiries about spending on gasoline and the main petroleum-derived inputs (oils and tires); for public transportation users, it collects the amounts spent on this service. Regarding direct household inputs, it asks about spending on electricity, gas, coal, or other fuel for heating.

17. Thanks to its biennial periodicity, ENIGH permits quantifying the change in spending on energy goods from 2018 to 2020. This statistical information allows decision-makers to quantify households' energy expenditure, in addition to knowing whether the increase in prices implied that the proportion of expenditure on these items is higher, which would impact the consumption of other essential goods. Identifying the groups with the most significant increase in their current expenditure before and during the first COVID impact is possible, emphasizing the lowest income deciles or vulnerable groups.

18. The ENIGH uses six information collection instruments. Five questionnaires and a booklet, with which, in addition to collecting basic information about the selected

1 For users to replicate the results of 2016 and 2018, INEGI incorporates the expansion factor adjusted by the new population estimate into the database for those periods. For more details, the user may review the document "File Descriptor (FD)" for those periods.

5

dwelling and the people who are part of the household, information is collected on household income, the occupation status of household members, and household expenditure, to mention the topics main of the survey. The questionnaires and booklet used in the ENIGH 2020 were: • Questionnaire of households and housing. • Questionnaire for people aged 12 and over. • Questionnaire for home businesses. • Questionnaire for people under 12 years old. • Household expenses questionnaire. • Booklet of daily expenses.

19. In 2020, it was considered to carry out a survey of a statistical exercise that complemented the ENIGH, called Seasonal ENIGH (ENIGH E, by its acronym in Spanish). In this way, it would capture the seasonal and temporary events that affect the income and expenditure results of households in Mexico.

20. Initially, the ENIGH E was planned to rise throughout 2020, starting on January 4 and concluding on December 28, with a sample size of 54,710 dwellings. However, as a result of the pandemic caused by COVID-19, the collection of information was suspended as of April, achieving until then the lifting of 9 periods of ten days (from January 4 to April 2, 2020), that is, the first quarter of the year (1-2020), which is why the information published so far corresponds to that period and a total sample of 13,822 dwellings.

21. The ENIGH E 1-2020 and the ENIGH Nueva Serie maintain the same conceptual and methodological elements, preserving consistency between them; the critical difference between both programs is the period of information collection and its reference period. Although the two surveys inquire into the population's income and expenses in monthly, quarterly, and semi-annual periods, the data presented correspond to different months, as shown in diagrams 1 and 2.

6

Diagram 1. Reference period of the income and expenditure of ENIGH 2020, according to concept or item.

ENIGH Reference period

Sources of income and Large items of expenditure

February March April May June July August September October November

Income from work Property Rent Transfers Estimation of the rent of the house Other current income Spending on food, beverages, and tobacco Spending on clothing and footwear Spending on housing and conservation services; Electric power, and fuels Spending on items and services for cleaning and care of the house; glassware, whites, and household utensils; household goods and furniture Healthcare Spending Transportation expenditure; acquisition, maintenance, accessories, and services for vehicles; and communications Expenditure on education and leisure items and services; Tour and Party Packages, Lodging and Accommodation Spending on personal care; accessories and personal effects; and other miscellaneous expenditure Expenditure transfers

7

Diagram 2. According to concept or item, the reference period of income and expenditure of ENIGH E 1-2020.

Seasonal ENIGH Reference period

Sources of income and Large items of expenditure

2019 2020

July August September October November December January February March

Income from work Property Rent Transfers Estimation of the rent of the house Other current income Spending on food, beverages, and tobacco Spending on clothing and footwear Spending on housing and conservation services; Electric power, and fuels Spending on items and services for cleaning and care of the house; glassware, whites, and household utensils; household goods and furniture Healthcare Spending Transportation expenditure; acquisition, maintenance, accessories, and services for vehicles; and communications Expenditure on education and leisure items and services; Tour and Party Packages, Lodging and Accommodation Spending on personal care; accessories and personal effects; and other miscellaneous expenditure Expenditure transfers

22. In diagrams 1 and 2, it can be seen that the ENIGH E 1-2020 manages to determine the flow of household income and expenditure during December and January, while the ENIGH Nueva Serie, by its design, fails to capture them.

23. In this way, ENIGH E 1-2020 presents the phenomenon's evolution concerning its predecessor year, ENIGH 2018, and provides an economic overview of Mexico before the health contingency caused by COVID-19. On the other hand, it also allows to measure the change with the results of the ENIGH 2020, motivated by the measures of confinement of the population and the closure of economic activity that led to changes in income and expenditure in Mexican households.

