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Low wages, employees and employers in Italy: a longitudinal analysis, ISTAT, Italy

This experimental exhaustive analysis of the Italian regular labour incomes is based on the integrated use of Istat statistical registers on income, population and businesses, and on microdata from social security records. The observed time span is 2015-2022.

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1

Low wages, employees and employers in Italy: a longitudinal

analysis

Paola Anitori, Carlo De Gregorio, Annelisa Giordano – ISTAT [email protected], [email protected], [email protected] 1

Table of contents Executive summary ........................................................................................................................................... 2

Introduction ....................................................................................................................................................... 3

Sources, methodological aspects and concepts ................................................................................................. 3

Part 1. Incomes from dependent employment ................................................................................................... 5

1.1. Italian employees during years 2015-2022 ........................................................................................ 5

1.2. Distributions by sector and main socio-demographic characteristics. .................................................. 10

1.3 Per capita earnings ................................................................................................................................. 13

Part 2. Employees with low earnings in industry and services between 2015 and 2022 ................................. 16

2.1 Gross earnings and their components .................................................................................................... 16

2.1. The evolution of annual gross earnings ................................................................................................ 18

2.3. The employees with low earnings ........................................................................................................ 20

2.4. Employees with low earnings on a longitudinal perspective ................................................................ 23

2.4. Employees who escaped the low-wage trap ......................................................................................... 27

2.5. Employees who never succeeded to escape the low-earnings trap ....................................................... 30

Part 3. Employers and low earnings ................................................................................................................ 34

3.1. Business structure, employment and employees .................................................................................. 34

3.2. Employers and gross earnings .............................................................................................................. 36

3.3. Employers and employees with low earnings ...................................................................................... 39

3.5. The enterprises and the employees who escape from the trap of low earnings .................................... 40

3.6. The enterprises and the employees in trap of low earnings .................................................................. 42

Concluding remarks ......................................................................................................................................... 43

References ....................................................................................................................................................... 44

1 The authors are the only responsible for the content of this work, which do not involve at any rate Istat. They also wish

to thank the colleagues of Istat unit PSV (Giovanni Battista Arcieri, Lucia Coppola, Tiberio Damiani, Stefano De Santis,

Daniela Ichim, Isabella Siciliani, Anna Maria Sgamba, Fabio Spagnuolo) with whom they shared the current work on the

income register; the colleagues working at the Population register and at the Business register; and the colleagues of the

unit in charge of data collection.

2

Executive summary

 This experimental exhaustive analysis of the Italian regular labour incomes is based on the integrated use of

Istat statistical registers on income, population and businesses, and on microdata from social security records.

The observed time span is 2015-2022.

Part 1. Labour incomes from employee jobs

 Between 2015 and 2022 real per capita labour income of 21 million Italian employees decreased significantly.

COVID19-pandemic and 2022 inflation are mostly responsible for this trend, although even before 2020 the

structural weakness of dependent work produced a sluggish income dynamics.

 A large share of employees shows very low labour income levels, with 25% of employees barely above 10,000

euro in 2022 and a half of them below 20,000 euro (at constant 2015 prices).

 Low-learnings mostly originates from the private sectors. Let alone agriculture and domestic workers, where

low incomes and undeclared work coexist, industry and services produce a large portions of low-earnings.

 The distribution of incomes in the public sectors is less critical, although public employment witnessed a

constant reduction in real gross earnings in the first part of the period, with a decrease of about 2,000 euro (-

7%) in its median level between 2015 and 2020.

 The structural weakness of incomes is also reflected by the gender gap, especially in the private sectors.

Between 2015 and 2022 it has been only slightly reduced, more often in presence of higher education level.

Part 2. Employees with low earnings in industry and services between 2015 and 2022

 Yearly gross earnings (YGE) declined in real terms: in general YGE were hit by the increased adoption of

labour contracts of lower quality, namely short-term and part-time jobs, although in 2022 the effect on inflation

worsened the situation.

 A substantial and rather stable share of employees dropped in the low-wage areas, especially low YGE,

essentially due to the low-intensity of jobs. This affected their personal income with severe consequences even

at the household level.

 Over the entire period, about 60 per cent of employees in 2022 experienced at least one year under the low-

pay thresholds. In particular, only a minor share of these employees managed to bring their pay back above

the thresholds, usually through better quality contractual conditions. A larger portion of the others either exited

the status of employment or never succeeded to get rid of the “low pay trap" permanently.

 The tie between standard jobs and the level of hourly wages inevitably implies that the firms providing better

pay conditions are also those where full-time, permanent jobs prevail. This is a relatively small subset of firms

although they are large enough to involve an importantamount of non-agricultural workforce; these firms

belong to the more advanced service and industry activities where average hourly wage is set on more than 15

euros.

Part 3. Employers and low-earnings

 As one moves away from these wage levels, operating on the wage spectrum is possible only by acting on job

intensity through part-time and fixed-term contracts. Low-paid employees gradually experiences lower

intensities and durations, while hourly wages remain quite below the average.

 Micro-enterprises and individual firms produce very low per capita annual earnings due to lower levels of all

the wage components: lower hourly earnings and lower intensity and duration of jobs.

 The economic activities with a high propensity to pay low wages emerge quite clearly. Most of them belong

to services. In Horeca and recreation, heavily affected by undeclared work, more than two employees out of

three is below YGE threshold. In support services, education and other household services more than 50% of

employees have low annual earnings.

 Quitting low-pay sectors is generally the only way to escape low earnings, in as much as there are a few

sectors there may be better opportunities to improve pay conditions. A higher propensity to change employer

and economic activity is thus associated to improvements in earnings conditions.

3

Introduction

In Italy, the level and distribution of employees’ labor earnings has been at the center of a both academic and

political debate, sometimes messy and mainly focused on the opportunity to set a minimum wage, generally

intended as a minimum threshold of hourly earnings. This attention derives from the fact that Italy, at present,

is among the five EU countries without a legal minimum wage. Although the European Parliament Directive

2022/2041 on adequate minimum wages does not compel Italy to enforce it by law, due to the high coverage

rate of collective bargaining, the issue has remained on top of the agenda of the public debate. Most of the

analyses emphasize the presence of a large share of low-wage workers in the private sector, and often stress

the importance of disentangling all the components determining the wage level. The spread of non-standard

forms of regular dependent employment – in particular part-time and short-term contracts - makes it

compelling to look into annual and monthly earnings by separating the effects due to hourly earnings (once

clearly defined) from those due to working time. This means going beyond hourly earnings (the usual target

of minimum wage proposals) to take more properly into account the income flows deriving from earnings2.

This paper does not enter directly into this debate nor into the details of what kind of minimum wage should

or should not be introduced or what the exact definition of wage should be considered. Based on previous

research conducted in Istat on the quality and on the earnings of employees3, it tries instead to provide details

and descriptive evidence on a longitudinal perspective (2015-2022) regarding a wide range of issues that

surround the more general theme of incomes from dependent employment. Many tables and charts are used in

order to document the empirical evidence of our investigations, and are founded on the use of large scale

databases such as Istat statistical registers on incomes, population and businesses. The integrated use of those

registers offers uncountable opportunities to deepen the analysis and to describe exhaustively the topic of

incomes from employment, with special attention to low incomes. A short premise provides thus a general

overview of the statistical sources used in this paper and of the methodological context and concepts through

which our analyses are developed.

Part 1 of the paper is dedicated to employees’ labour incomes. The analysis mainly focuses on some

distributive aspects of these incomes and it is based on individual data for more than 20 million employees,

examined for the first time by domain4: public sector and private sector, separately for industry and services,

agriculture and domestic workers. In this analysis we show the heterogeneity of incomes, and provide some

insights on the low income areas. In Part 2 the attention is shifted towards the gross earnings coming from the

private non-agriculture sectors, the largest and most heterogeneous set of Italian employees. Here the analysis

targets notional (or contractual) earnings in order to disentangle their nature independently from the windfall

factors influencing effective labour earnings. The elementary components of gross earnings are thus

investigated with reference to the nature of labour contracts: hourly earnings and working time components

are jointly analysed in order to determine which effects lay behind the areas of low earnings. Longitudinal data

in the eight-year period 2015-2022 helped us to characterize the cohorts of employees who succeeded to escape

from the low earnings trap and tose who never came out from low earnings conditions. Part 3 finally extends

the analysis of part 2 in order to find evidences on the characteristics of employers and their role in generating

poor employment conditions. At the end of the paper some conclusions are drawn and some of the numberless

areas of further research are evidenced.

Sources, methodological aspects and concepts

In this paper, we integrate anonymized microdata from Istat statistical registers and administrative data. In

particular, the population register reports for each individual some basic demographic information concerning

2 Bavaro 2022, Bavaro, Raitano 2023, Crettaz,Bonoli 2010, Filandri, Struffolino 2019, Grimshaw 2011, Hallerod et al.

2015, Jansson et al. 2020, Marucci, De Minicis 2019, Ministero del lavoro 2021, Raitano et al.2019. 3 Anitori, Arcieri, et al. 2019, Anitori, De Gregorio et al. 2019, De Gregorio, Giordano 2014, 2016, De Gregorio,

Giordano, Siciliani 2021, Istat 2019, 2022, 2023. 4 Many hints concerning the analysis of employees’ incomes have be extracted from Atkinson (2008), though our rich

database pushed us towards a more descriptive approach.

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age, gender, citizenship and educational level. Additional information identifies the individuals actually

resident in Italy, flagging those who are resident in private households. The available data used in the paper

range from 2015 to 2022 and we refer to residents in private households at 31 December of each year5. Thus,

we exclude from the analysis all employees not belonging to resident population, (about 3% of business register

total employment), the entrepreneurs who are employees in the same enterprise they own and all the individuals

who are in old-age pension schemes. In some analysis we also restrict the observed population to those aged

15-64 years.

The income register also refer to years 2015-2022 and it is structured in modules6: in this paper we focus on

gross labour incomes that include social contributions paid by the worker and income taxes7. We partitioned

the employees in four subgroups depending on the nature of the employer: public sector, private industry and

services8, private agriculture and domestic workers (where, though, the employer is a private household). This

partitioning, especially when referred to public administrations and private businesses, is based upon criteria

that might not correspond exactly with the official allocation of economic units used in S13 but it can be

improved in time as soon as Istat labour register will be available. In the paper we will also experiment a

provisional allocation of labour incomes by type of employer as provided by the business register. Moreover

we will also use data from the module of the income register dedicated where total disposable income is

estimated, mainly based on tax reports integrated with other non-taxable incomes, and from the module on

pensions, used to identify and exclude the retirees from the analysis. All the information we derive from the

income register is referred to regular incomes and does not include irregular incomes deriving from irregular

jobs or from irregularly worked hours within regular jobs (as in the case of false part-time jobs9). It is useful

to remind that, in the case of dependent employment, the irregularity rate is estimated by National Accounts

over 11%, with peaks of 32% in agriculture and more than 50% in domestic services. This information should

be kept in mind where examining the results described in Part 1 of the paper. According to the most recent

report from the Ministry of Economy and Finance, in 2020 the tax gap deriving from dependent employment

was 2,4% corresponding to 3,9 billion euro, to be added to an additional amount of 10,9 billion euro of gap in

social contributions, 2,5 billion of which to be paid by the workers10.

Table 1

5 The age of individuals is referred to 31 December of the year. 6 Modules relate to labour income (regular and irregular), pensions, non-pension monetary transfers and taxes. 7 Istat income register follows quite steadily the guidelines fixed in Unece Canberra Handbook (Unece 2011). 8 Often we shall refer for simplicity to these activities as “industry and service” omitting their private nature. 9 De Gregorio, Giordano (2014). 10 Ministero dell’Economia (2023). Notice that in the case of self-employment, the estimated income tax gap was 69,7%

in 2020.

Rate of non regular employment by economic activity. Year 2021

Economic activity %

Agricolture 32,0

Industry 4,8

Construction 14,3

Trade 5,5

Transportation 5,8

Horeca 14,9

Information 3,8

Finance 2,3

Other business services 6,1

Education 4,9

Human health 5,1

Recreation 19,4

Domestic services 51,8

Total 11,3

Source: Istat, National accounts

5

The business register is referred to 2015-202111. It contains structural business statistics data on the enterprises

in industry and services. There are about 4,5 million units in this domain, 1,5 million of which had at least one

employee enrolled in some part of the year. From this register we also derive structural (NACE, size,

governance) and performance indicators (profit and loss accounts) of each business. In particular, from the

associated linked employer-employee register we also draw some information on those business owners

enrolled as employees in the same enterprise they own.

Detailed data on the employees in the private non-agriculture sectors, and in particular on their jobs and labour

contracts, are derived from original social security data. From this source, it is possible to classify jobs

according to qualitative and quantitative data, like gross earnings, gross hourly earnings, and contractual

working time. Earnings in particular include social contributions paid by the worker and income taxes. We

deliberately chose to concentrate on the individual variables defined in labour contracts in order to exclude all

the events that affecting effective gross earnings (like public subsidies to employment such as labour retention

schemes). This remark has to be kept in mind to understand what is at the core of the paper. The analysis in

Part 1 in fact is based on effective (and strictly) labour incomes and this explains why, for example, the effects

of the pandemic come out so clearly. In Part 2 and Part 3 where we focus on the quality of jobs, considering

pay and duration of labour contracts, the extraordinary slump in labour earnings happened in 2020 is much

less visible.

Part 1. Incomes from dependent employment

1.1. Italian employees during years 2015-2022

Between 2015 and 2022, the number of individuals involved in dependent employment grew consistently in

Italy though the level and dynamics of their labour incomes have been overall weak and could not resist the

double impact of pandemic and inflation. Although these events were quite recent, the weakness of employees’

gross incomes appeared quite clearly also in the first part of that period: 25% of employees could count on

slightly more than 10.000 euro in the year before pandemics, and half of them hardly arrived at 20 thousand

euro (Table 1.1).

According to Istat income register, in 2022 there were about 21 million individuals with incomes from

employee jobs in Italy, accounting for a total of more than 460 billion euro in labour gross earnings12.

Compared to 2015, when there were just over 18 million employees, total earnings increased by 6,1 per cent,

recovering the sharp decline in 2020 due to the pandemic. Between 2021 and 2022, however, this catch-up has

been partially eroded by inflation: HICP grew in fact by 8,8% in 2022, and as a whole by 14,2% since 2015.

As a result, average per capita income in 2022 was at the lowest level (just over 22,000 euro) of the entire time

span, lower even than in the year of pandemics.

The distribution of per capita annual gross earnings shows quite heterogeneous dynamics depending on income

level. In 2015, for example, the median annual gross earning was slightly above 21,000 euro; in subsequent

years, median income suffered a slight erosion until 2019 and a conspicuous slowdown during pandemics,

while 2022 inflation nullified the partial recovery registered the year before. The labour incomes laying above

the median were, in general, progressively more resilient to the effects of pandemics, actually heavily loosing

purchasing power only when inflation picked up: during 2020, job retention schemes were in fact applied more

intensively to individuals in the medium and lower wage classes13 and this helped to preserve higher incomes.

Lower deciles, on the other hand have shown substantial resilience over time (with the exception of 2020),

11 The 2022 version was made available late march 2024, too late for this paper. 12 According to the definitions adopted in the income register, and derived from the Canberra Manual (Unece 2011),

labour income from dependent employment consists in the flows actually accruing to the employee from the employer,

gross of income tax and of social contributions charged on employees, excluding any transfer for social security purposes

but including job retention schemes. In this sense, in Part 1, we use the terms labour income and gross earnings as

synonyms. All values referred to in this section are at constant prices 2015. 13 De Gregorio et al. (2021).

6

especially with respect to inflationary pressures perhaps due to the likely impossibility of further earnings

compression.

On the other hand, over the period under observation, the growth in the number of employees came with some

appreciable changes in the composition of this workforce (Table 1.2). In 2015, more than 70 per cent of Italian

regular employees were in the non-agricultural private sector, around 20 per cent in the public sector whilst

the rest was almost equally divided between agriculture and domestic work14. Over 60 per cent of employees

were between 35 to 54 years old, males prevailed over females by around 8 p.p. and the presence of workers

with foreign citizenship was limited to a modest 10 per cent. Almost half employees had an upper secondary

level of education, one third primary or lower secondary education, and the remaining 22 per cent a bachelor

or doctoral level15.

This picture has not changed much over time, or changed very slowly. In the overall period, the share of

employees in the non-agricultural private sector has increased by 2 p.p. as of 2022, notwithstanding the

difficulties due to pandemics in 2020 and partially in 2021. At that time, social protection measures avoided

mass redundancies but some personnel cuts occurred all the same16. In specular contrast, the share of

employees in the public sector lost more than 1 p.p. between 2015 and 2022. The share of agriculture, on the

other hand, remained more or less constant until 2020 - a year in which it grew slightly, benefiting from the

fact that agriculture was not subject to the economic lockdowns - and started to reduce from 2021. The share

of domestic workers17 declined steadily, with the exception of 2020 when layoffs were discouraged through

job retention.

Looking at the composition by age, we observe a progressive reduction of individuals belonging to the middle

aged classes and a consequent increase in employees with 55 to 64 years. This trend was mainly caused by the

demographic ageing of the Italian population18. Also the number of new and younger employees progressively

increased, whilst the workers with 25-34 years decreased steadily up to 2020 and then showed a small recover

in the following years. Meanwhile, the presence of female remained substantially stable, with a slight increase

only at the end of the period. The share of employees with education above ISCED 3 also increased, quite

slowly though, while that of individuals with Italian citizenship slightly decreased.

If we divide the time span into two intervals, the first from 2015 to 2019 and the second from 2019 to 2022

(Table 1.3), we observe that in the first sub period the overall number of employees has grown by 1.9 per cent

but at the same time real per capita incomes did not move. This has been mainly due to the employees in

industry and services who, although grown in number by 2.5 per cent, registered an unchanged per capita

labour income. On the contrary, in the public sector the slight increase of the employees has been mirrored by

a slight reduction in per capita income. In agriculture, though, both the number of employees and per capita

YGE had increased, whilst domestic workers experienced a reduction in number but a growth of their incomes.

Looking at the dynamics by age group, between 2015 and 2019, there was a clear reduction in the number of

workers between the ages of 35 and 44 (with per capita gross earnings essentially unchanged) and an increase

in the extreme age classes, mostly due to the effect of demographic trends. It should be noted, however, that

per capita income of these classes shrunk, albeit slightly. The share of foreign employees from Africa and Asia

also increased, but only the former had suffered a reduction in the average income.

In the second sub-period from 2019 to 2022, total employment has increased by 1.6 per cent but per capita

gross earnings sharply declined by 2 per cent, and this contraction has been quite generalized. Public

employees suffered more than others the impact of inflation and pandemic: all age groups were affected, and

women more than men, regardless to occasional employment growth. Between 2019 and 2022, the average

14 From now on, we intend by domestic employees the personnel employed in activities of private households as

employers, corresponding to section T of NACE classification. 15 The data on the level of education are derived from the Population register, and the available series starts from 2018. 16 Most of them were probably retirements of voluntary exits. 17 The share of regular employees in agriculture and the household sector is very small due to extensive use of

undeclared or 'grey' work for tax and social contributions evasion purposes. 18 See Istat 2023.

7

gross earnings of those with upper secondary education has also lost ground and this also happened to

immediately higher ISCED levels.

Table 1.1

Indicators 2015 2016 2017 2018 2019 2020 2021 2022

N. employees (000) 18.324 18.633 19.130 19.500 19.729 19.646 20.073 20.705

Index (2015=100) 100 101,7 104,4 106,4 107,7 107,2 109,5 113,0

Total Income (000) 433.721 446.619 449.720 459.541 466.108 443.232 466.207 460.128

Index (2015=100) 100 103,0 103,7 106,0 107,5 102,2 107,5 106,1

Per capita Income 23.669 23.970 23.509 23.566 23.625 22.561 23.226 22.223

Index (2015=100) 100 101,3 99,3 99,6 99,8 95,3 98,1 93,9

Percentiles

p5 2.658 2.768 2.651 2.648 2.683 2.424 2.565 2.615

p10 4.612 4.834 4.644 4.636 4.706 4.021 4.427 4.566

p15 6.572 6.941 6.657 6.652 6.746 5.683 6.305 6.568

p20 8.596 9.035 8.714 8.709 8.800 7.398 8.252 8.494

First quartile 10.546 10.996 10.654 10.651 10.775 9.216 10.184 10.335

p30 12.633 13.109 12.731 12.724 12.875 11.147 12.195 12.279

p35 14.887 15.407 14.972 14.949 15.096 13.230 14.353 14.260

p40 17.241 17.677 17.230 17.173 17.295 15.333 16.545 16.214

p45 19.383 19.681 19.268 19.209 19.279 17.394 18.608 17.950

Median 21.206 21.486 21.070 21.022 21.067 19.298 20.471 19.537

p55 22.823 23.079 22.684 22.712 22.725 21.148 22.199 21.024

p60 24.435 24.634 24.218 24.248 24.235 23.004 23.782 22.548

p65 26.153 26.264 25.846 26.012 25.923 24.816 25.490 24.083

p70 28.227 28.281 27.842 27.924 27.860 26.836 27.430 25.865

Third quartile 30.510 30.577 30.134 30.263 30.192 29.291 29.703 28.193

p80 33.085 33.186 32.774 33.069 32.979 32.183 32.476 30.940

p85 36.606 36.640 36.245 36.609 36.530 35.865 36.088 34.397

p90 42.109 42.043 41.646 41.844 41.909 41.376 41.617 39.560

p95 54.062 54.281 53.528 53.464 53.699 53.301 53.748 50.575

Sources: Istat, Income Register 2015-2022, Population Register 2015-2022

Notes: (a) Only indiv iduals w ith annual gross earnings ov er 1.000 Euro

Distribution of gross earnings of total employees (a) by year. Years 2015-2022 (values at constant prices

2015. Index: base 2015=100)

8

Table 1.2

Economic sector,

Demographic characters 2015 2016 2017 2018 2019 2020 2021 2022

ECONOMIC SECTOR (b)

Public sector 19,9 19,6 19,1 18,9 18,6 19,1 19,0 18,7

Private Industry and services 71,6 72,1 72,6 72,9 73,2 72,4 72,8 73,6

Agricolture 4,1 4,1 4,1 4,1 4,0 4,2 4,1 3,8

Domestic employees 4,1 4,0 3,9 3,8 3,7 4,0 3,9 3,6

AGE CLASS (c)

15-24 5,7 5,8 6,3 6,6 6,8 6,4 7,0 7,5

25-34 19,5 19,3 19,0 18,9 18,8 18,7 18,9 19,2

35-44 27,7 26,9 25,9 24,9 24,0 23,5 22,7 22,1

45-54 29,2 29,2 29,0 28,8 28,6 28,7 28,2 27,6

55-64 16,8 17,5 18,1 18,7 19,4 20,2 20,6 20,9

GENDER

Females 46,0 46,0 46,0 45,9 45,9 45,8 45,9 46,1

Males 54,0 54,0 54,0 54,1 54,1 54,2 54,1 53,9

EDUCATION LEVEL

Up to Lower secondary

education (ISCED 0-2) 31,0 30,3 29,3 28,7 27,6

Upper secondary education

(ISCED 3) 47,6 48,0 48,0 47,9 48,2

Up to short-cycle tertiary

education (ISCED 4-5) 5,6 6,0 6,0 6,6 6,9

Bachelor’s or equivalent level

(ISCED 6) 15,1 15,0 15,9 16,0 16,4

Up to PhD or their equivalent

level (ISCED 7-8) 0,7 0,8 0,8 0,8 0,9

CITIZENSHIP (by area)

Italians 89,5 89,7 89,6 89,5 89,3 89,0 89,1 89,0

EU 3,4 3,4 3,3 3,3 3,2 3,3 3,1 2,9

Extra-EU 2,5 2,4 2,3 2,3 2,3 2,3 2,2 2,2

Africa 1,6 1,6 1,7 1,8 2,0 2,1 2,2 2,3

Asia 2,2 2,2 2,3 2,3 2,4 2,6 2,5 2,6

Other 0,8 0,8 0,8 0,8 0,8 0,9 0,9 0,9

Total 100 100 100 100 100 100 100 100

Source: Istat, Income Register 2015-2022, Population Register 2015-2022

Total employees (a), by economic sector, main demographic characters and year. Years

2015-2022 (%)

Notes: (a) Only indiv iduals w ith annual gross earnings ov er 1.000 Euros at constant 2015 prices; (b) residual sectors dropped.

The sum does not add up to 100; (c) Only age classes w ith a share > 0.5%.The sum does not add up to 100

9

Table 1.3

N. employees

Per capita

earnings N. employees

Per capita

earnings

ECONOMIC SECTOR

Public sector 0,3 -0,2 1,9 -2,8

Private Industry and services 2,5 0,0 1,9 -2,0

Agricolture 1,4 2,0 0,0 0,9

Domestic employees -0,4 1,1 1,0 -0,7

AGE CLASS (b)

15-24 6,7 0,2 5,0 -1,1

25-34 0,9 0,7 2,3 -0,3

35-44 -1,7 -0,1 -1,2 -1,6

45-54 1,3 -0,1 0,4 -2,0

55-64 5,7 -0,6 4,0 -2,9

GENDER

Females 1,8 0,2 1,7 -1,8

Males 1,9 -0,2 1,5 -2,2

EDUCATION LEVEL (c )

Up to Lower secondary

education (ISCED 0-2) … … -1,4 -2,2

Upper secondary education

(ISCED 3) … … 1,8 -2,7

Up to short-cycle tertiary

education (ISCED 4-5) … … 6,8 -0,8

Bachelor’s or equivalent level

(ISCED 6) … … 4,6 -2,9

Up to PhD or their equivalent

level (ISCED 7-8) … … 6,8 -0,2

CITIZENSHIP (by area)

Italian 1,8 -0,1 1,5 -2,1

EU 0,1 3,4 -0,9 0,7

Europa extra-EU -0,3 2,1 0,3 0,1

Africa 6,9 -0,6 6,9 0,4

Asia 4,5 1,8 4,7 -0,7

Other 1,9 1,6 4,8 -0,4

Total 1,9 0,0 1,6 -2,0

Sources: Istat, Income Register 2015-2022, Population Register 2015-2022

Notes: (a) Only indiv iduals w ith annual gross earnings >1000 Euros; (b) Only age classes w ith share > 0.5%; (c ) ISCED classification groupings:

respectly 0-1-2, 3, 4-5, 6, 7-8

Economic sector,

Demographic characters

Employees and gross earnings (a) by year, economic sector and main demographic characters. Years

2015-2022 (Average annual rate of change)

2015-2019 2019-2022

10

1.2. Distributions by sector and main socio-demographic characteristics.

The distribution of employees’ incomes in the period under scrutiny reflects their structural weakness and has

also been strongly marked by the pandemic and inflation. Nevertheless, one of the most important aspects to

be underlined has to do with the sector heterogeneity of incomes. This heterogeneity clearly places in the

private sectors most of low income earners while the public sector provide a sort of benchmark with relatively

stable and decent income levels, although at a standstill in nominal terms from the very initial years of the

period.

Chart 1.1 illustrates the distribution of employees by level of gross earnings19. Two elements are clearly

noticeable: compared to 2015, in the pandemic year the bulk of employees earning between 19,000 and 35,000

euro per year shrinked drastically. At the same time the number of individuals with gross earnings below 8,000

euro increased by one percentage point (around 1.9 million employees) and the number of those earning

between 11,000 and 19,000 euro by half a percentage point (around 900 thousand individuals). The effects of

job retention measures put in place by Government to offset the effects of the lockdown are evident. The partial

recovery in 2021 returned the curve to roughly the initial shape in 2015, but a net shift towards lower income

classes occurred in 2022, quite generalized although with some distinguishes. The number of income earners

below 7,000 euro was stable, those between 7,000 and 25,000 euro increased sharply and those over this

threshold decreased a little, catching up the levels of 2020 again.

More interestingly, when examined by economic sector, gross income distribution reveals a large

heterogeneity (Chart 1.2). The public sector has a relatively concentrated distribution while in industry and

services there is a large portion of low and very low income owners. In agriculture and domestic services

incomes are indeed very low as compared to the other sectors, with very few employees above 10 thousands

euro on an annual basis.

The distribution by income class in the public sector appears strongly concentrated between 17,000 euro and

40,000 euro. The quasi-bell shape is multi-modal in the central part, each peak probably representing a subset

of employees with the same contractual conditions (and therefore very similar annual gross earnings): the

public sector, in fact, is characterised by stronger and more homogeneous rules in labour contracts that

discipline the different working profiles in different administration bodies. The curve shows a rather small

backward shift towards the lower classes in the year of the lockdown and a further, but sharper, shift of the

same negative sign in 2022 following the blow of inflation. In this case, the increase of individuals sliding

towards lower income classes is progressively more pronounced for those who earned more than 27,000 euro

in the previous year.

With regard to industry and services, in 2020 the increase in the number of workers who moved into lower

income classes is spread over almost all classes; this was due (as mentioned before) to job retention schemes

and to the interruption of lower quality jobs (especially short-term). The most evident increase regarded two

subgroups: those who fell down into the classes up to 10,000 euro and those between 13,000 and 20,000 euro.

In 2021, the distribution partly recovered the 2015 shape, except for the slight increase in the number of

workers with less than 10,000 euro, who continued to benefit from the social measures started the year before.

In 2022, on the other hand, the curve shows a polarisation between those (over 27,000 euro and under 7,000

euro) who maintained their status (albeit for different reasons) and those within this range who clearly

worsened their income conditions.

By restricting the view to the extreme years of the period, if one looks at cumulate distributions, several facts

are worth noticing (Chart 1.3). Firstly, the difference between the performance of the public and the private

sector in 2022: the cumulate curve for civil servants shows a drift to the left and, as mentioned above, the worst

performance is associated with income classes with more than 27,000 euro. The lowest quintile in 2015 was

19 Income classes of 1,000 EUR (at constant prices 2015). Years from 2016 to 2019 have been omitted due to the

substantial similarity of their distributions to 2015.

11

about 21,000 euro and decreased to 17,000 euro in 2022. Similarly, the third decile decreased from 24,000 to

21,000 euro and then the gap narrows as income increases. In industry and services, on the other hand, we see

a greater resilience among the income classes below 10,000 euro (possibly due to job retention schemes in act

until 2022) and a smoother effect on the rest of the income classes. The deterioration of the situation is evident

mostly between the median and the third quartile of the curve. In 2015, for instance, the sixth decile was at

22,000 euro whilst in 2022 it went down to 20,000 euro.

The situation is different in agriculture where a partial improvement is detected in 2022 as the number of those

earning between 7,000 and 17,000 euro increases. This phenomenon should be better investigated but could

be linked to the partial emersion of undeclared or 'grey' jobs. A slight inversion of the trend can also be

observed in 2022 among domestic workers, although in this case it is much more contained: the rigidity of the

curve also indicates a very pronounced income compression, which could also in this case be linked to

phenomena of irregular work not captured by the data.

Chart 1.1. Distributions of Italian employees by gross earnings class and years (%, values in .000 euro at

constant prices 2015)

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

D en

si ty

%

Gross earnings (.000 euro)

Employees by gross earnings and year (values at constant 2015 prices)

2015 2020

2021 2022

0

10

20

30

40

50

60

70

80

90

100

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

D en

si ty

%

Gross earnings (.000 euro)

Employees by gross earnings and year (values at constant 2015 prices)

2015 2020

2021 2022

12

Chart 1.2. Distribution of Italian employees by sector, gross earnings class and years (%, values in .000 euro

at constant prices 2015)

Chart 1.3

0

1

2

3

4

5

6

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

D en

si ty

%

Gross earnings (at constant 2015 prices)

Public sector

2015 2020

2021 2022

0

1

2

3

4

5

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

D en

si ty

%

Gross earnings (at constants 2015 prices)

Private Industry and Services

2015 2020

2021 2022

0

2

4

6

8

10

12

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

D en

si ty

%

Gross earning (at constant 2015 prices)

Private Agricolture

2015 2020 2021 2022

0

1

2

3

4

5

6

7

8

9

10

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58

D en

si ty

%

Gross earnings (at constant 2015 prices)

Domestic employees

2015 2020 2021 2022

0

10

20

30

40

50

60

70

80

90

100

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

%

Gross earnings (.000 euro, at constant 2015 prices)

Cumulate distribution of employees by sector and income class. Years 2015 and 2022

Public 2015

Public 2022

Private Industry and Services 2015

Private Industry and Services 2022

Agricolture 2015

Agricolture 2022

Domestic 2015

Domestic 2022

13

1.3 Per capita earnings

The public and the private side of dependent employment tell different stories and seem to represent opposite

realities, with structural differences which do emerge clearly also if we consider how labour incomes are

distributed. The weakness of private employees are actually quite structural, and this fact is revealed quite

clearly if we consider age and gender gaps. Incomes from employment often do not imply acceptable

household incomes, especially if agriculture and domestic workers are considered.

In 2022 the average gross earnings of an employee with 25 years was about 18 thousands euro if working in

the public sector and about 14 thousands euro in the private sector. For those who were 50 years old their

respective YGE was respectively about 32 thousands and 26 thousands euro. The age gap is thus very high,

especially in industry and services while it is very low in agriculture (9.200 vs. 10.300 euro) and among

domestic workers (7.700 vs 8.300 euro), where earnings are very low and there is no room for meaningful age

gaps.

The analysis so far has shown that between 2015 and 2022, given the almost stable employment structure, the

main cause of the deterioration in the income conditions of Italian employees has depended more on inflation

than on pandemics. The trend in per capita gross earnings confirms a net reduction between 2015 and 2022 for

employees in all age classes, especially in the public sector (Chart 1.4). In 2020 the deterioration of per capita

levels for public employees was particularly sharp for those over 45 years, but further deterioration happened

in 2022, this time affecting all ages. In industry and services the reduction in per capita income in 2022

resembles the trend shown in 2020 although people with 25-34 years appear to be better off in 2022.

In agriculture, on the contrary, per capita levels increase both in 2020 and in 2022 regardless of pandemic or

of the inflation boost: in particular, in 2022 they increase more than in 2020 for those below 45 years. Among

domestic workers per capita gross earnings increased in 2022 only up to the age of 38, is table up to 52 and

decreased steadily after that age.

As the estimates by gender and age indicate (Chart 1.5), it’s no surprise that women show generally lower pay

levels than their male colleagues: but the gap is definitely greater in the private sector and in agriculture than

in the public sector. Among domestic workers, where male are a minority, the gap between genders is smaller

and decreases with age.

In the public sector a 40 years old woman earned on average less than 24.000 euros per year, and the gap with

men is about 6.000 euros; it remains constant at least until the age of 60, and then narrows slightly. Among the

private employees, a woman in her 40s earns about 16.000 euro, that is 8.000 euro less than a man of the same

age: but the gap widens as age increases. A similar trend can be seen in agriculture, although a woman in her

40s earns 8.000 euro and the gap with her male colleagues is about 3.000 euro. Among domestic workers the

gap remains constant at around 3.000 euro independently from ages, at least up to 58 years and then narrows

considerably.

Differences in per capita income by sex are quite remarkable also according to educational achievements.

Females with at most upper secondary education suffer the worst difference with the corresponding males

(more than 7.000 euro) and this difference remains constant over the years (Table 1.4). Things improve for

more educated females: although in 2018 a female bachelor earns around 28.000 euro against 44.000 euro of

an analogous male, the difference seems to start dropping in 2020 and more sharply in 2022. The same happens

for females with a master or a PhD. Those females with lower levels of education, instead, are constantly worse

off in all years and no improvements have really occurred over time.

Since agriculture and domestic workers are traditionally the least educated, we limit the comparison by gender

and education level to the employees of the two main economic sectors (Table 1.5). Here the difference in the

levels of gross earnings between stands out. In 2018, in the public sector, a woman with primary education (or

a diploma) earned 28 per cent (24 per cent) less than a man, while in the private sector a woman with the same

educational achievements earned 47 per cent less than her male colleague (46 per cent for graduates). These

gaps increased in the following years only in the public sector, as in the private sector the situation only altered

14

in the year of the pandemic and then returned to almost the initial level in 2022. Among those with an

educational qualification higher than ISCED 3, despite the fact that the gap between genders and sectors is

anyway still quite considerable, a slight narrowing of the gender gaps is observed in 2022 compared to 2018

in both sectors.

Things are very different among those who have at least a university degree. The gap between women and

men with a degree in the public sector is 40 per cent in 2018 and 2020 but drops to 34 per cent in 2022. In the

private sector, on the other hand, it reaches 70 per cent in 2015 and narrows to 61 per cent in 2022. In the

public sector the gender gap in gross earnings narrows among those above ISCED 6 (around 31 per cent in

2018 and 26 per cent in 2022). In industry and services, instead, having a doctorate or a master's degree means

for women earning 44 per cent less than men in 2022, a figure lower than that in 2018 but still quite impressive.

A final look at household disposable equivalent incomes reveals that the households with only public

employees or with at least one civil servant plus at least one private employee are best off since they have a

higher probability to belong to the upper quintiles. On the contrary, households whose members are private

employees lay more probably in the third quintile, while when members are employed in agriculture or as

domestic workers their households finish more probably in the lower quintiles: this happens either if there are

no members in the household working in other sectors and if there is at least one private employee.

Households with two members are more frequently placed in the highest quintile, presumably because there

are two incomes (e.g. both are labour incomes or one is labour income and the other one comes from a pension).

Households with three or more members, instead, are definitely in the lowest quantiles, probably because some

of the members are too young to work or because there are unemployed components.

Chart 1.4. Per capita gross earnings by sector and age. Years 2015, 2020 and 2022 (Values at constant prices

2015)

0

5000

10000

15000

20000

25000

30000

35000

40000

16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64

G ro

ss e

ar n

in g

s (a

t c on

st an

t 2 01

5 pr

ic es

)

Age

Public vs. Private Industry and Services

public 2015

public 2020

public 2022

private 2015

private 2020

private 2022

0

2000

4000

6000

8000

10000

12000

16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64

G ro

ss e

ar n

in g

s (a

t c on

st an

t 2 01

5 pr

ic es

)

Age

Agricolture vs. Domestic

agricolture 2015

agricolture 2020

agricolture 2022

domestic 2015

domestic 2020

domestic 2022

15

Chart 1.5. Per capita annual gross earnings by sector and gender. Year 2022 (Values at constant prices 2015)

Table 1.4

Table 1.5

0

5.000

10.000

15.000

20.000

25.000

30.000

35.000

40.000

45.000

16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64

G ro

ss e

ar n

in g

s (a

t c on

st an

t 2 01

5 pr

ic es

)

Age

Public sector

Male

Female

0

5.000

10.000

15.000

20.000

25.000

30.000

35.000

16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64

G ro

ss e

ar n

in g

s (a

t c on

st an

t 2 01

5 pr

ic es

)

Age

Private Industry and Services

Male

Female

0

2.000

4.000

6.000

8.000

10.000

12.000

14.000

16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64

G ro

ss e

ar n

in g

s (a

t c on

st an

t 2 01

5 pr

ic es

)

Age

Private Agricolture

Male

Female

0

2.000

4.000

6.000

8.000

10.000

12.000

16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64

G ro

ss e

ar n

in g

s (a

t c on

st an

t 2 01

5 pr

ic es

)

Age

Domestic

Male

Female

Females Males Females Males Females Males

Up to Lower secondary education (ISCED 0-2) 13.990 20.664 12.950 19.350 12.913 19.294

Upper secondary education (ISCED 3) 18.809 26.260 17.544 24.697 17.144 24.075

Up to short-cycle tertiary education (ISCED 4-5) 20.169 27.606 19.810 27.268 19.832 26.458

Bachelor’s or equivalent level (ISCED 6) 28.652 44.308 28.381 43.517 27.150 40.492

Up to PhD or their equivalent level (ISCED 7-8) 32.545 45.143 34.724 47.100 34.263 45.280

Total 19.674 26.583 18.960 25.493 18.704 24.911

Sources: Istat, Income Register 2015-2022, Population Register 2015-2022

Notes: (a) Only indiv iduals w ith annual gross earnings (at constant prices) >1000 Euros; (b) ISCED classification groupings: respectly 0-1-2, 3, 4-5, 6, 7-8

Per capita gross earnings (a), by year, gender and education(b). Years 2018,2020,2022 (values at constant 2015

prices)

Education level

2018 2020 2022

2018 2020 2022 2018 2020 2022

Up to Lower secondary education (ISCED 0-2) 128 130 131 147 151 148

Upper secondary education (ISCED 3) 124 128 128 146 148 145

Up to short-cycle tertiary education (ISCED 4-5) 128 129 126 145 149 143

Bachelor’s or equivalent level (ISCED 6) 140 140 134 170 169 161

Up to PhD or their equivalent level (ISCED 7-8) 131 130 126 152 147 144

Total 126 128 126 143 144 141

Sources: Istat, Income Register 2015-2022, Population Register 2015-2022

Education level

Public sector Private (industry and services)

Notes: (a) Only indiv iduals w ith annual gross earnings (at constant prices) >1000 Euros; (b) ISCED classification groupings: respectly 0-1-2, 3, 4-5, 6, 7-8

Gender-gap in per capita gross earnigs (a), by education level (b) and economic sector. Years 2018, 2020, 2022

(Index. Base: Females=100)

16

Table 1.6

Part 2. Employees with low earnings in industry and services between 2015 and

2022

2.1 Gross earnings and their components

The characterizing elements of per capita yearly gross earnings (we will use the acronym YGE in the rest of

the paper) have recently been analysed by separating three elementary components: hourly wages, monthly

intensities and duration of employment (Istat, 2022). The general conclusion was that wage inequalities derive

from the interaction of hourly wages and working time, and that referring solely to the level of hourly wages

– as it often happens in the national public and policy debate - is largely insufficient and cannot ensure

comprehensive explanations neither of wage variability nor of the large extent of low wage areas. In this

section we extend the analysis to the eight-year period 2015-2022 by adopting a similar theoretical approach.

In particular, we focus on gross earnings as defined in labour contracts, where they are intended as theoretical

gross earnings, in the sense that they represent the gross earnings the employee would have “theoretically”

received in the absence of events that may give rise to notional crediting or to occasional increase or decrease

of monthly pay. Production bonuses are thus excluded, as also the amounts due for untaken vacations or

vacations themselves, arrears due by law or by contracts related to previous years, and pay items related to

actual work performance (e.g., overtime). Instead, all recurring competencies normally found in monthly pay

(shift allowances, contracted overtime, and values subject to ordinary contributions referring to recurring

fringe benefits) are included. In this sense, earnings are gross of both income taxation and employee social

contributions. If actual gross earnings had been chosen for the analysis, they would have been affected by the

occurrence of such events especially in annual or monthly totals. Our objective here is focusing on a more

stable concept of earnings and that’s why: in the rest of the paper we shall use the term gross earnings while

referring to theoretical earnings.