24. An additional difference is that each program contains sample sizes of different magnitudes; in the case of ENIGH E 1-2020, its size only allows for generating results at the national level.

25. In December 2021, the survey of the Seasonal ENIGH was resumed, with a national sample size of 51,000 homes. The information collection takes place from December 25, 2021, ending on January 8, 2023. This project is expected to have a periodicity of execution every four years.

8

III. Conceptual design

26. The total current income of a household consists of monetary and non-monetary sources that satisfy these three criteria:

INCLUSION CRITERIA EXCLUSION CRITERIA

REGULARITY AVAILABILITY WEALTH

Income sources must be regular and subject to an uninterrupted flow in a given period.

Income sources must contribute to economic well- being; that is, they must be available to purchase goods and services that the household can use.

These income sources should not include the flows that modify the net wealth or the net value of the assets and debts of the household.

27. Given the transactional role of money, the total current income splits into current monetary and non-monetary income.

28. Current expenditures:

A household's expenditures are the expenses it must make to satisfy its needs and commitments.

The total current expenditures include all the expenses a household regularly incurs to purchase its basic consumption basket, plus non-regular consumption expenses. This concept does not consider the costs that modify the wealth of the householders.

29. Methods to measure consumption expenditure:

ENIGH measures consumption expenditure considering the purchase value of goods and services and whether they were paid in full in the reference period.

ENIGH reports consumption expenditure when purchases are made in cash or with credit cards, when goods are purchased using credit schemes provided by the seller, whether formal or informal, the consumption expenditure reported by ENIGH corresponds to paid consumption.

30. By composition, the current monetary expenditure of households is grouped into nine categories: 1. Food, beverages, and tobacco; 2. Clothing and footwear; 3. Housing and conservation services; Electric power and fuels; 4. Items and services for cleaning and care of the house; glassware, whites, and household utensils; household goods and furniture; 5. Healthcare Spending; 6. Transportation expenditure; acquisition, maintenance, accessories, vehicle services, and communications; 7. Expenditure on education, leisure items, and services; Tour and Party Packages, Lodging, and Accommodation; 8. Personal care; accessories, personal effects, and other miscellaneous expenditures; 9. Expenditure transfers.

9

IV. Results

31. Based on the ENIGH 2018 and 2020, we can conduct a series of analyses regarding average current monetary expenditure, both in national terms and concerning various population groups.

Average quarterly current monetary expenditure by income deciles, by survey year Dollars, at constant 2020 prices

Decile ENIGH 2018 ENIGH 2020 Percentage Change I 503 553 9.81% II 715 690 -3.54% III 867 826 -4.70% IV 1,021 954 -6.50% V 1,206 1,086 -9.98% VI 1,370 1,234 -9.91% VII 1,601 1,424 -11.06% VIII 1,902 1,650 -13.24% IX 2,411 2,072 -14.07% X 4,372 3,424 -21.68%

Total 1,597 1,391 -12.87 %

32. The average quarterly monthly expenditure per household of ENIGH 2020 was 1,391 dollars, in contrast to 1,597 dollars in the 2018 edition, representing a decrease of about 13%.

33. Analyzing this information by deciles2, it is observed that, in 2020, the first decile presented an average quarterly current monetary expenditure of 553 dollars, while the ENIGH 2018 reported 503 dollars, an increase of about 10 percent.

34. On the other hand, for households in the tenth decile, this average quarterly expenditure was 3,424 dollars, while for 2018, the expenditure was 4,372 dollars, a decrease of almost 22 percent.

35. In 2020, the quarterly average of current monetary expenditure per household in urban areas was 1,509 dollars, 1.6 times that of rural areas with 963 dollars, while for the ENIGH 2018 in urban areas, the expenditure was 1,776 dollars, and in rural areas, 996 dollars.

36. In urban areas, for 2020, the average quarterly current monetary expenditure in the first decile was 640 dollars, while for 2018, that decile reported an expenditure of 620 dollars, an increase of just over three percent. While the tenth decile of urban areas in 2020 reported an expenditure of 3,672 dollars, and in 2018 same, a decile of 4,791 dollars, a decrease of just over 23 percent.

37. In rural areas, households in the first decile of the ENIGH 2020 reported an expenditure of 421 dollars, while for the ENIGH 2018, the first decile reported 377 dollars, an increase of the order of 12%. In 2020, the expenditure of the tenth decile

2 Households can be grouped according to the income they receive. When ten sets of the same size are formed, they are known as "deciles", so the first decile is made up of the tenth part of the households that have the lowest incomes, and so on, until reaching the last decile, which is composed of the tenth part of the households with the highest incomes.

10

in rural areas was 2,150 dollars. In 2018 was 2,355 dollars, a decrease of about 9 percent.

Spending on food, beverages, and tobacco

38. Food, beverages, and tobacco accounted for the largest category, reaching 529 dollars in 2020 and 562 dollars in 2018, a decrease of about 6%. In contrast, the item of clothing and footwear reached 42 dollars in 2020 and 72 dollars in 2018, which represents a decrease of 42 percent.

39. Food, beverages, and tobacco represented the largest category, 38% of current monetary expenditure, while the proportion of these items in 2018 was just over 35%. In contrast, clothing and footwear accounted for only 3% of current monetary spending, compared to just over 4% in the 2018 edition.

40. For food, beverages, and tobacco, considering the first decile of the ENIGH 2020, an expense of 277 dollars was reported, while for the ENIGH 2018, this decile presented an expense of 251 dollars, an increase of just over 10 percent.

41. In the case of households of the tenth decile, for the ENIGH 2020, the expenditure in the category of food, beverages, and tobacco was 975 dollars, while for the 2018 edition presented an expense of 1,111 dollars, that is, a decrease of just over 12 percent.

42. In 2020, the current monetary expenditure in the category of food, beverages, and tobacco in urban areas was 561 dollars (quarterly average), 1.4 times that of rural areas with 414 dollars, while for the ENIGH 2018 in urban areas, the expenditure was 609 dollars, and in rural areas 405 dollars.

43. In urban areas, for 2020, the average quarterly current monetary expenditure for food, beverages, and tobacco for the first decile was 308 dollars, while for 2018, this decile reported an expense of 293 dollars, an increase of just over 5%. While the tenth decile of urban areas reported an expenditure of 1,024 dollars in 2020, in 2018 was 1,172 dollars, a decrease of about 13 percent.

44. For rural areas, households in the first decile of the ENIGH 2020 reported spending on the food, beverage, and tobacco category of 232 dollars, while for the ENIGH 2018, the first decile reported 211 dollars, an increase of almost 10%. For the tenth decile in 2020 in rural areas, the expenditure was 709 dollars, and in 2018 of 732 dollars, a decrease of just over 3 percent.

45. In the case of food and beverages, with the highest average expenditure consumed within the household in the quarter, meat stands out with 104 dollars in the case of ENIGH 2020 and 99 dollars for ENIGH 2018, which represents an increase of 5.2%; cereals in the ENIGH 2020 with 77 dollars and 76 dollars in the ENIGH 2018, that is, an increase of 0.1%; other miscellaneous foods3 with 63 dollars for the ENIGH 2020 and 52 dollars in the ENIGH 2018, that is, an increase of 21.1%, and vegetables presented an increase of 7.6%, being the expenditure of 54 dollars in the ENIGH 2020 and 50 dollars in the ENIGH 2018.

3 Rice cereal, oatmeal, mixed for baby; baby porridge; fresh mushrooms; custards, jellies, powdered puddings, etc.

11

Expenditure on electric power

46. Regarding the expenditure on electric power, the average quarterly current monetary expenditure per household of ENIGH 2020 was 32 dollars, in contrast to 30 dollars in the 2018 edition, which represents an increase of just over 9 percent.

47. Analyzing this information by deciles, it is observed that, in 2020, the first decile presented an expense in this area of 13 dollars, while the ENIGH 2018 reported 11 dollars, an increase of about 21 percent.

48. On the other hand, for households in the tenth decile, this expenditure was 73 dollars, while for 2018, the expenditure was 66 dollars, which is an increase of almost 12 percent.

49. In 2020, the average quarterly current monetary expenditure per household of urban areas in this item was 36 dollars, 1.8 times that of rural areas with 20 dollars. While for ENIGH 2018, the expenditure was 33 dollars in urban areas and rural areas 18 dollars.

50. In urban areas, for 2020, the average quarterly current monetary expenditure for the electric power item in the first decile was 16 dollars, while for 2018, this decile reported an expense of 15 dollars, an increase of just over 11 percent. While the tenth decile of urban areas in 2020 reported an expenditure of 79 dollars and 71 dollars in 2018, an increase of just over 11 percent.

51. In rural areas, households in the first decile of ENIGH 2020 reported an expenditure of 10 dollars, while for ENIGH 2018, the first decile reported 7 dollars, an increase of more than 31%. For the tenth decile in 2020 in rural areas, the expenditure was 42 dollars, and in 2018 of 36 dollars, an increase of more than 17 percent.