Households I quintile II quintile III quintile IV quintile V quintile

Sector of employement of

households components

Only private (I&S) sector 106 105 103 96 90

Only public sector 35 75 97 133 161

Only agricolture 271 128 61 28 11

Only domestic workers 287 141 50 14 8

Public and private (I&S) sectors 20 56 109 148 168

Private (I&S) and agricolture sectors 151 149 104 67 30

Private (I&S) and domestic sectors 167 170 101 48 14

Other combinations 124 127 109 85 55

N. of components

One 97 85 111 114 93

Two 77 91 91 104 136

Three 89 100 98 105 108

More than three 130 119 100 80 71

Citizenship of components

Only Italian 83 93 103 109 112

Mixed 164 140 92 59 44

Only foreign 238 152 72 27 10

N. of components below 14 yrs.

None 88 91 101 107 112

One 125 121 98 85 71

More than one 144 134 94 72 57

Sources: Istat, Income Register 2015-2022, Population Register 2015-2022

Households with at least one employee, by quintile of disposable equivalent income. Year 2021 (Specialization

rates)

17

The case of job retention schemes is paradigmatic. During pandemics, the use of job retention schemes was

extended to about one half of Italian employees in industry and services20. Employers were allowed to stop

paying for their employees, while the State subsidized, through social transfers, the income of those individuals

and above all their employers. In the analyses made in Part 1 of this work this effect was intended to emerge

clearly seen since the object of the analyses were the actual monetary flows from employers to employees. In

the analyses of Part 2 and 3, instead, the effect will not be visible, since low earnings dynamics are studied

independently from this kind of events: during labour retention schemes, formal labour contracts remained

unchanged. The use of theoretical gross earnings, as we adopted in this contexts, is thus intended to target the

structural components of remunerations.

At the individual level, YGE are split with simple algebra as the product of three components. Hourly gross

earnings (HGE) are derived as the ratio between annual amount of contractual gross earnings and total

workable hours (WH):

𝐻𝐺𝐸 = ∑𝑌𝐺𝐸

∑𝑊𝐻

Monthly intensity (MOI) is computed as the ratio between workable hours and the number of months in the

year in which the employee had a labour contract (NM) of whatever length:

𝑀𝑂𝐼 = ∑𝑊𝐻

∑𝑁𝑀

The duration (DUR) is the average number of months covered in the year, at least partially, by a labour contract

and the average is calculated with respect to the N employees belonging to the domain under scrutiny:

𝐷𝑈𝑅 = ∑𝑁𝑀

𝑁

Using this little formalization, YGE is given by the product of these three components:

𝑌𝐺𝐸 = 𝐻𝐺𝐸 ∗ 𝑀𝑂𝐼 ∗ 𝐷𝑈𝑅

The analysis of YGE and of its components is here extended to the period 2015-2022 by taking advantage of

the progressive availability of Istat statistical register, and of the opportunities opened up by their integrated

use, such as analyzing yearly data on earnings longitudinally and comparing trends in the observed period in

order to study the evolution of their wages and the transitions to and from low-wage areas.

To summarize some results, we found that over the period examined, YGE declined in real terms: while in

2022 this can be explained by the growth in the inflation rate, more generally YGE were hit by the increased

adoption of labour contracts of lower quality, namely short-term and part-time jobs. A substantial, and rather

stable over time, share of employees dropped in the low-wage areas, especially low YGE areas, essentially

due to the low-intensity of employment relationships which has affected their income conditions with

important consequences even at the household level. Over the entire period, about 60 per cent of the employees

experienced at least one year under the thresholds of low pay. In particular, only a minor portion of these

employees succeeded to bring their pay back to the above thresholds21 (usually through better quality

contractual conditions), while a large portion of the other workers either exited the status of employment

(probably not voluntarily) or never managed to permanently get rid of the “low pay trap".

20 De Gregorio et al. 2021. 21 As it will be cleared with larger detail further on, two thresholds have been estimate, one on YGE and one on HGE.

The first one is fixed at 60% of the corresponding overall median, the second at 66% of the median calculated on standard

jobs.

18

2.1. The evolution of annual gross earnings

Between 2015 and 2022, the number of employees in business and services rose by about two million (Table

2.1)22. This significant growth (+16%) has affected not so much the average age of employees (shallowly

increased from 39,5 to 40,3 years) as their whole age structure. In particular, we notice a sort of polarization.

On the one side, the increase in the weight of the younger age groups due to the flows of new entrants (despite

the stop in the year of the pandemic) and, on the other side, the increase in the older groups (due to the annual

drift of age cohorts) resulting in a partial retreat in the relative weight of the middle age classes. The weight of

foreign citizens grew by about one percentage point - albeit limited to the contribution of African and Asian

individuals – as well as the educational level of employees due to the slow extinction of people with lower

ISCED scores.

Over the same period, YGE lost 10.1 percent of their value in real terms, ranking below the level attained in

2015. This result was largely determined by the dynamics of consumer prices, particularly in 2022, but it can

be also linked to the decline in YGE already manifested in the previous years when inflation was decidedly

modest (Table 2.2). A significant exception is 2020. The extensive use of job retention schemes - with almost

half of the employees involved - supported employment relationships mainly among holders of standard jobs

(i.e. full-time open-ended contracts). Consequently, the growth in per capita YGE (+3.6 percent compared to

2019) derives from the decrease of employees with non-standard contracts (whose earnings are generally

lower).

Net of the pandemic year, the negative dynamics of YGE derives from the combination of the reduction in real

HGE and the decline in the intensity and duration of jobs that took place until 2018 and after 2020. Considered

by gender, this dynamic does not reveal any specific trend: the structural evidence that shows a YGE 30%

higher for men remains constant in the period, and depends only for a minor part by differences in HGE, while

much more important is the role played by the monthly intensity of labor relations. The latter evidence can be

explained, in turn, by the extensive use of female workforce with part-time contracts.

Changes in the composition of employees by type of contract also seem to have played a decisive role in

determining the decline in per capita wages (Table 2.3). While open-ended contracts (full-time or part-time)

reveal a substantial stability in YGE, since 2017 their weight has gradually and significantly decreased, albeit

net of the rebound observed in 2020. In 2022 the incidence of standard jobs as compared to 2015 lost about 4

percentage points in terms of employees, while the incidence of part-time positions, also on an open-ended

basis, decreased by about 2.5 percentage points. The increase in the number and relative weight of fixed-term

employees has proceeded hand in hand with the simultaneous reduction in hourly wages since the early years

of the observed period, and in the intensity and duration of employment relationships.

22 As in the rest of Part 2 and 3 of this paper, the figure refers to employees between the ages of 15 and 64 who are part

of the resident household population as of December 31 of the reference year, net of entrepreneurs and old-age pension

holders: entrepreneurs often result as employees of the same enterprise that they own, so we excluded them from the

analysis. Specifically we included all individuals with at least one earnings event from employment relationships with

non-agricultural private sector enterprises.

19

Table 2.1

Table 2.2

Demographic

characters 2015 2016 2017 2018 2019 2020 2021 2022

Total 13.026 13.279 13.814 14.144 14.339 14.107 14.530 15.059

GENDER

Females 41,1 41,2 41,6 41,7 41,8 41,2 41,3 41,6

Males 58,9 58,8 58,4 58,3 58,2 58,8 58,7 58,4

AGE CLASS

15-24 8,1 8,3 9,3 9,6 9,9 9,1 10,0 10,6

25-34 23,9 23,5 23,3 23,1 22,8 22,6 22,5 22,3

35-44 29,9 28,9 27,7 26,7 25,8 25,3 24,3 23,4

45-54 26,9 27,2 27,2 27,3 27,4 28,0 27,6 27,0

55-64 11,2 12,1 12,6 13,3 14,1 15,0 15,5 16,7

CITIZENSHIP (by area)

Italian 90,3 90,4 90,3 90,0 89,8 89,4 89,5 89,4

EU 3,0 3,0 3,0 3,0 2,9 3,0 2,9 2,8

Europe non EU 2,1 2,1 2,1 2,1 2,1 2,0 2,0 2,0

Africa 1,8 1,8 1,8 1,9 2,0 2,2 2,3 2,4

Asia 2,1 2,1 2,2 2,3 2,4 2,6 2,5 2,5

Other areas 0,7 0,7 0,7 0,8 0,8 0,8 0,8 0,9

EDUCATION LEVEL

ISCED 0-2 31,2 32,8 32,1 30,5 29,6

ISCED 3 51,0 50,3 50,8 51,1 51,4

ISCED 4-5 5,6 5,3 5,7 6,0 6,4

ISCED 6 11,9 11,2 11,1 12,0 12,2

ISCED 7-8 0,4 0,4 0,4 0,4 0,4

Employees, by year and main demographic characters. Years 2015-2022 (Number in thosusands. %

distributions)

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Notes: Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises, belonging to the resident population, liv ing in households, ex cluding

entrepreneurs and those w ho are in retirement. Data on education lev el are av ailable from the population register only from 2018.

Years HGE (c)

Monthly

intensity (d)

Duration

(e) HGE (c)

Monthly

intensity (d)

Duration

(e) HGE (c)

Monthly

intensity (d)

Duration

(e)

2015 13.026 20.908 13,4 154 10,1 41,1 17.258 12,3 140 10,0 23.458 14,0 164 10,2

2016 13.279 20.840 13,4 150 10,3 41,2 17.159 12,4 136 10,2 23.423 14,0 160 10,4

2017 13.814 20.188 13,2 149 10,2 41,6 16.548 12,3 134 10,1 22.778 13,8 159 10,4

2018 14.144 19.983 13,1 149 10,3 41,7 16.420 12,2 134 10,1 22.527 13,6 159 10,4

2019 14.339 20.044 13,1 149 10,3 41,8 16.472 12,2 134 10,1 22.605 13,6 159 10,4

2020 14.107 20.771 13,2 152 10,3 41,2 17.637 12,4 141 10,1 22.971 13,7 160 10,5

2021 14.530 20.201 13,1 150 10,3 41,3 16.699 12,2 136 10,0 22.662 13,5 160 10,4

2022 15.059 18.797 12,2 150 10,3 41,6 15.495 11,4 136 10,0 21.147 12,6 160 10,4

Rates of change (f)

2016 -0,3 0,5 -2,6 1,8 -0,6 0,9 -3,1 1,7 -0,1 0,3 -2,3 1,9

2017 -3,1 -1,4 -1,0 -0,8 -3,6 -1,1 -1,5 -1,1 -2,8 -1,6 -0,6 -0,6

2018 -1,0 -1,1 0,0 0,0 -0,8 -0,6 0,0 -0,1 -1,1 -1,3 0,1 0,2

2019 0,3 0,2 0,0 0,1 0,3 0,3 0,1 -0,1 0,3 0,1 0,0 0,2

2020 3,6 0,7 2,3 0,5 7,1 1,2 5,1 0,6 1,6 0,5 0,6 0,5

2021 -2,7 -1,2 -1,3 -0,3 -5,3 -1,1 -3,6 -0,7 -1,3 -1,4 0,1 -0,1

2022 -6,9 -6,8 0,0 -0,2 -7,2 -6,9 0,1 -0,3 -6,7 -6,6 0,0 -0,1

YGE of employees, by year, gender and YGE component. Years 2015-2022 (Values at constant 2015 prices)

Employees

(a)

YGE (b)

Women %

on total (a)

YGE (b) YGE (b)

YGE (b)

Components

YGE (b)

Components

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Note: (a) Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises, belonging to the resident population, liv ing in households, ex cluding entrepreneurs and those w ho are in retirement.(b) Av erage YGE. (c) YGE by

w orkable hour. (d) Workable hours in each month under contract. (e) Number of months w ith a contract. (f) w ith respect to the preceding y ear.

YGE (b)

Components

TOTALE WOMEN MEN

20

Table 2.3

2.3. The employees with low earnings

Following an analysis tool currently used in the literature, two thresholds are introduced to identify employees

with low earnings: one on YGE and the other on HGE (Table 2.4). The first threshold is fixed at 60 percent of

median YGE, where the median is calculated on all employees belonging to the resident population. The

second threshold identifies employees whose hourly wage is less than two-thirds of HGE median, calculated

only on standard labor relations, excluding apprentices. At constant prices, the value of the two thresholds

decreases from 2020, while at current prices they maintain a slightly increasing trend: in 2022 the threshold

for YGE is at current prices slightly above 12 thousand euros, while HGE threshold is about 8.5 euros per

hour23.

By 2022, the share of employees were the YGE threshold24 is just under 30 percent. Such proportion is quite

constant over the years but shows a slight decline from 2019 onwards. The number of individuals below the

threshold (which at the end of the period numbered 4.4 million, i.e. over 400,000 more than in 2015) follows

the increase in the total number of employees (Table 2.5). People with non-standard contracts, as expected, are

found more frequently in the low YGE area. It’s the case of about a half of part-time open-ended employees

whose incidence in the below-the-threshold group has fallen steadily since 2015. However, the most critical

situations are among employees with fixed-term jobs, especially those with part-time contracts. For these

categories, the duration and intensity of labour contract affect heavily the overall compensation.

Employees with low HGE are 1.4 million in 2022 (9.3 percent of the total), down from about 1.7 million

recorded in 2018. Again, short-term jobs are undoubtedly the most vulnerable, especially if they are also part-

time contracts.

Employees with standard jobs, although relatively less affected by low pay, significantly fuel the area of critical

pay: in 2022 about 400,000 low-YGE and 300,000 low-HGE employees came from standard job holders. At

the opposite end, about 3 million low-YGE workers held part-time jobs (Table 2.6). Young people, women

and foreign citizens were the most frequent figures in non-standard jobs and also those most associated with

23 The two thresholds are calculated on the same reference population used in the analysis, excluding entrepreneurs and

retired people. 24 From now on we shall refer to low YGE or low HGE to intend employees below the corresponding threshold.

2015 2022 YGE HGE (c)

Monthly

intensity (d) Duration (e) YGE HGE (c)

Monthly

intensity (d) Duration (e)

Only standard (g) 55,4 51,5 26.599 13,5 172 11,5 -0,9 -1,1 -0,2 0,3

Only full-time short-term

(h) 8,1 9,9 9.561 9,9 151 6,4 -1,3 -1,6 -0,4 0,6

Only part-time open-

ended (i) 19,4 16,9 11.591 9,9 107 11,0 -0,4 -1,2 -0,2 1,0

Only part-time short-term

(l) 5,1 7,6 3.959 8,4 83 5,6 -1,2 -1,5 -0,5 0,9

Mixed types, also

standard (m) 7,7 7,9 17.054 9,8 159 10,9 -0,8 -1,4 -0,1 0,7

Other mixed types (n) 4,2 6,2 8.683 8,5 110 9,3 -0,7 -1,4 0,2 0,5

Total 100 100 18.797 12,2 150 10,3 -1,5 -1,3 -0,4 0,2

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Notes: (a) Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises, belonging to the resident population, liv ing in households, ex cluding entrepreneurs and those

w ho are in retirement. (b) Av erage YGE. (c) YGE by w orkable hour. (d) Workable hours in each month under contract. (e) Number of months w ith a contract. (f) w ith respect to the preceding

y ear. (g) Employ ees w ith only standard jobs in the y ear. (h) Employ ees w ith only full-time short-term jobs in the y ear. (i) Employ ees w ith only part-time open-ended jobs in the y ear. (l)

Employ ees w ith only part-time short-term jobs in the y ear. (m) Employ ees w ith more than one ty pe of job in the y ear, among w hich also standard jobs. (n) Employ ees w ith more than one

ty pe of job in the y ear, among w hich nev er standard jobs. (p) Employ ees are clasified on the basis of the ty pe of jobs they ex perience during the y ear, indipendently of the number of

employ ers.

YGE, by year, type of job and component. Years 2015-2022 (Distribution and average annual rate of change. Values at constant 2015

prices )

Type of job (p)

Employees %

(a)

Per capita yearly gross earnings YGE (b)

2022 Average rate of change 2015-2022

Components Components

21

low earnings. In particular, two out of three young people are below YGE threshold, and those with low HGE

account for between one-quarter and one-third of the target subpopulation. Among the population with at least

a bachelor's degree, the incidence of low-YGE appears to be about half of the total figure.

When family ties are taken into account, households with low-YGE employees verge on 4 million at the end

of the observed period: they steadily account for about 35 percent of total households with employees and have

slightly more members (3 individuals per household versus 2,6), a figure that is also quite stable over time

(Table 2.7). The presence of low-YGE employees significantly affects household incomes: any other labour

income of their own or other household members' is unlikely to provide adequate support to the family’s

economic wellbeing. In fact, if we consider the equivalent disposable incomes25, the presence of employees

with low YGE is associated with a higher probability of ending up in the poorest fifth, nearly twice as high as

for the rest of households with employees, with a significant presence even in the second fifth.

Table 2.4

25 For each household, the sum of the individual incomes has been divided by a family coefficient based on OECD-

modified equivalence scale in order to take into account the different compositions.

Current prices

Constant 2015

prices (b) Current prices

Constant 2015

prices (b)

2015 11.564 11.564 8,0 8,0

2016 11.738 11.750 8,0 8,1

2017 11.477 11.330 8,1 8,0

2018 11.497 11.217 8,2 8,0

2019 11.621 11.261 8,3 8,0

2020 11.964 11.616 8,3 8,0

2021 11.975 11.405 8,3 7,9

2022 12.056 10.557 8,5 7,4

Notes: (a) YGE threshold is 60% of the median v alue, ex cluding entrepreneurs and old-age pension holders. HGE

threshold is equal to 66 percent of the median v alue calculated on standard jobs only , ex cluding apprentices,

entrepreneurs and old-age pension holders. (b) Values at constant prices refer to 2015 and are calculated by

apply ing changes in the general HICP index .

Thersholds (a) adopted to identify the employees with low earnings, by year

and type of threshold (values at current anc constant 2015 prices)

Year

YGE HGE

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps,

Uniemens 2015-2022.

22

Table 2.5

Table 2.6

Year

Total Only standard (c) Only full-time

short-term (d)

Only part-time

open-ended (e)

Only part-time

short-term (f)

Mixed types, also

standard (g)

Other mixed

types (h)

LOW YGE

2015 3.947 30,3 8,1 65,5 56,2 93,9 22,9 72,8

2016 3.912 29,5 5,8 63,9 54,0 93,9 23,4 73,3

2017 4.170 30,2 5,2 60,4 49,7 92,5 22,1 71,4

2018 4.260 30,1 5,0 58,2 48,0 91,7 18,9 69,5

2019 4.315 30,1 4,8 63,7 47,7 93,8 17,5 69,7

2020 4.213 29,9 4,0 68,9 49,4 94,5 18,5 73,2

2021 4.317 29,7 4,3 66,8 47,6 94,2 17,2 70,7

2022 4.413 29,3 5,1 63,4 47,0 93,8 15,2 66,9

LOW HGE

2015 1.222 9,4 4,6 17,9 12,8 22,5 11,5 20,6

2016 1.273 9,6 4,5 17,8 13,0 23,2 12,3 21,3

2017 1.564 11,3 4,8 20,3 14,3 26,2 14,1 24,5

2018 1.688 11,9 4,8 20,7 14,8 27,0 14,1 25,3

2019 1.650 11,5 4,6 21,7 14,2 25,9 13,2 24,4

2020 1.539 10,9 4,5 20,8 14,3 25,1 13,0 23,2

2021 1.531 10,5 4,2 19,8 13,2 25,0 12,0 22,5

2022 1.400 9,3 3,7 18,0 11,4 21,9 10,0 19,8

Employees with low earnings, by year, type of threshold and type of job. Years 2015-2022 (Number in thousands. % Incidence)

Employees

below

thresh. (b)

Incidence % by type of job (a)

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Note:(a) Employ ees are clasified on the basis of the ty pe of jobs they ex perience during the y ear, indipendently of the number of employ ers. (b) Indiv iduals w ith at least an earning ev ent w ith

priv ate non-agricolture enterprises, belonging to the resident population, liv ing in households, ex cluding entrepreneurs and those w ho are in retirement. (c) Employ ees w ith only standard jobs in the

y ear. (d) Employ ees w ith only full-time short-term jobs in the y ear. (e) Employ ees w ith only part-time open-ended jobs in the y ear. (f) Employ ees w ith only part-time short-term jobs in the y ear. (g)

Employ ees w ith more than one ty pe of job in the y ear, among w hich also standard jobs. (h) Employ ees w ith more than one ty pe of job in the y ear, among w hich nev er standard jobs.

Total 15-24 yrs 25-34 yrs Female Foreign. ISCED 0-2 ISCED 7-8

2015 3.947 30,3 67,4 37,3 39,0 47,0

2016 3.912 29,5 67,0 36,2 38,4 46,2

2017 4.170 30,2 69,0 36,6 39,0 46,1

2018 4.260 30,1 68,2 36,2 38,9 45,2 34,0 19,5

2019 4.315 30,1 68,1 35,8 39,1 44,6 34,2 18,1

2020 4.213 29,9 66,6 35,9 38,9 45,0 33,6 18,3

2021 4.317 29,7 68,5 34,8 39,0 43,4 33,4 18,3

2022 4.413 29,3 66,6 32,7 38,9 42,1 33,4 18,0

2015 1.222 9,4 28,2 11,9 11,7 18,4

2016 1.273 9,6 28,6 12,2 11,8 18,8

2017 1.564 11,3 31,8 14,1 13,8 21,2

2018 1.688 11,9 32,3 14,8 14,6 21,8 14,4 5,5

2019 1.650 11,5 31,6 14,1 14,2 20,4 13,9 4,9

2020 1.539 10,9 29,9 13,6 13,4 19,8 13,3 4,6

2021 1.531 10,5 28,5 12,8 13,1 18,3 12,8 4,4

2022 1.400 9,3 25,6 10,9 11,9 15,5 11,2 3,9

Notes: (a) Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises, belonging to the resident population, liv ing in households, ex cluding

entrepreneurs and those w ho are in retirement.

Employees with low earnings, by year, type of threshold and main demographic characters. Years 2015-2022

(Number in thousands. % Incidence)

Years

Employees

below the

thresh. (a)

Incidence %

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

LOW YGE

LOW HGE

23

Table 2.7

2.4. Employees with low earnings on a longitudinal perspective

In order to highlight some aspects of wage trends, the set of employees of private non-agricultural enterprises

between 2015 and 2022 was projected onto the resident population of 2022, restricting the analysis to

individuals who were between the ages of 25 and 60 in 2022 (and therefore 18-53 in 2015). This made it

possible, on the one hand, to look backward at the events and employment continuity of this sub-population

and, on the other hand, to examine the trajectories of entries and exits of the individuals from low-wage areas.

In 2022, those who had experience as employees between 2015 and 2022 totaled 16.5 million, or 58.5 percent

of the entire 25-60 population (Table 2.8)26. A large proportion of them (about 12.9 million) are still employed

in 2022, while the remaining 3.6 million who were without a contract in that year, were nonetheless among

employees in at least one of the previous years.

Among those who are in employment in 2022 there is a neat predominance of individuals with continuous

traces of employment in all years of the observed period (about 7.7 million27), while others – generally younger

people - show continuous traces of employment only from 2019 onward. These two cohorts together account

for 10 million individuals. A further set of about 1.4 million of individuals are new employees hired from

202028. The remaining lot consists of more than 1.2 million individuals with non-continuous job experience,

albeit in many cases repeated over several years. The last lot is composed by individuals no more in

employment in 2022: part of them since longer time (1.6 million had no signs since 2019), while an additional

2 million individuals gradually exited private employment between 2019 and 2022.

The cohort of persistent workers, in addition to being the largest, is also the cohort with the highest HGE and

YGE: their real YGE growth was slow but appreciable until the abrupt slowdown brought about by the surge

in inflation in 2022 (Table 2.9). Of course, this is a very heterogeneous cohort, as will be seen below.

Employees with more recent continuous job signals, although starting from very modest wage levels, show a

remarkable dynamics in YGE in the face of somewhat static HGE: these are individuals whose conditions have

improved through a greater intensity of labor relations and who have gone through the pandemic period

26 This subpopulation of employees accounts for about two-thirds of the male population and just over half of the female

population, more than 70 percent of the under-35s and just over 50 percent of the over-50s (the latter likely to be more

absorbed in public employment). Looking only at those employed in 2022, the incidence of younger people drops by

about 20 percentage points, confirming the greater intermittency of employment relationships during the observed period. 27 In the following, we will refer to these employees with pay signals in all years of the period by referring to them as

"persistent." 28 "New" in this case stands for "with no employment relationship from 2015 to 2019."

N (.000)

Incidence

% (c) Avg.

Incidence

% (e) First Second Third Fourth Fifth

2015 3.523 35,1 3,1 37,8 207 116 89 53 35

2016 3.491 34,2 3,1 37,0 207 118 88 53 34

2017 3.687 35,2 3,1 38,2 205 119 88 53 34

2018 3.766 35,2 3,1 38,3 205 119 87 54 35

2019 3.815 35,3 3,1 38,5 204 120 86 54 36

2020 3.748 34,9 3,0 37,7 199 122 87 56 35

2021 3.827 35,0 3,1 38,1 198 121 87 57 38

2022 (b) 3.939 35,0 3,0 38,2 188 122 88 58 40

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Notes: (a) The specialization rate is obtained by the ratio betw een the share of households w ith at least an employ eee below YGE threshold and the share of househods in the quintile. A v alue ov er 100marks an

ov er the av erage frequency . (b) For the y ear 2022 calculations are made based on 2021 personal incomes. (c) % of households w ith at least one component w ith low YGE. (d) Av erage number of components

in the households w ith at least one employ ee w ith low YGE. (e) % of components in households w ith at least an employ ee w ith low YGE.

Households with at least one component with low YGE, by quintile of disposable equivalent household income and year. Years 2015-2022

(Specialization rates with respect to the households of employees (a))

Years

Households

Number

Number of

components (d) Quintile of disposable equivalent household income

24

practically unscathed, achieving greater stability precisely in those years. The rest of the cohorts show more

irregular wage dynamics, as they are essentially characterized by modest earnings, especially in YGE.

In contrast, about 10 million employees ended up, even episodically, below one of the two thresholds. This is

more than one-third of the entire 2022 population and more than 60 percent of the subpopulation of employees

(Table 2.10). Individual experiences of low HGE are usually associated with low YGE, although the reverse

is valid more rarely. Of the 9.8 million individuals with low YGE events (including 6.7 million below the

threshold for more than two years), only 3.7 million also experienced low HGE. For the others, the annual

wage shortfalls enlighten a problem of employment intensity and contract breaks. Symmetrically, however,

among the 4 million employees who experienced low HGE during the period, fewer than 10 percent exceeded

the low YGE threshold each year: in short, then, when hourly wages are very low, it is unlikely that annual

wages are not also low.

Employees with persistent signals between 2015 and 2022 are less frequently in the low-pay areas, although

nearly a third of them spent at least one year in the low YGE class. Far greater is the incidence of low-pay

episodes in the other cohorts of employees: more than 80 percent of employees have experienced periods of

low annual pay, even among those no longer employed in 2022, with peaks of more than 90 percent among

employees with the most discontinuous work trajectories. A large portion of them experienced low contract

duration and extremely limited work intensity. Furthermore, if only the threshold on HGE is considered, trends

by cohort reveal a further sign of weakness among employees with more discontinuous labour contracts.

In the cohorts characterized by persistent labour relations, on the other hand, the annual incidence of the

employees with low earnings halved over the period. This is because a substantial group of workers (about 1.8

million) raised permanently their YGE as of 2019, and just under 1.2 million individuals raised their HGE

above the threshold (Table 2.11). At the opposite end of the spectrum, the portion of employees who have

never left permanently their wage insecurity is certainly very large. A group of 4.1 million individuals, in fact,

has never risen above the YGE threshold: of these almost 900,000 come from the persistent cohorts and more

than three million from those who in 2022 were without a contract (even for a long time), denoting clear signs

of pay weakness even when they were previously active. Employees who did not succeed in the observed

period to get out of low HGE are proportionately a smaller group (but still 900,000): for them it seems that

improving HGE over the threshold is relatively easier than resolving with the YGE threshold. Finally, if we

consider all employees who are unable to permanently break out of poor pay levels or those who see their

situation worsening, we observe that even in the most persistent cohorts of workers, there is less than 40 percent

of individuals who manage to cross the threshold of low pay, whether hourly or annual.

The cohort of the most persistent employees is definitely the largest and most heterogeneous in both

composition and evolution of their earnings. It is overwhelmingly composed of adults, males, with fewer

foreigners (and also fewer university graduates, due to a predictably age-related effect). On the opposite side,

if we look at the portion of those who experienced periods of low pay between 2015 and 2022, the share of

women, young people, and foreigners is markedly higher, and the share of university graduates appears

conspicuously lower (Table 2.12).

After all, women, young people and foreigners are generally the most associated with low earnings, which

regarded nearly 70 percent of women and more than 80 percent of young people and foreigners with

employment signals during the period. The cohorts of employees without a contract in 2022, especially in the

segments with low earnings, result to be characterized by a high female composition signaling a specific

tendency to undergo even long-term contract interruptions. Employees with persistent relationships as of 2019

are on average younger, as well as the cohort with more recent access to employment positions; the latter in

particular also show a significant presence of foreigners and university graduates. Finally, the cohorts of

employees with intermittent jobs also have a strong youth component.

25

Table 2.8

Table 2.9

Employees by cohot of persistence and year in whiche they were employed. Years 2015-2022 (thousands)

Cohorts (a) Employees (i) 2015 2016 2017 2018 2019 2020 2021 2022

Employed in 2022 12.885 8.859 10.447 10.584 10.807 10.813 11.917 12.116 12.885

Persistent since 2015 (b) 7.715 7.715 7.715 7.715 7.715 7.715 7.715 7.715 7.715

Other persistent since 2019 (c) 2.558 458 1.717 1.849 2.067 2.558 2.558 2.558 2.558

Entry in 2020 (d) 1.367 786 838 1.367

Discontinuous with some seniority (e) 932 602 837 851 832 434 728 817 932

Other discontinuous (f) 314 84 178 169 193 106 130 188 314

Employees only before 2022 3.644 2.056 2.838 2.851 2.876 1.636 1.782 1.590 …

Only before 2019 (g) 1.612 1.080 1.287 1.257 1.222

Other exited between 2019 and 2022 (h) 2.032 977 1.551 1.595 1.654 1.636 1.782 1.590 …

Total 16.530 10.916 13.285 13.435 13.682 12.449 13.699 13.706 12.885

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Notes: (a) Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises betw een 2015 and 2022, belonging to the resident population in 2022, liv ing in households,

ex cluding entrepreneurs and those w ho are in retirement and aged 25-60. Here they are classified on the basis of theri presence among employ ees; (b) Employ ees in ev ery y ear of the period;

(c) Others employ ees in ev ery y ear from 2019; (d) Other employ ees for the first time from 2020 on; (e) Employ ees present discontinuously in the period but at least for four y ears. (f) Other

discontinuous employ ees (g) Employ ees only until 2018, ev entually discontinuously ; (h) Other employ ees in 2019-2021, ev entually discontinuous; (i) Number of employ ees w ith earnings in at

least one month betw een 2015-2022.

Cohorts (a) 2015 2016 2017 2018 2019 2020 2021 2022

YGE

Employed in 2022 21.482 21.492 21.218 21.319 21.613 22.399 21.937 19.925

Persistent since 2015 (b) 22.895 23.828 24.221 24.622 25.110 25.622 25.825 24.142

Other persistent since 2019 (c) 11.130 8.892 10.580 12.642 13.882 15.955 17.057 16.484

Entry in 2020 (d) 8.924 11.228 11.203

Discontinuous with some seniority (e) 13.057 12.494 11.383 10.169 9.337 16.098 10.739 11.201

Other discontinuous (f) 8.672 5.365 3.327 3.502 3.872 4.706 7.152 8.196

Employees only before 2022 14.014 13.761 12.857 12.087 11.554 10.961 7.801

Only before 2019 (g) 12.628 11.873 10.039 6.724

Other exited between 2019 and 2022 (h) 15.547 15.432 14.765 14.124 11.554 10.961 7.801

Total 20.076 20.128 19.811 19.912 20.291 21.272 21.062 19.925

HGE

Employed in 2022 13,1 13,2 13,1 13,0 13,1 13,2 13,2 12,3

Persistent since 2015 (b) 13,3 13,4 13,5 13,6 13,8 14,0 14,0 13,3

Other persistent since 2019 (c) 11,0 10,7 10,3 10,4 10,6 10,9 11,0 10,6

Entry in 2020 (d) 11,0 11,0 10,4

Discontinuous with some seniority (e) 11,1 11,0 10,8 10,6 10,6 11,6 10,7 10,0

Other discontinuous (f) 11,4 10,8 9,6 9,4 9,5 9,7 10,7 9,7

Employees only before 2022 11,9 11,9 11,7 11,5 11,6 11,7 11,2

Only before 2019 (g) 11,9 11,9 11,6 11,1

Other exited between 2019 and 2022 (h) 11,8 11,9 11,7 11,6 11,6 11,7 11,2

Total 12,9 13,0 12,9 12,8 13,0 13,2 13,1 12,3

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Gross earnings, by cohort and year. Years 2015-2022 (values at constant 2015 prices)

Notes: (a) Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises betw een 2015 and 2022, belonging to the resident population in 2022, liv ing in

households, ex cluding entrepreneurs and those w ho are in retirement and aged 25-60. Here they are classified on the basis of theri presence among employ ees; (b)

Employ ees in ev ery y ear of the period; (c) Others employ ees in ev ery y ear from 2019; (d) Other employ ees for the first time from 2020 on; (e) Employ ees present

discontinuously in the period but at least for four y ears. (f) Other discontinuous employ ees (g) Employ ees only until 2018, ev entually discontinuously ; (h) Other employ ees in

2019-2021, ev entually discontinuous; (i) Number of employ ees w ith earnings in at least one month betw een 2015-2022.

26

Table 2.10

Table 2.11

Table 2.12

Cohorts (a) N

Incid.

% N Incid.% N

Incid.

% N

Incid.

%

Employed in 2022 12.885 6.935 53,8 6.656 51,7 2.953 22,9 2.674 20,7

Persistent since 2015 (b) 7.715 2.501 32,4 2.283 29,6 1.049 13,6 830 10,8

Other persistent since 2019 (c) 2.558 2.160 84,4 2.121 82,9 998 39,0 959 37,5

Entry in 2020 (d) 1.367 1.086 79,5 1.069 78,2 327 24,0 310 22,7

Discontinuous with some seniority (e) 932 882 94,6 878 94,2 437 46,9 433 46,5

Other discontinuous (f) 314 306 97,5 306 97,3 141 45,0 141 44,8

Employees only before 2022 3.644 3.124 85,7 3.099 85,0 1.099 30,2 1.074 29,5

Only before 2019 (g) 1.612 1.367 84,8 1.356 84,1 409 25,3 397 24,6

Other exited between 2019 and 2022 (h)2.032 1.756 86,4 1.743 85,8 690 34,0 677 33,3

Total 16.530 10.059 60,9 9.755 59,0 4.052 24,5 3.747 22,7

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Notes: (a) Indiv iduals with at least an earning event with private non-agricolture enterprises between 2015 and 2022, belonging to the resident population in

2022, liv ing in households, excluding entrepreneurs and those who are in retirement and aged 25-60. Here they are classified on the basis of theri presence

among employees; (b) Employees in every year of the period; (c) Others employees in every year from 2019; (d) Other employees for the first time from 2020

on; (e) Employees present discontinuously in the period but at least for four years. (f) Other discontinuous employees (g) Employees only until 2018, eventually

discontinuously ; (h) Other employees in 2019-2021, eventually discontinuous; (i) Number of employees with earnings in at least one month between 2015-2022.

Employees with low earnings, by cohort and type oft hreshold. Years 2015-2022 (Numbers in thousands)

Employees

(i)

of whom: with low earnings

YGE or HGE YGE HGE YGE and HGE

Cohorts (a)

Employees

(i) Always

No more

since 2019 From 2019

Other

intermittents

Employees

(i) Always

No more

since 2019 From 2019

Other

intermittents

Employed in 2022 6.656 2.088 1.663 1.106 1.799 2.953 423 968 822 739

Persistent since 2015 (b) 2.283 389 878 336 681 1.049 90 469 177 312

Other persistent since 2019 (c) 2.121 472 670 293 685 998 97 300 305 296

Entry in 2020 (d) 1.069 751 318 327 195 133

Discontinuous with some seniority (e) 878 277 83 121 397 437 22 153 142 120

Other discontinuous (f) 306 200 31 39 36 141 19 46 66 11

Employees only before 2022 3.099 2.058 118 253 670 1.099 470 201 155 274

Only before 2019 (g) 1.356 1.033 323 409 255 153

Other exited between 2019 and 2022 (h) 1.743 1.025 118 253 347 690 214 201 155 121

Total 9.755 4.146 1.781 1.359 2.468 4.052 893 1.169 977 1.013

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Notes: (a) Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises betw een 2015 and 2022, belonging to the resident population in 2022, liv ing in households, ex cluding

entrepreneurs and those w ho are in retirement and aged 25-60. Here they are classified on the basis of theri presence among employ ees; (b) Employ ees in ev ery y ear of the period; (c) Others

employ ees in ev ery y ear from 2019; (d) Other employ ees for the first time from 2020 on; (e) Employ ees present discontinuously in the period but at least for four y ears. (f) Other discontinuous

employ ees (g) Employ ees only until 2018, ev entually discontinuously ; (h) Other employ ees in 2019-2021, ev entually discontinuous; (i) Number of employ ees w ith earnings in at least one month

betw een 2015-2022.

Employees with low earnings, by cohort and type of threshold. Years 2015-2022 (thousands)

YGE HGE

When below the threshold When below the threshold

Cohorts (a)

25-34

yrs. Female Foreign.

ISCED

6-7-8

25-34

yrs. Female Foreign.

ISCED

6-7-8 Total 25-34 yrs. Female Foreign.

ISCED

6-7-8

Employed in 2022 12.885 26,1 41,9 10,8 14,2 6.935 38,5 48,2 15,5 11,9 53,8 79,4 61,8 77,1 45,1

Persistent since 2015 (b) 7.715 13,5 39,5 6,1 12,8 2.501 26,3 49,0 11,1 8,6 32,4 63,2 40,2 59,3 21,8

Other persistent since 2019 (c) 2.558 47,1 43,1 15,4 15,9 2.160 48,4 45,3 15,8 13,3 84,4 86,9 88,6 86,8 70,2

Entry in 2020 (d) 1.367 46,4 48,6 25,7 20,5 1.086 45,2 52,4 25,9 17,2 79,5 77,4 85,7 79,9 66,7

Discontinuous with some seniority (e) 932 36,3 46,2 13,8 11,0 882 37,7 47,2 14,0 10,4 94,6 98,2 96,6 96,3 89,9

Other discontinuous (f) 314 45,3 49,0 16,0 14,5 306 45,9 49,5 16,1 14,2 97,5 98,8 98,6 98,0 95,2

Employees only before 2022 3.644 29,4 53,8 12,4 15,9 3.124 32,3 56,2 13,4 15,2 85,7 94,1 89,4 92,6 82,2

Only before 2019 (g) 1.612 25,2 55,0 10,8 16,9 1.367 27,7 57,5 11,7 16,0 84,8 93,3 88,7 91,8 80,6

Other exited between 2019 and 2022 (h)2.032 32,7 52,9 13,6 15,0 1.756 35,9 55,2 14,7 14,6 86,4 94,7 90,0 93,1 83,6

Total 16.530 26,8 44,5 11,2 14,5 10.059 36,6 50,6 14,8 12,9 60,9 83,0 69,2 80,9 54,1

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Notes: (a) Indiv iduals w ith at least an earning ev ent w ith priv ate non-agricolture enterprises betw een 2015 and 2022, belonging to the resident population in 2022, liv ing in households, ex cluding entrepreneurs and those w ho

are in retirement and aged 25-60. Here they are classified on the basis of theri presence among employ ees; (b) Employ ees in ev ery y ear of the period; (c) Others employ ees in ev ery y ear from 2019; (d) Other employ ees for

the first time from 2020 on; (e) Employ ees present discontinuously in the period but at least for four y ears. (f) Other discontinuous employ ees (g) Employ ees only until 2018, ev entually discontinuously ; (h) Other employ ees in

2019-2021, ev entually discontinuous; (i) Number of employ ees w ith earnings in at least one month betw een 2015-2022.

Employees, by cohort, demographic characters and low earnings conditions. Years 2015-2022 (Numbers in thousand; %)

Employees of whom: at least one year with low YGE or HGE

Total

Composition % Incidence % on the respective total

Total (i)

Composizione %

27

2.4. Employees who escaped the low-wage trap

It is interesting to observe the trajectories of the employees who succeeded to exit from low-earnings or,

conversely, who never managed to fully exit the low-earnings trap: in order to do that, we focus on the largest

cohort of 7 million persistent employees, with signs of dependent employment in every year of the period

2015-2022.

A significant portion of these workers (about 878 thousands) experienced low YGE in the first part of the

period but succeeded to emancipate permanently in the second time span. It is interesting to shed some light

on the main reasons why this happened, whether it was because of an increase in HGE or in working time, if

there was any improvement in job quality, what happened during the pandemic and whether gross earnings

resisted or not to inflationary pressures.

For these individuals, the development of YGE was intense until 2020 but slowed down in the following years,

especially in 2022 when inflation pressures hit severely (Table 2.13). Ultimately, the exit from low YGE was

supported not so much by the dynamics of HGE (which increased appreciably in real terms up to 2021) but

rather by the increase in working time. In particular, up to 2019 the growth in YGE stemmed mostly from the

increase in the duration of contracts due to the transitions to open-ended jobs, but also from the growth in

monthly intensity due to transitions to full-time jobs. After 2020, however, margins for intensity growth

saturated and the dynamics of HGE were not sufficient to support YGE, especially in 2022.

The employees who gradually left behind a low-HGE status (about 470 thousands) showed similar growth in

YGE (until 2020) but at significantly lower absolute levels. Their HGE, although it rose appreciably over the

period, was never permanently above the average of 10 euros per hour. Up to 2017, moreover, its contribution

to the change in YGE was lower than the contribution of the duration of contracts. The gradual weakening of

these components led to a stagnation of wages that worsened in 2022 due to the inflation boost. The recovery

due to the increase in HGE only partially offset the low YGE, which remained dependent on the intensity and

duration of jobs.