Expenditure on fuels for vehicles

52. The average quarterly current monetary expenditure in the category of fuels for vehicles per household of the ENIGH 2020 was 83 dollars, in contrast to 100 dollars in the 2018 edition, which represents a decrease of just over 17 percent.

53. Analyzing this information by deciles, it is observed that, in 2020, the first decile presented an expense in this item of 12 dollars, while the ENIGH 2018 reported 10 dollars, an increase of 25 percent.

54. On the other hand, for households in the tenth decile, this expenditure was 263 dollars, while for 2018, the expenditure was 356 dollars, a decrease of just over 26 percent.

55. In 2020, the average quarterly current monetary expenditure in the fuels for vehicles per household in urban areas was 90 dollars, 1.6 times that of rural areas with 55 dollars. Meanwhile, for ENIGH 2018, in urban areas, the expenditure was 113 dollars, and in rural areas, 57 dollars.

56. In urban areas, for 2020, the average quarterly current monetary expenditure for the fuels for vehicles item in the first decile of urban areas was 13 dollars, while for 2018, this decile reported an expense of 12 dollars, an increase of just over 16%. While the tenth decile of urban areas in 2020 reported an expenditure of 276 dollars and 384 dollars in 2018, a decrease of just over 28 percent.

57. In rural areas, households in the first decile of ENIGH 2020 reported an expenditure of 10 dollars, while for ENIGH 2018, the first decile reported 7 dollars, an increase

12

of more than 41%. For the tenth decile in 2020 in rural areas, the expenditure was 182 dollars, and in 2018 of 206 dollars, a decrease of more than 11 percent.

Healthcare Spending

58. Regarding healthcare of the ENIGH 2020, the average quarterly current monetary expenditure was 59 dollars, in contrast to 42 dollars in the 2018 edition, representing an increase of about 41 percent.

59. Analyzing this information by deciles, it is observed that, in 2020, the first decile presented an expenditure in this item of 23 dollars, while the ENIGH 2018 reported 13 dollars, an increase of about 76 percent.

60. On the other hand, for households in the tenth decile, this expenditure was 174 dollars, while for 2018, the expenditure was 135 dollars, which is an increase of about 29 percent.

61. In 2020, the average quarterly monthly expenditure on healthcare in urban areas was 61 dollars or 1.2 times that of rural areas at 52 dollars; for ENIGH 2018, in urban areas, the expenditure was 43 dollars, and in rural areas, 37 dollars.

62. In urban areas, for 2020, the average quarterly current monetary expenditure on healthcare in the first decile was 25 dollars, while for 2018, that decile reported an expenditure of 12 dollars, an increase of just over 104%. Meanwhile, the tenth decile of urban areas in 2020 reported an expenditure of 183 dollars and 149 dollars in 2018, a decrease of just over 23 percent.

63. In rural areas, households in the first decile of ENIGH 2020 reported an expenditure of 19 dollars, while for ENIGH 2018, the first decile reported 11 dollars, an increase of more than 73%. For the tenth decile in 2020 in rural areas, the expenditure was 155 dollars, and 94 dollars in 2018, a more than 65 percent decrease.

Main results in the expenditure

64. The 20 main items of average expenditure based on the ENIGH 2020 represent 86% of the quarterly current monetary expenditure of households in Mexico. The first three places with the highest expenditure correspond to spending on meat at 104 dollars, personal care at 85 dollars, and education at 83 dollars, while for the ENIGH 2018, the first place corresponded to education at 137 dollars, the second to food outside the home with 128 dollars and the third to fuels for vehicles with 100 dollars.

65. The last three places on this list are the acquisition of vehicles, with 33 dollars in 2020, in contrast to 40 dollars in 2018, a decrease of 16.3%. Dressed presented an expenditure of 27 dollars in 2020 and 44 dollars in 2018, a decrease of just over 39%. Finally, from 20 dollars in 2020 to 39 dollars in 2018, recreation represents 50% less.

66. Based on ENIGH data, the proportion of expenditure on electric power went from 1.85% of current monetary expenditure in 2018 to 2.33% in 2020.

67. Finally, a table is presented with the percentage variation by item of expenditure reported between ENIGH 2018 and ENIGH 2020.

13

Households and their current monetary expenditure on selected items Percentage of current monetary expenditure

Item ENIGH 2018 ENIGH 2020 Percentage Change Foods and beverages 35.22% 38.05% 2.83% Clothing and footwear 4.48% 2.99% -1.49% Housing and services 9.54% 10.98% 1.44% Cleaning and care of the house 5.87% 6.55% 0.68%

Transport and communications 19.95% 18.56% -1.39%

Education and leisure 12.11% 7.68% -4.43% Personal care 7.40% 8.01% 0.60% Healthcare 2.62% 4.23% 1.61% Expenditure transfers 2.80% 2.96% 0.16%

V. Conclusions

68. Households in Mexico during 2020 allocated almost 40% of their monetary expenditure on food, nearly a fifth of their expenditure was used for transportation, and just over 10% to cover housing needs and domestic services. Education, in 2018, was an item to which 12.1% of total household spending was allocated; however, by 2020, this percentage dropped to 7.7%. On average per household, spending on education and leisure was reduced by almost 45%, it is the item of expenditure that fell the most in households compared to 2018. Similarly, average spending on clothing and footwear and, in addition, transportation fell by 42% and almost 20%, respectively, compared to the same year.

69. On the other hand, in 2020, average health expenditures increased by more than 40% compared to 2018; it is a result that reflects how households adapted their consumption habits in the face of the global health crisis. That is, households sacrificed expenses on education, clothing, and footwear and, in addition, on transportation; to meet their healthcare needs.

70. Overall, households in Mexico reduced their average spending by 13%; it can be same that there was no considerable change compared to 2018. However, not all households reduced their spending.

71. Households with the lowest incomes increased their quarterly average monetary expenditure by less than 10%, as was the case of households in the first income decile. The ENIGH 2020 reflects that lower-income households increased their expenses while higher-income households reduced them considerably. Households in the highest income decile reduced their monetary expenditure by almost 22%.

72. Healthcare spending was a growing area across the board. On the one hand, households with lower incomes increased their average health expenditures by 76%, and, at the same time, households with higher incomes in Mexico increased their disbursement in this area by almost 30%.

73. Spending on education, on the other hand, was an item with widespread declines. It is observed how all deciles reduced their average spending in this area, between 32

14

and 52%, compared to 2018. A similar case was that of spending on clothing and footwear.

74. Regarding average transport spending, households with lower incomes increased their disbursements on transport; but, on the other hand, households with higher incomes decreased their allocations in this area by almost 30%.

75. In summary, in 2020, household monetary expenditure fell significantly. However, lower-income households saw increased spending, while high-income households decreased their monetary disbursements.

76. Faced with widespread healthcare needs, households sacrificed spending on education, clothing, footwear, and transportation. On the other hand, spending on housing and domestic services was an item of expenditure that increased across the board compared to 2018.

77. Transportation spending increased in lower-income households but decreased significantly in high-income households; Reductions explain this in fuel consumption, foreign transport, and, to a lesser extent, by the fall in the acquisition of vehicles for private use.

78. Expenditure on electricity consumption had a general increase in all deciles and urban and rural areas.

79. Gini Coefficient allows appreciation of income inequality. The value of this coefficient for income with transfers by deciles of households was 0.415 in 2020. Besides, the Gini Coefficient will enable us to appreciate the positive effects of transfers on income distribution. If 2020 there were no income transfers, the concentration of these would have been higher because the Gini coefficient would have had a value of 0.468 against 0.415 with transfers.

15

Bibliography

INEGI. (2020). ENIGH 2020. Conceptual framework. Instituto Nacional de Estadística y Geografía (INEGI), Mexico. https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos /nueva_estruc/889463901204.pdf

INEGI. (2020). ENIGH 2020. Interviewer manual. Instituto Nacional de Estadística y Geografía (INEGI), Mexico. https://www.inegi.org.mx/contenidos/programas/enigh/nc/2020/doc/enigh2020_ns_entrevistador .pdf

INEGI. (2020). ENIGH 2020. Results presentation. Instituto Nacional de Estadística y Geografía (INEGI), Mexico. https://www.inegi.org.mx/contenidos/programas/enigh/nc/2020/doc/enigh2020_ns_presentacion _resultados.pdf

INEGI. (2020). ENIGH 2020. Sample design. Instituto Nacional de Estadística y Geografía (INEGI), Mexico. https://www.inegi.org.mx/contenidos/productos/prod_serv/contenidos/espanol/bvinegi/productos /nueva_estruc/889463901228.pdf

OECD. (2017). Terms of reference, OECD Project on the distribution of household incomes. Organization for Economic Cooperation and Development (OECD). http://www.oecd.org/els/soc/IDD-ToR.pdf

UNECE. (2017). Guide on poverty measurement. United Nations Economic Commission for Europe (UNECE). https://www.unece.org/fileadmin/DAM/stats/publications/2018/ECECESSTAT20174.pdf

  • I. Objective
  • II. Methodological aspects
  • III. Conceptual design
  • IV. Results
  • V. Conclusions
  • Bibliography

Can integrated statistical and geographical datasets provide insights on migrants in transit relevant municipalities in Mexico? An ongoing process towards a proxy of SDG 10.7.3

Languages and translations
English

Can integrated statistical and geographical datasets provide insights on migrants in transit- relevant municipalities in Mexico?"