Such dynamics are linked to important changes in the job quality: a significant proportion of employees who

passed over the low-YGE line have gradually gained access to standard jobs, the incidence of which raised in

the period from just over a quarter to almost two-thirds (Table 2.14). At the same time, short-term jobs (be

them full-time or part-time) decreased whilst open-ended part-time decreased much less, confirming the

presence of more stable relationships. The exit from low-he status came along with transitions to standard jobs

and at the expense of fixed-term contracts, although to a lesser extent. More often improvements were achieved

within the pre-existing contract.

As for the flows characterized by changes in the type of labour contract between 2015 and 2019, the exit from

the low YGE observed for almost half of the workers went along with an improvement in the job quality

because of the transit to standard or open-ended jobs (Table 2.15). The growth in YGE in this case exceeds

double digits due to the decisive contribution of the intensity and duration of employment, especially for those

who transited to full-time contracts. Part of the employees with standard jobs in 2015, gained the pay increase

through the consolidation of duration and, to a lesser extent, through the increase in HGE. For those individuals

(mostly women) who maintained open-ended part-time jobs during this period, YGE increased because of the

increase in contractual workable hours which also resulted in an intensification of monthly working

commitments. Finally, there is a share of employees (more than 10 percent) who came out of low pay even

with worsening contractual conditions, and this was due to increases in contract length or work intensity

although the quite modest, if not negative, contribution of HGE.

In the last three-year period, the improvements in the quality of labour contracts were less intense (Table 2.16).

About 60 percent of employees retained the contractual condition of 2019, albeit with stagnant YGE in real

terms. However, while full-time employees showed a relative growth in HGE, part-time workers could not

keep the pace of inflation: despite the increase in monthly intensity, there was a decrease in YGE. For who

gained access to standard jobs after 2019, coming largely from part-time contracts, the large increase in YGE

28

was determined by monthly intensity and resisted the inflationary blaze of 2022, whilst the contribution of

HGE was insignificant

The scenario partly changes if we consider the exits from low HGE: here the transition is not so tied to the

improvement in job quality that in many cases even worsened (Table 2.17). The YGE of these individuals

(about 16 thousand euros in 2019) saw a double-digit growth rate. It was higher for those whose contractual

conditions improved over the period and for those who remained with full-time and fixed-term jobs. In addition

to the generalized increase in HE (similar for all categories regardless of contractual changes) - those who

improved the quality of their jobs experienced a faster growth in the monthly intensity and those whose job

quality remained unchanged saw an increase in the job duration

In the last four-year period 2019-2022, the exits from low HGE were associated with a stagnation in YGE

(Table 2.18). Even so, only for the employees who could improve the quality of their jobs through access to

open-ended jobs (standard or part-time) this growth is the result of increased monthly intensity and contract

length. For other transitions to full-time, the increase is given by workable hours. Overall, YGE and HGE in

2022 are still quite modest, placing just above 16 thousand euros and on 9.6 euros, respectively.

Table 2.13

Table 2.14

Years Employees YGE HGE

Monthly

intensity Duration YGE HGE

Monthly

intensity Duration

YGE

2015 878 8.980 9,8 125 7,4 … … … …

2016 878 13.658 10,0 137 10,0 52,1 2,6 9,3 35,7

2017 878 15.391 10,1 144 10,6 12,7 0,7 5,3 6,2

2018 878 17.282 10,3 151 11,1 12,3 1,8 5,1 5,0

2019 878 19.713 10,6 158 11,8 14,1 3,1 4,5 6,0

2020 878 21.389 10,9 166 11,9 8,5 2,8 5,0 0,6

2021 878 21.392 11,0 163 11,9 0,0 1,4 -1,7 0,4

2022 878 20.179 10,5 162 11,9 -5,7 -4,6 -0,7 -0,4

HGE

2015 469 9.730 7,9 143 8,6 … … … …

2016 469 11.766 8,3 142 9,9 20,9 4,9 -0,4 15,7

2017 469 13.126 8,6 145 10,5 11,6 3,9 1,8 5,5

2018 469 14.696 9,1 149 10,8 12,0 4,9 3,3 3,2

2019 469 16.040 9,8 148 11,1 9,1 7,6 -1,0 2,5

2020 469 17.233 10,1 155 11,0 7,4 3,3 4,5 -0,5

2021 469 17.275 10,2 151 11,3 0,2 0,8 -2,5 1,9

2022 469 16.349 9,7 152 11,1 -5,4 -4,6 0,5 -1,4

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who definitively escaped from the trap of low earnings since 2019, by year, type of threshold and YGE

components (Numbers in thousands,% change over the previous year. Values at constant 2015 prices)

YGE % change over the previous year

YGE components YGE components

Note: (a) Only persistent employ ees

Type of job 2015 2019 2022 2015 2019 2022 2015 2019 2022 2015 2019 2022

YGE HGE

Standard 230 426 563 26,3 48,5 64,2 142 190 241 30,3 40,5 51,5

Full-time short-term 157 62 26 17,9 7,1 2,9 73 47 33 15,5 10,0 7,1

Part-time open-ended 220 186 178 25,0 21,1 20,3 100 101 103 21,3 21,4 22,0

Part-time short-term 80 7 2 9,1 0,8 0,2 44 19 12 9,3 4,1 2,7

Mixed types, also standard 100 151 92 11,4 17,2 10,5 61 68 51 13,0 14,5 11,0

Other mixed types 91 46 17 10,4 5,2 2,0 49 44 27 10,5 9,4 5,7

Total 878 878 878 100 100 100 469 469 469 100 100 100

Full-time 387 488 589 44,1 55,6 67,1 215 237 275 45,8 50,5 58,6

Part-time 299 193 180 34,1 22,0 20,5 144 120 116 30,6 25,6 24,7

Mixed types 191 197 109 21,8 22,4 12,4 110 112 78 23,6 23,9 16,7

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who definitively escaped from the trap of low earnings since 2019, by year, type of threshold

and type of job. Years 2015, 2019 e 2022 (Numbers in thousands; % compositions)

Note: (a) Only persistent employ ees

29

Table 2.15

Table 2.16

Table 2.17

25-34

yrs. Females Foreign.

ISCED

6-7-8 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019

UNCHANGED

Standard 158 18,0 32,6 26,6 7,5 16,7 24.004 8,9 11,7 2,6 173 1,2 11,9 4,9

Full-time fixed-term 22 2,5 28,0 21,8 10,3 6,9 19.191 16,6 10,7 1,9 168 3,9 10,6 10,1

Part-time open-ended 111 12,6 17,3 71,6 7,0 10,8 14.617 7,5 10,4 1,4 118 4,7 12,0 1,2

Part-time fixed-term 2 0,2 30,1 64,2 8,4 10,5 14.373 19,0 9,9 0,7 126 7,2 11,5 10,3

IMPROVED

Passed to Standard 268 30,5 41,0 33,2 11,4 12,0 21.654 16,7 10,5 2,7 173 6,7 11,9 6,5

Others passed to Full-time 41 4,6 36,5 43,2 13,4 9,5 18.537 20,5 9,9 1,7 161 14,2 11,6 3,8

Others passed to open-ended 119 13,5 37,3 48,1 10,8 10,1 17.481 17,1 10,0 1,7 148 6,7 11,8 8,0

WORSENED

Exited from Standard 72 8,3 25,9 30,1 10,4 8,5 18.873 7,4 10,3 -0,2 158 0,0 11,5 7,7

Other exited from Full-time 16 1,9 31,2 52,7 9,2 9,4 15.313 10,8 10,0 1,4 130 1,0 11,7 8,2

Others exited from open-ended 24 2,7 26,3 46,8 11,7 8,1 16.547 12,4 9,9 0,6 146 7,5 11,5 3,9

OTHER FLOWS 46 5,3 34,8 39,9 12,9 8,2 17.721 15,2 10,0 1,6 154 5,6 11,6 7,4

TOTAL 878 100 33,3 39,9 10,1 11,6 19.713 13,3 10,5 1,9 158 5,0 11,8 5,8

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who definitively escaped from the trap of low YGE since 2019, by type of change in job type and components of YGE. Years 2015-2019. (Numbers in

thousands, incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2015 and 2019 Total

Incidence % YGE HGE Monthly intensity Duration

Dist. %

Note: (a) Only persistent employ ees

25-34

yrs. Females Foreign.

ISCED

6-7-8 2022

Avg.growt

h rate

2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022

UNCHANGED

Standard 376 42,8 37,4 30,2 9,7 14,3 23.018 0,4 11,1 0,3 173 0,1 11,9 0,0

Full-time fixed-term 11 1,3 21,8 22,7 9,1 5,6 20.880 1,8 11,9 1,0 168 -0,1 10,5 0,9

Part-time open-ended 140 16,0 18,9 73,6 6,9 11,0 14.492 -0,4 9,8 -1,7 124 1,2 12,0 0,0

Part-time fixed-term 0 0,0 16,1 59,7 6,2 11,3 14.182 -0,8 9,7 -2,0 129 1,2 11,4 0,0

IMPROVED

Passed to Standard 188 21,4 34,4 32,2 12,2 9,0 20.988 3,7 10,2 0,1 173 2,7 11,9 0,9

Others passed to Full-time 18 2,0 34,3 49,9 10,8 10,2 17.735 5,2 9,7 -1,2 155 6,9 11,7 -0,3

Others passed to open-ended 41 4,6 30,0 52,1 10,3 8,8 16.738 1,3 9,5 -1,2 148 1,5 11,8 1,0

WORSENED

Exited from Standard 50 5,7 41,7 34,5 12,3 9,4 18.441 -4,1 9,9 -0,8 161 -2,6 11,6 -0,7

Other exited from Full-time 10 1,1 34,3 63,0 7,8 11,5 15.238 -4,8 9,8 -1,2 131 -4,0 11,9 0,4

Others exited from open-ended 13 1,5 31,0 45,1 12,3 7,3 16.095 -0,4 9,4 -1,6 149 2,1 11,5 -0,8

OTHER FLOWS 31 3,6 36,9 35,6 13,4 7,5 18.433 -0,2 9,7 -0,8 164 0,5 11,7 0,0

TOTAL 878 100 33,3 39,9 10,1 11,6 20.179 0,8 10,5 -0,3 162 0,9 11,9 0,2

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who definitively escaped from the trap of low YGE since 2019, by type of change in job type and components of YGE. Years 2019-2022. (Numbers in thousands,

incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2019 and 2022 Total

Incidence % YGE HGE Monthly intensity Duration

Dist. %

Note: (a) Only persistent employ ees

25-34

yrs. Females Foreign.

ISCED

6-7-8 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019

UNCHANGED

Standard 94 20,1 48,2 25,8 12,5 6,5 19.802 7,1 9,7 5,6 173 0,0 11,8 1,5

Full-time fixed-term 17 3,6 34,2 27,5 13,4 6,3 16.619 17,5 11,7 7,1 166 3,7 8,6 5,7

Part-time open-ended 56 12,0 21,7 69,9 13,0 7,1 11.251 4,6 9,3 5,3 102 -1,6 11,8 1,0

Part-time fixed-term 5 1,1 36,9 64,8 9,6 8,7 6.639 8,0 9,4 4,1 91 -0,6 7,8 4,5

IMPROVED

Passed to Standard 96 20,4 47,5 30,4 15,9 7,5 20.167 16,6 9,9 5,6 173 4,4 11,8 5,7

Others passed to Full-time 19 4,0 45,3 41,1 17,6 7,0 15.597 18,4 9,8 5,5 157 9,9 10,2 2,2

Others passed to open-ended 56 11,9 41,5 48,8 13,9 7,7 14.717 18,7 9,7 5,2 133 4,0 11,5 8,5

WORSENED

Exited from Standard 48 10,1 43,4 32,9 14,1 5,9 14.975 4,4 9,8 5,1 145 -3,3 10,6 2,7

Other exited from Full-time 17 3,7 41,4 50,2 13,0 6,8 10.740 7,2 9,5 4,7 113 -4,4 10,0 7,1

Others exited from open-ended 21 4,6 38,2 49,1 15,5 6,0 11.127 5,0 9,6 4,5 121 0,7 9,6 -0,2

OTHER FLOWS 39 8,4 42,1 45,4 15,2 6,5 13.204 12,0 9,7 5,1 134 1,3 10,2 5,2

TOTAL 469 100 41,6 40,2 14,2 6,9 16.040 11,0 9,7 5,4 147 1,4 11,1 3,9

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who definitively escaped from the trap of low HGE since 2019, by type of change in job type and components of YGE. Years 2015-2019. (Numbers in thousands,

incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2015 and 2019 Total

Incidence % YGE HGE Monthly intensity Duration

Dist. %

Note: (a) Only persistent employ ees

30

Table 2.18

2.5. Employees who never succeeded to escape the low-earnings trap

A significant share of low-earnings (annual or hourly) employees never permanently exited their condition

(Table 2.19). Those who showed a low YGE dynamics up to 2017, in the following years experienced

significant reductions in the annual earnings due to lower duration and monthly intensity of labour contracts.

The rebound in intensity and duration occurred in 2021 was insufficient to offset the compression occurred

during the pandemic and to bring YGE back to early periods. In 2022, these individuals were also severely

affected by the inflationary flare-up and their HGE returned below the level attained in 2015.

Those who never recovered steadily from low HGE showed similar features. However, they exhibited a greater

overall resilience of YGE thanks mainly to monthly intensity an duration. The dynamics of their HGE though

was very critical, and their level was never permanently above 8 euros: after all, the level of YGE itself only

occasionally exceeded 12 thousand euros.

Among those who never succeeded in raising their earnings, there was the prevalence of part-time contracts,

especially permanent contracts, even among those suffering low HGE (Table 2.20). Among employees with

low YGE, there was a rather significant decrease in standard jobs especially between 2015 and 2019, and an

increases in the intra-year mobility between types of contracts that denoted instability in labor relations.

More than half of these employees did not change the type of job, particularly in the case of part-time open-

ended employees where women prevailed. Nearly stationary HGE were accompanied by weak dynamics of

job intensity (Table 2.21). The wage dynamics of who experienced a worsening of job quality was decidedly

more critical: the termination of standard jobs came along with a significant reduction in employment intensity

and a decline in HGE.

Over 2019-2022, HGE declined significantly in real terms also for those whose job quality remained

unchanged or even worsened, particularly for those who had a standard job in 2019 (Table 2.22). HGE

worsened as well for part-time permanent jobs. The evolution of employees who never left low HGE appears

critical. Up to 2019, the decline in HGE is quite conspicuous and widespread, regardless of changes in

contractual conditions (Table 2.23). Duration and intensity of labor grew slightly, containing the regressive

dynamics of total remuneration. The latter keeps growing only for employees who have been able to improve

their job quality. On average in 2022 the annual pay of those who never exceeded YGE threshold remains very

low (below 12 thousand euros), as does the hourly pay (below 8 euros) (Table 2.24). HGE deteriorated further

after 2019 and this decline was finally offset by increases in intensity and duration only for individuals whose

contractual conditions improved.

The YGE of persistent employees grew appreciably only for the youngest who started from very low levels in

2015 and that remained firmly below the levels of older employees (Table 2.25 and Table 2.26). About three-

quarters of young people aged 25-29 in 2022 (who were therefore 18-22 in 2015) experienced a YGE below

25-34

yrs. Females Foreign.

ISCED

6-7-8 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022

UNCHANGED

Standard 160 34,2 47,2 27,2 13,8 7,2 19.920 -0,4 9,9 0,4 172 -0,2 11,7 -0,5

Full-time fixed-term 15 3,2 30,9 29,9 13,5 5,4 16.574 0,5 11,9 -0,6 167 -0,1 8,3 1,2

Part-time open-ended 74 15,9 22,6 71,3 12,2 7,4 10.734 -1,9 8,9 -1,6 105 0,7 11,4 -1,0

Part-time fixed-term 4 0,9 29,7 67,4 9,4 7,4 6.107 0,9 8,8 -2,2 92 2,2 7,5 0,9

IMPROVED

Passed to Standard 81 17,3 46,1 29,8 15,7 7,2 19.713 5,7 9,9 0,3 172 3,3 11,6 2,0

Others passed to Full-time 14 2,9 43,9 43,4 16,1 6,4 13.853 6,3 9,2 -0,8 153 9,6 9,8 -2,2

Others passed to open-ended 35 7,5 41,3 51,8 13,7 6,6 13.916 7,2 9,3 -1,3 133 3,9 11,3 4,5

WORSENED

Exited from Standard 30 6,3 51,6 33,3 16,4 5,9 15.384 -7,0 9,5 -0,4 154 -3,9 10,6 -2,8

Other exited from Full-time 10 2,1 41,4 53,7 13,3 6,6 10.827 -5,9 9,2 -1,4 118 -6,0 10,0 1,6

Others exited from open-ended 15 3,1 40,6 50,1 15,9 5,4 10.709 -4,9 9,1 -1,4 125 1,6 9,5 -5,1

OTHER FLOWS 31 6,5 44,1 44,8 14,7 6,0 13.031 1,1 9,3 -1,1 138 1,6 10,1 0,6

TOTAL 469 100 41,6 40,2 14,2 6,9 16.349 0,7 9,6 -0,4 151 1,0 11,1 0,1

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who definitively escaped from the trap of low HGE since 2019, by type of change in job type and components of YGE. Years 2019-2022. (Numbers in

thousands, incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2019 and 2022 Total

Incidence % YGE HGE Monthly intensity Duration

Dist. %

Note: (a) Only persistent employ ees

31

the threshold. Among the employees who never experienced low earnings, YGE increased with age, passing

from 23,000 euros of the youngest to 31,000 euros of the eldest. The same does not happen for individuals

below the threshold: the 20,000 euros recorded in 2022 by the segment that succeeded to escape low YGE

remained almost constant by age group, as did the 10,000 euros of the segment that never managed to

permanently exit from low YGE. A similar dynamic has been recorded for HGE.

As expected, gender wage gaps have widened over the observed period: the wage dynamics were

systematically weaker for women not only in terms of HGE but because of the lower intensity of labour

contracts.

Table 2.19

Table 2.20

Years Employees YGE HGE

Monthly

intensity Duration YGE HGE

Monthly

intensity Duration

YGE

2015 1.405 11.492 9,9 123 9,5

2016 1.405 12.230 9,9 120 10,3 6,4 0,4 -2,7 8,9

2017 1.405 12.515 10,0 120 10,5 2,3 0,3 0,3 1,8

2018 1.405 12.058 9,8 118 10,4 -3,6 -1,4 -2,0 -0,3

2019 1.405 11.459 9,8 114 10,2 -5,0 0,1 -3,0 -2,1

2020 1.405 11.007 9,8 113 9,9 -3,9 0,3 -0,7 -3,5

2021 1.405 11.794 9,8 118 10,3 7,2 -0,8 3,8 4,1

2022 1.405 10.199 9,0 118 9,6 -13,5 -7,7 0,4 -6,7

HGE

2015 580 10.647 8,5 133 9,4

2016 580 11.653 8,5 133 10,3 9,5 -0,2 -0,2 10,0

2017 580 11.634 8,4 132 10,5 -0,2 -1,7 -0,6 2,2

2018 580 11.541 8,1 134 10,6 -0,8 -3,0 1,6 0,6

2019 580 11.469 7,9 136 10,6 -0,6 -2,4 1,9 -0,1

2020 580 11.331 8,0 137 10,3 -1,2 1,2 0,2 -2,6

2021 580 12.015 8,1 139 10,7 6,0 0,7 1,9 3,4

2022 580 11.600 7,8 141 10,6 -3,5 -4,1 1,3 -0,6

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Note: (a) Only persistent employ ees

Employees (a) who never definitively escaped from the trap of low earnings, by year, type of threshold and YGE components

(Numbers in thousands,% change over the previous year. Values at constant 2015 prices)

YGE % change over the previous year

YGE components YGE components

Condizioni contrattuali 2015 2019 2022 2015 2019 2022 2015 2019 2022 2015 2019 2022

YGE HGE

Standard 309 228 246 22,0 16,3 17,5 153 156 180 26,4 26,8 31,0

Full-time short-term 164 180 185 11,7 12,8 13,1 67 62 60 11,6 10,7 10,3

Part-time open-ended 555 611 590 39,5 43,5 42,0 184 184 176 31,6 31,7 30,4

Part-time short-term 125 101 93 8,9 7,2 6,6 56 38 34 9,6 6,5 5,9

Mixed types, also standard 115 111 135 8,2 7,9 9,6 59 65 66 10,2 11,2 11,4

Other mixed types 137 174 157 9,7 12,4 11,2 61 75 63 10,6 13,0 10,9

Total 1.405 1.405 1.405 100 100 100 580 580 580 100 100 100

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who never definitively escaped from the trap of low earnings, by year, type of threshold and

type of job. Years 2015, 2019 e 2022 (Numbers in thousands; % compositions)

Note: (a) Only persistent employ ees

32

Table 2.21

Table 2.22

Table 2.23

25-34

yrs. Females Foreign.

ISCED

6-7-8 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019

UNCHANGED

Standard 154 10,9 14,8 34,2 9,2 8,1 21.063 -2,9 11,5 0,9 169 -1,9 10,9 -1,9

Full-time fixed-term 81 5,8 21,1 35,6 11,3 6,3 10.811 0,5 10,0 -0,4 147 0,4 7,3 0,5

Part-time open-ended 438 31,2 12,2 76,6 10,2 7,3 9.604 -0,4 9,5 0,0 87 -0,6 11,7 0,1

Part-time fixed-term 30 2,2 27,1 68,2 9,6 8,2 6.158 1,2 9,2 -0,5 89 -0,1 7,5 1,7

IMPROVED

Passed to Standard 75 5,3 34,4 37,5 15,5 8,4 17.216 5,2 9,9 0,3 169 4,4 10,3 0,4

Others passed to Full-time 47 3,3 32,6 47,5 15,5 6,8 11.148 3,1 9,4 -0,4 142 7,9 8,4 -4,2

Others passed to open-ended 138 9,8 30,4 62,2 12,7 8,3 10.299 4,9 9,1 0,0 102 0,8 11,1 4,1

WORSENED

Exited from Standard 156 11,1 22,5 41,2 10,4 6,4 11.074 -13,5 9,7 -1,9 124 -8,4 9,2 -3,9

Other exited from Full-time 66 4,7 29,3 53,4 12,2 7,1 9.119 -3,3 9,3 -0,5 103 -6,3 9,5 3,7

Others exited from open-ended 98 7,0 26,4 57,5 13,3 6,4 8.538 -6,6 9,2 -1,2 104 -1,1 8,9 -4,3

OTHER FLOWS 124 8,8 32,1 54,4 13,5 6,9 9.803 1,1 9,2 -0,5 116 -0,1 9,2 1,8

TOTAL 1.405 100 21,7 56,7 11,5 7,3 11.459 -1,6 9,7 -0,3 116 -1,1 10,2 -0,2

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who never definitively escaped from the trap of low earnings, by type of change in job type and components of YGE. Years 2015-2019. (Numbers in

thousands, incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2015 and 2019 Total

Incidence % YGE HGE Monthly intensity Duration

Dist. %

Note: (a) Only persistent employ ees

25-34

yrs. Females Foreign.

ISCED

6-7-8 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022

UNCHANGED

Standard 122 8,7 18,3 32,1 10,3 9,1 10.195 -20,6 10,0 -4,0 162 -1,3 6,3 -16,2

Full-time fixed-term 85 6,0 19,0 34,8 12,0 5,8 10.988 0,1 9,4 -1,7 151 0,5 7,7 1,3

Part-time open-ended 472 33,6 12,6 77,4 10,0 7,7 8.513 -3,8 8,8 -2,3 89 0,8 10,9 -2,4

Part-time fixed-term 28 2,0 20,1 69,6 8,6 7,8 5.924 0,0 8,6 -2,2 92 1,3 7,5 1,0

IMPROVED

Passed to Standard 124 8,8 33,3 34,5 13,6 7,6 16.695 16,7 9,3 -0,8 170 10,8 10,5 6,2

Others passed to Full-time 65 4,6 30,5 48,3 14,0 6,6 11.832 6,1 8,8 -1,9 149 12,5 9,0 -3,9

Others passed to open-ended 134 9,5 29,9 55,5 12,5 7,0 12.133 11,2 8,9 -1,2 124 5,4 11,0 6,8

WORSENED

Exited from Standard 106 7,6 24,7 39,0 12,4 7,2 9.824 -20,0 9,1 -4,6 132 -8,1 8,2 -8,7

Other exited from Full-time 50 3,6 26,9 54,5 11,4 6,9 8.831 -8,4 8,8 -2,7 110 -7,6 9,2 1,9

Others exited from open-ended 96 6,8 24,6 61,2 12,5 6,5 8.394 -9,4 8,7 -3,3 110 0,9 8,8 -7,1

OTHER FLOWS 124 8,9 29,8 54,4 12,5 6,2 9.919 1,1 8,7 -2,0 122 2,0 9,4 1,1

TOTAL 1.405 100 21,7 56,7 11,5 7,3 10.199 -2,6 9,0 -2,4 121 1,6 9,6 -2,1

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who never definitively escaped from the trap of low earnings, by type of change in job type and components of YGE. Years 2019-2022. (Numbers in

thousands, incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2019 and 2022 Total

Incidence % YGE HGE Monthly intensity Duration

Dist. %

Note: (a) Only persistent employ ees

25-34

yrs. Females Foreign.

ISCED

6-7-8 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019 2019

Avg.growth

rate 2015-

2019

UNCHANGED

Standard 91 15,7 24,1 37,2 11,1 4,9 16.359 -3,3 8,0 -1,2 178 -2,2 11,5 0,1

Full-time fixed-term 23 4,0 26,3 36,9 12,7 5,9 10.049 -0,4 8,3 -4,5 155 2,7 7,9 1,5

Part-time open-ended 122 21,0 15,6 73,0 14,4 5,0 9.276 -0,2 7,9 -1,4 101 1,1 11,7 0,2

Part-time fixed-term 11 1,8 29,4 66,7 11,5 6,7 5.530 0,0 8,0 -2,6 90 1,0 7,6 1,7

IMPROVED

Passed to Standard 65 11,2 40,9 36,3 16,8 5,3 15.623 7,6 7,8 -1,7 177 6,0 11,3 3,3

Others passed to Full-time 23 4,0 37,1 46,7 17,9 5,5 11.306 5,5 7,9 -3,5 153 11,2 9,3 -1,7

Others passed to open-ended 61 10,6 33,1 55,8 16,4 5,7 10.751 7,3 7,9 -2,0 121 4,2 11,3 5,1

WORSENED

Exited from Standard 62 10,7 28,0 38,7 12,6 4,6 11.213 -10,2 8,1 -4,5 142 -4,3 9,7 -1,8

Other exited from Full-time 24 4,1 33,8 51,0 15,3 5,9 8.506 -2,7 8,0 -2,7 112 -4,4 9,5 4,7

Others exited from open-ended 40 6,9 27,5 57,8 15,6 4,9 8.243 -5,2 8,1 -3,4 110 1,5 9,3 -3,4

OTHER FLOWS 57 9,9 32,4 53,7 15,5 5,9 9.989 2,0 8,0 -2,6 130 2,3 9,7 2,4

TOTAL 580 100 27,7 51,3 14,4 5,3 11.469 -0,1 7,9 -2,3 136 1,3 10,6 1,0

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who never definitively escaped from the trap of low earnings, by type of change in job type and components of HGE. Years 2015-2019. (Numbers in

thousands, incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2015 and 2019 Total

Incidence % YGE HGE Monthly intensity Duration

Dist.

%

Note: (a) Only persistent employ ees

33

Table 2.24

Table 2.25

25-34

yrs. Females Foreign.

ISCED

6-7-8 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022 2022

Avg.growth

rate 2019-

2022

UNCHANGED

Standard 111 19,1 29,5 35,8 13,1 5,1 15.668 -0,8 7,6 -0,4 181 0,3 11,4 -0,7

Full-time fixed-term 23 3,9 22,7 36,6 13,0 5,6 10.691 2,8 8,3 0,5 159 0,2 8,1 2,1

Part-time open-ended 131 22,5 15,5 73,9 14,3 5,2 8.669 -2,2 7,4 -1,2 103 0,2 11,3 -1,2

Part-time fixed-term 9 1,6 22,4 68,3 10,0 6,6 5.373 1,6 7,8 -1,1 91 1,3 7,6 1,4

IMPROVED

Passed to Standard 69 11,9 36,8 34,6 16,1 5,3 15.777 8,6 8,0 0,0 175 5,2 11,3 3,2

Others passed to Full-time 26 4,5 33,6 48,4 16,7 5,4 11.358 6,3 7,9 -1,2 154 11,0 9,3 -3,1

Others passed to open-ended 54 9,3 30,7 54,3 15,3 5,5 11.575 10,1 7,9 -0,1 131 4,2 11,2 5,8

WORSENED

Exited from Standard 44 7,7 35,2 39,4 14,4 4,9 11.606 -10,2 7,9 -2,2 148 -5,0 9,9 -3,4

Other exited from Full-time 19 3,4 30,8 53,9 13,7 5,4 8.601 -5,2 7,8 -1,1 116 -7,6 9,5 3,7

Others exited from open-ended 35 6,0 27,8 60,6 15,6 4,9 8.266 -6,7 7,7 -2,3 116 1,1 9,2 -5,6

OTHER FLOWS 58 10,1 31,8 54,1 14,3 5,4 9.954 1,5 7,9 -0,8 129 1,0 9,8 1,3

TOTAL 580 100 27,7 51,3 14,4 5,3 11.600 0,5 7,8 -0,8 141 1,2 10,6 0,1

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Employees (a) who never definitively escaped from the trap of low earnings, by type of change in job type and components of HGE. Years 2019-2022. (Numbers in

thousands, incidence%, Annual average rates of growth. Values at constant 2015 prices)

Changes in job type between

2019 and 2022 Total

Incidence % YGE HGE Monthly intensity Duration

Dist.

%

Note: (a) Only persistent employ ees

25-29 30-34 35-39 40-44 45-49 50-54 55-60 Female Male

Total employees 7.715 298 744 1.015 1.216 1.480 1.484 1.477 3.049 4.666

Never below any threshold 70,4 26,3 49,4 65,9 72,8 76,1 77,8 77,8 62,4 75,7

Above the thresholds from 2019 11,4 37,2 24,4 14,2 10,0 8,2 7,2 6,2 11,5 11,3

Never definitively above the threshold 18,2 36,6 26,2 20,0 17,2 15,6 15,0 15,9 26,1 13,0

YGE in 2022

Total 24.142 17.701 19.862 22.124 23.687 24.859 26.163 26.613 20.535 26.499

Never below any threshold 28.390 23.388 24.085 26.036 27.359 28.422 29.861 30.762 25.582 29.903

Above the thresholds from 2019 20.179 19.998 20.985 20.398 19.680 19.876 20.058 19.669 18.644 21.200

Never definitively above the threshold 10.199 11.286 10.866 10.452 10.459 10.131 9.888 9.051 9.321 11.348

Total 0,8 9,0 4,4 2,1 0,9 0,3 -0,1 -0,7 0,5 0,9

Never below any threshold 0,1 3,1 2,3 1,4 0,5 0,0 -0,3 -0,7 -0,1 0,3

Above the thresholds from 2019 12,3 18,3 15,1 12,4 10,5 10,1 9,9 9,4 11,3 12,8

Never definitively above the threshold -1,7 7,9 1,3 -1,6 -2,1 -2,6 -2,8 -5,0 -1,7 -1,7

HGE in 2022

Total 13,3 10,1 11,1 12,3 13,0 13,5 14,2 14,7 12,5 13,7

Never below any threshold 14,3 11,3 12,0 13,2 13,9 14,4 15,1 15,5 13,7 14,6

Above the thresholds from 2019 10,5 10,1 10,8 10,7 10,4 10,3 10,5 10,5 10,3 10,6

Never definitively above the threshold 9,0 8,8 8,9 9,0 9,1 9,0 9,0 9,1 8,8 9,2

Total 0,1 1,9 1,6 1,2 0,5 -0,1 -0,4 -0,7 -0,1 0,0

Never below any threshold 0,3 2,8 2,1 1,6 0,8 0,2 -0,2 -0,5 0,2 0,3

Above the thresholds from 2019 1,2 2,7 2,5 1,5 0,6 0,0 0,1 -0,1 0,9 1,1

Never definitively above the threshold -1,2 0,6 -0,2 -0,9 -1,3 -1,6 -1,7 -2,1 -1,2 -1,4

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

% Composition

Average annual rate of growth 2015-2022

Average annual rate of growth 2015-2022

Note: (a) Only persistent employ ees

Gross earnings of employees (a), by age class, gender, and level of gross earnings (Numbers in thousands, % composition, Average annual

rates of change. Values at constant 2015 prices)

Level of gross earnings

Age class Gender

Totale

34

Table 2.26

Part 3. Employers and low earnings

One of the side issue in the debate on low earning is which economic activities and what kind of enterprises

generate them. Although poor pay conditions are spread across all types of businesses and economic activities,

there are important differences that are worth of investigation. Based on the results and approaches described

in Part 2 of the paper, in this paragraph we analyze, firstly, low earnings on a cross-section perspective. By

exploiting the link between employees and their main employer on a yearly basis, we provide an insight of the

general characteristics of the business structure (such as economic activity, size, and type of governance)

associated to the level of gross earnings in years 2015-2022. Secondly, we investigate on a longitudinal

perspective, which conditions and which characteristics of the employers were associated to the transitions of

workers from below to above the thresholds, or to the employees that never had the opportunity to escape from

the low-earnings trap.

3.1. Business structure, employment and employees

Istat business register (BR) counts about 4,5 mln enterprises: one out of three has at least one employee, so

about 1,5 mln enterprises are involved in our analysis each year (Table 3.1). Most of them (1,25 mln) are

micro-enterprises with less than 10 persons employed. More than 500 thousands are individual enterprises,

although 75% of the employees in the register are concentrated in enterprises with more complex governance

and a significant portion of employees work in larger enterprises. The business register estimates 12,7 mln

Totale 0-2 3 4-5 6 7-8

Total employees 7.715 2.311 4.048 367 958 30

Never below any threshold 70,4 66,3 70,9 66,5 79,3 80,8

Above the thresholds from 2019 11,4 11,1 11,4 16,4 10,2 11,0

Never definitively above the threshold 18,2 22,6 17,7 17,1 10,4 8,2

YGE in 2022

Total 24.142 20.120 23.406 25.784 35.852 39.122

Never below any threshold 28.390 23.777 27.286 30.669 40.639 43.613

Above the thresholds from 2019 20.179 19.180 19.541 21.797 24.550 27.171

Never definitively above the threshold 10.199 9.858 10.359 10.645 10.530 10.934

Total 0,8 0,1 0,6 2,5 1,7 2,5

Never below any threshold 0,1 -0,5 -0,1 1,4 1,1 1,8

Above the thresholds from 2019 12,3 10,3 11,8 16,1 17,1 20,4

Never definitively above the threshold -1,7 -2,0 -1,5 -0,1 -2,1 -1,5

HGE in 2022

Total 13,2 11,2 12,9 14,2 19,2 20,6

Never below any threshold 14,3 11,9 13,8 15,6 20,7 22,1

Above the thresholds from 2019 10,5 9,8 10,3 11,4 12,9 14,2

Never definitively above the threshold 9,0 8,8 9,0 9,4 9,7 10,1

Total 0,0 -0,5 -0,2 1,0 1,0 1,6

Never below any threshold 0,3 -0,4 0,1 1,5 1,4 2,0

Above the thresholds from 2019 1,0 0,2 0,9 2,3 2,7 3,5

Never definitively above the threshold -1,3 -1,3 -1,3 -0,8 -1,4 -1,1

Sources: Istat, Population register 2015-2022, Business register 2015-2021, Income register 2015-2022. Inps, Uniemens 2015-2022.

Average annual rate of growth 2015-2022

Gross earnings of employees (a), by level of education and level of gross earnings (Numbers in

thousands, % composition, Average annual rates of change. Values at constant 2015 prices)

Posizione rispetto alla soglia

Education level (ISCED)

Average annual rate of growth 2015-2022

% Composition

Note: (a) Only persistent employ ees

35

employees29. The difference between the number of employees estimated in the register (a weekly average)

and the headcount of the individuals enrolled during the whole year – at least for a few weeks – deserves some

comment. In those domains where lower quality contracts are more frequently used, especially fixed-term

contracts, the duration of labour relations is shorter. The difference between headcounts and the standard

measure of employment, that would be null if jobs were ideally continuous along the year, is present in every

economic sector and size class, and it is extremely interesting since it describes at a glance the degree of

stability of jobs in each specific domain. In 2021, the 14,5 mln individuals that were enrolled in industry and

services correspond to 12,7 mln employees in the average week. This 14% scrap is an average, and varies

considerably across sectors, the difference being greater in some services, like for instance horeca, support

services (mainly cleaning and temporary work agencies), recreation, education, and constructions. About 4,5

mln workers are involved in these sectors and they count for 3,4 mln employees. The same scrap is lower than

5% in Industry and Finance where more than 70% of individuals experienced only standard jobs during the

year. In some sectors (horeca, recreation, support services) there is a widespread use of fixed term contracts

while in others (trade and most services serving households) part-time contracts prevail30.

Table 3.1

29 According to international standards, the business register reports the annual average of the weekly number of

employees by enterprise. An individual enrolled for 6 months, for example, is equivalent to 0,5 employees on an annual

basis. 30 It is important to make clear that in this context of our analysis the term employee is not intended as we are not using

the definition of the register since we concentrate on the individuals traced in the pay roll of the enterprises, with a head-

count approach, and the term employee will be used to address these individuals and not their equivalent measure in terms

of employment. That definition is anyway adopted to determine for example the size class of enterprises.

Employers and employees, by type of contract, Nace, business size and governance. Year 2021

Nace

N

(.000) % Avg.

always

Standard

always

Full-time

fixed term

always Part-

time open-

ended

always

Part-time

fixed-term

other

combi-

nations

Total 1.468 12.746 100 8,7 14.530 100 14,0 52,0 9,8 18,0 7,7 12,5

NACE

C MANUFACTURING 212 3.261 25,6 15,4 3.373 23,2 103 76,0 5,2 9,3 1,7 7,8

B,D,E REST OF INDUSTRY 10 304 2,4 30,6 312 2,1 2,5 79,2 4,5 7,4 2,3 6,6

F CONSTRUCTION 183 929 7,3 5,1 1.100 7,6 18,4 58,7 15,7 6,9 2,7 16,0

G TRADE 356 2.215 17,4 6,2 2.435 16,8 9,9 49,1 5,0 26,5 7,5 11,9

H TRANSPORTATION 51 1.012 7,9 19,9 1.108 7,6 9,5 62,5 9,5 9,8 4,5 13,7

I HORECA 215 999 7,8 4,6 1.503 10,3 50,4 15,6 20,3 25,0 19,0 20,2

J INFORMATION 40 511 4,0 12,9 561 3,9 9,9 71,5 7,5 13,1 2,2 5,7

K FINANCE 22 452 3,5 20,6 474 3,3 5,0 79,8 1,7 14,2 0,6 3,7

L,M PROFESSIONAL 140 685 5,4 4,9 760 5,2 10,9 57,8 6,6 21,4 4,4 9,9

N SUPPORT SERVICES 57 1.297 10,2 22,6 1.616 11,1 24,7 23,4 20,9 20,6 15,8 19,3

P EDUCATION 11 83 0,7 7,3 104 0,7 24,3 26,1 5,9 29,2 26,9 11,9

Q HUMAN HEALTH 69 653 5,1 9,5 723 5,0 10,7 32,2 4,2 37,4 13,4 12,8

R RECREATION 21 103 0,8 5,0 172 1,2 66,3 20,2 23,4 17,4 23,9 15,1

S OTHER SERVICES 81 242 1,9 3,0 290 2,0 19,5 31,5 6,2 36,9 10,9 14,5

SIZE CLASS

Micro (<10 pers.employed) 1.257 3.119 24,5 2,5 3.972 27,3 27,3 35,3 10,1 28,3 11,7 14,7

Small (10-49 pers.employed) 182 3.182 25,0 17,5 3.552 24,4 11,6 54,7 10,4 14,6 6,6 13,7

Medium (50-249 pers.employed) 24 2.356 18,5 97,5 2.534 17,4 7,6 63,3 8,8 11,5 5,5 10,9

Large (>250 pers.employed) 4 4.089 32,1 955,5 4.472 30,8 9,4 58,4 9,7 15,2 6,2 10,5

GOVERNANCE

Individual firms 522 1.062 8,4 2,0 3.972 9,4 134 27,2 9,0 35,6 13,4 14,8

Other partnerships 284 1.064 8,4 3,7 3.552 8,8 127 40,2 11,1 24,6 9,9 14,2

Joint-stock companies 616 9.361 73,4 14,9 2.534 72,2 118 58,5 10,2 13,5 6,0 11,8

Other companies 45 1.259 9,9 27,9 4.472 30,8 118 37,6 6,8 28,9 12,9 13,8

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Notes: (a) Indiv iduals in the resident population (only residents in household). (b) Av erage w eekly employ ees.

diff%

Head-

count

vs. BR

Business Register (BR) data

Employees (b) by type of labour contract in the year (incidence, % )

Individuals (a) in the pay-roll of the enterprises during the year

No.

enter-

prises

(.000)

Number

(.000) %

36

3.2. Employers and gross earnings

The tie between standard jobs and the level of hourly wages previously highlighted inevitably implies that the

firms providing better pay conditions are also those where full-time, permanent jobs prevail (Table 3.2). Recent

studies remark that the number of these firms is relatively small but that they are large enough to recruit the

bulk of non-agricultural workforce and the most performing activities where hourly wages are on average

above 15 euros (Chart 3.1)31. As one moves away from them, hourly wages become poorer as firms seem

acting mostly on job intensity offering part-time and fixed-term contracts. Our entire set of low-paid employees

gradually experienced lower and lower intensities and durations of the employment relationships, showing

hourly wages steadily below the average. In this respect the firm scale and the type of governance also matter.

Employees of micro-enterprises and individual firms show very low annual earnings due to lower levels of all

the wage components.