An ongoing process towards a proxy of SDG 10.7.3

UNECE. Group of Experts on Migration Statistics

Geneva, October 2022

https://cnnespanol.cnn.com/video/comentario-gabriela-frias-accidente-migrantes-chiapas-carretera-redaccion-mexico/

https://www.dw.com/es/m%C3%A9xico-pide-a-honduras-contener-las-caravanas-migrantes/a-56252173

Our roadmap

Objective

1

Specifying data needs on

“migrants in transit”

Defining and integrating

data

Initial results and next steps

2 3 4

• To develop a proxy of SDG 10.7.3 “Number of people who died or disappeared in the process of migration towards an international destination” by

• identifying migrant-relevant municipalities (geographic areas) of interest

• processing vital statistics -deaths recorded by nationality

3

Objective1

https://treneando.com/tag/la-bestia/

(Liliana Nieto del Río / para The Times). (Los Angeles Times)

4 Specifying data needs on “migrants in transit

• International references

2

Indicator 10.7.3. Number of people who died or disappeared in the process of migration towards an international destination was identified.

2021.UNSD’s Updated SDG’s metadata

2019 – 2022. ECLAC WG on international migration (IM) - National Statistical System assessment – UNSD –ECLAC- IOM

2022. UNECE’s Use of new data sources for international migration Database of Innovation in Migration Statistics (DIMIS) as a hub of references.

• Develop methodologies to characterize specific populations related to international migration: social assistance shelters, border control offices, migration routes.

• Analyze the possibility of the use of new data sources for international mobility and or migration

2021. Statistical and geographical activities for SDG disaggregation Using available statistics to maximize disaggregation (geographical units, specific populations

(indigenous, people with disabilities, afromexicans, “migrants”.), sex and age group)

5

International Organization for Migration (Berlin, Costa Rica & MX): Metadata (Scope, definitions, international methodology)

Identified key concepts, data sources and literature available

2

Approved within SNIEG’s WG on International Migration and Mobility

• Understanding Indicator 10.7.3

6

Defining and integrating data3

• Identifying “High Value Datasets”

7 3

1. 2020. Population and Housing Census. Location of collective households, shelters and centers for migrant’s assistance and country of birth of “migrants” using these facilities.

2. Yearly. National Road Network. Key national transport corridors and railways (operating, out of operation and in construction).

3. Yearly. Statistics on events of “ people” with undetermined migratory status identified by migratory authorities. (by nationality and municipality (NUTS 3).

4. Yearly. Points of detection. Location where Migration authorities found people under an irregular stay.

Defining and integrating data • Making partnerships and selecting variables

5. Yearly. Administrative records on foreigners found death. Depending on the situation, records may include sex, age, country of birth, (NUTS 3), location of death recorded.

6. Yearly. Administrative records of official points of entry (operating and out of operation).

7. 2016-2017. Migratory routes . Reported in migrant survey (Guatemalan, Hondurans and Salvadorians “migrants”)

8. Yearly. Geostatistical catalogue. NUTS 1, 2 and 3

9. Airports (excluding military) and ports

10. Main rivers. Defined by Ministry of the Environment.

11. Desertic areas. Defined by Ministry of the Environment.

Who was using the migrant shelters?  Characterize the populations (Native- born – Born-in-other-

country)

 Proportion of Born-in-other-country by shelter

 22% of the shelters did not have Born-in-other- country registered

 Selection of shelters when 3 of 10 were Born-in-other-country (123 out of 181)

Where are those shelters  Deploying common “persistent unique identifiers” for shelters’

statistical data as

a means of geocoding data into an integrated repository

Defining and integrating data • Transforming variables

Geocoding statistics of persons under irregular conditions

NUTS 3 (polygons)

Ra ilw

ay tr

ac ks

De te

ct io

n po

in ts

Point-based and line based data

Geocoding admin record of detentions and railway tracks geospatial data by

administrative units (NUTS 3)

Georeferenced airports Georeferenced airports by type

National Road Network has gereferenced all airports but for its purpses they are not dissaggregated by type and we exclude the military ones.