On the other hand, if we compare the distribution of employees by YGE and by HGE it is clear that the first

distribution is less concentrated than the latter (Chart 3.2). Take for example the median value in YGE

distribution: it was about 18 thousands euro in 2021 (at constant 2015 prices). This median ranges from 31,000

for an employees in finance and communications to 12,000 euro for an employees in personal services, passing

through 23,000 euro for industry and to 19,000 in construction, 16,000 in business services and 13,000 in

Horeca. One half of all employees has a YGE between 9 and 25,000 euro. Consider now HGE. The overall

median is 10,4 euro, ranging from 9,3 in personal services to 11,5 in industry, peaking 16,3 in finance and

communications. Interquartile range is between 9 and 13 euro. Only for a small part of workers the variability

in YGE can be explained by HGE, while intensity and duration play a major role in explaining the differences

among economic activities.

Between 2015 and 2022 the dynamics of real gross earnings showed a sharp decline (Table 3.3): the average

annual rate of decrease was 1,5%, and more than 2% in some services while in the largest sectors was almost

in the average. Up till 2018, the decline has been harder in Horeca and in some services serving household.

The reduction in monthly intensity of jobs drove the decline, due a more intense use of part-time and short-

term contracts. In the same time span, real HGE also lost ground continuing the decline also in the following

years until 2021, although with some remarkable differences among sectors. In manufacturing, for instance,

real hourly earnings were quite stable, in construction and trade they decreased, in Horeca they marginally

recovered from the former decline. Between 2018 and 2021, duration and monthly intensity had the most

important role in driving the increase in annual earnings. In 2022, the rise in inflation cut real earnings quite

uniformly across sectors.

31 These aspects were studied in a cross section analysis in Istat (2022).

37

Table 3.2

Gross earnings of employees, by Nace, size class and governance. Year 2021 (values at constant 2015 prices)

Nace

Hourly

gross

earnings

Monthly

intensity

(b)

Duration

(c)

Hourly

gross

earnings

Monthly

intensity Duration

Total 14.530 20.201 13,1 150,4 10,3 100 100 100 100

NACE

C MANUFACTURING 3.373 25.175 13,5 165,2 11,3 125 104 110 110

B,D,E REST OF INDUSTRY 312 29.215 15,7 163,1 11,4 145 120 108 111

F CONSTRUCTION 1.100 18.916 11,7 162,1 10,0 94 90 108 97

G TRADE 2.435 19.603 12,6 145,7 10,7 97 97 97 104

H TRANSPORTATION 1.108 21.755 12,8 157,9 10,7 108 98 105 105

I HORECA 1.503 9.046 9,7 117,4 7,9 45 74 78 77

J INFORMATION 561 28.548 16,5 160,9 10,7 141 126 107 105

K FINANCE 474 42.680 23,2 157,2 11,7 211 178 105 114

L,M PROFESSIONAL 760 24.036 14,8 152,8 10,6 119 113 102 103

N SUPPORT SERVICES 1.616 12.744 10,1 137,7 9,2 63 77 92 89

P EDUCATION 104 12.029 11,9 110,6 9,2 60 91 74 89

Q HUMAN HEALTH 723 14.610 10,8 131,3 10,3 72 83 87 100

R RECREATION 172 17.836 15,7 152,6 7,4 88 121 101 72

S OTHER SERVICES 290 11.270 8,8 129,0 9,9 56 67 86 96

SIZE CLASS

Micro (<10 pers.employed) 3.972 13.597 10,7 134,7 9,4 67 82 90 91

Small (10-49 pers.employed) 3.552 19.091 11,9 154,9 10,4 95 91 103 101

Medium (50-249 pers.employed) 2.534 23.678 13,8 159,4 10,8 117 105 106 105

Large (>250 pers.employed) 4.472 24.978 15,1 154,1 10,7 124 116 102 104

GOVERNANCE

Individual firms 3.972 11.251 9,7 124,6 9,3 56 74 83 91

Other partnerships 3.552 14.468 10,4 142,1 9,8 72 80 94 95

Joint-stock companies 2.534 22.375 13,7 155,9 10,5 111 105 104 102

Other companies 4.472 17.568 12,4 137,7 10,2 87 95 92 100

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Notes: (a) Indiv iduals in the resident population (only residents in household). (b) Number of w orkable hours per month. (c) Number of months as

employ ees

Components

Number

(.000)

Components

Per capita gross earnings in 2021

Annual

gross

earnings

Annual

gross

earnings

Indices. Base: Total=100

38

Chart 3.1. NACE sections and Manufacture divisions by average hourly gross earnings and monthly

intensity of jobs. Bubbles are proportional to average duration of labour contracts. Year 2021

a) NACE sections

b) Manufacture divisions

Chart 3.2

BDE C

F

G

H

I

J

M

N

P

Q

R

S

100

110

120

130

140

150

160

170

8 9 10 11 12 13 14 15 16 17

M o

n th

ly w

o rk

ab le

h o

u rs

Hourly gross earnings

10

11

12

13

14

1516

17

18

19

20

21 22

23

24

25 2627 28

29 30

31

32

33

140

145

150

155

160

165

170

175

180

10 12 14 16 18 20 22

M o

n th

ly w

o rk

ab le

h o

u rs

Hourly gross earnings

0

10

20

30

40

50

60

70

80

90

100

0 4.000 8.000 12.000 16.000 20.000 24.000 28.000 32.000 36.000 40.000 44.000 48.000 52.000 56.000 60.000

Yearly gross earnings (YGE)

Employees by Yearly gross earnings (YGE) and Nace (Year 2021. Cumulated %)

Industry Construction Trade and Horeca

Business services Communication and Finance Personal services

TOTAL

0

10

20

30

40

50

60

70

80

90

100

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Hourly gross earnings (HGE)

Employees by Hourly gross earnings (HGE) and Nace (Year 2021. Cumulated %)

Industry Construction Trade and Horeca

Business services Communication and Finance Personal services

TOTAL

39

Table 3.3

3.3. Employers and employees with low earnings

The economic activities with a high propensity to pay low wages emerge quite clearly from Table 3.4 and most

of them belong to services. In Horeca and recreation, the sectors most affected by undeclared employment,

more than two employees out of three is below YGE threshold. In support services (where subcontracting is

frequent), education and other household services more than 50% of employees have low YGE. In construction

the incidence of employees below the annual threshold, which was more than 30% in 2015, declined due

mainly to an increase in the duration of labour contracts: this sector benefited strongly from the specific fiscal

incentives provided by the Government to refurbish private outlets.

The incidence of low HGE follows in part the same scheme: nevertheless, it should be noticed the higher share

of individuals below the threshold in support services and in the other services where contractual hourly wages

are generally very low and subcontracting very high.

Low earnings have a relatively minor impact in manufacturing; nonetheless. These activities still account for

a remarkable share of low wage earners, given that they involve a large number of employees. If we consider

the threshold on annual earnings, nearly 10% of employees below the threshold come from manufacturing and

another 15% from trade activities. Horeca accounts almost for another 25%, and support services nearly 20%.

On the other side, if we consider hourly gross earnings we find that support services account for 30% of total

individuals below the threshold. Manufacturing, trade and the other services serving households also provide

larger shares. All these sectors together explain more than two thirds of individuals with low hourly earnings.

If we consider two-digit NACE, and in particular the first twenty divisions with higher incidence of employees

below the threshold of YGE, we find out that they account for more than 50% of total employees in industry

and services and nearly 80% of employees with low annual earnings (Table 3.5). At the top of the rank, we

find services like recreation, Horeca, cleaning services, personal and education services, employment agencies.

In all these cases, more than a half of employees are in the low-wage area. We find in this rank also

manufacturing activities dealing with food products and wearing apparels. Also construction belong to this

restricted set. The average incidence of individuals with low annual earnings of this top twenty activities is

45,1%, more than the triple than the rest of NACE divisions.

Most of the sectors rank similarly with reference to low hourly gross earnings. In this case, the top twenty

divisions include textiles and leather in manufacturing and all the logistics. At the top of the rank we find the

activities related to security and cleaning of offices and buildings, often tied to outsourcing by larger enterprises

and public administration. These top twenty divisions account for more than 75% of the employees with low

hourly wages.

Nace

2015-

2018

2018-

2021

2015-

2022 (d)

2015-

2018

2018-

2021

2015-

2022 (d)

2015-

2018

2018-

2021

2015-

2022 (d)

2015-

2018

2018-

2021

2015-

2022 (d)

Total -1,5 0,4 -1,5 -0,7 -0,1 -1,3 -1,2 0,4 -0,4 0,3 0,1 0,2

C MANUFACTURING -0,7 0,4 -1,2 -0,6 0,0 -1,1 -0,4 0,1 -0,2 0,4 0,2 0,1

B,D,E REST OF INDUSTRY -0,5 0,0 -1,4 -0,9 -0,8 -1,8 0,2 0,6 0,4 0,1 0,2 0,1

F CONSTRUCTION -0,9 0,5 -0,8 -1,1 -0,4 -1,5 -1,0 -0,1 -0,5 1,3 1,0 1,2

G TRADE -0,7 0,0 -1,3 0,1 -0,5 -1,2 -1,4 0,2 -0,4 0,6 0,4 0,3

H TRANSPORTATION -0,5 0,0 -1,3 -0,3 -0,3 -1,2 -0,6 0,1 -0,2 0,4 0,1 0,2

I HORECA -3,3 -0,9 -2,2 -0,8 0,4 -1,3 -3,1 0,2 -1,1 0,5 -1,4 0,1

J INFORMATION -1,8 -0,6 -2,0 -1,1 -0,5 -1,6 -1,1 0,2 -0,4 0,4 -0,3 0,0

K FINANCE 0,1 0,2 -1,1 -0,1 0,1 -1,1 -0,5 0,0 -0,2 0,8 0,0 0,2

L,M PROFESSIONAL 0,7 0,3 -0,6 0,1 -0,2 -0,9 -0,5 0,3 -0,1 1,1 0,2 0,5

N SUPPORT SERVICES -1,0 1,3 -0,8 -0,9 0,0 -1,3 -0,3 1,2 0,3 0,2 0,1 0,2

P EDUCATION -2,0 -0,5 -1,8 0,0 -0,5 -1,3 -3,6 0,3 -1,3 1,7 -0,3 0,8

Q HUMAN HEALTH -2,5 0,2 -2,1 -0,6 0,7 -1,0 -2,2 0,0 -0,9 0,3 -0,5 -0,2

R RECREATION -0,3 5,0 0,0 0,5 2,3 -0,8 -1,4 3,3 0,7 0,6 -0,7 0,1

S OTHER SERVICES -2,4 0,4 -1,8 -0,6 -0,2 -1,3 -2,4 0,1 -0,9 0,6 0,5 0,3

Per capita gross earnings of employees, by Nace and component. Years 2015-2022 (average annual rates of growth at constant 2015 prices)

Annual gross earnings Hourly gross earnings Monthly intensity (b) Duration (c)

Notes: (a) Indiv iduals in the resident population (only resients in household). (b) Number of w orkable hours per month. (c) Number of months as employ ees. (d) 2022 data are prov isional, since

Business register data are referred to 2021

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

40

Table 3.4

Table 3.5

3.5. The enterprises and the employees who escape from the trap of low earnings

In Part 2 we described the behavior of the cohort of employees persistently in employment in the period 2015-

2022; , in particular, we referred to the exit of some individuals from conditions of low earnings. The focus

was on the group of 878 thousand employees who improved their earning conditions starting from 2019 after

Employees (a) with low gross earnings, by Nace and type of threshold. Years 2015-2022

2015 2016 2017 2018 2019 2020 2021 2022 (b) 2015 2016 2017 2018 2019 2020 2021 2022 (b)

LOW ANNUAL EARNINGS

TOTALE 30,3 29,5 30,2 30,1 30,1 29,9 29,7 29,3 100 100 100 100 100 100 100 100

C MANUFACTURING 13,7 13,0 13,0 12,7 12,4 12,4 12,2 12,4 11,4 10,9 10,3 10,0 9,7 9,9 9,5 9,5

B,D,E REST OF INDUSTRY 9,5 9,2 9,1 9,2 9,1 9,0 8,9 8,9 0,7 0,7 0,7 0,6 0,6 0,7 0,6 0,6

F CONSTRUCTION 30,3 28,3 28,0 27,6 26,6 25,3 24,9 22,8 7,7 7,0 6,4 6,2 6,0 6,0 6,4 6,4

G TRADE 27,7 26,3 26,1 26,0 26,3 26,4 26,2 26,8 15,4 15,1 14,6 14,5 14,7 14,9 14,8 14,8

H TRANSPORTATION 20,4 19,4 19,3 18,8 19,0 18,9 18,2 18,0 5,3 5,2 5,0 4,8 4,9 4,9 4,7 4,7

I HORECA 66,9 65,9 67,2 66,9 67,0 69,4 69,5 65,8 21,6 22,3 24,4 24,8 25,3 24,2 24,2 24,2

J INFORMATION 15,8 13,8 13,9 14,8 15,1 14,1 15,9 15,8 1,9 1,7 1,7 1,8 1,8 1,8 2,1 2,1

K FINANCE 6,5 4,9 4,7 4,9 4,7 4,9 5,2 5,9 0,8 0,6 0,5 0,5 0,5 0,5 0,6 0,6

L,M PROFESSIONAL 28,1 24,7 24,3 24,4 23,8 22,8 23,7 24,0 4,3 4,1 3,9 4,0 3,9 3,9 4,2 4,2

N SUPPORT SERVICES 53,8 53,2 52,9 51,8 51,5 53,0 51,0 49,4 17,7 18,4 18,9 18,9 18,5 19,1 19,1 19,1

P EDUCATION 58,6 55,6 54,7 54,0 53,6 54,1 54,2 52,8 1,3 1,3 1,2 1,2 1,2 1,3 1,3 1,3

Q HUMAN HEALTH 37,9 38,7 37,1 37,0 37,2 37,9 37,5 37,8 5,7 6,2 5,8 5,9 6,0 6,4 6,3 6,3

R RECREATION 64,7 62,8 64,7 65,4 66,4 64,7 65,5 65,6 2,5 2,5 2,7 2,8 2,8 2,5 2,6 2,6

S OTHER SERVICES 56,8 56,2 56,4 56,9 57,3 57,4 56,1 55,9 3,8 3,9 3,9 4,0 4,0 4,0 3,8 3,8

LOW HOURLY EARNINGS

TOTALE 9,4 9,6 11,3 11,9 11,5 10,9 10,5 9,3 100 100 100 100 100 100 100 100

C MANUFACTURING 5,3 5,4 6,2 6,4 5,6 5,6 5,2 4,1 14,3 14,0 13,2 12,6 11,4 12,1 11,5 11,5

B,D,E REST OF INDUSTRY 2,3 2,5 3,2 3,6 3,3 3,1 2,9 2,4 0,6 0,6 0,6 0,6 0,6 0,6 0,6 0,6

F CONSTRUCTION 5,6 6,3 7,6 8,2 7,8 8,1 7,1 5,1 4,6 4,8 4,6 4,7 4,6 5,3 5,1 5,1

G TRADE 4,0 4,0 4,6 4,9 4,8 4,9 4,9 4,5 7,2 7,0 6,9 6,8 7,1 7,5 7,8 7,8

H TRANSPORTATION 8,7 8,7 9,3 9,3 8,8 8,0 7,6 6,5 7,3 7,1 6,4 6,0 5,9 5,7 5,5 5,5

I HORECA 14,6 14,3 18,5 18,9 18,5 16,5 16,1 15,3 15,2 14,9 17,9 17,7 18,2 15,8 15,8 15,8

J INFORMATION 3,0 3,0 3,5 4,0 4,0 3,5 3,9 3,5 1,2 1,1 1,1 1,2 1,3 1,2 1,4 1,4

K FINANCE 1,3 1,3 1,4 1,5 1,5 1,5 1,6 1,5 0,5 0,5 0,4 0,4 0,4 0,4 0,5 0,5

L,M PROFESSIONAL 8,1 7,1 8,0 8,7 8,7 7,7 7,7 7,1 4,0 3,6 3,4 3,6 3,8 3,6 3,8 3,8

N SUPPORT SERVICES 25,1 25,9 28,6 30,5 30,0 30,3 28,4 24,8 26,7 27,6 27,3 28,1 28,2 29,9 30,0 30,0

P EDUCATION 11,8 12,4 14,0 14,5 14,3 14,6 15,9 12,7 0,8 0,9 0,8 0,8 0,8 0,9 1,1 1,1

Q HUMAN HEALTH 8,6 8,9 11,7 13,0 11,6 8,6 8,3 7,1 4,1 4,4 4,9 5,2 4,9 4,0 3,9 3,9

R RECREATION 16,7 17,0 20,5 21,6 22,2 21,4 22,5 22,1 2,1 2,1 2,3 2,3 2,5 2,3 2,5 2,5

S OTHER SERVICES 52,7 52,9 54,8 56,3 55,8 56,0 55,7 52,6 11,5 11,4 10,0 9,9 10,3 10,6 10,5 10,5

Incidence on total employees Distribution

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Notes: (a) Indiv iduals aged 15-64 y rs. in the resident population (only residents in household, ex cludind those in retirement and entrepreneurs), in the pay -roll of enterprises in eah y ear. (b) 2022 data are

prov isional, since Business register data are referred to 2021

Nace code and description (section and two-digit) Total

with low

annual

earnings Nace code and description (section and two-digit) Total

with low

hourly

earnings

R 93 RECREATION Recreation and sports 74,7 0,7 1,9 S 96 OTHER SERVICES Other personal services 58,3 1,9 10,3

I 56 HORECA Food and beverage 69,5 8,1 19,0 N 80 SUPPORT SERVICES Security and investigation 56,4 0,6 3,4

I 55 HORECA Accommodation 69,3 2,2 5,2 N 81 SUPPORT SERVICES Services to buildings and landscape 41,5 3,5 13,7

N 81 SUPPORT SERVICES Services to buildings and landscape 61,2 3,5 7,2 R 93 RECREATION Sports, amusement and recreation 28,6 0,7 2,0

S 96 OTHER SERVICES Other personal services 57,7 1,9 3,6 M 73 PROFESSIONAL Advertising and market research 27,2 0,5 1,2

P 85 EDUCATION Education 54,2 0,7 1,3 N 82 SUPPORT SERVICES Office and business support 22,2 2,0 4,1

N 78 SUPPORT SERVICES Employment 53,1 4,5 8,0 C 14 MANUFACTURING Manufacture of wearing apparel 20,8 1,1 2,2

Q 88 HUMAN HEALTH Social work without accommodation 49,7 1,5 2,5 N 78 SUPPORT SERVICES Employment 19,5 4,5 8,3

N 82 SUPPORT SERVICES Office and business support 39,7 2,0 2,6 I 56 HORECA Food and beverage service 16,9 8,1 13,0

L 68 REAL ESTATE Real estate 38,8 0,7 0,9 P 85 EDUCATION Education 15,9 0,7 1,1

Q 87 HUMAN HEALTH Residential care 38,3 1,4 1,8 C 15 MANUFACTURING Manufacture of leather 14,2 0,9 1,2

M 73 PROFESSIONAL Advertising and market research 38,1 0,5 0,6 I 55 HORECA Accommodation 13,0 2,2 2,8

N 80 SUPPORT SERVICES Security and investigation 33,3 0,6 0,7 Q 88 HUMAN HEALTH Social work without accommodation 12,5 1,5 1,8

G 47 TRADE Other retail sale in specialised stores 32,6 9,2 10,1 H 52 TRANSPORTATION Support for transportation 12,1 2,8 3,3

C 14 MANUFACTURING Manufacture of wearing apparel 30,5 1,1 1,1 Q 87 HUMAN HEALTH Residential care 9,8 1,4 1,3

C 10 MANUFACTURING Manufacture of food products 28,4 2,6 2,5 L 68 REAL ESTATE Real estate 9,7 0,7 0,6

Q 86 HUMAN HEALTH Human health 28,2 2,1 2,0 G 45 TRADE Trade and repair of motor vehicles 9,7 1,8 1,6

F 41 CONSTRUCTION Construction of buildings 27,8 2,2 2,0 F 43 CONSTRUCTION Specialised construction 9,5 4,7 4,3

F 43 CONSTRUCTION Specialised construction 25,5 4,7 4,1 C 13 MANUFACTURING Manufacture of textiles 9,0 0,7 0,6

M 69 PROFESSIONAL Legal and accounting activities 24,1 1,3 1,0 M 74 PROFESSIONAL Other professional, technical etc. 8,4 0,6 0,5

Total 20 with highest incidence below the threshold (b) 45,1 51,4 78,0 Total 20 with highest incidence below the threshold (b) 29,3 40,9 77,3

Rest of Nace 13,4 48,6 22,0 Rest of Nace -56,1 59,1 22,7

TOTAL 29,7 100 100 TOTAL 10,5 100 100

Share on total

employees (a)%

Employess

below the

threshold

Two-digit Nace with the highest incidence of employees with low gross earnings, by type of threshold. Year 2021

LOW ANNUAL GROSS EARNINGS LOW HOURLY GROSS EARNINGS

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Share on total

employees (a)%

Employess

below the

threshold

Notes: (a) Indiv iduals aged 15-64 y rs. in the resident population (only residents in household, ex cludind those in retirement and entrepreneurs), in the pay -roll of enterprises in eah y ear. (b) Only Nace tw o-digit w ith at least 50 thousands employ eees

41

experiencing low annual earnings in the years before. In this section we try to answer few questions: how did

they get this result? Did they remain in the same enterprise? or did they change employer in the same sector?

or more drastically did they change economic activity?

If we look at the distribution of these workers across NACE sections we observe that more than 70% of those

employees improved its conditions by changing employer between 2015 and 2022 (Table 3.6)32. By

considering the characteristics of the employer at the beginning and at the end of the period, we see that this

sub-population of employees moved towards sectors (such as industry, finance, transportation, human health,

information) generally characterized by higher annual gross earnings, and they moved from low earnings

activities, like Horeca, support services and recreation where only less than 20% of 2015 employees succeeded

to overcome the low earnings threshold. In Manufacturing, for instance, one third of employees remained with

the same employer and more than 70% remained in the same NACE: something similar happened to those that

in 2015 were employed in finance, or trade, transportation and human health. The case of construction is partly

different: most employees tended to change employer remaining in the same sector.

Interesting details can also be observed considering the characteristics of the employer (Table 3.8). Here the

change of employer regarded mostly employees that left micro-enterprises for larger businesses, in particular

for medium enterprises. The change took place even for those who stayed with a same employer in all the

previous years: changing employer was often associated with higher YGE or implied a shift towards more

structured businesses (joint stock companies), away from individual firms and other partnerships.

The escape from low pay sectors is thus generally the only winning strategy from low earnings, since sectors

where there are more opportunities to improve general pay conditions are few.

The origin-destination flows of who improved their conditions by changing NACE put in evidence frequent

transitions to Manufacture from support services, construction, trade and Horeca (Table 3.7). Also frequent

are the transitions towards the rest of business services especially from manufacture, and again construction,

trade and horeca.

Table 3.6

32 The fact that more than 40% had more than two employers in the period witnesses the mobility of these individuals.

Cohoort of persistent 2015-2022 employees over the threshold of low earnings from 2019, by events of change of enterprise and Nace, and year

Nace 2015 2021 % change N incid.% N incid.% N incid.% N incid.%

C MANUFACTURING 140.095 203.895 45,5 101.233 72,3 46.013 32,8 55.220 39,4 38.862 27,7

B,D,E REST OF INDUSTRY 10.324 17.454 69,1 6.491 62,9 3.858 37,4 2.633 25,5 3.833 37,1

F CONSTRUCTION 84.585 79.713 -5,8 55.700 65,9 16.956 20,0 38.744 45,8 28.885 34,1

G TRADE 159.596 173.887 9,0 111.146 69,6 54.161 33,9 56.985 35,7 48.450 30,4

H TRANSPORTATION 71.487 84.543 18,3 47.809 66,9 19.688 27,5 28.121 39,3 23.678 33,1

I HORECA 105.043 62.424 -40,6 50.675 48,2 19.934 19,0 30.741 29,3 54.368 51,8

J INFORMATION 27.939 31.211 11,7 17.529 62,7 8.720 31,2 8.809 31,5 10.410 37,3

K FINANCE 13.561 18.178 34,0 11.156 82,3 8.172 60,3 2.984 22,0 2.405 17,7

L,M PROFESSIONAL 43.776 45.577 4,1 23.298 53,2 14.287 32,6 9.011 20,6 20.478 46,8

N SUPPORT SERVICES 145.610 87.391 -40,0 47.579 32,7 16.362 11,2 31.217 21,4 98.031 67,3

P EDUCATION 5.669 5.847 3,1 3.784 66,7 2.959 52,2 825 14,6 1.885 33,3

Q HUMAN HEALTH 38.682 44.944 16,2 32.524 84,1 18.267 47,2 14.257 36,9 6.158 15,9

R RECREATION 11.240 6.103 -45,7 3.647 32,4 2.210 19,7 1.437 12,8 7.593 67,6

S OTHER SERVICES 20.240 16.680 -17,6 11.444 56,5 6.370 31,5 5.074 25,1 8.796 43,5

Total 877.847 877.847 0,0 524.015 59,7 237.957 27,1 286.058 32,6 353.832 40,3

Change Nace

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Notes: (a) Indiv iduals aged 15-64 y rs. in the resident population (only residents in household, ex cludind those in retirement and entrepreneurs), in the pay -roll of enterprises persistently in the y ears 2015-2022.

Number of employees

Same Nace

Total Same enterprise Change enterprise

42

Table 3.7

Table 3.8

3.6. The enterprises and the employees in trap of low earnings

Turning attention to the complementary cohort of persistent workers who could never escape the low earnings

trap, we see that they amount to 1,4 million, and have a higher propensity to remain in the original sector and

are more tied to the same employer over time (Table 3.9 vs. Table 3.6). When they moved to other sectors,

they did it towards business and personal services sectors, included the weakest ones with respect to the level

of YGE. Horeca and recreation were the activities progressively abandoned, in this case by more than 10% of

those employees33.

33 Pandemics might have had a role in this unfavorable dynamics.

Nace 2015 Total

Other industry

(B,C,D,E) Construction (F)

Trade & Horeca

(G, I)

Communication &

Finance (J,K)

Other business

services

(H,L,M,N)

Other personal

services (P,S)

C MANUFACTURING 38.862 3,6 14,4 32,0 4,0 41,3 4,6

B,D,E REST OF INDUSTRY 3.833 22,6 17,0 13,6 2,6 40,7 3,4

F CONSTRUCTION 28.885 44,4 15,2 2,7 34,6 3,0

G TRADE 48.450 37,4 6,8 5,2 9,1 34,4 7,1

H TRANSPORTATION 23.678 33,1 9,0 23,6 2,9 27,5 3,9

I HORECA 54.368 27,8 5,3 27,0 3,9 27,8 8,1

J INFORMATION 10.410 21,1 4,3 20,5 6,4 41,2 6,6

K FINANCE 2.405 17,3 3,7 21,0 11,1 40,7 6,1

L,M PROFESSIONAL 20.478 26,3 6,8 23,5 18,3 18,4 6,8

N SUPPORT SERVICES 98.031 44,9 6,4 21,1 5,1 16,7 5,8

P EDUCATION 1.885 15,3 2,7 17,8 10,7 30,1 23,4

Q HUMAN HEALTH 6.158 20,2 3,8 22,5 4,7 36,3 12,5

R RECREATION 7.593 22,3 6,6 30,6 6,3 26,6 7,7

S OTHER SERVICES 8.796 25,1 5,5 24,0 5,1 30,3 10,0

Total 353.832 32,1 6,8 21,1 5,9 27,9 6,3

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Notes: (a) Indiv iduals aged 15-64 y rs. in the resident population (only residents in household, ex cludind those in retirement and entrepreneurs), in the pay -roll of enterprises persistently in the y ears 2015-2022.

Only employ ees abov e the threshold after 2019, and formerly below the threshold betw een 2015 and 2018.

Nace 2021

Cohoort of persistent 2015-2022 employees over the threshold of low earnings from 2019 who changed their Nace, by Nace 2015 and Nace 2021

2015 2021 % ch. 2015 2021 % ch. 2015 2021 % ch.

BUSINESS SIZE

Micro (<10 pers.employed) 340.177 220.200 -35,3 97.175 82.320 -15,3 243.002 137.880 -43,3

Small (10-49 pers.employed) 210.222 246.616 17,3 55.180 62.615 13,5 155.042 184.001 18,7

Medium (50-249 pers.employed) 113.914 168.291 47,7 29.695 33.532 12,9 84.219 134.759 60,0

Large (>250 pers.employed) 213.534 242.740 13,7 55.907 59.490 6,4 157.627 183.250 16,3

Total 877.847 877.847 0,0 237.957 237.957 0,0 639.890 639.890 0,0

GOVERNANCE

Individual firms 115.251 67.187 -41,7 28.578 28.578 0,0 86.673 38.609 -55,5

Other partnerships 100.543 78.689 -21,7 30.700 30.700 0,0 69.843 47.989 -31,3

Joint-stock companies 559.698 651.432 16,4 153.547 153.547 0,0 406.151 497.885 22,6

Other companies 102.355 80.539 -21,3 25.132 25.132 0,0 77.223 55.407 -28,3

Total 877.847 877.847 0,0 237.957 237.957 0,0 639.890 639.890 0,0

Notes: (a) Indiv iduals aged 15-64 y rs. in the resident population (only residents in household, ex cludind those in retirement and entrepreneurs), in the pay -roll of enterprises

persistently in the y ears 2015-2022. Only employ ees abov e the threshold after 2019, and formerly below the threshold betw een 2015 and 2018.

Cohoort of persistent 2015-2022 employees who passed over the threshold of low gross annual earnings from 2019, by

business size, type of governance and year. Years 2015 and 2021

Total employees With the same employer Who changed employer

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

43

Compared to those who overcame low wages, these employees were generally more involved with micro-

enterprises and with individual firms in 2015: in that year, a larger portion worked for Horeca enterprises. On

average, they come from enterprises where earning levels were worse. Changes of employer, when they

occurred, were prevalently addressed toward large-scale businesses and towards joint stock companies, while

the flow towards medium sized businesses was quite shallow. These evidences suggest a certain difficulty to

move across sectors and some difficulties in escaping low pay also when moving to large services enterprises,

especially when (as we saw in Part 2) part-time and fixed term jobs tend to prevail.

Table 3.9

Table 3.10

Concluding remarks

The analysis carried out in this paper uses for the first time a relatively large 2015-2022 longitudinal dataset

deriving from the integration of ISTAT's statistical registers on population, incomes and businesses. It delivers

a rather critical picture of the wage conditions of more than 20 million of Italian employees distributed in four

economic sectors: public sector, private (industry and services), agriculture and domestic workers. The study

concerns both the levels and the dynamics of labour incomes. The most critical issues regard domestic and

Nace 2015 2021 % change N incid.% N incid.% N incid.% N incid.%

C MANUFACTURING 195.802 189.437 -3,3 134.894 68,9 71.037 36,3 63.857 32,6 60.908 31,1

B,D,E REST OF INDUSTRY 9.716 12.216 25,7 5.350 55,1 3.179 32,7 2.171 22,3 4.366 44,9

F CONSTRUCTION 76.837 82.517 7,4 52.892 68,8 16.166 21,0 36.726 47,8 23.945 31,2

G TRADE 236.838 233.713 -1,3 168.590 71,2 85.513 36,1 83.077 35,1 68.248 28,8

H TRANSPORTATION 76.643 83.668 9,2 47.132 61,5 16.304 21,3 30.828 40,2 29.511 38,5

I HORECA 312.660 272.798 -12,7 222.399 71,1 80.396 25,7 142.003 45,4 90.261 28,9

J INFORMATION 21.906 23.280 6,3 12.806 58,5 7.454 34,0 5.352 24,4 9.100 41,5

K FINANCE 11.233 12.221 8,8 9.003 80,1 5.991 53,3 3.012 26,8 2.230 19,9

L,M PROFESSIONAL 57.475 64.129 11,6 38.186 66,4 24.969 43,4 13.217 23,0 19.289 33,6

N SUPPORT SERVICES 227.672 247.015 8,5 150.236 66,0 46.833 20,6 103.403 45,4 77.436 34,0

P EDUCATION 8.927 10.320 15,6 6.677 74,8 5.066 56,7 1.611 18,0 2.250 25,2

Q HUMAN HEALTH 87.003 98.999 13,8 74.431 85,5 43.638 50,2 30.793 35,4 12.572 14,5

R RECREATION 25.782 21.718 -15,8 12.820 49,7 7.331 28,4 5.489 21,3 12.962 50,3

S OTHER SERVICES 56.843 53.306 -6,2 41.016 72,2 23.964 42,2 17.052 30,0 15.827 27,8

Total 1.405.337 1.405.337 0,0 976.432 69,5 437.841 31,2 538.591 38,3 428.905 30,5

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Notes: (a) Indiv iduals aged 25-60 y rs. in 2022 resident population (only residents in household, ex cludind those in retirement and entrepreneurs), in the pay -roll of enterprises persistently in the y ears 2015-2022.

Cohoort of persistent 2015-2022 employees never permanently above the threshold of low earnings, by events of change of enterprise and Nace, and year

Number of employees

Same Nace

Change NaceTotal Same enterprise Change enterprise

2015 2021 % ch. 2015 2021 % ch. 2015 2021 % ch.

BUSINESS SIZE

Micro (<10 pers.employed) 606.265 548.785 -9,5 220.452 213.245 -3,3 385.813 335.540 -13,0

Small (10-49 pers.employed) 331.511 331.714 0,1 89.364 92.522 3,5 242.147 239.192 -1,2

Medium (50-249 pers.employed) 178.013 180.462 1,4 45.924 45.679 -0,5 132.089 134.783 2,0

Large (>250 pers.employed) 289.548 344.376 18,9 82.101 86.395 5,2 207.447 257.981 24,4

Total 1.405.337 1.405.337 0,0 437.841 437.841 0,0 967.496 967.496 0,0

GOVERNANCE

Individual firms 261.634 224.439 -14,2 99.818 99.818 0,0 161.816 124.621 -23,0

Other partnerships 206.237 177.851 -13,8 77.554 77.554 0,0 128.683 100.297 -22,1

Joint-stock companies 744.848 827.439 11,1 203.559 203.559 0,0 541.289 623.880 15,3

Other companies 192.618 175.608 -8,8 56.910 56.910 0,0 135.708 118.698 -12,5

Total 1.405.337 1.405.337 0,0 437.841 437.841 0,0 967.496 967.496 0,0

Cohoort of persistent 2015-2022 employees never permanently above the threshold of low earnings, by business size,

type of governance and year. Years 2015 and 2021

Total employees With the same employer Who changed employer

Sources: Istat, Business register 2015-2021, Income register (2015-2022), Population register (2015-2022)

Notes: (a) Indiv iduals aged 25-60 y rs. in 2022 resident population (only residents in household, ex cludind those in retirement and entrepreneurs), in the pay -roll of enterprises

persistently in the y ears 2015-2022. Only employ ees abov e the threshold after 2019, and formerly below the threshold betw een 2015 and 2018.

44

agricultural work: in these sectors more than 70 percent of employees have yearly labor incomes of less than

10,000 euros. These activities are notoriously characterized by a high incidence of non-regular or "grey"

employment that emphasize the effects of low wages. They are also sectors where public intervention plays a

prominent role in supporting employees' income indirectly by financing their employers with fiscal incentives,

a further element helping to explain the low level of wages.

More varied is the picture emerging from the analysis of the largest segment of employees relating to the non-

agricultural private sector. In this case, given also the size of the activities (we talk about 15 million

individuals), some specializations related to low wage workers can be identified. Some service sectors

evidently generate poorly paid labor: this is the case, for instance, in accommodation and food service activities

and personal services, where median incomes are just over 10 thousand euros. Nevertheless, there are other

business services activities that offer rather poor wages, such as temporary employment agencies, cleaning and

security services where the presence of intense outsourcing commissioned by medium and large economic

units amplifies the spread of low paid work. The seven NACE divisions with the highest rate of low-earners

(recreation and sports, food and beverage, accommodation, services to buildings, personal services, education,

and employment and recruiting agencies) explain more than a half of total employees below the threshold of

yearly gross earnings. Industry is more rarely involved in low earnings, although food and textile industries

are - like construction - more risky with their relatively higher rate of low-wage earners.

More generally, the economic activities where yearly gross earnings are lower are also those where hourly

gross earnings are lower. Although an adequate level in hourly earnings is a necessary condition to have decent

yearly earnings, we argue that poor work remains essentially a problem of low incomes from work: duration

and intensity of labour contract is often insufficient to sustain individual earnings. Low income of employees

depends on the quality of their jobs. When quality is scarce, and contracts are short-termed or with a low

intensity, for a large part of employees low-earnings become a sort of a trap, a sort of a swamp from which it

is difficult to get out. The exit from the low-earning condition most of the times the only escape from the

enterprises and the economic activities that offer low quality jobs. Job quality, though, is also an issue relating

the quality of the employer: for people who succeed to escape the low-earnings trap it is often fundamental to

work in enterprises that grows in size and performance.

Given this picture, we think that further steps of this research could be dedicated to a large amount of subjects.

We just highlight a few of them. On the one hand, the role of the employer in the dynamics of employees

earnings needs to be more exploited: the evolution of its profit & loss accounts, the characteristics of its

workforce, the distribution of earnings among employees , the type of reference market (local or foreign). On

the other hand, some light must be shed over the interactions between low-earnings and the government support

to individual incomes: the short but meaningful and troubled story of these measures in Italy interacts with the

events in the labour market for a large set of employees. We already know what comes from the most critical

service sectors but a deeper exploitation of the longitudinal information delivered by the statistical registers

can really support a better knowledge of these welfare policy issues. Finally, geographic aspects of the issues

need to be revealed by placing on the territory low-wage earners and their employers.

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A sensitivity test on stocks and CFC estimates of Italy: implementation of European recommendations for harmonization and comparability among Member States

Languages and translations
English

Economic Commission for Europe Conference of European Statisticians Group of Experts on National Accounts Twenty-third session Geneva, 23-25 April 2024 Item 3 of the provisional agenda Improvement of measures of consumption of fixed capital

A sensitivity test on stocks and CFC estimates of Italy: implementation of European recommendations for harmonization and comparability among Member States

Prepared by Italian National Institute of Statistics1

Summary

Since 2020, Istat has been engaged in research and analysis aimed at improving the use of the Perpetual inventory method (PIM) for estimating stocks and consumption in fixed capital (CFC) in the Italian national accounts. The paper describes the work carried out so far. Firstly, the current Italian practice was analyzed in the European context, in light of the recommendations formalized by the DMES Task Force on fixed assets and estimation of consumption of fixed capital under ESA 2010 (TF FIXCAP). The analysis allowed for a self- assessment of our estimation method, also considering the data sources used for the choice of functions and parameters in our practice: we identified the assumptions adopted in Italy underlying PIM that are robust, as they are based on empirical evidence, recent and specific to our country. In this regard, we describe the business surveys periodically conducted in Italy to measure the service lives of some capital assets. On the other hand, there are some current assumptions in the Italian practice that need to be revised, since they are based on hypotheses that lack empirical bases, are often dated and diverging from what is recommended at the European level. Therefore, exercises are described to measure the sensitivity of stock and CFC estimates by modifying these assumptions, aligning them with the European recommendations, in order to achieve greater methodological harmonization in the international context and better data comparability. These sensitivity tests are conducted on Total Economy and on General Government sector, for which the CFC estimates are particularly crucial given their impact on its output estimation. Finally, conclusions and plans are presented.

1 Prepared by Paola Santoro and Nicola Vallo.

United Nations ECE/CES/GE.20/2024/22

Economic and Social Council Distr.: General 11 April 2024 Original: English

ECE/CES/GE.20/2024/22

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I. Introduction2

1. In Italian national accounts, the estimates of consumption of fixed capital (CFC) and capital stocks (net and gross) are obtained through the Perpetual Inventory Method (PIM). The gross capital stock is estimated by cumulating past flows of gross fixed capital formation (GFCF) of assets over their estimated service life, i.e. the length of time that assets are retained in the capital stock before being retired because the asset is exported, sold for scrap, dismantled or abandoned. Not all assets in a cohort (i.e. all the assets of a particular type that are acquired in a specific year) are removed at the same time, therefore retirement profile is required to model the retirement process of a cohort (frequently used distributions are normal, lognormal, Winfrey, or Weibull). Moreover, aggregate stock figures (namely, net capital stock) should reflect the fact that similar assets of different ages have different values because of depreciation (or CFC), that is the loss in value of an asset or a class of assets as they age. The depreciation function shows how the asset value declines due to physical deterioration (wear and tear), normal obsolescence or normal accidental damage. Under geometric depreciation, the value declines by a constant rate each period; under linear depreciation, the value declines by a constant amount each period.

2. The PIM is the most common method used worldwide but, in practice, National Statistical Institutes (NSIs) may apply it in very different ways, depending on their choices of functions and key parameters. The best option for determining them is to obtain empirical evidence at national level but, generally, this information is sparse and often dated. Consequently, national accountants are forced to make assumptions.

3. With the aim of supporting NSIs in the compilation of stocks of capital assets and CFC, in 2020 Eurostat established the DMES Task Force on fixed assets and estimation of consumption of fixed capital under ESA 2010 (TF FIXCAP). It was closed in December 2022 and a set of recommendations was developed in order to improve harmonization of practices and comparability of data across countries. Istat participated in this international debate as a member of the TF FIXCAP, supporting its work also through a project granted by EUROSTAT titled “Improvement in the quality of consumption of fixed capital and capital stock estimates in Italy” (2020-2022).

4. In this paper we describe the work carried out by Istat with the aim of improving the Italian official estimates of CFC and capital stocks, in the context of the grant support and the TF FIXCAP work. First, the approach currently used and the main data sources are presented. We then compare the current Italian assumptions underlying the PIM (functions and service lives) with the practices of other countries and with the international recommendations of the TF FIXCAP, thus identifying the most robust assumptions and the weakest ones. The analysis takes into account the data sources used, i.e., we assess whether the divergences in our assumptions to the international guidelines are driven by Italian specific characteristics, based on empirical evidence or not (Sections 2 and 3). Then, the impact of revisions on our estimates of stocks and CFC through sensitivity analyses is measured (Section 4). Finally, conclusions and plans are presented (Section 5).

II. Depreciation function and retirements distribution in the Italian practice

A. Assumptions and data sources

5. In Italy, official estimates of net capital stocks and CFC are obtained assuming a straight line model of depreciation: an asset with a service life of T years loses a constant proportion (1/T) of the initial asset value each period, becoming zero at the end of year T.

2 The views expressed in this paper are those of the authors and do not necessarily reflect the views of

the Istat. The paper derives from the joint work of all authors; however, the paragraphs were authored as follows: §1, 2, 3, 4.1 and 5 were written by P. Santoro; § 4.2 by P. Santoro and N. Vallo.