Dissaggregating by existing categories of operating infrastructure

• Integrating a harmonized and disaggregated geocoded by NUTS 3 database

With “high” migrant-presence With “medium” migrant-presence

• Detentions • Records of dead migrants • Born-in-other-country -

shelters • Events of persons on

irregular condiitons • Border municipalities (North

and South) • Migrant routes

• Entry points • Main transport corridors • Railway network • Airports (international,

national, local) • Border municipalities (West

and East Coast) • Ports

• Without matches

• Defining variables to select migrant-relevant areas (NUTS3).

With “low” migrant-presence

With “high” migrant-presence With “medium” migrant-presence With “low” migrant-presence

367 802 1,302

Number of municipalities

• Migrant-relevant areas (NUTS3). Initial results4

2020 2021 Changes 2020-2021

- 183 Municipalities change from 2020 - 2021

- Ongoing validation

• Migrant-relevant areas (NUTS3). Initial results by year

International mobility análisis

Defined geographic areas to provide services for temporary populations (accomodation, health)

Defined criteria to use non-traditional data source (web scraping, big data).

• Other potential uses

Ministry of the Interior (SEGOB)

• Graciela Martínez Caballero

Engaged team

INEGI (NSO)

Geographic Divison

• Trinidad Carrillo

• Violeta Zamora

Ministry of the environment

• Cleotilde Arellano

INEGI (NSO) VP Socio Demographic Info • Adriana Oropeza • Naghielli Álvarez

INEGI (NSO) Research – Data Science Lab • Alejandra Figueroa • Elio Villaseñor • Olinca Páez • Víctor Cuevas • Abel Coronado

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  • Engaged team
Russian

Могут ли комплексные наборы статистических и географических данных дать представление о транзитных мигрантах в муниципалитетах Мексики?

Текущий процесс перехода к косвенному показателю ЦУР 10.7.3

ЕЭК ООН. Группа экспертов по статистике миграции

Женева, октябрь 2022 г.

https://cnnespanol.cnn.com/video/comentario-gabriela-frias-accidente-migrantes-chiapas-carretera-redaccion-mexico/

https://www.dw.com/es/m%C3%A9xico-pide-a-honduras-contener-las-caravanas-migrantes/a-56252173

Наша дорожная карта

Задача

1

Определение потребностей

в данных о «транзитных мигрантах»

Определение и интеграция

данных

Первые результаты и следующие

шаги

2 3 4

• Разработать косвенный показатель ЦУР 10.7.3 «Количество людей, умерших или пропавших без вести в процессе миграции в направлении международного назначения» путем

• определения муниципалитетов, представляющих интерес для мигрантов (географические области)

• обработка статистики естественного движения населения - смерти зарегистрированные по гражданству

3

Задача1

https://treneando.com/tag/la-bestia/

(Liliana Nieto del Río / para The Times). (Los Angeles Times)

4Определение потребностей в данных о «транзитных мигрантах»

• Международные документы

2

Индикатор 10.7.3. Выявлено количество людей, которые погибли или пропали без вести в процессе миграции в направлении международного назначения.

2021. Обновленные метаданные ЦУР СОООН

2019 – 2022. РГ ЭКЛАК по международной миграции (ММ) - Оценка национальной статистической системы – СОООН – ЭКЛАК- МОМ

2022. Использование ЕЭК ООН новых источников данных для международной миграции

База данных инноваций в миграционной статистике (DIMIS) как центр справочной информации.

• Разработка методологии для характеристики конкретных групп населения, связанных с международной миграцией: приюты социальной помощи, пункты пограничного контроля, маршруты миграции.

• Анализ возможности использования новых источников данных о международной мобильности и/или миграции

2021. Статистические и географические мероприятия по дезагрегации ЦУР Использование имеющихся статистических данных для максимальной дезагрегации (географические единицы, конкретные группы населения (коренное население, люди с ограниченными возможностями, афро-мексиканцы , «мигранты», пол и возрастная группа)

5

Международная организация по миграции (Берлин, Коста-Рика и Мексика): метаданные (сфера охвата, определения, международная методология)

Выявлены ключевые концепции, источники данных и доступная литература

2

Одобрено рабочей группой SNIEG по международной миграции и мобильности

• Понимание Индикатора 10.7.3

6

Определение и интеграция данных3

• Определение «ценных наборов данных»

Мигранты в транзитных

муниципалитетах

C. Административные документы

А. Официальная статистика

В. Географическая информация

7 3

1. 2020. Перепись населения и жилого фонда. Расположение коллективных домохозяйств, приютов и центров помощи мигрантам и страны рождения «мигрантов», использующих эти объекты.