ECE/CES/GE.20/2024/22

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6. Istat combines the straight line depreciation profile for single assets with a truncated normal retirement profile. The retirements are distributed around the average service life (constant over time) according to a truncated normal distribution (with truncation limits set at +/- 35% of average service life and the variance set so that 90% of retirements occur between +/- 25% of the average service life). A truncated normal distribution was chosen because we exclude that an asset, once entered, is never retired.

7. This approach is adopted to estimate the capital stock and CFC for all assets, except R&D and weapons systems, calculated using a geometric depreciation function. For them, net capital stock and consumption of fixed capital are obtained by means of the formula Kt = Kt-1 (1- δ) + It where Kt and Kt-1 are, respectively, the net stock at the end of period t and t-1, It is the gross fixed capital formation (at chain-linked values) of year t and δ represents the depreciation rate. The constant annual depreciation rate (δ) is calculated using information about average service life of the two asseets with the “declining balance method”: δ = R/T, where R is the declining-balance rate and T is the average service life. For R&D R is set to 2 (the double-declining balance rate is used), for weapons systems it is set to 1.65. Consumption of fixed capital in period t is simply obtained multiplying the depreciation rate δ for the capital stock at the end of period t-1.

8. Other countries’ practices, international manuals and guidelines are the main sources3.

9. The retirement function is assumed to be bell-shaped as in many other countries. The parameters (truncation limits and the variance) were defined on the bases of several tests and preliminary works carried out using alternative assumptions4. For R&D the main source is the “Manual on measuring Research and Development on ESA 2010”5; for weapons systems the depreciation rate was derived from a technical study on several type of assets (including weapons) made by the U.S. Bureau of Economic Analysis (BEA)6.

10. Almost all our assumptions about retirement and depreciation functions were made many years ago and they have never been reviewed.

B. International comparison

11. Italy is in line with the most chosen methods used by the other European countries. Based on the information collected by the TF FIXCAP7, eighteen EU/EFTA countries use a linear model of depreciation in combination with a retirement function. Assumptions could vary by asset, as also in Italy happens (i.e. for R&D and weapons system).

12. When a retirement function is used, almost all countries (17) use a bell-shaped retirement function (various mathematical functions), as also Italy does.

Table 1 Depreciation functions by country

Depreciation functions Countries Linear BE, CZ, DE, EE, ES, FI, FR, HR, HU, IT, LU, LT, LV, MT, PT, RO, SI, SK Others AT, BG, DK, EE, EL, ES, FI, IE, IS, IT, LT, LV, NL, NO, PT, SE, SI

Source: Eurostat, (2023)

Table 2 Retirement functions used by country

Retirement functions Countries Bell-shaped BE, CY, CZ (partly), DE, EE, ES, FI, FR, HU, HR, IE, IT, LT, LU, LV, MT, NL Others CZ (partly), RO, PT, SI SK

Source: Eurostat, (2023)

3OECD, (2009), OECD, (2010), Eurostat, (2014). 4Lupi, C., Mantegazza, S., (1994). 5 Eurostat, (2014). 6 BEA, (2003). 7Eurostat, (2023).

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13. The European TF FIXCAP expresses the following recommendations8:

• Recommendation 1: A bell-shaped retirement function should be used (without preferring a specific bell-shaped function).

• Recommendation 2: Within the context set by ESA2010 regarding depreciation functions, the recommendation is limited to using a convex cohort depreciation function.

14. The recommendation is to apply a convex cohort depreciation function. In the case of Italy, the combination of a straight line depreciation function for an individual asset in a given cohort and a normal (hence bell-shaped) function results into a convex cohort depreciation function: the asset cohort’s value tends to decline more rapidly initially and less rapidly later. For R&D and weapons systems, the geometric depreciation function is used, coherently to the TF recommendations.

III. Service lives in the Italian practice

A. Assumptions and data sources

15. In our estimates, service lives (ASLs) are constant over time and are asset and industry specific. For all institutional sectors other than General Government, the same ASLs are used. For General Government they may differ.

16. Service lives were first set during the 1990s relying on expert advice, other countries’ practices and the international manuals. Since then, they have been updated over time, on empirical evidence from business surveys or international recommendations. The following table presents the main data sources for the service lives currently used in Italy, by asset.

Table 3 Sources for service lives by asset

Asset Source N.111 Dwellings Expert advice

N.112 Other buildings and structures Buildings other than dwellings: expert advice; Road works and Other civil engineering works: recommendations from the GNP Committee on the Consumption of Fixed Capital on Roads, Bridges etc. (GNIC 011, 2003)

N. 1131 Transport equipment Expert advice/other countries’ practices

N. 11321 Computer hardware

Business surveys data: the survey on business confidence by Istat, 2018, and the survey of industrial and service firms by Bank of Italy, 2019

N. 11322 Telecommunications equipment N. 1139 Other machinery and equipment of which Furniture

N. 114 Weapons system Other countries’ practices

N. 115 Cultivated biological resources Expert advice/other countries’ practices

N. 1171 Research and development Manual on measuring Research and Development on ESA 2010, Eurostat, 2014

N. 1172 Mineral exploration and evaluation

Expert advice/other countries’ practices/OECD “Handbook on deriving capital measures of intellectual Property Products”, 2010

N. 1173 Computer Software and databases

Expert advice/other countries’ practices/OECD “Handbook on deriving capital measures of intellectual Property Products”, 2010

N. 1174 Entertainment, literary or artistic originals

GNP Committee on Entertainment, Literary and Artistic Originals (GNIC/010 and GNIC/022). OECD “Handbook on deriving capital measures of intellectual Property Products”, 2010

8 Eurostat, (2023).

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17. Three business surveys were run in Italy aimed to measure the service lives for some assets on empirical evidence:

• the first one involved a sub-sample of Italian firms participating in the Bank of Italy’s annual Survey of industrial and service firms (Invind), in cooperation with Istat, in 20119;

• a questionnaire on service lives was included in the Survey on business confidence conducted by Istat, in 201810;

• a specific section was added in the questionnaire of the Bank of Italy’s annual Survey Invind, in cooperation with Istat, in 201911.

18. The results of the three surveys (2011, 2018, 2019) were used to measure the service life of computer hardware, telecommunications equipment, other machinery and equipment, with a separate evidence for furniture. Other capital goods were excluded, because it is difficult to estimate their service lives through surveys (e.g., buildings)12 or because other sources are available, such as administrative data (e.g., motor vehicles)13 or international guidelines (IPP).

19. Service lives for computer hardware, telecommunications equipment, furniture and other machinery and equipment were detected with different levels of breakdown by product in the three questionnaires. In the survey by Istat (2018) four types of assets were included (one-to-one correspondence between the goods included in the questionnaire and those on which PIM works in Italy). A very detailed glossary was provided to support respondents in identifying types of capital goods included in each category covered by the survey. In the two questionnaires of Invind (2011, 2019), three types of assets (computer hardware, telecommunications equipment and furniture) were uniquely identified by three assets covered by the survey; instead, other machinery and equipment was detailed in 23 (in 2011) and 5 (in 2019) categories of products. Then the service life for the total asset “other machinery and equipment” was obtained as an average of the service lives collected in the survey for all these products (identified at the 4-digit level of the CPA14, reclassified in Products used in the National Accounts Supply and Use table - SUT system), weighted with the relevant GFCF in these products.

20. In all the three surveys, the questionnaire was kept simple and convenient to fill in.

21. In 2011 and 2019, contacts and interviews were carried out by the Bank of Italy territorial branches. Questionnaires were administered by means of an electronic form, taking advantage of a web platform. Istat defined the questionnaires, the criteria for the treatment of data and provided useful information for the computation of the estimates, elaborated by the Bank of Italy.

22. In the Istat’s survey on business confidence (2018), data collection was carried out using the CATI technique (computer assisted telephone interviewing). The questionnaire and some useful information were sent before the interview to all respondents by e-mail.

23. The three surveys (2011, 2018, and 2019) were all based on a sample, with different sizes, increasing over time:

9 Tartaglia-Polcini R., (2013). 10 https://www.istat.it/it/files//2018/11/Business-and-consumer-confidence-November-2018.pdf 11 https://www.bancaditalia.it/pubblicazioni/indagine-imprese 12 The standard approach to gather empirical evidence on average service lives of capital goods is to

collect information on the age of the assets at the time they are retired from the production process. However, the standard approach cannot be adopted to estimate ASLs of buildings. In fact, they are not (usually) scrapped but they are subject to major maintenance and repair that it is needed in order to prevent it from falling into disrepair and from collapsing.

13 Forestieri P., Santoro P., (2023) 14 The Statistical classification of products by activity, abbreviated as CPA, is the classification of

products (goods as well as services) at the level of the European Union.

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• 359 firms belonging to the industry and service sector with at least 20 employees, in 2011

• 3,182 enterprises in the manufacturing sector with a size of 10-500 employees, in 2018

• 4,775 firms, with at least 20 employees belonging to the all private non-financial services industries and firms with at least 10 employees for construction, in 2019.

Table 4 Sample composition by size class and economic sector - business surveys on service lives, 2011, 2018 and 2019

Number % 2011

Size class (employees) 20-49 96 27 50+ 263 73 Sector Manufacturing 271 76 Services 40 11 Construction 48 13 Total 359 100

2018 Size class (employees) 10 – 49 1,541 48 50-249 1,245 39 250-500 396 12 Sector Manufacturing 3,182 100 Total 3,182 100

2019 Size class (employees) 10-49 1,787 37 50 – 199 1,821 38 200 – 499 660 14 500+ 507 11 Sector Manufacturing 2,996 63 Services 1,215 25 Construction 564 12 Total 4,775 100

Source: elaboration on business surveys Table 5 Number of answers by asset - business surveys on service lives, 2011, 2018 and 2019

2011 2018 2019

Computer hardware 170 1,786 1,395

Communication equipment 64 1,239 642

Furniture 97 556 543

Other machinery and equipment 625 1,600 1,697

Total 956 5,181 4,277 Source: elaboration on business surveys

24. Service life was defined on the basis of a notion of economic life, not physical or engineering life. The three surveys asked firms, as users of capital assets, to report the service

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life (age) of assets retired from the production, in the previous five years, because they had reached the end of their life (discards due to physical deterioration or obsolescence). Firms had to report the age of the capital goods at the time of retirement from production, not the age of the assets at the moment of the write-off from the balance sheet.

25. It was indicated to exclude all productive assets transferred, for any reason, to other enterprises, because sales of productive assets to other firms are not included in the definition of service life.

26. For assets that were acquired new, firms were asked to indicate the service life from the time of acquisition; for assets that were acquired second-hand, firms were asked to indicate the total service life, i.e., that at the time of acquisition plus the number of years elapsed between acquisition and discard.

27. If more than one asset of the same type was disposed of, respondents had to indicate the average life of the assets for which the highest price had been paid at the time of purchase.

28. The main outcomes are reported in table 6.

29. Results indicate shorter service lives for telecommunication equipment in the two recent surveys compared to the one of 2011, while they are longer for hardware in all industries, for furniture in manufacturing, shorter for other machinery and equipment in manufacturing.

30. Based on these results, Istat updated the service lives for computer hardware, telecommunications equipment, other machinery and equipment, with a separate evidence for furniture, in occasion of the general revisions of the Italian national accounts in 2014 (the 2011 survey was used) and in 2019 (2018 and 2019 surveys were used). In both occasions, we decided to revise the time series backwards (stock and CFC), using the new service lives.

31. In 2019, revisions affected the final value of the total CFC by +0.3%, the total net capital stock by +0.4% and the total gross capital stock by +0.5%, with reference to the year 2016. Table 6 Results - business surveys on service lives, 2011, 2018 and 2019

Computer hardware

Telecommunications equipment

Furniture Other machinery and equipment

2011

Manufacturing 5.9 9.4 12.8 15.4

Other ind. excl. Constr.

6.1 13.7 15.1 23.5

Services 5.8 6.5 12.8 9.1

Construction 5.2 5.2 14.1 9.4

2018

Manufacturing 7.0 7.3 14.5 14.4 2019

Manufacturing 6.3 6.2 14.5 14.7 Other ind. excl. Constr. 7.2 6.7 9.4 14.7 Services 6.1 5.2 12.5 11.0 Construction 6.0 4.6 10.0 9.8

Source: elaboration on business surveys

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B. International comparison

32. Table A.1 and table A.2 in the Annex show service lives currently used in Italy for S1 and S13 respectively. All industries are reported for each asset even if in some activities, GFCF, stock and CFC in that asset may be zero.

33. Service life can vary considerably from one country to another. Many Member States do not have direct evidence to establish assumptions on this crucial parameter. As different service lives could have important impacts on estimates of capital stock and CFC, the TF FIXCAP recommends average service lives by assets, as indicated in table A.3 of the Annex. In some cases, a range is specified. Member States that use an ASL outside the range should support their decision by evidence (recommendation 3)15.

34. Comparing Italian assumptions with the international recommendations, in some cases our service life differs from that expressed by the TF FIXCAP. A detailed comparison is described in paragraph 4.

35. Therefore, in 2021, Istat conducted a sensitivity analysis to measure the impact on estimates of introducing new harmonized service lives. The following section presents the methodological approach and main results.

IV. Sensitivity analysis on the service lives

A. Methodological approach

36. The sensitivity analysis has involved Total Economy (S1) and General Government (S13) sectors and has been conducted on all the fixed assets owned by the two institutional sectors.

37. In order to choose service lives for our exercise, we have compared the current Italian average service life to the ones recommended by the TF, for each asset.

38. First, we have removed decimals from our service lives in line with the other NSIs and the TF FIXCAP16.

39. In our current estimates, for some assets, the service life varies between the two sectors. In this analysis, we have used the same service life for Total Economy and General Government, by asset; in fact, no detailed empirical information is available to justify the differences for the two sectors in Italy. The goal is to ensure greater consistency.

40. For those assets for which the TF indicates a range, we first have checked whether or not the current parameter used in Italy is included in that range:

• when our ASL by asset (and industry) falls within the range indicated by the TF, we have confirmed our parameter;

• in case our ASL by asset (and industry) is out the range but empirical evidence exists (i.e. computer hardware, telecommunications equipment, other machinery and equipment, of which furniture, see section 3), we have confirmed the Italian current parameter;

• in case our ASL by asset (and industry) is not within the range and empirical evidence does not exist, we have revised our ASL: if our ASL is shorter, we have used the minimum level indicated by the TF; if our ASL is longer, we have chosen the maximum level recommended by the TF.

15 Eurostat, (2023). 16 Decimals of current Italian parameters are generated by averaging responses collected in the business

surveys on service life or by averaging detailed data by product.

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41. If a range is not indicated by the TF (few assets: aircrafts, trains, some types of weapons systems, computer software e databases), we have compared our service lives with the ASL by the TF directly and we have adopted TF indications for all assets for which our service life differs and no empirical evidence exists.

42. Based on the comparison between the current service lives used in Italy, by asset and industry, and those recommended by the TF, we have decided on the new parameters to be used in the PIM for this sensitivity analysis, as shown in the table A.4 of the Annex.

43. The following sections explain our choices in more detail, by asset; the described analysis refers, for simplicity, mainly to the S1 sector. Table A.5 and table A.6 of the Annex present the differences between the ASLs used for the sensitivity test and the ASLs currently adopted in Italy for S1 and for S13 (in empty cells the difference is zero). Then, the results for both sectors are presented in paragraph 4.2.

AN. 111 Dwellings 44. For a consistent comparison, it is necessary to take into account the different composition of the assets to which the ASL refers.

45. Service life of 79.1 years for dwellings in Italy relates to the buildings (new buildings and renovation and upgrades) and not to the costs of ownership transfer; they are treated separately, with an ASL equal to 25 years. On the other hand, the recommended service life indicated by the TF (ASL 70, range 65-75) relates to a combined asset: new buildings, renovation and upgrades, costs of ownership transfer.

46. If the costs of ownership transfer were included, then the average service lives for dwellings would be shorter in Italy. Therefore, we have calculated a new service life for dwellings including costs of ownership transfer. It is an average of the Italian service lives of two components (79.1 and 25) weighted by the relevant gross fixed capital formation estimates cumulated for the years 1995-2020. The result is 72 for the combined asset, comparable to the one of the TF, and is within the international range. Accordingly, we have confirmed our service lives: 79 for dwellings (including renovation and upgrades) and 25 years for costs of ownership transfer, as we prefer to continue estimating the two components separately.

47. For General Government, service lives differ by industry. In this exercise, an ASL of 79 years has been used for all industries also for S13, consistent with S1 assumptions.

AN. 1121 Buildings other than dwellings 48. For this asset the average service life varies by industry in Italy (the minimum value is 35, the maximum one is 80). TF recommendations are expressed by type of buildings. We have used the type of industry as a proxy for the type of building, as shown in the table below.

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Table 7 Correspondence between type of buildings and NACE17

TF recommendations NACE Warehouse and industrial buildings B-F Commercial buildings G Educational buildings P Health buildings Q86 Buildings and structures for military use - Other buildings All other industries

49. Also for buildings other than dwellings the Italian service lives relate just to the buildings (new buildings and renovation and upgrades) and not to the costs of ownership transfer (treated separately, with an ASL of 25 years), while the ones recommended by the TF refer to the combined asset (new buildings, renovation and upgrades, costs of ownership transfer). We have calculated a new service life for non-residential buildings including costs of ownership transfer as an average of the separate Italian service lives of two components weighted by the relevant GFCF estimates cumulated for the years 1995-2020. The results, which vary by industry, in some cases are within the range set by the TF, so we have confirmed our current parameter; in other cases they are outside it, so for this exercise we have adopted the extreme value of the range closest to our current parameter. This value has been adjusted (re-proportioned) to refer only to buildings net of costs of ownership transfer, since we intend to continue estimating the two components separately.

AN. 1122 Other structures 50. The comparison has led to very significant revisions for some industries. However, in those with the largest GFCF amounts in this asset (NACE H Transportation and storage, J61 Telecommunications, O Public administration and defence; compulsory social security), the current ASLs used in Italy are confirmed (they are all included in the TF range, 50-60 years).

AN. 1123 Land improvement 51. In Italy, the value of this asset is totally allocated in the activity A, Agriculture, where the Italian ASL (51) is included in the TF range (50-60 years). Therefore, we have confirmed it (removing decimals as for the other assets).

AN. 1131 Transport equipment 52. For vehicles, the Italian current service life is the same as the ASL recommended by the TF (10) for all industries except for NACE O. Therefore, we have changed this parameter just in this activity (from 14 years to 10 years).

53. The TF provides separate guidance for trains, aircraft, ships, while in Italy the PIM method runs on “other transport equipment” as a whole (sum of trains, aircraft, and ships), using a single parameter for it; separate ASLs are not available. For the exercise, it was necessarily to calculate a unique service life (ASL and range) based on the recommendations made by TF and to compare it with the parameter currently used in our estimation. The ASL and range have been obtained by averaging service

17 NACE is the “statistical classification of economic activities in the European Community” and is

the subject of legislation at the European Union level which imposes the use of the classification uniformly within all the Member States; see https://ec.europa.eu/Eurostat/documents/3859598/5902521/KS-RA-07-015-EN.PDF

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lives proposed by the TF (ASL and range) for trains, aircraft, ships, weighted by the relevant GFCF expenditure in Italy cumulated over several years (from 1995 onwards). The resulting ASL for “other transport equipment” is 24 year (the range is 21-26 years). Since the current Italian ASL is 18 years (20 years in the O activity), we have used the minimum value of the calculated range for the homogeneous asset “Other transport equipment” in our sensitivity analysis (21 years).

AN. 11321 Computer hardware and AN. 11322 Telecommunications equipment 54. For these assets, the ASLs in Italy are based on empirical evidence and are reviewed periodically (business surveys on the service life for fixed assets, see section 3). The rounded values of the parameter (computer hardware: 6 or 7 years; telecommunications equipment: 5 or 7 years, depending on the industry), without decimals, are within the range recommended by the TF (5-7 and 4-7 years, respectively). The exception is telecommunications equipment in NACE A (Agriculture, forestry and fishing), for which the Italian ASL is higher (9 years); since this activity is out of the scope of the surveys, the parameter has been reduced from 9 to 7 years, indicated as the maximum value by the TF.

AN. 1139 Other machinery and equipment 55. In Italy, separate estimates of capital stock and CFC are obtained for

1) other machinery and equipment (excluding furniture)

2) furniture.

56. Service lives for these two assets are based on empirical evidence and reviewed periodically through the business surveys. They vary by industry.

57. It is not possible to directly compare the service lives with those recommended by the TF for “other machinery and equipment” (expressed by CPA 2-digit), as there is no one-to-one correspondence, except for furniture (CPA 31). Therefore, for the comparison of this asset it was necessary to calculate a service life (ASL and range) by averaging service lives expressed by the TF for CPA (ASL and range) weighted by the relevant cumulated GFCF expenditure in Italy by CPA. For every industry, the parameter is calculated (ASL and range) and then compared to our ASLs. They are aligned, except for NACE F (Constructions) and G (Wholesale and retail trade; repair of motor vehicles and motorcycles), where the rounded service lives currently used in Italy (10 and 11 years respectively) are shorter than the minimum value of the calculated range (14 and 12 years respectively). However, since these industries are in the scope of the surveys, we have confirmed our current parameters for all industries.

AN. 1139 (CPA 31) Furniture 58. The range set by the TF for CPA 31 (furniture) includes the service lives used in Italy except for four industries (NACE B, D, E, and F). In any case, we have confirmed our parameters as they are based on empirical evidence (business surveys) and reviewed periodically.

AN. 114 Weapons systems 59. The TF recommendations and the Italian practice for this asset vary depending on the type of weapon. Although the breakdown is not exactly the same, the comparison is simple and fairly straightforward.

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60. Because the service lives currently used in Italy for ships, armoured vehicles and tanks (27 years) are longer than the ones recommended by the TF, for our sensitivity analysis we modified the parameter for these types of weapon systems (25 years for ships, 20 years for armoured vehicles and tanks).

AN. 1151 Animal resources yielding repeat products and AN. 1152 Tree, crop and plant resources yielding repeat products 61. In Italy, capital stock for AN. 1151 is obtained through a direct approach (price x quantity). Service life is not used.

62. The ASL currently used in Italy for AN. 1152 is 18 years, included in the TF range (10-20). We have confirmed our value.

AN. 117 Intellectual property products 63. For all assets included in the intellectual property products (IPP), our service lives are included in the range recommended by the TF (except for software in NACE O, reduced from the current value of 7 years to the recommended value of 5 years, in line with all the other industries in Italy).

B. Main results

64. The following tables (tables 8 – 13) show the impact of introducing the new service lives on the estimates of the net stock and consumption of fixed capital in Italy, for S1 and S13.

65. Based on the exercise described, the effect is not material for both Total Economy and General Government in terms of levels, growth rates and composition.

66. Total net capital stocks for the two sectors increase (tables 8-9). For S1 the total value is higher than the current one from +0.3% to +0.4% over the time series 1995-2020 (+8 billion EUR in 1995, +21 billion EUR in 2020). For S13 the level changes from +0.1% to +0.4% (+615 million EUR in 1995, +4 billion EUR in 2020).

67. CFC revisions (tables 10-11) for S1 are -113 million EUR in 1995 (-0.1%) e +608 million EUR in 2020 (+0.2%); for S13, the impact on estimates is +128 million EUR in 1995 (+0.6%) and +191 million EUR (+0.4%) in 2020.

68. The impact on the total value of stocks and CFC is driven by revisions of opposite sign by asset.

69. The levels of the stock change in the same direction as changes in service lives: the value of each asset increases or decreases accordingly to changes in its service life.

70. Analyzing the impact on net capital stock for S1 by asset for the year 2016 (table 12):

• the main revisions are on buildings other than dwellings (-4.6%, -53 billion EUR), due to shorter service lives for almost all industries (mainly L Real Estate activities and G Wholesale and retail trade), except for Q86 Human health activities (where the revised service life is longer);

• with opposite sign, significant revisions are on other structures (+5.8%, +57 billion EUR), mainly for the longer service life in NACE D Electricity, gas, steam and air conditioning supply;

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• estimates increase for transport equipment (+7.4%, +9.4 billion EUR), due to the longer service life for transport equipment other than vehicles (on the other hand, vehicles decrease slightly due to the reduction in the industry O);

• the level increases for the asset other machinery and equipment (+2.1%, +11 billion EUR), due to decimal rounding;

• in some cases, revisions are quite marginal in level: dwellings (-0.1%, -1,8 billion EUR), ICT (-1.1%, -379 million EUR), IPP (-0.8%, -1,5 billion EUR, due to software revision in the industry O). They depend on the elimination of decimals and/or revisions of service lives in very few industries (or just one industry).

71. For the net capital stock of S13, in 2016 (table 13):

• the main significant revision is on dwellings (+48%, +13 billion). This asset is mainly owned by the industry O Public administration and defense compulsory social security; in this industry the ASL revision is +19 years (from 60 to 79). The remaining significant portion of GFCF in dwelling is allocated to the industry Education (P), where the average life has been increased of +21.8 years (from 57.2 to 79);

• the revision for buildings other than dwellings is with opposite sign (-2.9%, - 9 billion). The NACE activity O Public Administration and Defense accounts for 75% of S13 GFCF in this asset (average calculated on 2018-2020) and its ASL has changed from the current 60 years to 56 years. A remaining 11% of GFCF is placed in the Q86 Human health activities, where the parameter has been increased of 11 years;

• the revision on other assets are not significant in level: other structures (+0.2%, 1 billion EUR); transport equipment (+6.1%, +495 million EUR), ICT (-7.5%, -272 million EUR), other machinery and equipment (-0.6%, -368 million EUR);

• IPP do not change.

72. Depreciation generally changes in the opposite direction of changes to service lives; that is, increasing the service lives reduces the amount of depreciation because with longer service lives each asset is written off over a longer period. In some years, however, the increase in the number of assets in the stock due to the use of longer service lives outweighs the reduction in the amounts of consumption of fixed capital charged to each asset and total consumption of fixed capital increases with longer service lives.

73. By asset, the main impact on CFC is for buildings other than dwellings and other structures for S1 (table 12) and dwellings and non-residential buildings for S13 (table 13).

74. Composition by asset does not changes significantly (tables 12-13).

75. Due to low revisions on levels, growth rates differ very marginally for both sectors (tables 8-11 and figure 1).

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Table 8 The impact of service lives revisions on net capital stock estimates; levels at current replacement costs (million EUR) and growth rates. Total Economy, 1995-2020

Year

Level growth rate

new ASL* Istat ASL** difference Relative

difference %

new ASL* Istat ASL** new ASL*

1995 2,866,292 2,858,147 8,145 0.3

1996 2,986,876 2,978,318 8,558 0.3 4.2 4.2 - 0.00

1997 3,120,024 3,111,031 8,993 0.3 4.5 4.5 0.00

1998 3,244,575 3,235,223 9,353 0.3 4.0 4.0 0.00

1999 3,341,229 3,331,650 9,580 0.3 3.0 3.0 - 0.00

2000 3,533,121 3,522,738 10,383 0.3 5.7 5.7 0.01

2001 3,696,572 3,685,299 11,273 0.3 4.6 4.6 0.01

2002 3,908,104 3,896,238 11,866 0.3 5.7 5.7 - 0.00

2003 4,072,377 4,060,089 12,288 0.3 4.2 4.2 - 0.00

2004 4,301,327 4,288,241 13,086 0.3 5.6 5.6 0.00

2005 4,550,200 4,536,749 13,452 0.3 5.8 5.8 - 0.01

2006 4,786,913 4,772,489 14,424 0.3 5.2 5.2 0.01

2007 5,050,767 5,035,397 15,370 0.3 5.5 5.5 0.00

2008 5,299,914 5,283,245 16,669 0.3 4.9 4.9 0.01

2009 5,424,101 5,406,958 17,143 0.3 2.3 2.3 0.00

2010 5,595,411 5,577,290 18,121 0.3 3.2 3.2 0.01

2011 5,810,575 5,792,216 18,359 0.3 3.8 3.9 - 0.01

2012 5,887,528 5,867,916 19,612 0.3 1.3 1.3 0.02

2013 5,868,575 5,848,300 20,275 0.3 - 0.3 - 0.3 0.01

2014 5,851,288 5,830,555 20,733 0.4 - 0.3 - 0.3 0.01

2015 5,862,496 5,841,397 21,099 0.4 0.2 0.2 0.01

2016 5,870,694 5,849,624 21,070 0.4 0.1 0.1 - 0.00

2017 5,926,875 5,905,459 21,416 0.4 1.0 1.0 0.00

2018 6,027,878 6,006,382 21,496 0.4 1.7 1.7 - 0.00

2019 6,065,882 6,044,614 21,268 0.4 0.6 0.6 - 0.01

2020 6,048,728 6,027,613 21,116 0.4 - 0.3 - 0.3 - 0.00 Source: elaboration on Istat data * ASLs used in the sensitivity analysis ** ASLs currently used in Italy

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Table 9 The impact of service lives revisions on net capital stock estimates; levels at current replacement costs (million EUR) and growth rates. General Government, 1995-2020

Year

Level growth rate

new ASL* Istat ASL** difference relative difference % new ASL* Istat ASL** new ASL*

1995 509,329 508,713 615 0.1

1996 525,327 524,657 671 0.1 3.1 3.1 0.01

1997 544,847 544,121 726 0.1 3.7 3.7 0.01

1998 564,389 563,650 738 0.1 3.6 3.6 - 0.00

1999 577,718 576,922 796 0.1 2.4 2.4 0.01

2000 608,134 607,112 1,022 0.2 5.3 5.2 0.03

2001 663,118 661,746 1,371 0.2 9.0 9.0 0.04

2002 685,688 684,129 1,559 0.2 3.4 3.4 0.02

2003 711,162 709,445 1,718 0.2 3.7 3.7 0.01

2004 751,368 749,536 1,832 0.2 5.7 5.7 0.00

2005 791,717 789,809 1,909 0.2 5.4 5.4 - 0.00

2006 832,765 830,868 1,897 0.2 5.2 5.2 - 0.01

2007 873,249 871,225 2,024 0.2 4.9 4.9 0.00

2008 915,696 913,512 2,184 0.2 4.9 4.9 0.01

2009 946,611 943,888 2,723 0.3 3.4 3.3 0.05

2010 990,610 987,829 2,781 0.3 4.6 4.7 - 0.01

2011 1,035,919 1,033,103 2,816 0.3 4.6 4.6 - 0.01

2012 1,037,275 1,034,035 3,240 0.3 0.1 0.1 0.04

2013 1,028,326 1,024,959 3,367 0.3 - 0.9 - 0.9 0.01

2014 1,028,882 1,025,408 3,474 0.3 0.1 0.0 0.01

2015 1,020,662 1,017,063 3,599 0.4 - 0.8 - 0.8 0.01

2016 1,014,540 1,009,540 5,000 0.5 - 0.6 - 0.7 0.14

2017 1,012,613 1,010,850 1,763 0.2 - 0.2 0.1 - 0.32

2018 1,018,171 1,013,967 4,204 0.4 0.5 0.3 0.24

2019 1,020,783 1,016,670 4,113 0.4 0.3 0.3 - 0.01

2020 1,016,784 1,012,586 4,198 0.4 - 0.4 - 0.4 0.01 Source: elaboration on Istat data * ASLs used in the sensitivity analysis ** ASLs currently used in Italy

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Table 10 The impact of service lives revisions on CFC estimates; levels at current replacement costs (million EUR) and growth rates. Total Economy, 1995-2020

Year

Level growth rate

new ASL* Istat ASL** difference relative difference % new ASL* Istat ASL** new ASL*

1995 144,509 144,621 - 113 - 0.1

1996 151,376 151,499 - 123 - 0.1 4.8 4.8 - 0.01

1997 158,360 158,511 - 151 - 0.1 4.6 4.6 - 0.01

1998 165,582 165,788 - 206 - 0.1 4.6 4.6 - 0.03

1999 171,744 171,989 - 245 - 0.1 3.7 3.7 - 0.02

2000 182,583 182,841 - 258 - 0.1 6.3 6.3 0.00

2001 192,460 192,709 - 249 - 0.1 5.4 5.4 0.01

2002 203,671 203,926 - 256 - 0.1 5.8 5.8 0.00

2003 211,882 212,137 - 255 - 0.1 4.0 4.0 0.01

2004 222,920 223,186 - 266 - 0.1 5.2 5.2 0.00

2005 233,566 233,837 - 271 - 0.1 4.8 4.8 0.00

2006 244,939 245,257 - 319 - 0.1 4.9 4.9 - 0.01

2007 256,781 257,160 - 379 - 0.1 4.8 4.9 - 0.02

2008 268,357 268,793 - 436 - 0.2 4.5 4.5 - 0.02

2009 273,557 273,947 - 390 - 0.1 1.9 1.9 0.02

2010 282,177 282,532 - 355 - 0.1 3.2 3.1 0.02

2011 291,067 291,378 - 310 - 0.1 3.2 3.1 0.02

2012 296,368 296,632 - 264 - 0.1 1.8 1.8 0.02

2013 295,266 295,414 - 148 - 0.1 - 0.4 - 0.4 0.04

2014 296,149 296,166 - 17 - 0.0 0.3 0.3 0.04

2015 300,079 299,993 85 0.0 1.3 1.3 0.03

2016 300,873 300,686 187 0.1 0.3 0.2 0.03

2017 306,059 305,792 267 0.1 1.7 1.7 0.03

2018 311,557 311,196 361 0.1 1.8 1.8 0.03

2019 315,700 315,214 486 0.2 1.3 1.3 0.04

2020 317,507 316,900 608 0.2 0.6 0.5 0.04 Source: elaboration on Istat data * ASLs used in the sensitivity analysis ** ASLs currently used in Italy

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Table 11 The impact of service lives revisions on CFC estimates; levels at current replacement costs (million EUR) and growth rates. General Government, 1995-2020

Year

Level growth rate

new ASL* Istat ASL** difference relative difference % new ASL* Istat ASL** new ASL*

1995 22,865 22,737 128 0.6

1996 23,340 23,221 119 0.5 2.1 2.1 - 0.05

1997 24,009 23,904 105 0.4 2.9 2.9 - 0.07

1998 24,884 24,788 96 0.4 3.6 3.7 - 0.06

1999 25,552 25,463 89 0.3 2.7 2.7 - 0.04

2000 27,081 26,972 109 0.4 6.0 5.9 0.06

2001 29,369 29,240 129 0.4 8.5 8.4 0.04

2002 31,022 30,863 159 0.5 5.6 5.6 0.08

2003 32,591 32,440 151 0.5 5.1 5.1 - 0.05

2004 34,583 34,440 143 0.4 6.1 6.2 - 0.05

2005 36,466 36,344 122 0.3 5.4 5.5 - 0.08

2006 38,469 38,359 110 0.3 5.5 5.5 - 0.05

2007 40,390 40,284 106 0.3 5.0 5.0 - 0.03

2008 42,408 42,287 121 0.3 5.0 5.0 0.02

2009 43,688 43,522 166 0.4 3.0 2.9 0.10

2010 45,852 45,643 209 0.5 5.0 4.9 0.08

2011 47,665 47,415 250 0.5 4.0 3.9 0.07

2012 47,978 47,770 208 0.4 0.7 0.7 - 0.09

2013 47,958 47,794 164 0.3 - 0.0 0.1 - 0.09

2014 48,187 48,048 139 0.3 0.5 0.5 - 0.05

2015 48,220 48,084 136 0.3 0.1 0.1 - 0.01

2016 48,413 48,187 226 0.5 0.4 0.2 0.19

2017 48,827 48,629 198 0.4 0.9 0.9 - 0.06

2018 49,184 49,016 168 0.3 0.7 0.8 - 0.06

2019 49,568 49,385 183 0.4 0.8 0.8 0.03

2020 49,832 49,641 191 0.4 0.5 0.5 0.01 Source: elaboration on Istat data * ASLs used in the sensitivity analysis ** ASLs currently used in Italy

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Table 12 The impact of service lives revisions on net capital stock and CFC estimates, by asset; levels at current replacement costs (million EUR) and composition (%). Total Economy, 2016

Asset

Level

difference relative difference %

Composition

new ASL* Istat ASL** new ASL* Istat ASL**

Net capital stock, S1 Dwellings including ownership transfer costs 2,807,616 2,809,412 - 1,796 - 0.1 48% 48%

Buildings other than dwellings including ownership transfer costs 1,099,458 1,152,415 - 52,957 - 4.6 19% 20% Other structures and land improvements 1,040,211 983,474 56,737 5.8 18% 17% Transport equipment 137,913 128,419 9,494 7.4 2% 2% ICT 35,095 35,474 - 379 - 1.1 1% 1%

Other mach. and equip. Including furniture and weapons systems 557,336 545,872 11,464 2.1 9% 9% Biological resources 5,867 5,867 - - 0% 0% IPP 187,197 188,690 - 1,493 - 0.8 3% 3% Total 5,870,694 5,849,624 21,070 0.4 100% 100%

CFC, S1 Dwellings including ownership transfer costs 66,657 66,614 44 0.1 22% 22%

Buildings other than dwellings including ownership transfer costs 41,985 40,541 1,444 3.6 14% 13% Other structures and land improvements 35,054 36,548 - 1,494 - 4.1 12% 12% Transport equipment 20,630 20,731 - 102 - 0.5 7% 7% ICT 11,297 11,281 16 0.1 4% 4%

Other mach. and equip. Including furniture and weapons systems 79,209 78,944 265 0.3 26% 26% Biological resources 348 348 - - 0% 0% IPP 45,694 45,679 15 0.0 15% 15% Total 300,873 300,686 187 0.1 100% 100%

Source: elaboration on Istat data * ASLs used in the sensitivity analysis ** ASLs currently used in Italy

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Table 13 The impact of service lives revisions on net capital stock and CFC estimates, by asset; levels at current replacement costs (million EUR) and composition (%). General Government, 2016

Asset

Level

difference relative difference %

Composition

new ASL* Istat ASL** new ASL* Istat ASL**

Net capital stock, S13 Dwellings including ownership transfer costs 39,733 26,880 12,853 47.8 4% 3%

Buildings other than dwellings including ownership transfer costs 291,948 300,683 - 8,735 - 2.9 29% 30% Other structures and land improvements 563,918 562,891 1,027 0.2 56% 56%

Transport equipment 8,637 8,142 495 6.1 1% 1%

ICT 3,379 3,651 - 272 - 7.5 0% 0%

Other mach. and equip. Including furniture and weapons systems 59,272 59,639 - 368 - 0.6 6% 6%

Biological resources IPP 47,654 47,654 - - 5% 5%

Total 1,014,540 1,009,540 5,000 0.5 100% 100%

CFC, S13 Dwellings including ownership transfer costs 1,111 1,262 - 151 - 12.0 2% 3%

Buildings other than dwellings including ownership transfer costs 9,665 9,484 180 1.9 20% 20% Other structures and land improvements 18,447 18,450 - 3 - 0.0 38% 38%

Transport equipment 1,038 1,109 - 72 - 6.5 2% 2%

ICT 1,103 1,302 - 199 - 15.3 2% 3%

Other mach. and equip. Including furniture and weapons systems 6,303 5,833 470 8.1 13% 12%

Biological resources IPP 10,747 10,747 - - 22% 22%

Total 48,413 48,187 226 0.5 100% 100% Source: elaboration on Istat data * ASLs used in the sensitivity analysis ** ASLs currently used in Italy

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Figure 1 The impact of service lives revisions on net capital stock and CFC growth rates, Total Economy and General Government, 1995-2020

Source: elaboration on Istat data * ASLs used in the sensitivity analysis ** ASLs currently used in Italy

V. Conclusion

76. In recent years, harmonizing compilation practices and improving the quality and comparability of estimates of capital stock and CFC have gained importance and attention. In this context, Istat has conducted numerous studies and tests in order to review and update its practice of measuring stocks of fixed assets and CFC in the Italian national accounts.

77. Based on the results of our analyses, some Italian assumptions are empirically based and/or are aligned with international guidelines (the depreciation function and the retirements distribution; service lives for many assets, such as dwellings, buildings other than dwellings in some industries, other structures in some industries, land improvements, IPP, ICT equipment and other machinery and equipment). In contrast, some hypotheses should be revised as they differ from the recommendations made by the TF and they are not justified by empirical evidence (service life for buildings other than dwellings in some industries, other structures in some industries, transport equipment, some types of weapons systems). Also, the same service life for all sectors should be adopted, if the current ASLs differ by sector and specific detailed information is not available, in order to have greater methodological harmonization.

-1,0 -

1,0 2,0 3,0 4,0 5,0 6,0 7,0

Net capital stock, S1

new ASL* Istat ASL**

-2,0

-

2,0

4,0

6,0

8,0

10,0

Net capital stock, S13

new ASL* Istat ASL**

-1,0

-

1,0

2,0

3,0

4,0

5,0

6,0

7,0

CFC, S1

new ASL* Istat ASL**

-2,0

-

2,0

4,0

6,0

8,0

10,0

CFC, S13

new ASL* Istat ASL**

ECE/CES/GE.20/2024/22

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78. A sensitivity analysis has measured the effect of revisions on our estimates of stocks and CFC. The exercise shows that no significant impacts are expected on the estimates of stocks and CFC and on key macroeconomic indicators, through the measurement of non-market output by the sum of costs. Changing these parameters, the total net capital stocks for S1 is higher than the current one from +0.3% to +0.4% over the time series 1995-2020, CFC revision is from -0.1% to +0.2% over the same period. The impact on the total value is determined by revisions with opposite signs by asset. Growth rates differ very marginally.

79. However, these are provisional estimates and plans, as the works are not yet completed.

80. First, the new survey is underway to update the service life of computer hardware, communication equipment, other machinery and equipment, furniture (a specific questionnaire has been included in the annual Invind survey, conducted by the Bank of Italy, in collaboration with Istat, in 2024).

81. Furthermore, Istat is testing the use of time-varying service lives for some assets (i.e., the average service life changes over time). The standard approach adopted by Istat (and by many other National Statistical Institutes) involves using constant average service lives over the time series. However, this approach does not take into account changes in the composition of gross fixed capital formation over time in products with different service lives (e.g. new constructions vs major maintenance) nor the fact that lives of assets of the same type may change over time due to variations in the rate of obsolescence.

82. Revision will be introduced in our estimates during the next EU harmonized benchmark revisions of national accounts, scheduled for 2024.

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References

BEA, BUREAU OF ECONOMIC ANALYSIS (2003). Fixed assets and consumer durable goods in the United States, 1925-1997

Bobbio E., Iommi M. and Tartaglia-Polcini R., (2014). New Evidence on Service Lives of Capital Goods in Italy: Implications for Capital Stock Measurement and TFP Growth, IARIW 33rd General Conference, Rotterdam, the Netherlands, August 24-30, 2014

Eurostat, (2010). European System of Accounts ESA 2010, Luxemburg.

Eurostat, (2014). Manual on measuring Research and Development on ESA 2010, Luxemburg.

Eurostat, OECD (2015). Survey of National practices in estimating net stock of structures, Luxemburg.

Eurostat, (2023). DMES Task Force on fixed assets and estimation of consumption of fixed capital under ESA 2010 (TF FIXCAP). Final report – May 2023

Forestieri P., Santoro P., (2023). The estimate of consumption of fixed capital and stock of vehicles in Italian national accounts, Scientific ASA Conference on Statistics, Technology and Data Science for Economic and Social Development, University of Bologna. Paper in press.

Lupi C., Mantegazza S., (1994). Ricostruzione delle serie degli investimenti per branca utilizzatrice per branca proprietaria e calcolo dello stock di capitale. Quaderni di ricerca, n.14. Roma, ISTAT. Journal Article.

OECD, (2009). Measuring Capital Second edition, Paris.

OECD, (2010). Handbook on deriving capital measures of intellectual Property Products, Paris.

Tartaglia-Polcini R., (2013). Service lives of other machinery & equipment in Italy: evidence from business survey data. Mimeo, 2013.

UNITED NATIONS STATISTICAL COMMISSION, (2008). System of National Accounts, New York.

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ANNEX Table A.1 ASLs currently used in Italy, by industry and asset, S1

Industry

N111 N1121 N1122 N1123 N111_N112 N1131 N1131 N11321 N11322 N1139 N1139 N114 N1152 N1171 N1172 N1173 N1174 Dwellings Buildings

other than dwellings

Other structures

Land improv.

Ownership transfer costs

Vehicles Other transport

equipment

Computer hardware

Telecomm. equipment

Other mach. and

equip.

Furniture Weapons systems

Tree, crop and plant resources

R&D Mineral exploration

and evaluation

Computer software

and databases

Entert., literary or

artistic originals

A Agriculture, forestry and fishing 79.1 51.1 51.1 51.1 25 10 18 6.0 9.4 13.9 12.8 18 10 34 5 10 B Mining and quarrying 79.1 35 35 35 25 10 18 7.2 6.7 14.7 9.4 18 10 34 5 10 C10T12 Manufacture of food products, beverages and tobacco products 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10 C13T15 Manufacture of textiles, wearing apparel, leather and related products 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

C16T18 Manufacture of wood and of products of wood and cork, paper and paper products and printing and reproduction of recoded media 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

C19 Manufacture of coke and refined petroleum products 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10 C20 Manufacture of chemicals and chemical products 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10 C21 Manufacture of basic pharmaceutical products and preparations 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

C22_23 Manufacture of rubber and plastic products and other non-metallic mineral products 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

C24_25 Manufacture of basic metals, fabricated metal products, except machinery and equipment 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

C26 Manufacture of computer, electronic and optical products 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10 C27 Manufacture of electrical equipment 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10 C28 Manufacture of machinery and equipment n.e.c. 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

C29_30 Manufacture of motor vehicles, trailers and semi-trailers, and other transport equipment 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

C31T33 Manufacture of furniture, other manufacturing, repair and installation of machinery and equipment 79.1 35 35 35 25 10 18 6.7 6.8 14.5 14.5 18 10 34 5 10

D Electricity, gas, steam and air conditioning supply 79.1 35 35 35 25 10 18 7.2 6.7 14.7 9.4 18 10 34 5 10 E Water supply; sewerage, waste management and remediation activities 79.1 40 40 40 25 10 18 7.2 6.7 14.7 9.4 18 10 34 5 10 F Construction 79.1 35 35 35 25 10 18 6.0 4.6 9.8 10.0 18 10 34 5 10 G Wholesale and retail trade; repair of motor vehicles and motorcycles 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 H Transportation and storage 79.1 50 50 50 25 10 18 6.1 5.2 13.9 12.5 18 10 34 5 10 I Accommodation and food service activities 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 J58T60 Publishing, audiovisual and broadcasting activities 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 J61 Telecommunications 79.1 50 50 50 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 J62_63 IT and other information services 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 K Financial and insurance activities 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 L Real estate activities 79.1 80 80 80 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10

M69T71 Legal and accounting activities, activities of head offices, management consultancy, architecture and engineering activities, technical testing and analysis 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10

M72 Scientific research and development 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10

M73T75 Advertising and market research, other professional, scientific and technical activities, veterinary activities 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10

N Administrative and support service activities 79.1 65 65 65 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 O Public administration and defence; compulsory social security 79.1 60 60 60 25 14 20 6.1 5.2 11.0 12.5 10-30 20 10 34 7 10 P Education 79.1 57 57 57 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 Q86 Human health activities 79.1 35 35 35 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 Q87_88 Residential care and social work activities 79.1 56 56 56 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 R Arts, entertainment and recreation 79.1 56 56 56 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 S Other services activities 79.1 56 56 56 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 T Act. of HH as employers; undif. G&S-producing activities of HH for own use 79.1 56 56 56 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10 U Activities of extraterritorial organizations and bodies 79.1 56 56 56 25 10 18 6.1 5.2 11.0 12.5 18 10 34 5 10

ECE/CES/GE.20/2024/22

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Table A.2 ASLs currently used in Italy, by industry and asset, S13

Industry

N111 N1121 N1122 N1123 N111_N112 N1131 N1131 N1132 N1139 N1139 N114 N1152 N1171 N1172 N1173 N1174 Dwellings Buildings

other than dwellings

Other structures

Land improv.

Ownership transfer costs

Vehicles Other transport

equipment

ICT Other mach. and

equip.

Furniture Weapons systems

Tree, crop and plant resources

R&D Mineral exploration

and evaluation

Computer software and

databases

Entert., literary or

artistic originals

A Agriculture, forestry and fishing 51.1 51.1 51.1 25 10 18 6.3 15 12.8 10 5 10 B Mining and quarrying 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10 C10T12 Manufacture of food products, beverages and tobacco products 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10 C13T15 Manufacture of textiles, wearing apparel, leather and related products 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10

C16T18 Manufacture of wood and of products of wood and cork, paper and paper products and printing and reproduction of recoded media 35.5 35.5 35.5 25 10 18 6.3 15 12.8 10 5 10

C19 Manufacture of coke and refined petroleum products 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10 C20 Manufacture of chemicals and chemical products 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10 C21 Manufacture of basic pharmaceutical products and preparations 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10

C22_23 Manufacture of rubber and plastic products and other non-metallic mineral products 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10

C24_25 Manufacture of basic metals, fabricated metal products, except machinery and equipment 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10

C26 Manufacture of computer, electronic and optical products 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10 C27 Manufacture of electrical equipment 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10 C28 Manufacture of machinery and equipment n.e.c. 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10

C29_30 Manufacture of motor vehicles, trailers and semi-trailers, and other transport equipment 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10

C31T33 Manufacture of furniture, other manufacturing, repair and installation of machinery and equipment 35.0 35.0 35.0 25 10 18 6.3 15 12.8 10 5 10

D Electricity, gas, steam and air conditioning supply 40.0 40.0 40.0 25 10 18 6.3 23 12.8 10 5 10 E Water supply; sewerage, waste management and remediation activities 40.0 40.0 40.0 25 10 18 6.3 23 12.8 10 5 10 F Construction 35.0 35.0 35.0 25 10 18 6.3 9 12.8 10 5 10 G Wholesale and retail trade; repair of motor vehicles and motorcycles 65.0 65.0 65.0 25 10 18 6.3 9 12.8 10 5 10 H Transportation and storage 50.0 50.0 50.0 25 10 18 6.3 9 12.8 10 5 10 I Accommodation and food service activities 65.0 65.0 65.0 25 10 18 6.3 9 12.8 10 5 10 J58T60 Publishing, audiovisual and broadcasting activities 56.2 56.2 56.2 25 10 18 6.3 9 12.8 10 5 10 J61 Telecommunications 50.0 50.0 50.0 25 10 18 6.3 9 12.8 10 5 10 J62_63 IT and other information services 56.2 56.2 56.2 25 10 18 6.3 9 12.8 10 5 10 K Financial and insurance activities 65.0 65.0 65.0 25 10 18 6.3 9 12.8 10 5 10 L Real estate activities 79.1 79.1 79.1 25 10 20 7.5 9 18.0 10 5 10

M69T71 Legal and accounting activities, activities of head offices, management consultancy, architecture and engineering activities, technical testing and analysis 79.1 79.1 79.1 25 10 18 6.3 9 12.8 10 5 10

M72 Scientific research and development 79.1 79.1 79.1 25 10 18 6.3 9 12.8 10 5 10

M73T75 Advertising and market research, other professional, scientific and technical activities, veterinary activities 79.1 79.1 79.1 25 10 18 6.3 9 12.8 10 5 10

N Administrative and support service activities 79.1 79.1 79.1 25 10 18 6.3 9 12.8 10 5 10 O Public administration and defence; compulsory social security 60.0 60.0 60.0 25 10 18 6.0 15 13.0 10-30 10 5 10 P Education 57.2 57.2 57.2 25 10 18 6.3 9 12.8 10 5 10 Q86 Human health activities 35.1 35.1 35.1 25 10 18 6.3 9 12.8 10 5 10 Q87_88 Residential care and social work activities 35.1 35.1 35.1 25 10 18 6.3 9 12.8 10 5 10 R Arts, entertainment and recreation 56.2 56.2 56.2 25 10 18 6.3 9 12.8 10 5 10 S Other services activities 56.2 56.2 56.2 25 10 18 6.3 9 12.8 10 5 10

T Act. of HH as employers; undif. G&S-producing activities of HH for own use 0 0 0 0 0 0 0 0 0 0 0 10

U Activities of extraterritorial organizations and bodies 0 0 0 0 0 0 0 0 0 0 0 10

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Table A.3 Recommendations for ASL by the TF FIXCAP

Asset code Asset ASL Years Range AN.111 Dwellings 70 65-75 AN.1121 Buildings other than dwellings Warehouse and industrial buildings 30 25-35 Commercial buildings 50 45-55 Educational buildings 50 45-55 Health buildings 50 45-55 Buildings and structures for military use 50 45-55 Other buildings 50 45-55 AN.1122 Other structures 55 50-60 AN.1123 Land improvements 55 50-60 AN.1131 Transport equipment Aircraft 20 Trains 25 Ships 25 20-30

Vehicles (possible differentiation e.g. trucks, trailers, buses, cars) 10 8-12

AN.11321 Computer hardware 6 5-7 AN.11322 Telecommunications equipment 5 4-7 AN.1139 Other machinery and equipment

CPA 26: computer, electronic and optical products (except groups 261 and 262) 10 8-12

CPA 27: electrical equipment 15 12-18 CPA 28: machinery and equipment n.e.c. 20 15-25 CPA 31: furniture 15 12-18 CPA 32: other manufactured goods 10 8-12 AN.114 Weapons systems Aircraft 25 20-30 Ships 25 Tanks 20 Armoured vehicles 20 Electronic equipment 10 Other 15 5-25 AN.1151 Animal resources yielding repeat products (no CFC) 10 AN.1152 Tree, crop and plant resources yielding repeat products 15 10-20 AN.1171 Research and development 10 8-12 AN.1172 Mineral exploration and evaluation 30 20-40 AN.1173 Computer software and databases 5 AN.1174 Originals 7 5-10 AN.1179 Other intellectual property products No recommendation

ECE/CES/GE.20/2024/22

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Table A.4 ASLs used in the sensitivity analysis for S1 and S13, by industry and asset

Industry

N111 N1121 N1122 N1123 N111_N112 N1131 N1131 N11321 N11322 N1139 N1139 N114 N1152 N1171 N1172 N1173 N1174 Dwellings Buildings

other than dwellings

Other structures

Land improv.

Ownership transfer costs

Vehicles Other transport

equipment

Computer hardware

Telecomm. equipment

Other mach. and

equip.

Furniture Weapons systems

Tree, crop and plant resources

R&D Mineral exploration

and evaluation

Computer software

and databases

Entert., literary or

artistic originals

A Agriculture, forestry and fishing 79 51 51 51 25 10 21 6 7 14 13 18 10 34 5 10 B Mining and quarrying 79 35 50 50 25 10 21 7 7 15 9 18 10 34 5 10 C10T12 Manufacture of food products, beverages and tobacco products 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10 C13T15 Manufacture of textiles, wearing apparel, leather and related products 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

C16T18 Manufacture of wood and of products of wood and cork, paper and paper products and printing and reproduction of recoded media 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

C19 Manufacture of coke and refined petroleum products 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10 C20 Manufacture of chemicals and chemical products 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10 C21 Manufacture of basic pharmaceutical products and preparations 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

C22_23 Manufacture of rubber and plastic products and other non-metallic mineral products 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

C24_25 Manufacture of basic metals, fabricated metal products, except machinery and equipment 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

C26 Manufacture of computer, electronic and optical products 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10 C27 Manufacture of electrical equipment 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10 C28 Manufacture of machinery and equipment n.e.c. 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

C29_30 Manufacture of motor vehicles, trailers and semi-trailers, and other transport equipment 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

C31T33 Manufacture of furniture, other manufacturing, repair and installation of machinery and equipment 79 35 50 50 25 10 21 7 7 15 15 18 10 34 5 10

D Electricity, gas, steam and air conditioning supply 79 35 50 50 25 10 21 7 7 15 9 18 10 34 5 10 E Water supply; sewerage, waste management and remediation activities 79 36 50 50 25 10 21 7 7 15 9 18 10 34 5 10 F Construction 79 35 50 50 25 10 21 6 5 10 10 18 10 34 5 10 G Wholesale and retail trade; repair of motor vehicles and motorcycles 79 58 60 60 25 10 21 6 5 11 13 18 10 34 5 10 H Transportation and storage 79 50 50 50 25 10 21 6 5 14 13 18 10 34 5 10 I Accommodation and food service activities 79 58 60 60 25 10 21 6 5 11 13 18 10 34 5 10 J58T60 Publishing, audiovisual and broadcasting activities 79 59 60 60 25 10 21 6 5 11 13 18 10 34 5 10 J61 Telecommunications 79 50 50 50 25 10 21 6 5 11 13 18 10 34 5 10 J62_63 IT and other information services 79 60 60 60 25 10 21 6 5 11 13 18 10 34 5 10 K Financial and insurance activities 79 57 60 60 25 10 21 6 5 11 13 18 10 34 5 10 L Real estate activities 79 58 60 60 25 10 21 6 5 11 13 18 10 34 5 10

M69T71 Legal and accounting activities, activities of head offices, management consultancy, architecture and engineering activities, technical testing and analysis 79 58 60 60 25 10 21 6 5 11 13 18 10 34 5 10

M72 Scientific research and development 79 59 60 60 25 10 21 6 5 11 13 18 10 34 5 10

M73T75 Advertising and market research, other professional, scientific and technical activities, veterinary activities 79 58 60 60 25 10 21 6 5 11 13 18 10 34 5 10

N Administrative and support service activities 79 58 60 60 25 10 21 6 5 11 13 18 10 34 5 10 O Public administration and defence; compulsory social security 79 56 60 60 25 10 21 6 5 11 13 10 - 30 20 10 34 5 10 P Education 79 56 57 57 25 10 21 6 5 11 13 18 10 34 5 10 Q86 Human health activities 79 46 50 50 25 10 21 6 5 11 13 18 10 34 5 10 Q87_88 Residential care and social work activities 79 56 56 56 25 10 21 6 5 11 13 18 10 34 5 10 R Arts, entertainment and recreation 79 56 56 56 25 10 21 6 5 11 13 18 10 34 5 10 S Other services activities 79 56 56 56 25 10 21 6 5 11 13 18 10 34 5 10 T Act. of HH as employers; undif. G&S-producing activities of HH for own use 79 56 56 56 25 10 21 6 5 11 13 18 10 34 5 10 U Activities of extraterritorial organizations and bodies 79 56 56 56 25 10 21 6 5 11 13 18 10 34 5 10

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Table A.5 Differences between the ASLs used in the sensitivity analysis and the ASLs currently used in Italy, by industry and asset, S1

Industry

N111 N1121 N1122 N1123 N111_N112 N1131 N1131 N11321 N11322 N1139 N1139 N114 N1152 N1171 N1172 N1173 N1174 Dwellings Buildings

other than dwellings

Other structures

Land improv.

Ownership transfer costs

Vehicles Other transport

equipment

Computer hardware

Telecomm. equipment

Other mach. and

equip.

Furniture Weapons systems

Tree, crop and plant resources

R&D Mineral exploration

and evaluation

Computer software

and databases

Entert., literary or

artistic originals

A Agriculture, forestry and fishing -0.1 -0.1 -0.1 -0.1 3.0 -2.4 0.1 0.2 B Mining and quarrying -0.1 15.0 15.0 3.0 -0.2 0.3 0.3 -0.4 C10T12 Manufacture of food products, beverages and tobacco products -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5 C13T15 Manufacture of textiles, wearing apparel, leather and related products -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

C16T18 Manufacture of wood and of products of wood and cork, paper and paper products and printing and reproduction of recoded media -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

C19 Manufacture of coke and refined petroleum products -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5 C20 Manufacture of chemicals and chemical products -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5 C21 Manufacture of basic pharmaceutical products and preparations -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

C22_23 Manufacture of rubber and plastic products and other non-metallic mineral products -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

C24_25 Manufacture of basic metals, fabricated metal products, except machinery and equipment -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

C26 Manufacture of computer, electronic and optical products -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5 C27 Manufacture of electrical equipment -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5 C28 Manufacture of machinery and equipment n.e.c. -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

C29_30 Manufacture of motor vehicles, trailers and semi-trailers, and other transport equipment -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

C31T33 Manufacture of furniture, other manufacturing, repair and installation of machinery and equipment -0.1 15.0 15.0 3.0 0.3 0.2 0.5 0.5

D Electricity, gas, steam and air conditioning supply -0.1 15.0 15.0 3.0 -0.2 0.3 0.3 -0.4 E Water supply; sewerage, waste management and remediation activities -0.1 -4.0 10.0 10.0 3.0 -0.2 0.3 0.3 -0.4 F Construction -0.1 15.0 15.0 3.0 0.4 0.2 G Wholesale and retail trade; repair of motor vehicles and motorcycles -0.1 -7.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5 H Transportation and storage -0.1 3.0 -0.1 -0.2 0.1 0.5 I Accommodation and food service activities -0.1 -7.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5 J58T60 Publishing, audiovisual and broadcasting activities -0.1 -6.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5 J61 Telecommunications -0.1 3.0 -0.1 -0.2 0.5 J62_63 IT and other information services -0.1 -5.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5 K Financial and insurance activities -0.1 -8.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5 L Real estate activities -0.1 -22.0 -20.0 -20.0 3.0 -0.1 -0.2 0.5

M69T71 Legal and accounting activities, activities of head offices, management consultancy, architecture and engineering activities, technical testing and analysis

-0.1 -7.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5 M72 Scientific research and development -0.1 -6.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5

M73T75 Advertising and market research, other professional, scientific and technical activities, veterinary activities -0.1 -7.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5

N Administrative and support service activities -0.1 -7.0 -5.0 -5.0 3.0 -0.1 -0.2 0.5 O Public administration and defence; compulsory social security -0.1 -4.0 -4.0 1.0 -0.1 -0.2 0.5 -2.0 P Education -0.1 -1.0 3.0 -0.1 -0.2 0.5 Q86 Human health activities -0.1 11.0 15.0 15.0 3.0 -0.1 -0.2 0.5 Q87_88 Residential care and social work activities -0.1 3.0 -0.1 -0.2 0.5 R Arts, entertainment and recreation -0.1 3.0 -0.1 -0.2 0.5 S Other services activities -0.1 3.0 -0.1 -0.2 0.5 T Act. of HH as employers; undif. G&S-producing activities of HH for own use -0.1 3.0 -0.1 -0.2 0.5 U Activities of extraterritorial organizations and bodies -0.1 3.0 -0.1 -0.2 0.5

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Table A.6 Difference Differences between the ASLs used in the sensitivity analysis and the ASLs currently used in Italy, by industry and asset, S13

Industry

N111 N1121 N1122 N1123 N111_N112 N1131 N1131 N1132 N1139 N1139 N114 N1152 N1171 N1172 N1173 N1174 Dwellings Buildings

other than dwellings

Other structures

Land improv.

Ownership transfer costs

Vehicles Other transport

equipment

ICT Other mach. and

equip.

Furniture Weapons systems

Tree, crop and plant resources

R&D Mineral exploration

and evaluation

Computer software

and databases

Entert., literary or

artistic originals

A Agriculture, forestry and fishing 27.9 -0.1 -0.1 3.0 -0.3 -1.0 0.2 B Mining and quarrying 44.0 15.0 3.0 0.7 -3.8 C10T12 Manufacture of food products, beverages and tobacco products 44.0 15.0 3.0 0.7 2.2 C13T15 Manufacture of textiles, wearing apparel, leather and related products 44.0 15.0 3.0 0.7 2.2 C16T18 Manufacture of wood and of products of wood and cork, paper and paper products

and printing and reproduction of recoded media 43.5 -0.5 14.5 3.0 0.7 2.2 C19 Manufacture of coke and refined petroleum products 44.0 15.0 3.0 0.7 2.2 C20 Manufacture of chemicals and chemical products 44.0 15.0 3.0 0.7 2.2 C21 Manufacture of basic pharmaceutical products and preparations 44.0 15.0 3.0 0.7 2.2 C22_23 Manufacture of rubber and plastic products and other non-metallic mineral

products 44.0 15.0 3.0 0.7 2.2 C24_25 Manufacture of basic metals, fabricated metal products, except machinery and

equipment 44.0 15.0 3.0 0.7 2.2 C26 Manufacture of computer, electronic and optical products 44.0 15.0 3.0 0.7 2.2 C27 Manufacture of electrical equipment 44.0 15.0 3.0 0.7 2.2 C28 Manufacture of machinery and equipment n.e.c. 44.0 15.0 3.0 0.7 2.2 C29_30 Manufacture of motor vehicles, trailers and semi-trailers, and other transport

equipment 44.0 15.0 3.0 0.7 2.2

C31T33 Manufacture of furniture, other manufacturing, repair and installation of machinery and equipment 44.0 15.0 3.0 0.7 2.2

D Electricity, gas, steam and air conditioning supply 39.0 -5.0 10.0 3.0 0.7 -8.0 -3.8 E Water supply; sewerage, waste management and remediation activities 39.0 -4.0 10.0 3.0 0.7 -8.0 -3.8 F Construction 44.0 0.0 15.0 3.0 -0.3 1.0 -2.8 G Wholesale and retail trade; repair of motor vehicles and motorcycles 14.0 -7.0 -5.0 3.0 -0.3 2.0 0.2 H Transportation and storage 29.0 3.0 -0.3 5.0 0.2 I Accommodation and food service activities 14.0 -7.0 -5.0 3.0 -0.3 2.0 0.2 J58T60 Publishing, audiovisual and broadcasting activities 22.8 2.8 3.8 3.0 -0.3 2.0 0.2 J61 Telecommunications 29.0 3.0 -0.3 2.0 0.2 J62_63 IT and other information services 22.8 3.8 3.8 3.0 -0.3 2.0 0.2 K Financial and insurance activities 14.0 -8.0 -5.0 3.0 -0.3 2.0 0.2 L Real estate activities -0.1 -21.1 -19.1 1.0 -1.5 2.0 -5.0

M69T71 Legal and accounting activities, activities of head offices, management consultancy, architecture and engineering activities, technical testing and analysis -0.1 -21.1 -19.1 3.0 -0.3 2.0 0.2

M72 Scientific research and development -0.1 -20.1 -19.1 3.0 -0.3 2.0 0.2

M73T75 Advertising and market research, other professional, scientific and technical activities, veterinary activities -0.1 -21.1 -19.1 3.0 -0.3 2.0 0.2

N Administrative and support service activities -0.1 -21.1 -19.1 3.0 -0.3 2.0 0.2 O Public administration and defence; compulsory social security 19.0 -4.0 3.0 -4.0 P Education 21.8 -1.2 -0.2 3.0 -0.3 2.0 0.2 Q86 Human health activities 43.9 10.9 14.9 3.0 -0.3 2.0 0.2 Q87_88 Residential care and social work activities 43.9 20.9 20.9 3.0 -0.3 2.0 0.2 R Arts, entertainment and recreation 22.8 -0.2 -0.2 3.0 -0.3 2.0 0.2 S Other services activities 22.8 -0.2 -0.2 3.0 -0.3 2.0 0.2 T Act. of HH as employers; undif. G&S-producing activities of HH for own use U Activities of extraterritorial organizations and bodies

  • Group of Experts on National Accounts
  • Twenty-third session
  • A sensitivity test on stocks and CFC estimates of Italy: implementation of European recommendations for harmonization and comparability among Member States
    • Prepared by Italian National Institute of Statistics0F
  • II. Depreciation function and retirements distribution in the Italian practice
    • A. Assumptions and data sources
    • B. International comparison
  • III. Service lives in the Italian practice
    • A. Assumptions and data sources
    • B. International comparison
  • IV. Sensitivity analysis on the service lives
    • A. Methodological approach
    • B. Main results
  • V. Conclusion
  • References
  • ANNEX

A regional estimate of General Government GFCF of Italy: different strategies for different assets and subsectors and implementation of European recommendations

Languages and translations
English

Economic Commission for Europe Conference of European Statisticians Group of Experts on National Accounts Twenty-third session Geneva, 23-25 April 2024 Item 4 of the provisional agenda Subnational and regional accounts

A regional estimate of General Government GFCF of Italy: different strategies for different assets and subsectors and implementation of European recommendations

Prepared by the Italian National Institute of Statistics1

Summary

Since 2020, Istat has been engaged in analysis aimed at realizing regional estimates for General Government sector GFCF. The paper describes the work carried out so far. The practice adopted was analyzed in light of the recommendations formalized by the Eurostat Task Force on Regional Investment. The potential in Local Government current data sources to provide accurate regional estimates has been tested, while the different issues in regionalizing GFCF of Central Government Units and Social Security Funds has been identified. The analysis was firstly aimed at regionalizing the GFCF expenditure in infrastructures, as a strategic asset for regional cohesion, but then also focused on remaining assets, and specifically on weapons and Research & Development. Therefore, exercises are described to obtain a regional distribution of General Government GFCF aligning them with the European recommendations, in order to achieve methodological harmonization in the international context and better data comparability. Finally, conclusions and future plans are presented.

1 Prepared by Nicola Vallo, Francesca Brunaccini.

United Nations ECE/CES/GE.20/2024/23

Economic and Social Council Distr.: General 12 April 2024 Original: English

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I. Introduction2

1. In June 2020, aware of the crucial relevance of monitoring public investment at regional level in order to provide proper indicators for cohesion policies, Eurostat launched the Task Force on Regional Investments, whose works continued until September 2023. As a member of the Task force, ISTAT participated to the debate and to the finalization of recommendations for regionalizing General Government GFCF. These recommendations were then collected in the final report of the Task Force. The participation of ISTAT was supported through a project granted by Eurostat titled “Pilot project on regional gross fixed capital formation by the government sector”. The project aimed at realizing test estimates of the regional distribution of General Government GFCF for the years 2018, 2019 and 2020.

2. The first section of this document describes of General Government GFCF of Italy by subsector and asset. Subsequently a description of how the regionalization has been realized is provided. Section number 3 focuses on the regionalization of Local Government units, while section 4 and 5 on Social Security Funds and Central Government. Section 6 is dedicated to R&D, while section 7 provides the results of the analysis for the year 2020. In Section 8 some provisional conclusions and future plans are presented.

II. Public investments of Italy and structure of the analysis

3. Table 1 shows the split of Italy General Government GFCF expenditure by subsectors. The Public administration sector, S.13, is divided in three subsectors: Central Government (CG), S.1311, Local Government (LG), S1313, and Social Security Funds (SSF), S.1314. In Italy the State subsector, S.1312 is not present.

Table 1 Split of Italy General Government GFCF expenditure by subsectors

2018 2019 2020 GG 37.766 41.470 43.077 CG 16.504 17.699 18.496 LG 20.859 23.461 24.322 SSF 403 310 259 GG 100% 100% 100% CG 43,7% 42,7% 42,9% LG 55,2% 56,6% 56,5% SSF 1,1% 0,7% 0,6% ISTAT, October 2023 release, dati.istat.it

4. LG plays constantly a prominent role in the overall level of Italian public investment. An effective regionalization of this subsector could give a good contribution to the regionalization of the entire GG sector. Nonetheless the contribution of CG is also very relevant, and in this subsector a big percentage of total expenditure in infrastructures is included. SSF instead represent the subsector with the smaller level of GFCF.

2 The views expressed in this paper are those of the authors and do not necessarily reflect the views of the ISTAT. The paper derives from the joint work of all authors; however, the paragraphs were authored as follows: §1, 2, 3, 4, 6, 7, 8 were written by N. Vallo; § 5 by N. Vallo and F. Brunaccini.

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Table 2 General government GFCF division into subsectors, 2018

2018 GG CG LG SSF GG CG LG SSF

Dwellings, Building other than dwellings and Other structures 19.155 7.504 11.648 3 100,0% 39,2% 60,8% 0,0%

of which Other structures 11.968 5.964 6.004 - 100,0% 49,8% 50,2% 0,0%

Machinery equipment and intellectual property products 18.611 9.000 9.211 400 100,0% 48,4% 49,5% 2,1%

of which machinery equipement 4.142 1.617 2.474 51 100,0% 39,0% 59,7% 1,2%

of which R&D 7.881 2.387 5.385 109 100,0% 30,3% 68,3% 1,4%

of which computer software and database and Entertainment, literary or

artistic originals 2.967 1.375 1.352 240 100,0% 46,3% 45,6% 8,1%

ISTAT, October 2023 release, dati.istat.it

Table 3 General government GFCF division into subsectors, 2019

2019 GG CG LG SSF GG CG LG SSF

Dwellings, Building other than dwellings and Other structures 21.253 7.802 13.475 - 24 100,0% 36,7% 63,4% -0,1%

of which Other structures 14.037 6.663 7.374 - 100,0% 47,5% 52,5% 0,0%

Machinery equipment and intellectual property products 20.217 9.897 9.986 334 100,0% 49,0% 49,4% 1,7%

of which machinery equipement 4.795 1.655 3.095 45 100,0% 34,5% 64,5% 0,9%

of which R&D 8.123 2.455 5.562 106 100,0% 30,2% 68,5% 1,3%

of which computer software and database and Entertainment, literary or

artistic originals 3.038 1.526 1.329 183 100,0% 50,2% 43,7% 6,0%

ISTAT, October 2023 release, dati.istat.it

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Table 4 General government GFCF division into subsectors, 2020

2020 GG CG LG SSF GG CG LG SSF

Dwellings, Building other than dwellings and Other structures 21.207 7.773 13.556 - 122 100,0% 36,7% 63,9% -0,6%

of which Other structures 13.443 6.737 6.706 - 100,0% 50,1% 49,9% 0,0%

Machinery equipment and intellectual property products 21.870 10.723 10.772 381 100,0% 49,0% 49,3% 1,7%

of which machinery equipement 6.607 2.714 3.830 63 100,0% 41,1% 58,0% 1,0%

of which R&D 8.262 2.618 5.529 115 100,0% 31,7% 66,9% 1,4%

of which computer software and database and Entertainment, literary or

artistic originals 3.329 1.719 1.413 203 100,0% 51,6% 42,4% 6,1%

ISTAT, October 2023 release, dati.istat.it

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5. Tables 2-4 show how, for the years considered, the general government GFCF is divided into subsectors and kind of asset. Regarding CG is clear that a big part of the expenditure is related to Other Structures and R&D. The strategy adopted in regionalizing CG took into consideration this relevance, isolating the specific subgroups of units responsible for this part of GFCF and assessing the possibility of including complementary information to those already present in their data sources currently used for national accounts.

6. Similarly to what is done for the estimates of annual accounts, realized for homogenous group of units within each subsector and then assembled to obtain the GG figures, the regionalization has been realized in the same way, not considering the subsector as a whole, but each group of units within a given subsector.

7. For central administration this groups are: the State group (including Ministries and Prime Minister's Office, the Chamber of Deputies, the Senate, the Presidency of the Republic, the Constitutional Court, Other Bodies of Constitutional status, which constitutes the core central administration), Anas, RFI, National Research Bodies, National Television, National Economic Bodies and other minor central units.

8. Local Government includes Regions, Provinces, Municipalities, Local Health Units, Universities, Chambers of Commerce, Port Authorities and other minor local units.

9. Social Security Funds are a group of 22 institutions. Despite their lower impact on GG investments, this group of units is characterized by a strong level of dwellings disposals, and this element has been carefully considered in distributing by regions their investments.

10. At first for each group of unit the availability of regional information in the data sources already used for annual accounts has been checked; if not available, the characteristics of the unit have been considered: when uniregional, the GFCF has been allocated to the region where the unit was located, if multiregional, additional other resources have been explored to obtain the regional distribution of their assets. Within each group of units the total GFCF expenditure has been split in three parts regionalized separately:

(a) Assets other than R&D and Weapons

(b) R&D

(c) Weapons (only for State)

11. Within the GFCF portion of “Assets other than R&D and Weapons” - which includes Dwellings, Buildings other than dwellings, Other structures, Transport equipment, Machinery equipment, ICT, Software and Entertainment, literary or artistic originals – all the imputations, i.e. elements of expenditure included in P51g according to ESA 2010 and Manual on Government Deficit and Debt (Eurostat, 2022), have been regionalized separately: own account software, financial leasing, decommissioning costs, expenditure related to PPP and EPC contracts classified as on balance, to rerouted assets in the context of concession contracts.

12. Own account software has been regionalized using regional distribution of the single group of units compensation of employees, information from PPP and EPC contracts has been used to locate the work realized in the specific region of the unit involved, decommissioned assets has been located in the region of the actual decommissioning, and similarly happened for road works realized by concessionaires classified in S.13 and construction assets rerouted to S.13 perimeter.

III. Local Government (S.1313) GFCF regionalization

13. For Local Government (S1313) a detailed regionalization by single asset has been realized. This was possible thanks to the structure of data sources already used for compiling annual accounts, reporting regional information both for accrual and cash amounts. Regions, Municipalities, Provinces and Local Health Units, which together represent the 94% of Local Government P51g expenditure, share this same level of detail.

14. For the residual 6% (Mountains Development Bodies, Chambers of Commerce, Port Authorities, and other local units) a similar approach has been used when possible.

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15. The main components of the Local Government investments are Building and Other structures and Research & Development. Municipalities are responsible for the higher level of expenditure within the group and, with Regions and Provinces, cover more than the 80% of the expenditure in Buildings and other structures of the subsector. Universities alone cover almost entirely the expenditure of the subsector in R&D.

16. The data source for annual accounts of Regions and Autonomous Provinces is the Istat Survey on Financial Statements of Regions and of Autonomous Provinces. The data source is organized by region and for all the aggregates, including GFCF, is pretty straightforward to obtain regional accounts. The Regions and Autonomous Provinces subgroup of units is multiregional and constituted by single units resident in specific regions.

17. For Provinces and Municipalities the sources used for annual accounts are the “Final Accounting Reports” (Certificati del rendiconto di bilancio), collected every year by the Ministry of Internal Affairs. Since 2019 data are uploaded in the MoF General Government database (Banca Dati Amministrazioni Pubbliche-BDAP). In this case the overall amount of GFCF could be split by region using the split by region of the original data source, at asset level.

18. For Local Health Units, profit and loss accounts collected by the Ministry of Health are available. These data, consolidated at regional level, are transmitted by every LHU to the NSIS System (Nuovo Sistema Informativo Sanitario) of the Ministry of Health and then to Istat. The regional split of stocks, and consequently of flows of P51g, is available at asset level. In this case non-movable assets are only Buildings other than dwellings.

19. As for Universities, the data source is a census survey on the Universities accounting documents carried out by the Ministry of Education and Research. Also in this case the structure of the annual data source allowed a detailed split by asset. Universities, together with Research Bodies for Central Government, are mainly responsible for the expenditure in Research and Development. The regionalization of this specific asset will be described in section 6.

20. The nature of the units, the straightforward identification of the regional local-KAUS, and the use of surveys and administrative data as data sources allowed to use a bottom up approach in performing the GFCF regional estimates, with GFCF expenditure effectively allocated to the regions where is used and whose value added estimation contributes to.

21. S.1313 group of units are multiregional, with local KAUs resident in specific regions. For each local KAU information on the aggregates involved in the estimation of P.1 at costs (D1, P2, D29 paid, with the exclusion of P51c which follows the regional distribution of P51g) and on P51g is available. P51g is automatically allocated to the region of the owner (owner principle), and P51g and value added, resulting from the estimation of P1, are strictly associated.

22. This ascending/ bottom up approach is applicable almost for the entire S.1313 subsector. Being based on surveys and administrative data, this method of regionalization is an A-method in terms of accuracy, according to Manual on regional accounts methods (Eurostat,2013).

23. The “other local units” subgroups covers a residual part (6% on average for the years considered) of LG investments. This subgroup is composed by multiregional units like Chambers of Commerce or Port Authorities or Mountain Development Bodies, for which the sources are available at regional level, but also by units classified in the S.13 sector, most of them uniregional. This subgroup also contains local roads concessionaires classified in S.13. For these units a pseudo-bottom up approach has been applied in regionalizing P51g. This units are involved in the construction and maintenance of roads. In this cases the notional local KAU of the concessionaires has been estimated using as indicator the regional distribution of the road network, following a territorial criterion to allocate the P51g. Nonetheless, the percentage of S.1313 P51g regionalized using the pseudo bottom up approach is very low (1%).

24. Normally Local Government units are involved in realizing works via Public Private Partnership contracts (PPP). Regions, Municipalities and Local Health Units in particular. If the contract is classified as on balance, i.e. the entire cost of the works has to be included in

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General Government net lending/net borrowing according to ESA2010, a regional distinction by asset (mostly Buildings other than dwellings, and Other structures), coming from PPP contracts is available and, in compiling the estimates, this particular expenditure has been regionalized accordingly.

IV. Social Security Funds (S.1314) regionalization

25. Social security Funds data source for annual accounts is a statistical survey, carried out by ISTAT, on final budgets of Social Security Funds.

26. A regional distribution of gross fixed capital formation of SSF is not available in the data sources, so different strategies have been used according the peculiar characteristics of this part of GFCF expenditure of GG, which nonetheless normally amounts to a residual percentage of it, as you can see in Table 1, reported in section 1.

27. The gross fixed capital formation of Social Security Funds consists mainly of software, acquired R&D, ICT equipment, buildings other than dwellings and dwellings. For dwellings in particular, the level of disposal – realized in the past also via securitizations - is historically quite relevant. This is especially true for the years 2001- 2005, but all the following years, including the years 2018, 2019 and 2020 analyzed for the project, were affected.

28. A first test estimate has been realized using a top down indicator: the regional information on fixed assets expenditure of Public Administration contained in the Territorial Public Accounting System (CPT)3 survey, provided by the Italian Agency of Cohesion. This survey collects cash payments related to different aggregates realized by each unit of general government participating to the survey. Given the level of aggregation of the information collected (the assets are grouped in moveable assets and non-moveable assets only) and considering a difference of perimeter between the SSF participating to the survey and the universe of SSF included in S.13 perimeter, this first option has been abandoned.

29. The second test estimate aimed at giving also particular attention to disposals of Dwellings. This led to a separate regionalization of acquisitions and disposals. The acquisition and disposals of new Machinery and equipment, ICT and software has been regionalized using the regional distribution of SSF employees. The acquisition of new and used dwellings and building other than dwellings has been regionalized using the regional distribution of areas of these assets owned by SSF reported in the annual survey Patrimonio della PA4 realized by the Treasury Department (Ministry of Finance): each General Government unit communicates data on public real estate (buildings and land), providing information on area, location, type of use, real estate characteristics of the asset. The database of the Department of the Treasury contains information on the properties of approximately 11,000 public administrations, both central and local, using homogeneous criteria and classification systems. The regional distribution of the areas of dwellings and Building other than dwellings owned by SSF and contained in this survey has been used.

30. The disposal of dwellings has been regionalized separately using information contained in the balance sheets of Funds involved in the disposals, indicating the location and the region of the sold dwellings.

V. Central Government (S.1311) GFCF regionalization

31. The regionalization of GFCF of Central Government (S.1311) was more difficult due to the absence of regional information in the data sources used for compiling annual accounts. The regionalization of S.1311 GFCF needs necessarily to rely on external indicators.

32. Excluding weapons, in charge of State, and R&D, mainly realized by Central Research Bodies, the remaining assets are 90% covered by the regionalization of three

3 https://www.agenziacoesione.gov.it/sistema-conti-pubblici-territoriali/ 4 https://portaletesoro.mef.gov.it/

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subgroup of units: State, Anas and RFI. The last two are mainly engaged in realizing roads and railways.

33. The remaining Other Central Units group (National Economic Bodies, National Assistance Bodies, National Television) is mainly constituted by uniregional units. The RIDDCUE5 survey used for annual accounts estimation of these units contains the region where each unit is located and allows a proper regionalization.

34. For State subgroup the State Budget Reporting provided by the State General Accounting Department (Ragioneria Generale dello Stato) of the Ministry of Economy and Finance contains only aggregated information for the total annual expenditure with no indication about the counterpart or the local office responsible for the expenditure.

35. Initially a counterpart approach (Eurostat, 1999) in regionalizing this expenditure was considered, trying to allocate the expenditure to the region where the asset is purchased and the seller located, but this hypothesis has been subsequently abandoned due to the lack of complete and reliable information in the sources available. Also in this case, a first test estimate was realized using the regional distribution of expenditure provided by the Territorial Public Accounting System (CPT). Finally a second estimate has been realized using a top down approaches for different assets: for moveable assets, the regional distribution of employees of Ministries, Schools and National Security has been used; for dwellings and buildings other than dwellings, instead, the information on stocks of these assets, owned by State Administration, reported in the Patrimonio della Pa survey realized by the Treasury Department of the Ministry of Finance, also mentioned in section 4, was used. The regional distribution of the areas of dwellings and building other than dwellings owned by State has been used as indicator.

36. Finally, more than the 50% of State GFCF consists of weapons systems. For this type of asset, according to Task Force recommendations, the allocation of movable expenditures (warships, submarines, military aircraft, tanks, missile carriers and launchers) to central headquarters has been avoided. The compensation of employees, of military staff, has been considered a better indicator to allocate by region this category of assets. In particular different types of military personnel (terrestrial, navy and air force) has been used for the different assets included in military expenditure.