2. Ежегодно. Национальная дорожная сеть. Ключевые национальные транспортные коридоры и железные дороги (действующие, бездействующие, строящиеся).

3. Ежегодно. Статистика событий «людей» с неопределенным миграционным статусом, выявленных миграционными органами. (по гражданству и муниципалитету (NUTS 3).

4. Ежегодно. Точки обнаружения. Место, где миграционная служба обнаруживает лиц с незаконным пребыванием.

Определение и интеграция данных • Установление партнерских отношений и выбор переменных

5. Ежегодно. Административные данные по иностранцам, найденным мертвыми. В зависимости от ситуации, записи могут включать пол, возраст, страну рождения (NUT 3), указание места смерти.

6. Ежегодно. Административные данные официальных пунктов въезда (действующие и недействующие).

7. 2016-2017 . Миграционные маршруты.. Указываются в обследовании мигрантов (гватемальцы, гондурасцы и сальвадорцы в качестве «мигрантов»)

8. Ежегодно . Геостатистический каталог. NUTS 1, 2 и 3

9. Аэропорты (исключая военные) и морские порты

10. Основные реки. По определению министерства защиты окружающей среды.

11. Пустынные зоны. По определению министерства защиты окружающей среды.

Кто обращался в приюты для мигрантов?  охарактеризовать группы населения (коренное население –

родившиеся в другой стране)

 Доля родившихся в другой стране в разрезе по приютам

 22 % приютов не имеют зарегистрированных как родившиеся в другой стране

 Выбор приютов, когда 3 из 10 были рождены в другой стране (123 из 181)

Где находятся приюты

 Развертывание общих «постоянных уникальных идентификаторов» для статистических данных приютов в качестве средства геокодирования данных в интегрированном депозитарии.

Определение и интеграция данных • Преобразование переменных

Статистика геокодирования лиц, находящихся в нелегальных условиях

Геокодирование административных данных об задержаниях и геопространственные данные о

железнодорожных путях по административным единицам (NUTS 3)

NUTS 3 (полигоны)

Ж /д

п ут

и То

чк и

об на

ру ж

ен ия

Данные на основе точек и линий

Аэропорты с географической привязкой

Аэропорты с географической привязкой по типу

Национальная дорожная сеть имеет географическую привязку всех аэропортов, но для ее целей они не разбиты по типам, и исключены военные аэропорты.

Дезагрегация по существующим категориям операционной инфраструктуры

• Интеграция согласованной и дезагрегированной геокодированной базы данных NUTS 3

• Без совпадений

• Задержания • Записи о погибших

мигрантах • Рожденные в другой

стране - приюты • События лиц в

нелегальных условиях • Приграничные

муниципалитеты (север и юг)

• Маршруты мигрантов

• Точки въезда • Основные транспортные

коридоры • Железнодорожная сеть • Аэропорты

(международные, национальные, местные)

• Приграничные муниципалитеты (западное и восточное побережье)

• Морские порты

С «высоким» присутствием мигрантов

Со «средним» присутствием мигрантов

• Определение переменных для выбора миграционных областей (NUTS3)

С «низким» присутствием мигрантов

С «высоким» присутствием мигрантов Со «средним» присутствием мигрантов С «низким» присутствием мигрантов

367 802 1302

Количество муниципалитетов

• Миграционные области (NUTS3). Первоначальные результаты4

2020 2021 Изменения 2020-2021 гг.

- 183 муниципалитета, изменения в 2020 -2021 гг. - Текущая валидация

• Миграционные области (NUTS3). Первоначальные результаты по годам

Анализ международной мобильности

Определение географических областей для предоставления услуг временному населению (проживание, здравоохранение)

Определение критериев для использования нетрадиционных источников данных (веб-агрегирование, большие данные).

• Другое потенциальное применение

Министерство внутренних дел (СЕГОБ ) • Грасиела Мартинес

Кабальеро

Наша команда

INEGI (NSO)

Географический отдел

• Тринидад Каррильо

• Виолета Замора

Министерство защиты окружающей среды

• Клеотильда Арельяно

INEGI (NSO) Вице-президент по социально- демографической информации • Адриана Оропеса • Нагиелли Альварес

INEGI (NSO) Лаборатория исследований и науки о данных • Алехандра Фигероа • Элио Вильясеньор • Олинка Паес • Виктор Куэвас • Абель Коронадо

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  • Наша команда

Leadership, talent management and productivity

Rodrigo Nuñez (INEGI: National Institute of Statistics and Geography Mexico)

Languages and translations
English

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