37. ANAS (acronym for "Azienda Nazionale Autonoma delle Strade") activity is road design and construction and subsequent ordinary and extraordinary maintenance. It manages the road system and road safety along the entire network of state roads and freeways under direct management and in coordination with other bodies. RFI - Rete Ferroviaria Italiana is a public company whose activity is the management and maintenance of the railway network. The level of difficulties in splitting by region the GFCF realized by these two units is quite similar: both the units have a balance sheet and a profit and loss accounts, both the units are exclusively engaged in realizing public infrastructure. In both cases the information and tables contained in the balance sheets did not provide any useful indication for the regional distribution of the works realized.

38. ANAS and RFI are annually responsible for more than the 40% of the expenditure in Other structures (tab. 5), and while a remaining relevant part is realized by Municipalities, whose regionalization is much more accurate giving the data sources available, it is indeed important to achieve an accurate regionalization of this two big central units responsible for the main infrastructures included in General Government GFCF.

39. In a first test estimate the regionalization of these two units has been realized using the length of the infrastructures network by region, available on ANAS and RFI websites. A territorial criterion appeared the best approach to define the economic owner of the infrastructures. Nonetheless this estimation strategy had its weakness: using the network structure as indicator is implicitly assuming that all the annual expenditure is distributed to all the regions where the network is, each year for the same quota. Even if assuming that only

5 Rilevazione di informazioni, dati e documenti necessari alla classificazione di unità economiche nei settori istituzionali stabiliti dal sistema europeo dei conti 2010 (sec 2010). www.istat.it

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maintenance could follow this pattern (and is debatable), this indicator does not capture the realization of new works, that could happen in a given region on a given year.

40. To face this problem a further exploration of ANAS and RFI available published reports and documents has been done to separate extraordinary maintenance from realization of new works and try a separate regionalization. These additional documents, multiyear investments plans and planning contracts, although extensively providing details on planned and approved works, concluded works, nature of the works (ordinary and extraordinary maintenance and new works) even at regional level, does not allow an effective reconciliation between this set of information and the works then realized and reported in a given year balance sheet – and subsequently classified in national accounts as P51g. At the moment, provisionally, the use of the roads and railways network as an indicator remains the only available choice.

Table 5 Contribution of groups of units within each subsector to Other structures expenditure

CG LG

ANAS RFI

OTHER

CENTRAL

UNITS

REGIONS MUNICIPALITIE

S

OTHER

LOCAL

UNITS

2.018

Other structures 100% 10% 35% 5% 10% 32% 8%

of which roads 30% 0% 2% 18% 42% 8%

of which other structures

other than roads

0% 53% 6% 6% 27% 8%

2.019

Other structures 100% 9% 32% 7% 8% 34% 11%

of which roads 24% 0% 1% 12% 47% 15%

of which other structures

other than roads

0% 50% 10% 5% 26% 8%

2.020

Other structures 100% 11% 33% 7% 7% 34% 9%

of which roads 28% 0% 1% 8% 46% 16%

of which other structures

other than roads

0% 52% 10% 7% 27% 4%

41. The final subgroups of central units consists of Research Bodies, whose expenditure is mainly due to own account R&D (and this asset regionalization is described in section 6), and other central units such as National Economic Bodies and National Assistance Bodies. For these last group the regional information again comes directly from the ISTAT survey RIDDCUE used for annual accounts estimation. Most of the units included in this subgroup are uniregional, and the overall expenditure in P51g of this units is only the 10% of the total S.1311 P51g. This subgroup of units contains the decommissioning costs included in GFCF, allocated to the region where the nuclear site to be decommissioned is.

42. The regionalization for S.1311 has been mainly realized using a top down approach and a territorial criterion. For State subgroup the indicators used come from administrative data and surveys, and in this case in terms of accuracy an A-method has been applied. On the contrary, the use of the length of the network for ANAS and RFI led to a classification of this choice as B-Method in terms of accuracy. For the remaining uniregional and multiregional units included in Other Central Units, local KAUS can be identified and a bottom up approach based on a survey has been applied. Also in this case an A-Method is used. Overall, for CG a mixed methods choice has been applied.

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VI. Research & Development

43. For R&S the main data source is the survey “Rilevazione statistica sulla Ricerca e Sviluppo nelle Istituzioni Pubbliche”6, carried out by ISTAT. The survey is exhaustive and it is methodologically based on the OECD Frascati Manual (Oecd, 2015). It collects data on expenditure for research, staff, types of research and funding sources, and considers two type of the expenditures: those for the institutional mission of the unit (intramural) and those for the research commissioned by external institution (extramural).

44. Research and development represents approximatively the 20% of General Government GFCF. It consists mainly on research realized by National Research and Development Bodies included in Central Government and Universities and Local Health Units for Local Government.

45. As discussed in the Task Force on Regional Investments, for R&D it is desirable to follow in regional accounts the same methodology used at national level and explained in Manual on measuring Research and Development in ESA 2010 (Eurostat, 2014). Nonetheless all the elements necessary at national level for the estimates are hardly available at regional level. A possible alternative envisaged is to use the compensation of employees.

46. Following the recommendation expressed in the Task Force, a stronger link with regional information coming from the survey on R&D has been ensured for National Research Bodies and Local Health Units. In this case the regional distribution of costs for intramural R&D reported in the survey has been used as in indicator to regionalize their R&D expenditure. For Universities, not covered by the survey, and other units involved in this kind of expenditure, a separate regionalization has been realized using compensation of employees.

VII. Results of the analysis

47. In the following graphs the regional distribution at NUTS-27 level for the year 2020 is showed. Graph 1 below shows the distribution of General Government GFCF by NUTS- 2.

Graph 1 Percentage of General Government gross fixed capital formation by NUTS-2, year 2020

6 https://www.istat.it/it/archivio/210604 7Regulation (EC) No 1059/2003

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48. Considering the total amount of GFCF and the entire General Government sector, Lazio and Lombardia regions seem to be the regions where the higher percentage of General Government gross fixed capital formation is concentrated.

49. Graph 2 below shows the regional distribution by group of assets of General Government GFCF.

Graph 2 Percentage of General Government gross fixed capital formation by group of assets by NUTS2 (year 2020)

50. Considering the split of R&D, weapons and GFCF other than R&D and weapons is evident the relevance of weapons and R&D in the composition of Lazio region investments. In this region is concentrated the higher level of terrestrial and air military forces, and this led to an higher level of expenditure of weapons allocated to this region. Only Puglia region has a comparable level of weapons expenditure allocated, due to the higher concentration of navy forces. As for R&D, more than the 50% of the intramuros research activity of National Research Bodies is concentrated in Lazio, and a high percentage of R&D realized by Universities is also allocated to the same regions.

51. It is useful to isolate the regional distribution of the GFCF other than R&D and weapons only.

0%

5%

10%

15%

20%

25%

30%

35%

GFCF by asset, by NUTS-2 regions (2020)

GFCF other R&D and weapons R&D weapons

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Graph 3 Percentage of General Government GFCF other than R&D and weapons by NUTS-2 (year 2020)

52. Graph 3 shows that, isolating this part of GFCF, while Lazio and Lombardia are still the regions where the higher percentage of public investments are allocated, Lombardia region appears the region with a more relevant role. Also other regions of the North and the south of Italy show important percentage of public investments, especially Veneto and Campania.

53. A further level of analysis is isolating within this group of assets the expenditure in Other structures, where public investments in roads, railways and infrastructures are recorded. Graph 4 shows the percentage of expenditure in infrastructure allocated by regions.

Graph 4 Percentage of Other structures by subsector by NUTS2 (year 2020)

54. Local Government –especially Municipalities- plays the most important role in this part of GFCF expenditure, this is particularly true for the Autonomous Provinces of Trento and Bolzano, but also for Campania, Veneto and Lombardia, which benefits of the highest percentage of expenditure both from Central Government and Local Government.

0% 2% 4% 6% 8%

10% 12% 14% 16%

IT C1

_P IE

M O

N TE

IT C2

_V AL

D 'A

O ST

A

IT C4

_L O

M BA

RD IA

IT H1

_B O

LZ AN

O

IT H2

_T RE

N TO

IT H3

_V EN

ET O

IT H4

_F RI

U LI

IT C3

_L IG

U RI

A

IT H5

_E M

IL IA

IT I1

_T O

SC AN

A

IT I2

_U M

BR IA

IT I3

_M AR

CH E

IT I4

_L AZ

IO

IT F1

_A BR

U ZZ

O

IT F2

_M O

LI SE

IT F3

_C AM

PA N

IA

IT F4

_P U

GL IA

IT F5

_B AS

IL IC

AT A

IT F6

_C AL

AB RI

A

IT G

1_ SI

CI LI

A

IT G

2_ SA

RD EG

N A

IT ZZ

_E XT

RA R

EG IO

GFCF other than R&D and weapons, by NUTS 2 regions (2020)

0% 5%

10% 15% 20% 25%

IT C1

_P IE

M O

N TE

IT C2

_V AL

IT C4

_L O

M BA

R…

IT H1

_B O

LZ AN

O

IT H2

_T RE

N TO

IT H3

_V EN

ET O

IT H4

_F RI

U LI

IT C3

_L IG

U RI

A

IT H5

_E M

IL IA

IT I1

_T O

SC AN

A

IT I2

_U M

BR IA

IT I3

_M AR

CH E

IT I4

_L AZ

IO

IT F1

_A BR

U ZZ

O

IT F2

_M O

LI SE

IT F3

_C AM

PA N

IA

IT F4

_P U

GL IA

IT F5

_B AS

IL IC

AT A

IT F6

_C AL

AB RI

A

IT G

1_ SI

CI LI

A

IT G

2_ SA

RD EG

N A

IT ZZ

_E XT

RA …

% of Other Structures, by subsector by NUTS-2 (2020)

CG LG

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VIII. Conclusions

55. In realizing a regional distribution of public investments, it was useful to disaggregate the General Government GFCF not only by subsectors, but also by different groups of unit within each subsectors and, within each group of units, by different assets. Additionally, for certain asset, it was helpful to regionalize separately particular operations which led to imputations on P51g.

56. In doing so, different strategies needed to be applied in regionalizing, in order to capitalize on the information available in data sources already available to compile S.13 annual accounts and to obtain usable information from additional data sources, where needed.

57. The tests realized represents a first provisional result in regionalizing Italy General Government gross fixed capital formation. They show that a big percentage of S.13 GFCF could be easily allocated by region, but also highlight areas where work still needs to be done to obtain a reliable regional estimate.

58. If for Local Government the structure of data sources available for Italian national accounts allows easily a bottom up approach, Central Government and SSF regionalization necessarily need to rely on indicators and a top down approach. For some assets this indicators come from additional administrative data or surveys, in other cases a territorial criterion of regionalization was provisionally chosen. This last choice, especially for central government infrastructure, given their strategic relevance, does not describe well the regional distribution of these assets. For this reason in the next future for this part of expenditure a further research of more suitable indicators or the possibility of obtaining direct information from the data source providers will be explored.

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References

Eurostat, (2010). European System of Accounts ESA 2010, Luxemburg.

Eurostat, (2013). Manual on regional accounts methods, Luxembourg

Eurostat, (1999).Regional accounts methods tables of general government, Luxembourg

Eurostat, (2023). Task Force Regional Investment, Draft Final report – October 2023

Eurostat, (2022). Manual on Government Deficit and Debt – Implementation of ESA 2010 – 2022 edition, Luxembourg

Eurostat, (2014). Manual on measuring Research and Development on ESA 2010, Luxemburg

OECD, (2015). Frascati Manual 2015, Guidelines for Collecting and Reporting Data on Research and Experimental Development, OECD Publishing, Paris

Regulation (EC) No 1059/2003 of the European Parliament and of the Council of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS)

  • Group of Experts on National Accounts
  • Twenty-third session
  • A regional estimate of General Government GFCF of Italy: different strategies for different assets and subsectors and implementation of European recommendations
    • Prepared by the Italian National Institute of Statistics0F
  • I. Introduction1F
  • II. Public investments of Italy and structure of the analysis
  • III. Local Government (S.1313) GFCF regionalization
  • IV. Social Security Funds (S.1314) regionalization
  • V. Central Government (S.1311) GFCF regionalization
  • VI. Research & Development
  • VII. Results of the analysis
  • VIII. Conclusions
  • References

EIOT framework for the Italian economy: How to complete it and its analytical potential

Languages and translations
English

eIOT FRAMEWORK FOR THE ITALIAN ECONOMY: HOW TO COMPILE IT AND ITS ANALYTICAL POTENTIAL

GENA 2024 Geneva, April 2024

Istat | National Accounts FEDERICO SALLUSTI, EMANUELE PALLOTTI, STEFANIA CUICCHIO

Outline

o Introduction

o The eSUT framework

o Building eIOT

o Analytical potential

o Conclusion and way forward

2 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

Introduction

o This presentation shows how eIOTs for the Italian economy are obtained based on the information provided by a (partial) eSUTs framework

o eIOTs are built by exploding the aggregated IOTs using the information coming from the eSUTs framework

o This allows to maintain the overall consistency while gaining higher granularity in the representation of the economy and enhancing the potential of IOTs in terms of both structural and impact analyses

o eIOTs have the following structure:

• I/O linkages consider 64 industries and 48 typologies of business unit (3072x3072 matrix) • All relevant aggregates (including distribution of income) are included • A detailed breackdown for imports (by origin) and exports (by destination) is provided

3 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

The eSUT framework | Aggregates

o The eSUT framework developed as yet provides information related to the following aggregates…

• Output • Intermediate costs • Value added • Compensation of employees • Gross operating surplus • Imports • Exports

o …considering, for each industry, 48 typologies of business unit

4 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

The eSUT framework | Typologies of business unit

o Business units are clustered according to three classification layers:

• Size-class (4 categories):  Micro-firms: 1-10 workers  Small firms: 10-50 workers  Medium firms: 50-250 workers  Large firms: 250+

• Governance status (3 categories):  Domestic-owned  MNE with Italian GDC  MNE with foreign GDC

• Degree of participation in GVCs (4 categories, following Veugelers et al., 2013):  No participation  Low participation (Single-mode)  Medium participation (Dual-mode)  High participation (Full-mode)

5 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

The eSUT framework | Coverage

o As yet, the extended information is partial in terms of coverage…

o …in terms of the compilation of all product x industry matrices needed to complete the SUT scheme

o …in terms of the breakdown by product of the results obtained by industry for output and intermediate costs

6 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

Building eIOTs | Concepts

o eIOTs are built by exploiting two sets of information:

• The eSUTs framework, which provides, for each aggregate and industry, the breakdown by typology of business unit

• The aggregated IOTs, which provide the full structure representing the Italian economy

o The main idea is to use the eSUTs to explode aggregated IOTs figures, using the latter as constraint to assure the consistency of the extended system of accounts

7 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

1,1 1:48,1:48

IOT

eIOT

o eIOTs framework is composed by the three customary matrices (industry by industry with fixed structure of sales):

• Total eIOT (compiled) • Domestic eIOT (compiled) • Import eIOT (obtained as Total eIOT –

Domestic eIOT)

Building eIOTs | Structure of matrices

8 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

TOTAL AND DOMESTIC eIOTs IMPORT eIOTs

FC G C

F

C H

IN V

EX P

I/O LINKAGES

IMP BY PARTNER

64 industries and 48 typologies

x 19 origins

FC G C

F

C H

IN V

EX P

TAXSUB

IC

IMP

OUTPUT

VALUE ADDED

COMP EMP

EXP BY PARTNER

64 industries and 48 typologies x 19 destinations

I/O LINKAGES 64 industries

and 48 typologies

TASUPR

OP SURP

qIC(j)

Building eIOTs | Compilation

o For total and domestic eIOTs, I/O linkages are compiled for each (&#x1d456;&#x1d456;, &#x1d457;&#x1d457;) exploiting the information of the eSUT framework (the share of output and intermediate costs by typology) to explode the related value in the IOT matrices

9 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

0.7*qOUT*Z on diagonal 0.3*qOUT*Z on non diagonal

I/O LINKAGES

i (1:48) x

j (1:48) (i,j)=Z

qO U

T( i)

*

RAS (i,j)=Z

*

Building eIOTs | Compilation

o Exports and imports (total and by partner) for each &#x1d456;&#x1d456; and &#x1d457;&#x1d457; are compiled exploting the structure of eSUT framework to split the values from IOT

10 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

i (1:48)EXP EXP(i) qEXP(i)= *

i (1:48)EXP by partner = EXP(i) qEXPp(i)*

j (1:48)IMP IMP(j) qIMP(j)= *

j (1:48)IMP by partner = IMP(j) qIMPp(j)*

Building eIOTs | Compilation

o Final consumption, Gross capital formation and Change in inventories are compiled for each &#x1d456;&#x1d456; by assigning domestic total final use to the different components following the row-structure of the IOT

11 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

i (1:48)GCF

i (1:48)CH INV

qCF(i)

qGCF(i)

qCHINV(i)

i (1:48)FC i (1:48)DFUSE

i (1:48)DFUSE

i (1:48)DFUSE

i (1:48)DFUSE i (1:48)SUPPLY i (1:48)IC i (1:48)EXP

=

=

=

=

*

*

*

- -

Building eIOT | Compilation

o The other aggregates are compled as follows:s

12 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

j (1:48)TAXSUB

j (1:48)IC

j (1:48)VAL ADD

j (1:48)C EMP

j (1:48)TASUPR

j (1:48)OP SURP

j (1:48)IO LINK j (1:48)IMP j (1:48)TAXSUB

j (1:48)OUTPUT j (1:48)IC

TAXSUB(j)

j (1:48)VAL ADD j (1:48)C EMP j (1:48)TASUPR

CEMP(j) qCEMP(j)

TASUPR(j)

qOUT(j)

qOUT(j)

=

=

=

=

=

=

*

*

*

– –

+ +

Analytical potential | Granularity

o IOTs can be used to analyse the structure of sectoral relationships (network approach) and the impact of different types of shock (Leontief approach)

o eIOTs allow to increase the granularity of both types of analysis, considering heterogeneous behaviours and reponses of different typologies of business unit, thus improving the precision of results

o Stepping from IOTs to eIOTs involves considering 3072 agents instead of taking into account only 64 industries

o In terms of à la Leontief analysis this applies to both shocks (detail of impulses) and responses (detail of impacts)

o In the following excersice the impact of exports to Germany on the Italian economy is analysed by using eIOTs framework

13 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

Analytical potential | Customary sectoral analysis

o Exports to Germany represent about 10% of overall Italian exports (50 bn euros)

o Zeroing exports to Germany, the Italian value added lowers by 2.1%, mainly in Manufacturing

o Compensation of employees decreases by 2.0%, while operating surplus by 2.1%. Imports lowers by 4.6%

14 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

o The new set-up of the system involves:

• Higher degree of integration

• Lower dependency from imports

• Higher profit share

Analytical potential | Classification layers

15 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

o eIOTs allows to consider a finer breakdown of results, accounting for the heterogeneity across different typologies of business units

o Effects are concentrated:

• In medium (-4.6%) and large firms (-3.1%)

• In Italian MNEs (-4.5%)

• In business units more involved in GVCs (-6.1% for GVC3 and -5.1% for GVC2)

Analytical potential | Typology focus

16 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

o eIOTs also permit to consider the effects by typology of business units, independently from the industry

o Italian MNEs (independently from size class and GVC participation) shows the lager effects

o Medium firms are more affected if involved in GVCs

Analytical potential | Sectoral focus

o Typologies of business unit can be used to map the effects within a given industry (Machinery in this example)

o The impact on Machinery is mainly connected with medium and large firms involved in GVCs

o MNEs shows a lower response to the shock with respect to domestic

17 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

Analytical potential | Mapping effects

18 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

GVC0 GVC0 GVC0 GVC0 GVC1 GVC1 GVC1 GVC1 GVC1 GVC1 GVC1 GVC1 GVC1 GVC1 GVC1 GVC1 GVC2 GVC2 GVC2 GVC2 GVC2 GVC2 GVC2 GVC2 GVC2 GVC2 GVC2 GVC2 GVC3 GVC3 GVC3 GVC3 GVC3 GVC3 GVC3 GVC3

DOM DOM DOM DOM DOM DOM DOM DOM MNEIT MNEIT MNEIT MNEIT MNEFR MNEFR MNEFR MNEFR DOM DOM DOM DOM MNEIT MNEIT MNEIT MNEIT MNEFR MNEFR MNEFR MNEFR MNEIT MNEIT MNEIT MNEIT MNEFR MNEFR MNEFR MNEFR

1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+ 1-10 10-50 50-250 250+

A04

A05

A06

A07

A08

A09

A10

A11

A12

A13

A14

A15

A16

A17

A18

A19

A20

A21

A22

A23

Conclusion and way forward

19 EIOT FRAMEWORK FOR THE ITALIAN ECONOMY | SALLUSTI, PALLOTTI, CUICCHIO

o Using a (partial) eSUTs framework eIOTs can be obtained starting from the aggregated IOTs

o eIOTs allows to gain in granularity of representation and analysis of the Italian economy, enanching the capability of IO analysis to grasp heterogeneous behaviours and different responses to stimuli across the different typologies of business units

o Istat is now setting up a thematic area to develop eSUTs and eIOTs frameworks in the next years, having the following goals:

1. Integrating this preliminary version of eIOT for Italy into multi-regional IOT (Figaro, ICIO) following a preceding experience (completion expected by the 3rd trimester 2024)

2. Obtaining a complete eSUTs framewrok (completion expected by the 2nd trimester 2025) 3. Obtaining final eIOTs from the complete eSUTs framework (completion expected by 4th trimester 2025)

thank you FEDERICO SALLUSTI | [email protected]

  • eIOT FRAMEWORK FOR THE ITALIAN ECONOMY: HOW TO COMPILE IT AND ITS ANALYTICAL POTENTIAL
  • Outline
  • Introduction
  • The eSUT framework | Aggregates
  • The eSUT framework | Typologies of business unit
  • The eSUT framework | Coverage
  • Building eIOTs | Concepts
  • Building eIOTs | Structure of matrices
  • Building eIOTs | Compilation
  • Building eIOTs | Compilation
  • Building eIOTs | Compilation
  • Building eIOT | Compilation
  • Analytical potential | Granularity
  • Analytical potential | Customary sectoral analysis
  • Analytical potential | Classification layers
  • Analytical potential | Typology focus
  • Analytical potential | Sectoral focus
  • Analytical potential | Mapping effects
  • Conclusion and way forward
  • thank you

Ethics Boot Camp Introduction. Angela Leonetti (Istat, Italy)

Languages and translations
English

ETHICS BOOTCAMP INTRO

WORKSHOP ON ETHICS IN MODERN STATISTICAL ORGANISATIONS Geneva, 26 March 2024

Istat | HRMT DIRECTORATE | Anticorruption & Transparency Unit ANGELA LEONETTI

Contents

o The purpose

o Conflict of interest and ethical dilemma

o The Cressey’s Triangle

o The sample situations and what to do with them

o The tools: Cressey’s triangle in two versions, checklists

o Any questions?

2 ETHICS BOOTCAMP | ANGELA LEONETTI

The purpose

3

“People often conceive morality as an intangible and idealized dimension, however, when it comes to violating ethical

standards, they do so in a concrete manner.”

ETHICS BOOTCAMP | ANGELA LEONETTI

Pacilli, M.G., Spaccatini, F., Giovannelli, I., 2019.

This bootcamp is intended to be an invitation to discuss how an individual in a particular situation can behave ethically/unethically «in a concrete manner».

The purpose is not to find out the right solution, there isn’t anything like that. The purpose is to discuss a particular situation and, through doing that, to cooperate in order to agree on priority values and proposals.

Conflict of interests & ethical dilemma

o Conflict of interests: that happens when we know what would be the right thing to do (we know where the public good is and how to achieve it) but, our needs/desires put pressure on us and circumstances offer us the opportunity to compromise, to not be compliant.

o Ethical dilemma: that happens when we want to do the right thing but, circumstances seem to show that whatever we decide we’ll break some rules, we’ll not be able to satisfy one or more ethical requirements.

4 ETHICS BOOTCAMP | ANGELA LEONETTI

5 ETHICS BOOTCAMP | ANGELA LEONETTI

It is a model of interaction between an individual and his/her organisational context.

It gives us information about three dimensions that can drive the individual to behave unethically:

1) pressure of internal needs/desires or external requests;

2) dimension of opportunities (to behave unethically);

3) dimension of self-justification (rationalization).

The Donald Cressey’s* triangle

https://en.m.wikipedia.org/wiki/File:Fraud_Triangle.png* https://en.wikipedia.org/wiki/Donald_Cressey

The sample situations

6

 We set up no. 2 ethics sensitive situations in two different contexts: one related to using statistical information and one related to HR support activities (overtime payments)

 Each group is provided with printouts of: the two situations, the two versions of the Cressey’s Triangle, three checklists

 Each group is invited to choose one of the two situations and work on it as follows

ETHICS BOOTCAMP | ANGELA LEONETTI

What to do with the chosen situation

7

What do you think about Ms. Right’s/Mr. Right’s reasoning/decision?

Here are some suggestions to work on that.

1. Discuss the short story and try to find whether it presents conflict(s) of interest or/and ethical dilemma(s), or/and other kind of ethical problems – or none;

2. Use the (first) Cressey’s triangle to analyse the conflict/dilemma; help yourselves to the check lists if you need. The lists are not completed so any items you may add to one or more of the checklists will be really appreciated;

3. Use the (second) Cressey’s triangle and try to agree on suggestions “to solve” the situation or to prevent similar situations in the future;

4. Feel free to contribute anywhere with your own work experience;

5. Appoint a Rapporteur who will report to the audience your considerations and suggestions.

ETHICS BOOTCAMP | ANGELA LEONETTI

How to apply the Cressey’s triangle to the situation

8

Analyse the situation and try to agree on how Ms. Right/Mr. Right justifies herself/himself. Pick items

from the «rationalisation» check list or add new ones. ETHICS BOOTCAMP | ANGELA LEONETTI

Analysis

How to use the Cressey’s triangle to prevent similar situations in the future

9

Discuss and try to agree on how to reduce the use of self-justification

ETHICS BOOTCAMP | ANGELA LEONETTI

Proposals

The checklists (you’ll find other items in the printouts)

10

� Ambition, career expectations

� Money troubles

� (…)

Pressures/needs

Opportunities

Rationalisation

� Short time left for action or decision

� Random unexpected events

� (…)

� Everybody does the same

� I did nothing bad, actually I did that to help other people/colleagues

� (…)

ETHICS BOOTCAMP | ANGELA LEONETTI

11

Any questions?

ETHICS BOOTCAMP | ANGELA LEONETTI

Have fun! ANGELA LEONETTI | [email protected]

  • ETHICS BOOTCAMP INTRO
  • Contents
  • The purpose
  • Conflict of interests & ethical dilemma
  • Slide Number 5
  • The sample situations
  • What to do with the chosen situation
  • How to apply the Cressey’s triangle to the situation
  • How to use the Cressey’s triangle to prevent similar situations in the future
  • The checklists (you’ll find other items in the printouts)
  • Slide Number 11
  • Have fun!

Introducing Session 1: Ethics in Institutional Contexts. Fabrizio Rotundi (Istat, Italy)

Languages and translations
English

1

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Workshop on Ethics in Modern Statistical Organisations

26-28 March 2024 Geneva, SwitzerlandFabrizio Rotundi, Italy

2

INTRODUCING SESSION 1: ETHICS IN INSTITUTIONAL CONTEXTS 1st Day, 26.03.24

3

TASK TEAM ON ETHICAL LEADERSHIP – THE EXPERT MEETING ON ETHICS 2024 MEETING PURPOSE SESSIONS SESSIONS GOALS

The Workshop on Ethics in Modern Statistical Organisations aims to bring together experts from Statistical organizations to exchange their experiences and lessons learned to meet the challenges they have to deal with:

1. maintain public trust

2. train leaders as moral agents

3. build a strong business culture able to support the highest ethical standards

4. meet the growing demand for refined, diverse, and timely data in larger quantities

5. find new technical and methodological ways

6. integrate communication with ethical values and practices

• Presentation Sessions

1. Ethics in institutional contexts

2. Ethics in daily worklife

3. Ethics for new data sources and technology

4. Ethics and proactive communication

• Interactive Sessions

«Ethics Camp» + «Ethics Lab»

1. compare the ethics management practices and share possible assessment systems to evaluate the compliance of behaviours to the institutional ethics;

2. outline any practices on ethics shared by the NSOs with their stakeholders and discuss day- by-day situations that challenge the discretional power;

3. develop awareness of the new data sources and technologies and discuss real case studies to identify ethical dilemmas and considerations coming from new technology use;

4. compare different ways to communicate ethical values and discuss the role of proactive listening.

UNECE, Geneva, 26-28 March 2024

4

SESSION 1: ETHICS IN INSTITUTIONAL CONTEXTS

Ethical leadership is also crucial

Managers should demonstrate outstanding ethical behaviour to inspire others to follow suit; they should build strong communication skills to champion the values of official statistics and to foster open dialogue on ethical matters.

Leaders should motivate their staff on the significance of ethical protocols and practices to adhere to the offices’ values, roles, and objectives, thereby increasing performance and fostering a positive work environment.

Institutional ethics are a growing area of interest

The application of ethics in an organization refers to how an institution itself promotes ethical behaviours with the sense of responsibility and accountability.

NSOs should consider themselves as moral agents to let ethical awareness grow promoting and adhering (but not only) to codes of conduct.

The organization model has an effect on the ethical behaviour of any entity; e.g. proper accountability of power, none unnecessary bureaucracy, and promotion of an environment that contributes to the health and ethical practices of offices.

5

SESSION 1: ETHICS IN INSTITUTIONAL CONTEXTS - PRESENTATIONS AUTHOR/ PRESENTER ORGANISATION TITLE OF THE CONTRIBUTION AT A GLANCE

Luca Di Gennaro Splendore

University of Malta Democracy dies in darkness without Official Data

Statistics (like employment rates, gross domestic product and pandemic data) are the lifeblood of democracy, influencing policymaking, media narratives and electoral choices. This work explores quantitative relationship between democracy and official statistics, ONS's role and the reasons why the NSOs disseminate their statistics free of charge.

Timo Koskimäki Statistics Finland Structure of ethical issues in new data ecosystems

The current ethical frameworks for statistics, and for social research, rely conceptually on the idea of an organisation collecting data directly from the units they aim to study. Trust-enhancing measures like ethical commissions, ethics-related web segments and ethics communication strategies has been set up to overcome the everyday distrust situations. The paper analyses the new ethical issues using the concept of professional ethics as a tool for analysis through examples of recent ethical debates, related to data and statistics, from the point of view of professional ethics.

Markus Baumann Federal Statistical Office (FSO)

Revision of the Swiss Official Statistics Charter: opportunities and risks

The UN Fundamental Principles of Official Statistics were used as a basis for the drafting of the European Statistics Code of Practice by the European Union. The FSO and the Swiss Conference of regional statistical offices (CORSTAT) have developed and adopted the Charter of Swiss Official Statistics in 2002, which applies to all statistical services producing and disseminating official statistics. The Charter complements the legal framework that governs the statistical authorities, and it is based on a set of basic principles and indicators, similar to the ES CoP. The planned presentation by the Swiss Federal Statistical Office and the Swiss Ethics Council is intended to shed light on the process and the challenges and proposed solutions arising from the revision of the Swiss Official Statistics Charter

Nicola Shearman Office of National Statistics (UK)

Ethical pinch-points: effectively communicating ethical consideration in research design

The presentation aims to communicate the first three year’s work of the UK Statistic Authority’s Centre for Applied Data Ethics (CADE), present key ethical considerations to researchers working with population, administrative and other secondary data, and describe the innovative methods that have been used to review over 1000 pieces of research in those three years.

Katia Ambrosino Istat Ethical management in NSOs

Implementing ethics and ethics management in an organization is crucial for enhancing performance, efficiency, effectiveness, and reputation, as well as improving working relationships and well-being. This presentation summarizes the findings of the surveys on ethics management conducted by the Task Team on Ethical Leadership in 2021 and 2022 under the coordination of the UNECE HLG-MOS. It provides an overview of the different ways some NSOs handle ethics, and describes adopted policies and procedures, as well as common ethical challenges organizations face.

22

Thank you for your attention!

  • UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS�Workshop on Ethics in Modern Statistical Organisations
  • Slide Number 2
  • Slide Number 3
  • Slide Number 4
  • Slide Number 5

Introduction to the Open discussion for the Reference Book on Ethics. Fabrizio Rotundi (Istat, Italy)

Languages and translations
English

1

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Workshop on Ethics in Modern Statistical Organisations

26-28 March 2024 Geneva, SwitzerlandFabrizio Rotundi, Italy

6

OPEN DISCUSSION FOR THE REFERENCE BOOK ON ETHICS 3° Day, 28.03.24

7

THE TASK TEAM ON ETHICAL LEADERSHIP

8

TASK TEAM ON ETHICAL LEADERSHIP: PEOPLE & BEGINNINGS

The Task Team on Ethical Leadership

• since 2021 has been focusing both on ethics management as a key strategy to all processes and activities within an organization, namely “business ethics”, and on data ethics;

• acknowledges the excellent work carried out by the Task Team, coordinated by CSO Ireland, on mapping and describing a list of “core values to the Fundamental Principles of Official Statistics”, proposed to the CES Bureau at the Plenary Session of the Conference of European Statisticians (CES) held in June 2021.

Among the others, the Task teams goals, approved by the HLG-MOS are:

1. To identify possible common practices in ethics management;

2. To define a common vocabulary and give concrete suggestions to support NSOs’ leadership

3. To provide a reference book to figure out how to deal with potential behavioural dilemmas

Angela Leonetti, Katia Ambrosino, Italy Elsa Dhuli, Albania Martin-J Beaulieu, Milana Karaganis, Statistics Canada Matt Short, ONS UK Tine Petsaj, Slovenia Emma MacDonald, Statistics New Zealand Lukasz Augustyniak, Eurostat Orla O'Gorman, CSO Ireland Andrea Ordaz-Németh InKyung Choi, Andrew Tait and Tetyana Kolomiyets, UNECE

Co-chairs: Fabrizio Rotundi, Italy, ....

TASK TEAM MEMBERS 2024

The Task Team on Ethical Leadership started its activity in 2021 as a follow up of the Risk Management Framework and related Guidelines, released in 2017 by the Modernisation Committee on Organisational Framework and Evaluation, under the coordination of the UNECE HLG-MOS.

9

TASK TEAM ON ETHICAL LEADERSHIP: THE BUSINESS CASE 2024

Purpose

• With reference to institutional ethics: NSOs must act as moral agents by upholding ethical behaviour, not just relying on individual staff members. Leaders in NSOs play a vital role in promoting ethical practices through effective communication, emphasising the organisation’s values, and making employees aware of the consequences of not adhering to codes of conduct.

• With reference to data ethics: NSOs face the challenge of meeting the growing demand for refined, diverse, and timely data in larger quantities. To address this, they are expanding beyond traditional methods, embracing alternative data sources, and incorporating data science and modern data integration techniques. Maintaining public trust is crucial, requiring proactive communication of ethical values and practices to prevent any loss of trust in statistical offices.

Deliverables

With reference to institutional ethics:

• Organise the Workshop on Ethics (26 to 28 March 2024 in Geneva) in collaboration with WP on institutional ethics. • Complete the analysis of the third survey results. • Incorporate ethics within various areas of GAMSO and GSBPM in collaboration with GSBPM/GAMSO revision task team

under the Supporting Standards Group. • Complete a Reference Book on Ethics for NSOs.

With reference to data ethics:

• Organise the Workshop on Ethics (26 to 28 March 2024 in Geneva) in collaboration with WP on data ethics. • Develop a common international definition of data ethics and a common understanding of its relevance for NSOs. • Develop a principle based international data ethics framework that can be used by NSOs. • Collect training materials / guidance on ethics and best practices on defining, applying, and communicating data ethics

across different NSOs. Provide case studies of good practices and the impacts this has had.

10

TASK TEAM ON ETHICAL LEADERSHIP –TRENDS OF ACTIVITY

SUBJECT PURPOSE STATE OF PLAY OUTPUT(S)

1. Survey results analysis Investigate practices on ethics and identify common features while removing biases related to different cultural contexts

Finalized Report about the practices found and analysed

2. Drafting of a Reference Book

Set up a common vocabulary on ethics; help NSOs detect and manage ethical dilemmas especially related to data ethics

In progress Reference Book

3. Integrate ethics in GAMSO/GSBPM

Revising models’ activities: focus on strategies and providing ethical terms of reference

First proposal submitted and awaiting in-depth opportunities

Statements and topics for GAMSO/GSBPM; new version(s) of the model(s)

11

INTEGRATING ETHICS IN THE GAMSO MODEL

Ethics cuts across the entire Business Architecture framework: understanding how it comes through strategies to production processes – thanks to proper assets and tools – is a key to turn ethical principles into action.

Ethics implementation must be measurable and that is possible through providing, for example: strategic objectives on ethics implementation (strategy); training on ethics (capability); set up of ethics implementation systems or anticorruption systems (corporate support) and their application to all production as well as supporting processes.

12

INTRODUCING THE REFERENCE BOOK ON ETHICS

13

THE CONCEPTUAL FRAMEWORK

Performance & Quality

Business Ethics

Ethics in an organisational context is a set of ethical values and behaviours an organisation choosen to implement its mission and vision as well as to improve effectiveness, efficiency and affordability of its overall performance

Ethical Leadership

Management of Ethics

Organizational

Ethical Values

14

SOME DEFINITIONS

Ethical leadership can be defined as “the demonstration that managers normatively conduct appropriate Ethics management through personal actions and interpersonal relationships, and the promotion of such conduct to followers through two-way communication, reinforcement, and decision-making’’.

Ethics management and Ethical leadership are two interrelated components, because only those leaders who can behave ethically can effectively manage Ethics within their organisation.

o in Ethics management all employees have their own role in integrating ethics in their daily actions;

o according to Ethical leadership, exercising power and authority as a manager inherently entails ethical challenges, which means that all leaders/managers need to embed ethical activity in their own managing role.

15

REFERENCE BOOK ON ETHICS

The Task Team on Ethics planned to produce a sort of Handbook as a reference

guide providing a common high level data ethics framework for statistics.

The so-called “Reference Book” aims at supporting NSOs’ leadership and

giving concrete suggestions in real-work-type regarding potential behavioural

dilemmas (for example, in terms of data processing, personal data protection,

conflict of interests and so on).

It also should provide guidance on how to implement Ethics management

across the statistical production chain, and how to communicate the benefits

of practicing good data ethics to a wide variety of stakeholders (e.g. data

suppliers, the public, researchers and civil society).

16

REFERENCE BOOK ON ETHICS: TABLE OF CONTENTS

Introduction

1. Conceptual groundwork

1. What is ethics (in official statistics)?

2. What do we mean by official statistics?

2. Ethics in official statistics

1. Ethics in the statistical business production process

2. Ethics in the institution

3. Ethical dilemmas

1. Ethical dilemmas by country/by topic

2. Fantastic failures

4. Results of ethics surveys

1. Results of surveys

2. Analysis of surveys

5. Ethics and communication in official statistics

1. Effective communication strategies

6. Conclusions

7. References

8. Bibliography

TABLE OF CONTENTS

17

REFERENCE BOOK ON ETHICS: REVISING THE TABLE OF CONTENTS

Table of contents Draft

Themes/Issues from the workshop/ Bootcamp to be

included in the RB (From the Chairs)

New themes from the Bootcamp to be included in the RB

(From the Researchers and Rapporteurs)

Matches and differences from the 3 Surveys Findings

(From the Researchers)

Proposals to revise the Table of Contents

(From the audience)

1. Conceptual Framework

1. What is ethics (in official statistics)?

2. What do we mean by official statistics?

2. Ethics in Official Statistics

1. Ethics in the statistical business production process

2. Ethics in the institution

3. Ethical Dilemmas

1. Ethical dilemmas by country/by topic

2. Fantastic failures

5. Ethics and Communication in Official Statistics

1. Effective communication strategies

18

OPEN QUESTIONS

19

REFERENCE BOOK ON ETHICS: OPEN QUESTIONS

1. Ethics and law: National Statistics Offices (NSOs) operate within the confines of statistics laws, which define specific regulations governing their activities. However, laws alone may not encompass all ethical considerations inherent in statistical work. Reflecting on this, explore two scenarios: i) Cases where actions are legally permissible but may still be regarded as unethical; ii) Cases where actions are technically prohibited by law, yet are arguably ethical;

2. Ethics and code of conduct: In the field of official statistics, Fundamental Principles of Official Statistics and Code of Conducts (e.g., European Statistics Code of Practice, national code of conduct) provides essential guidelines that producers of official statistics should abide to. Considering the specific principles and codes within your regional or national context, are there any areas or codes you believe are lacking or absent? Please reflect on potential gaps and discuss how these could be addressed to further enhance ethical practices within statistical organizations.

3. Ethics and strategies: What do you think the relationship between ethics and trust? While being ethical is undoubtedly crucial, do you believe it is sufficient on its own for a statistical organization to gain trust? What additional factors or strategies do you consider necessary to build and maintain trust with stakeholders?

Ethics & Compliance

20

Ethics in daily works & new data / technology

REFERENCE BOOK ON ETHICS: OPEN QUESTIONS

3. How can we effectively measure the level of commitment to and implementation of the (business and data) ethics within an NSO?

4. To what extent changing societal expectations impact ethics and ethical practices within statistical organizations?

5. Do NSOs have an ethical obligation to use their expertise not only to produce statistical information and products, but also, to work with other partners to strengthen ethical use of data? Does this type of activities fall under social responsibility dimension of NSOs?

6. With the exponential development of technology, how do new technologies impact traditional ethical practices and dilemmas within NSOs?

7. Private industry has started moving towards publishing the sustainability reports on the environmental, social and governance impacts of their activities. Could this type of reporting provide a vehicle for NSOs to measure implementation of business ethics within NSO?

1. What do you think the difference between business ethics and data ethics in statistical organizations? Are these concepts referring to distinct areas of work, or do they overlap significantly? Could you provide examples from daily works where they do overlap or differ?

2. In what ways do ethical considerations influence decisions related to the selection of data sources, methodologies, and indicators in official statistical work? Is there any institutionalized mechanism to support this process in your organization?

21

Ethical dilemmas

Systematic analysis of ethical dilemmas could significantly enhance decision-making processes. Are there established ethical frameworks or principles that you recommend applying when conducting such analyses? How do these frameworks guide the identification, evaluation, and resolution of ethical dilemmas in practice?

Ethics and proactive communication

REFERENCE BOOK ON ETHICS: OPEN QUESTIONS

For example, an interviewer who learns about serious crime from the respondents and being torn between reporting to authority and keeping the confidentiality, what are the ethical (or moral) values conflicting in this situation?

In your experience, what are the most common barriers to communication of ethics within statistical organizations, and how can these be overcome?

22

Thank you for your attention!

  • UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE CONFERENCE OF EUROPEAN STATISTICIANS�Workshop on Ethics in Modern Statistical Organisations
  • Slide Number 6
  • Slide Number 7
  • Slide Number 8
  • Slide Number 9
  • Slide Number 10
  • INTEGRATING ETHICS IN THE GAMSO MODEL
  • Slide Number 12
  • Slide Number 13
  • Slide Number 14
  • Slide Number 15
  • Slide Number 16
  • Slide Number 17
  • Slide Number 18
  • Slide Number 19
  • Slide Number 20
  • Slide Number 21

Proposals to Promote Change from Compliance to Ethical Commitment in ISTAT. Angela Leonetti (ISTAT, Italy)

Languages and translations
English

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT

WORKSHOP ON ETHICS IN MODERN STATISTICAL ORGANISATIONS Geneva, 26 March 2024

Istat | HRMT DIRECTORATE | Anticorruption & Transparency Unit ANGELA LEONETTI

Contents

o The reference framework

oWhat Istat has already achieved (as far as ethics is concerned)

o The declaration of commitment to prevent corruption

o The campaign training methods

o Brief remarks on the target

o The contribution of psychosocial perspective

o The elements of the campaign

2 PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

The reference framework

3 PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

Ethics for official

statistics professions

Ethics for legality

Ethics in the public

sector

Offence against the public sector

Maladministration Corruption

Words have a weight

4

To prevent Corruption

Ethics

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

What Istat has already achieved (as far as ethics is concerned)

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI5

Since 2021, participation in the Task Team on Ethical Leadership

 In 2021 Istat got the UNI ISO 37001:2016 certification (Anti- bribery management systems standard). This first certification is expiring on 27 July 2024

Updating of the Code of Conduct for all staff in 2022

Specific training (webinars) dedicated to the new Code provisions

 In order to keep the 37001 certification, in June 2023 the Istat Board adopted a Declaration of Commitment to Prevent Corruption.

The Declaration of commitment to prevent corruption

6 PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

 The Declaration was adopted by Istat Board on 8 June 2023  The Declaration:

• summarizes the objectives of Istat anticorruption management system and the different roles engaged in keeping its efficiency and compliance

• highlights the importance of an organisational culture ethically oriented in a proactive way, that is, not just aimed at avoiding penalties;

• summarizes the key elements for anticorruption strategy, among which more space than in the past is now given to awareness and training on ethics and legality for all staff

Declaration

The Declaration of commitment to prevent corruption

7 PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

The Declaration points at a comprehensive strategy based on the following ethical features:

 avoid/reject any pressure or influence contrasting with law and the Code of Conduct;

 avoid/reject any actual or prospective conflict of interests;

 privilege public interest in every situation;

 commit to opt for public good whenever involved in an ethical dilemma

 remember we are civil servants even in private situations, and behave consequently when any private circumstances can have an impact on work activities.

8

Not just a signature

 The Declaration is now expected to be signed and endorsed by all middle and high managers in Istat. About the signing, the International Standard is very clear in saying not to consider it a technicality: through signing managers are expected to make an ethical commitment, that is, are expected to take the responsibility to behave ethically as well as to promote ethics among their staff.

 The above implies the setting up of a campaign to make the Declaration well known and understood in its different requirements.

 In order the campaign to be successful, we have taken into consideration three elements so far: the target (Istat managers), the campaign/training methods and the contribution of psychosocial perspective (together with legality and professional aspects) in drafting the campaign contents.

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

9

The campaign/training methods

 Also because of the pandemic, the training on anticorruption, transparency, code of conduct etc. in the last years has been delivered through webinars.

 This method has been very useful and efficient in releasing information and updating about law evolution on this subjects.

 However, when it comes to ethics training, webinars can be a passive strategy: people receive information, very few break the ice to ask questions, and sometimes – due to sudden work demands, beyond the screen there can be persons whose mind is temporarily away.

 If in Istat we want to go beyond compliance, beyond the signature, we must find ways to actively engage people.

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

10

That means:  Using methods that facilitate active listening

and discussion and sharing of experiences, such as the bootcamp we have just gone through;

 Building a training cycle of continuous improvement (PLAN-DO-CHECK-ACT) that, starting from a kick-off event (the signing of the Declaration) will keep open the debate on different ethical perspectives and situations.

The campaign/training methods

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

11

As regards the target

 In Italy the legislation to prevent corruption has developed with a strong connection to administrative matters, and the idea of ethics promoted by such legislation goes beyond single professions in the public sector, because it focuses on being a civil servant.

 Of course, official statistics professionals are civil servants too but, as far as Istat is concerned, they are mainly focused on professional identity and professional ethics (deontology). And that is understandable!

12 Administrative

managers (high+middle

level)

60 technical managers (high+middle level, most statistics, few

ICT and other)

Five times as much

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

As regards the target

12

 In other words, statistics managers in Istat have a very strong professional identity that comes from dedicated legislation and ethics and adds to their number and their professional link to the Istat mission and vision.

 On the other hand, the ethics campaign that anticipates and will follow the signature of the Declaration to prevent corruption is targeted for all Istat managers and so it must be, even though it comes from different legislation.

 Therefore, from a communication-and-training point of view, the challenge will be to engage and keep engaged this significant sub- group of managers, and there are clues that lead us to believe that this will only be possible through sharing ethical problems and discussing on real experiences.

PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

The contribution of psychosocial perspective

13 PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

 “an initial step to combat unethical behavior within organizations is (…) to set aside the idea that people are essentially moral or immoral, [… and] focus on the idea that an individual’s choice to act in moral or immoral manner depends upon a complex interaction – determined on a case-by-case basis – between psychological, social and contextual factors”;

Pacilli, M.G., Spaccatini, F., Giovannelli, I., 2019.

The individual +

Context +

Society

Cressey, D., Other People’s Money: A Study in the Social Psychology of Embezzlement, Glencoe, 1953

 That is what the Donald Cressey’s Triangle is about.

Apply the Cressey’s triangle to the analysis of a situation

14

Analyse the situation and try to agree on how Ms. Right/Mr. Right justifies herself/himself. Pick items

from the «rationalisation» check list or add new ones.

Apply the Cressey’s triangle to reduce ethical risk in the future

15

Discuss and try to agree on how to reduce the use of self-justification

The elements of the campaign

16 PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT | ANGELA LEONETTI

 Intranet  Kick-off event  Intranet  Standing working group

Thank you for your attention ANGELA LEONETTI | [email protected]

  • PROPOSALS TO PROMOTE CHANGE FROM COMPLIANCE TO ETHICAL COMMITMENT IN ISTAT
  • Contents
  • The reference framework
  • Words have a weight
  • What Istat has already achieved (as far as ethics is concerned)
  • The Declaration of commitment to prevent corruption
  • The Declaration of commitment to prevent corruption
  • Not just a signature
  • The campaign/training methods
  • The campaign/training methods
  • Slide Number 11
  • As regards the target
  • The contribution of psychosocial perspective
  • Apply the Cressey’s triangle to the analysis of a situation
  • Apply the Cressey’s triangle to reduce ethical risk in the future
  • The elements of the campaign
  • Thank you for your attention

Investigating Ethical Practices in NSOs - Surveys Results. Katia Ambrosino (Istat, Italy)

Languages and translations
English

1

Investigating Ethical Practices in NSOs – Surveys results

Katia Ambrosino - Researcher at the Italian National Institute of Statistics Workshop on Ethics in Modern Statistical Organizations - 26 - 28 March 2024, Geneva, Switzerland

Abstract Improving integrity is a significant concern in the public sector and National Statistical Offices (NSOs), encompassing data ethics and organizational ethics. Studies show that implementing ethics and ethics management within an organization often leads to outstanding performance and is crucial for improving working relationships and overall well-being. Many NSOs already have effective ethics management practices and tools, but there is still a widespread need for conceptual clarity and practical examples when dealing with ethical concerns.

The purpose of this paper is to provide a comprehensive summary of the findings of the surveys on ethics management conducted by the Task Team on Ethical Leadership under the coordination of the UNECE HLG- MOS. The paper aims to offer an overview of the various approaches some NSOs employ in handling ethics and describe the tools and policies they have adopted.

Summary Introduction ....................................................................................................................................................... 2

1. Business ethics ........................................................................................................................................... 2

2. Surveys ....................................................................................................................................................... 3

2.1 Preliminary survey ................................................................................................................................... 3

2.2 Main survey ............................................................................................................................................. 3

2.3 In-depth survey ........................................................................................................................................ 3

3. Surveys results ........................................................................................................................................... 4

3.1 Ethics and Compliance Governance ........................................................................................................ 5

3.2 Ethics Management Organization ........................................................................................................... 6

3.3 Ethics implementation procedures and tools ......................................................................................... 7

3.4 Ethics dissemination ................................................................................................................................ 9

3.5 Ethics Management and Performance .................................................................................................. 10

3.6 Ethical Dilemmas ................................................................................................................................... 11

4. Conclusions .............................................................................................................................................. 11

References ....................................................................................................................................................... 11

2

Introduction The Task Team on Ethical Leadership was established in 2021 as a direct result of the Risk Management Framework and related Guidelines, released in 2017 by the Modernisation Committee on Organisational Framework and Evaluation under the coordination of the UNECE HLG-MOS.

The team's primary objective is to develop a Reference Book on Ethics in the National Statistical Offices (NSOs) with the intent to create a common ethical framework and provide a guide to support NSOs in upholding ethical standards and promoting a culture of transparency, accountability, and integrity across all organizational processes and activities, not limited to statistical production and research. To this purpose, the Task Team conducted three surveys in 2021 and 2022 in order to gain valuable insights into effective ethics management practices within NSOs.

This paper highlights the key findings of the surveys, examining the current state of ethical practices within selected NSOs and assessing the extent to which essential tools and procedures for ethical systems have been integrated. This preliminary analysis will serve as a foundation for further discussion and identifying best practices to help develop the Reference Book.

1. Business ethics Business ethics is a broad and encompassing concept that underpins all organizational activities and processes. It refers to the values, principles, and standards that guide the behavior of individuals and groups in an organization. Under this umbrella, organizations are expected to uphold moral and ethical standards in their decision-making and actions, promoting honesty, integrity, fairness, transparency, and accountability across all levels of the organization and all areas of activity, such as administration, communication, support, and production processes.

Studies show that implementing ethics and ethics management within an organization often leads to outstanding performance. Organizations that apply ethical practices are more likely to cultivate trust with employees and stakeholders, leading to increased loyalty and support. Additionally, organizations that uphold ethics are more likely to attract and retain skilled employees, as workers are more engaged and motivated. This can lead to higher productivity, innovation, and overall job satisfaction, which in turn can contribute to excellent organizational performance.

Employees need to be able to identify ethical issues in their work, develop the necessary cognitive tools to make ethical decisions and receive unwavering support from the organizational environment when making those choices. Hence, organizations need to establish a robust and efficient ethical system to provide employees with clear guidance and support in ethical matters. This system should be built upon a solid framework and a recognized program, ensuring that ethical standards and principles of conduct are well- defined and effectively communicated to all employees.

The ethics management system should include ethics training, communication channels, advice lines and offices, and tools such as anonymous reporting systems for reporting misconduct. Yet, simply creating a formal system does not guarantee effective ethics management. In fact, for a formal system to influence behavior, it should also be integrated into a broader, coordinated, and coherent cultural system that consistently promotes ethical conduct. By fostering a comprehensive ethical framework and cultivating an ethical culture, organizations can create a professional and morally sound environment that encourages ethical conduct on a daily basis.

3

2. Surveys The Task Team conducted three surveys at different intervals to gather information from interested NSOs. These surveys aimed to assess how NSOs incorporated the main tools used in ethical systems within their organizations.

The surveys were conducted in 2021 and 2022 and were constructed gradually. The questions were posed progressively, increasing the analysis's details and depth.

2.1 Preliminary survey The preliminary survey was conducted in February 2021 to gather initial information that helped develop the subsequent surveys.

The primary focus of the questionnaire was to understand the extent to which NSOs had implemented policies, procedures, and programs related to ethics management. Additionally, the survey aimed to evaluate their interest in participating in upcoming surveys exploring successful ethics management practices from NSOs that have either developed or were developing such practices. Accordingly, the questionnaire consisted of six simple and generic questions.

The preliminary survey was distributed to as many NSOs as possible to facilitate participation and collect many experiences. Out of all the NSOs that were contacted, a total of 29 responded by promptly returning the questionnaire1.

2.2 Main survey Between July and October 2021, the Task Team submitted the main survey to investigate the ethical tools and procedures implemented by NSOs to manage ethics in both statistical production and research, and organizational processes.

The survey focused on three key areas. First, it examined Ethics Policies and Organizational models, including aspects such as the presence of a Code of Ethics, the existence of an Ethics Committee, and the processes employed to investigate ethical breaches. Second, it delved into ethical strategies and practices, specifically focusing on the ethical review of statistical projects, which ensures that ethical considerations are taken into account during the planning and execution of statistical projects. Lastly, the survey explored Training on Ethics, aiming to determine whether NSOs have implemented training programs to enhance ethical awareness and understanding among their staff. Out of the 65 NSOs approached, 45 completed the survey2.

2.3 In-depth survey An extensive follow-up survey was conducted from June to August 2022 to investigate the suggestions derived from analyzing the responses received in the previous two surveys. The survey was sent to the NSOs

1 The preliminary survey collected responses from Australia, Belgium, Brazil, Canada, Chile, Colombia, Costa Rica, Czech Republic, Denmark, Finland, Greece, Ireland, Italy, Kyrgyz Republic, Lithuania, Luxembourg, Poland, Republic of Armenia, Republic of Belarus, Republic of Bulgaria, Romania, Serbia, Slovak Republic, Slovenia, Sweden, The Netherlands, Turkey, UK, and Ukraine. 2 The main survey collected responses from Albania, Argentina, Australia, Austria, Belgium, Canada, Colombia, Costa Rica, Croatia, Czech Republic, Ecuador, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Luxembourg, Malta, Mexico, New Zealand, Poland, Republic of Armenia, Republic of Azerbaijan, Republic of Belarus, Republic of Bulgaria, Republic of North Macedonia, Romania, Russia, Serbia, Slovak Republic, Slovenia, Sweden, The Netherlands, Turkey, UK, Ukraine, and Uzbekistan.

•February 2021Preliminary survey

•July-October 2021Main survey

•June-August 2022

In-depth survey

Figure 1 - The three surveys

4

that participated in the previous survey and confirmed that they had already implemented the ethical policies and measures analyzed. Of the 45 NSOs contacted, 25 responded by returning the questionnaire3.

The NSOs were divided into three groups and asked different questions based on their previous responses. The survey focused on four key areas crucial in promoting ethical practices within an organization (Figure 2).

 The first area of focus was Ethics and compliance Governance. This section delved into the establishment of business ethics values and the allocation of performance objectives directly linked to the implementation of ethical standards.

 The second area of emphasis was Ethics dissemination and implementation tools. This section explored the methods used to educate employees on the Code of Conduct and the development of a whistleblowing channel.

 The third area of examination was Ethics Management Organization. This section focused on implementing a dedicated Ethics/Compliance Officer and their organizational role.

 Lastly, the survey explored the Ethics Management Process. This section assessed the existence of a due diligence procedure and a risk assessment.

3. Surveys results The survey findings offer valuable insights into the ethical practices and procedures adopted by NSOs worldwide, providing a comprehensive overview of the current state of ethical practices within them.

All the surveys yielded positive results, with a high response rate from the recipients, highlighting the strong interest of NSOs in this subject. The overall responses aligned with the results gathered in the first preliminary survey, which showed that nearly 72% expressed keen interest in the Task Teams' work, and approximately 67% expressed their willingness to participate in future surveys.

The analysis emphasizes the prevalence of ethics management in various NSOs that successfully implemented ethical practices through diverse methods and tools, such as strategic initiatives, processes, structures, and inclusive training programs. The findings are presented based on six key areas to provide a framework for assessing effectiveness and adherence to ethical standards:

 Ethics and Compliance Governance: this section examines the extent to which organizations have established robust governance frameworks that promote ethical decision-making and behavior.

 Ethics Management Organization: this section explores the structures and processes that enable organizations to manage ethical practices effectively. This includes investigating the presence of dedicated ethics departments or committees as well as the allocation of resources to support ethical initiatives.

3 The in-depth survey collected responses from Albania, Armenia, Australia, Austria, Belgium, Canada, Colombia, Costa Rica, Czech Republic, Estonia, Eurostat, Finland, France, Hungary, Italy, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Malta, Mexico, New Zealand, Slovak Republic, Turkey, and Ukraine.

Ethics & Compliance Governance

Ethics dissemination & implementation tools

Ethics Management Organization

Ethics Management Process

Figure 2 – Focus areas of the in-depth survey

5

 Ethics implementation procedures and tools: this section describes organizations' methods and tools for implementing and monitoring ethical practices, such as protocols for investigating breaches and risk assessment procedures.

 Ethics dissemination: this section deals with NSOs' methods for effectively communicating and promoting their ethical values and policies inside and outside the organization.

 Ethics Management and Performance: this section investigates whether NSOs prioritizing ethics have improved NSOs' performance, employees’ satisfaction, and well-being.

 Ethical Dilemmas: this section explores the types of ethical challenges that organizations face and how they navigate these complex situations.

3.1 Ethics and Compliance Governance Ethics Governance refers to the principles, practices, and policies guiding an organization's decision-making processes. Its primary objective is to foster a culture of integrity, transparency, accountability, and social responsibility. A shared and transparent ethical framework serves as a guide and a constant reminder of the organization's commitment to upholding the highest ethical standards, ensuring everyone is fully aware of their responsibilities and obligations at all levels.

In the exploratory survey, among the responding NSOs, approximately 83% confirmed that their organization already had established Ethical management policies, programs, procedures, or practices. Of these, 88% implemented these measures over three years ago. However, when exploring these aspects, it appears that NSOs mainly refer to data Ethics documents. In fact, according to the in-depth survey, only 9 NSOs have developed comprehensive formal ethics programs.

Strategic Values Any organization needs to establish strong value statements within its strategic goals. A values statement provides a concise summary of the organization's principles, the values that its employees are expected to uphold, and the contributions its activities aim to bring to society. As also recognized by the CES Task Team on core values in their work presented at the 2022 Conference of European Statisticians in February 2022, in NSOs, as in any ethical organization, these statements become ingrained principles that guide and support decision-making and interactions with governments, society, and other stakeholders.

In the in-depth survey, about 80% of participants answered positively when asked if the NSO's vision, governance, or leadership statement considers an ethical perspective. Sometimes, the NSOs explicitly cite ethical and professional principles in their vision and mission statements. Other times, they refer to the Fundamental Principles of Official Statistics. In some cases, the NSOs declare that they promote and respect ethical practices, but there is no explicit mention of the word "ethical" in their strategic documents.

Q.: Do any Vision, Governance, and Leadership statements in your Organisation take into account ethical perspective?

~ * ~

A.: “We do a lot of what ethics is without calling it ethics.”

A.: “The emphasis of the NSO’s mission statement is on statistical production and the many ways this can serve policymakers, researchers, and the wider public. That said, the core values […] connect to the

pursuance of the highest ethical standards in statistical and dissemination processes.”

A.: “Geared to satisfying society’s information needs, official statistics shall be based upon a clear set of principles that are aimed at maintaining the quality of statistics and at retaining the confidence of

end users and providers of information in statisticians and statistical agencies.”

Source: In-depth survey

6

Codes of conduct and Codes of ethics The Code of Ethics and the Code of Conduct are other crucial tools for creating solid ethics policies in an organization. A Code of Ethics consists of general principles that promote ethical behavior among employees. It communicates an organization's values and helps guide its leadership's choices. A Code of Conduct is a written set of principles and policies that usually specify the organization's values statement. It includes specific standards of conduct expected in various realistic situations, offering employees a framework to navigate complex scenarios. It guides all employees, ensuring they understand what is acceptable and unacceptable regarding workplace behavior.

Interestingly, according to the main survey results, 73% of responding NSOs have developed or are developing their Ethical Code or a similar document outlining their employees' expected ethical behavior. However, the in-depth survey responses indicate that the documents these NSOs refer to are only partially the Code of Ethics and the Code of Conduct. Instead, they primarily include Fundamental Principles of Official Statistics and Data Ethics Codes or refer to the Ethical Code of Civil Servants. About 20% have a Code of Ethics, and 24% have a Code of Conduct.

3.2 Ethics Management Organization One highly effective tool for managing ethics within an organization is establishing specific and dedicated roles, such as an Ethics Committee and a dedicated Ethics/Compliance Officer. These structured functions are a point of reference for all employees and are responsible for implementing and administering an ethics management program, ensuring the application of ethics policies and procedures, overseeing training initiatives, and resolving ethical dilemmas.

According to the main survey findings, approximately 36% of respondents reported having a dedicated Ethics Committee within the NSO. This committee consists of members who are either internal to the organization (24%), external and independent (4%), or a combination of both (7%). When ethical issues are assigned to existing offices without creating a specific structure, the percentage rises to 77% (Figure 3).

According to the in-depth survey, 8 out of 14 NSOs also have a dedicated Ethics/Compliance Officer or a similar

role responsible for managing ethics. These officers usually work closely with the Ethics Committee and provide regular reports to the Director General of NSOs and sometimes to an external National Commission of Ethics as well.

The Committees and the Offices engage in activities such as:  Promote knowledge, understanding, and adherence to ethical values and Codes.  Conduct studies related to the ethics system and suggest necessary adjustments.  Provide advice and recommendations on practical situations where ethical issues arise and serve as

a consultative and specialized advisory body for applying the Code.  Receive reports of ethical violations committed by workers and provide recommendations.  Identify the need for ethical training, develop training programs, and conduct staff training on ethical

behavior.  Implement the development of ethical plans and other programs to meet ethical requirements.

Source: Main survey

Figure 3 - Does your NSO have an Ethics Committee

7

3.3 Ethics implementation procedures and tools Applying ethical procedures and tools is also crucial for cultivating an ethical culture within organizations. Guidelines, protocols, and assessment methods serve as a roadmap, guiding individuals at all levels of the organization to practice ethical behavior as they provide clear indications to recognize and investigate breaches or ethically review new projects. The tools used to report violations and assess ethical risks are equally vital since they permit to face and mitigate real or potential ethical risks.

Protocols to investigate ethical breaches Ethical breaches occur when there is a deviation from accepted practice or a violation of ethical rules and procedures. However, not all instances of misconduct are necessarily considered ethical breaches. Therefore, it is highly beneficial to establish a well-defined protocol for handling allegations of suspected breaches and addressing confirmed violations. Clear and transparent procedures facilitate recognizing and reporting ethical misconduct and gathering proper evidence for investigations. Moreover, they enable organizations to assess whether an ethical breach has occurred, determine the extent of the violation, and recommend appropriate subsequent actions.

The main survey indicates that almost 80% of NSOs do not face significant ethical violations. However, it appears that while most of these organizations established a Code of Conduct (69%), none implemented procedures to investigate ethical breaches. Conversely, almost all organizations that faced misconduct (8 out of 9) have an Ethical Code and a protocol to investigate violations of ethics.

These NSOs investigate ethics breaches through various means. Information is usually collected by receiving complaints regarding ethical norms and rules violations.

In a specific case, additional investigations are carried out through a census distributed to all employees to gather the information that will enable the establishment of new working plans and ethics programs, as well as workshops to understand staff perceptions regarding values and other ethical topics.

Considering the 9 NSOs that reported policies on ethical breach management, these concern different areas, such as the statistical production area (89%) and the administration (78%) and the governance areas (78%) (Figure 4).

Ethical Reviews of Projects According to over half the respondents, their NSOs' in-house projects undergo ethical reviews; among them, such assessment occurs regularly (Figure 5).

Personal data protection, transparency, and public good support have been reported as the central values to consider before deciding whether to start a project or a data acquisition. Integrity and upholding the highest ethical standards have been included among such central values by 59% of the NSOs (Figure 6).

Figure 4 – Areas covered by procedures to investigate ethical breaches

Source: Main survey

8

Source: Main survey Source: Main Survey

Risk management Organizations can proactively anticipate and address potential misconducts by adopting risk management practices. Risk management is the process of identifying, assessing, and prioritizing potential risks that may affect the achievement of any organizational goal. It involves analyzing the likelihood and impact of various risks and developing strategies to avoid or mitigate their negative consequences. Risk management is a valuable tool for ethics management because it helps navigate and address potential challenges even during organizational changes, ensuring a smooth transition.

The in-depth survey findings suggest that most interviewed NSOs (57%) have implemented a risk management methodology within their institutional processes. These procedures often include audits to ensure thoroughness (Figure 7).

The primary focus areas in risk assessment are compliance with legal requirements and preventing fraud and corruption. Notably, two NSOs have successfully obtained ISO certifications, including ISO 9001:2015, ISO 27001:2013, and ISO 37001:2016, which is a testament to their successful implementation of risk management practices.

Due diligence Due diligence is an essential tool to reinforce controls that counter unethical practices. By conducting thorough due diligence, organizations can effectively identify and mitigate potential risks associated with unethical behavior. Due diligence is a meticulous research and analysis process that organizations perform to thoroughly assess the integrity and reputation of partners, suppliers, and other stakeholders before engaging in business relationships that may expose the organization to legal, financial, or reputational harm.

In addition to its risk mitigation benefits, due diligence fosters a culture of integrity and trust internally and externally in the eyes of stakeholders and the wider community. By thoroughly examining potential partners' backgrounds and records, organizations can ensure alignment with their values and ethical standards.

Remarkably, 50% of respondents conduct due diligence assessments for all third parties as an integrated process in all calls for tenders, contracts, and grant agreements (Figure 7). Moreover, a selected few also

Figure 5 – Does your NSO conduct Ethical Reviews on projects? Figure 6 – Values considered in projects Ethical Reviews

Figure 7 – Tools used by NSOs to assess ethics

Source: In-depth survey

9

extend this practice to the recruitment process, ensuring that the attitude and background of potential candidates align with the organization's core values.

Whistleblowing Whistleblower channels have become an increasingly important and widely used tool to develop an ethical conscience across organizations worldwide. Whistleblower channels enable individuals to report illegal or unethical behaviors while remaining anonymous and protecting them from potential harm or discrimination. Creating a safe and confidential platform encourages more informants to step forward and expose misconduct without fearing negative consequences in their personal and professional lives. Furthermore, these channels serve as practical tools for collecting essential information, as they are specifically designed to investigate reports, ensuring they are handled appropriately.

According to in-depth survey results, 71% of NSOs have implemented safe and confidential reporting channels for disclosing workplace misconduct (Figure 7). These systems were primarily introduced in the early 2000s to comply with the national law concerning the Public Civil Servants Disclosure Protection Act. Despite their widespread implementation, most NSOs have not yet received any reports through these channels.

3.4 Ethics dissemination To foster a healthy and competitive environment that benefits all parties involved, organizations should engage in fair and transparent practices with employees, customers, suppliers, and competitors. Regular communication, ongoing training, and stakeholder engagement campaigns are essential to this process. These efforts not only promote transparency and accountability but also instill a sense of trust and confidence in the organization's unwavering commitment to ethical behavior.

Ethics Training Comprehensive and continuous ethics training is crucial in creating awareness about the significance of ethics in the workplace and fostering a culture of integrity and accountability in the organizational culture because it empowers employees to make ethical decisions and hold others accountable. The training should cover the fundamental principles of ethics and provide a detailed understanding of the organization's ethics policies and procedures.

Source: Main survey

According to the main survey, around 80% of respondents reported having specific training courses on ethics for all employees (Figure 8). This shows that many NSOs are paying attention to this issue. However, in most cases, such training is only offered occasionally: it is usually provided to recruits or employees involved in new projects and is delivered only once during their employment. Approximately 22% of these organizations conduct annual ethics training (Figure 9), which is primarily voluntary.

Figure 8 – Target audience of training on Ethics Figure 9 –Frequency of training on Ethics

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Stakeholder consultation By actively involving stakeholders through consultations to gather their insights and feedback, organizations can align with the community's expectations and values. This inclusive approach helps identify potential ethical concerns and enables organizations to address them, proactively reducing the risk of reputational damage. As a result, it strengthens relationships with stakeholders, fostering long-term trust and credibility.

Q.: Does your Organisation run any stakeholder consultation to promote reputation and reliability based on the development of Ethics?

~ * ~

A.: “There is a process for suggestions and complaints in charge of the Institutional Management, whose purpose is for users to express their disagreements about behaviors or situations that they observe regarding institutional personnel in the performance of their duties, or in the delivery of

institutional products (goods and services).”

A.: “When we consult our stakeholders, the ethical elements of our work are always present.”

Source: In-depth survey

To establish themselves as reliable entities and improve their reputation, 50% of the respondents engage in external stakeholder consultations regarding ethical development. These consultations typically focus on projects that are considered high-priority, strategic, or sensitive. Consultations may involve participation from citizens or groups representing various stakeholders. In some instances, external consultants or experts may also be engaged. In one case, an NSO conducted a study to gauge the perception of ethics within the organization, too.

3.5 Ethics Management and Performance As already mentioned, implementing ethics and ethics management within an organization often leads to outstanding performance. It appears from the in-depth survey that most of the respondents from the NSOs recognize the positive impact of ethics management on employees’ well-being and performance. According to the in-depth survey, 68% of the 25 respondents acknowledged that Ethics management has significantly improved working relations and overall well-being, while 60% agreed that it has improved performance.

Q.: On the basis of your experience, has the Ethics management increased the performance of your Organisation?

~ * ~

A.: “This has not been analyzed explicitly. Employees are informed as soon as they begin their employment of the frameworks and policies in place. Transparency is also demonstrated to the general public with the website. With these proactive actions the performance we can assume performance is

better compared to a reactive mode.”

“We are confident that Ethics management, using the set procedures ensuring transparency, will improve the wellbeing of all staff.”

Source: In-depth survey

However, none of the respondents implemented a monitoring system or a specific analysis within the organization to provide concrete evidence and enable a more accurate assessment of the impact of Ethics management on the workplace.

11

3.6 Ethical Dilemmas Ethical dilemmas are complex situations that arise when individuals face conflicting values and principles, making it challenging to determine the appropriate course of action. To make ethical decisions, individuals must carefully consider the potential consequences of their actions and balance competing moral obligations. This involves considering the organization's ethical framework and its Codes of Conduct while embracing transparency and accountability in their choices.

Regarding ethical dilemmas, in the in-depth survey, most NSOs expressed concerns regarding data ethics, with confidentiality and data privacy being the most mentioned areas. Five organizations explicitly mentioned organizational ethical dilemmas. Among them, the most frequently mentioned concerns revolve around merging positions, conflicts of interest, integrity, general corruption risks, compliance with professional ethics rules beyond the office environment, and the tension between public interest and protecting private or business interests.

4. Conclusions Overall, the three surveys the Task Team on Ethical Leadership conducted indicate that numerous NSOs have implemented robust measures to manage ethics, demonstrating a solid commitment to ethical practices. The high response rate, the willingness of NSOs to participate in the surveys and future information activities, their actual commitment to ethical practices, demonstrate their active engagement and desire to contribute to developing best practices in this area.

The analysis reveals that many NSOs have widely adopted ethics management practices. These organizations excel in implementing ethical practices through various means, such as strategic values, streamlined processes, organizational structures, and efficient tools. Given the nature of their work, most NSOs primarily focus on data ethics and the quality principles applied to statistics. Nonetheless, while this focus is essential, several NSOs also acknowledge the need for further development of business ethics to establish a comprehensive ethics management system extending beyond statistical production and research.

Continuous improvement and evolution of ethical frameworks can actually enable NSOs to effectively address emerging ethical challenges and fulfill their mission of providing accurate and reliable data to the public. And the insights collected from the surveys can serve as valuable starting point for stimulating further discussion, raising awareness, and generating increased interest among NSOs concerning ethics management practices.

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Badaracco, J. L. (2016). How to Tackle Your Toughest Decisions. Harward Business Review, September.

Bazerman, M. H., & Tenbrunsel, A. E. (2011). Blind spots: Why we fail to do what's right and what to do about it. Princeton University Press.

Bazerman, M.H. and Tenbrunsel, A.E. (2011). Blind spots: Why we fail to do what's right and what to do about it. Degruyter.com.

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Caldwell, C. and Verl, A.A. (2020). Business ethics: perspectives, management, and issues” Nova Science Publishers.

Cooper, T.L. (2019). Handbook of administrative ethics” Second edition Revised and Expanded, Routledge Taylor & Francis Group, New York London.

Crawshaw, J. R., Cropanzano, R., Bell, C. M., & Nadisic, T. (2013). Organizational justice: New insights from behavioural ethics. Human Relations.

Epley N. and Kumar A. (2019). How to Design an Ethical Organization A behavioral approach. Harward Business Review, May–June.

OECD (2000). Trust in Government. Ethics Measures in OECD Countries. OECD Publishing.

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Seltzer W. (2005). Official Statistics and Statistical Ethics: Selected Issues. International Statistical Institute, 55th Session.

Stark, A. (1993). What’s the matter with business ethics. Harward Business Review, June.

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Treviño L.K. and Brown M.E. (2004). Managing to be ethical: Debunking five business ethics myths. Academy of Management Executive, Vol. 18.

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Whitton H. (2001). Implementing effective ethics standards in government and the civil service. Transparency International, February.

INVESTIGATING ETHICAL PRACTICES IN NSOs SURVEYS RESULTS

Istat | Italian National Institute of Statistics

Geneva, Switzerland, 26 - 28 March 2024

WORKSHOP ON ETHICS IN MODERN STATISTICAL ORGANISATIONS

Katia Ambrosino

o The Task Team on Ethical Leadership

o Surveys

o Surveys results

Contents

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO2

Task Team on Ethical Leadership

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO3

Produce a Reference Book on Ethics in

NSOs

Create a common

framework

Support NSOs in upholding

standards Promote a culture of

transparency, accountability, and integrity

Surveys

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO4

Offer valuable insights into ethical practices in NSOs

The basis for further discussion

Best practices and lessons learned

Surveys findings

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO5

High response rate Willingness to cooperate with

TTeam

Widespread use of ethics management in numerous NSOs

Need for development of

ethical frameworks

Surveys findings

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO6

Ethics and Compliance Governance

Ethics Management Organization

Ethics implementation procedures and

tools

Ethics dissemination

Ethics Management and

Performance Ethical Dilemmas

Ethics & Compliance Governance

7

Q.: Are there any Ethics management policies/programmes/procedures/practices in your

Organisation?

Ethics & Compliance Governance

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO8

Q.: How long ago have them been implemented?

Yes, 82.8%

No, 17.2%

1 to 3 years, 13%

more than 3 years, 88%

Q.: Do any Vision, Governance and Leadership statements in your Organisation take into account ethical perspective?

Strategic Values

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO9

Q.: Does any governance provision by your organization establish business ethics values?

«…it is implicit, both in the institutional values, as well as in the mission and vision…»

«…We do a lot of what ethics is without calling it ethics…»

«…The emphasis of the NSO’s mission statement is on statistical production and the many ways this can serve policymakers, researchers, and the wider public…»

«…Geared to satisfying society’s information needs, official statistics shall be based upon a clear set of principles that are aimed at maintaining the quality of statistics and at retaining the confidence of end users and providers of information in statisticians and statistical agencies…»

Q.: Does your NSO have an ‘Ethical Code’ or a document regarding the expected ethical behavior of its employees?

Codes of conduct and Codes of ethics

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO10

65%

27%

4%

0%

4%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Yes No

Under review

Under construction

Fundamental Principles of Official Statistics and Data Ethics Codes

Ethical Code of Civil Servants

Code of Conduct

Code of Ethics

Ethics Management Organization

11

Ethics Committee or Officer

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO12

Q.: Does your NSO have an Ethics Committee or Officer?

Provide regular reports to

o Director General of NSOs or Head of Staff

o external National Commission of Ethics

Information collected through

o Questionnaires to workers

o In case of violations

Ethics Management Procedures and Tools

13

Protocols to investigate ethical breaches

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO14

Q.: Does your organization have a process to investigate a breach of professional ethics or scientific integrity?

Q.: Is your organisation facing particular Ethical dilemmas? 80%

100%

NO

NO

20%

90%

YES

YES

Q.: This process covers which areas?

Assessment tools

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO15

Q.: Do you conduct Ethics & Compliance risk assessments?

Q.: Do you conduct due diligence on your third parties?

Q.: Does your organisation have a whistleblowing channel which allows for anonymous reporting?

ISO 9001:2015, ISO 27001:2013, and ISO 37001:2016

0% 20% 40% 60% 80% 100% 120%

Risk assessment

Due diligence

Whistleblowing channel

YES NO

Ethics dissemination

16

Training

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO17

Q.: Do your employees receive training on the Code of Conduct?

Target audience

Frequency

Stakeholder consultation

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO18

«… There is a process for suggestions and complaints in charge of the Institutional Management, whose purpose is for users to express their disagreements about behaviors or situations that they observe regarding institutional personnel in the performance of their duties, or in the delivery of institutional products (goods and services)…»

«… When we consult our stakeholders, the ethical elements of our work are always present…»

Q.: Does your Organisation run any stakeholder consultation to promote reputation and reliability based on the development of Ethics?

Ethics Management and Performance

19

Ethics Management and Performance

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO20

On the basis of your experience, has the Ethics management increased the performance of your Organisation?

On the basis of your experience, has the Ethics management within your Organisation improved well-being at work?

On the basis of your experience, have you found a connection between the delivery of training on the Code and a decrease of disciplinary measures?

«… This has not been analyzed explicitly. Employees are informed as soon as they begin their employment of the frameworks and policies in place. Transparency is also demonstrated to the general public with the website. With these proactive actions the performance we can assume performance is better compared to a reactive mode…»

«… We are confident that Ethics management, using the set procedures ensuring transparency, will improve the wellbeing of all staff…»

«… Surely there is a positive impact, however, it has not been measured…»

Ethical Dilemmas

21

Ethical Dilemmas

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO22

Merging positions

Conflicts of interest

Integrity

General corruption risks

Compliance with professional ethics rules beyond the office environment

The tension between public and private interest

Conclusions

INVESTIGATING ETHICAL PRACTICES IN NSOS – SURVEYS RESULTS | KATIA AMBROSINO23

o Robust measures to manage ethics

o Solid commitment to ethical practices

o Support for development of ethical frameworks

Ethics management systems Data ethics

Reference Book

KATIA AMBROSINO | [email protected]

All images from Pixabay

  • INVESTIGATING ETHICAL PRACTICES IN NSOs �SURVEYS RESULTS
  • Contents
  • Task Team on Ethical Leadership
  • Surveys
  • Surveys findings
  • Surveys findings
  • Ethics & Compliance Governance
  • Ethics & Compliance Governance
  • Strategic Values
  • Codes of conduct and Codes of ethics
  • Ethics Management Organization
  • Ethics Committee or Officer
  • Ethics Management Procedures and Tools
  • Protocols to investigate ethical breaches
  • Assessment tools
  • Ethics dissemination
  • Training
  • Stakeholder consultation
  • Ethics Management and Performance
  • Ethics Management and Performance
  • Ethical Dilemmas
  • Ethical Dilemmas
  • Conclusions
  • Slide Number 24

Ethics and proactive communication: The Istat case. Giulia Peci, Michela Troia (Istat)

Languages and translations
English

1

UNITED NATIONS ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Workshop on Ethics in Modern Statistical Organisations 19 February 2024

26-28 March 2024, Geneva, Switzerland

Ethics and proactive communication: the Istat case

Giulia Peci (Istat, Italy)

[email protected]

Michela Troia (Istat, Italy)

[email protected]

Abstract

Why is ethics so important today?

We live in a mass-media-driven world, which can result in a disregard for the audience. Less attention is paid to

the concrete needs of different user groups, because everyone is indiscriminately subjected to the mechanisms

of advertising.

Talking about communication, this abuse of the media generally leads to the neglect of rules and principles.

This creates a need for ethics. Public communication and, in our case, statistical communication, cannot escape

this need.

Ethics in communication refers to the principles and guidelines that govern the responsible and fair exchange of

information among individuals or groups of people. This includes the moral considerations and values which

must guide the way we communicate.

To build trust, maintain positive reputation and relationships and contribute to a healthy and respectful

communications culture, it is essential to adhere to ethical standards in communication.

In the Anglo-American cultural area, the ethics of communication has found important developments in

contemporary thinking. In Italy these issues came late, but recent decades have made up for lost time, partly

thanks to research in mass communication studies.

In line with this vision of a proactive and ethical communication and in compliance with the Fundamental

Principles of Official Statistics, as well as with its own mission and vision, Istat has defined some key modes

for communicating with its target audiences, such as:

• using highly recognizable and consistent visual branding (both on social channels and the

institutional website)

• engaging directly with audiences through timely, transparent and data-driven communication

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• respecting for the right of all citizens, regardless of level of knowledge of statistics, to receive

adequate information, with no exclusion of categories or groups and with the same recognition and

visibility for all

• respecting gender and diversity in the use of language, taking into account all the different conditions

that characterize people and their way of life

• designing for all: accessibility by default and by design. Using images with alternative text,

infographics with text versions, websites built for screen readers, using captions in YouTube videos,

paying attention to color contrast in social cards and more

• to be a bridge and not a barrier to understanding through the use of simplified language for a wide audience. In

this paper, we would like to share with you the key concepts from which we started to outline our

style of communicating statistical information.

We will do this by sharing our experiences, our lessons learned and our next goals.

3

Ethics and proactive communication: the Istat case

Paper

Outline

Ethics and proactive communication: the Istat case ...........................................................................................

Abstract .....................................................................................................…………………………………….

1. Introduction ................................................................................................................................................. 3

2. What are the main ethical principles that govern communication? ............................................................ 4

3. How does Istat respect such principles? ..................................................................................................... 4

3.1. Transparency .............................................................................................................................................. 4

3.2. Impartiality ................................................................................................................................................. 5

3.3. Privacy ........................................................................................................................................................ 5

3.4. Cultural sensitivity ..................................................................................................................................... 5

3.5. Empathy ..................................................................................................................................................... 6

3.6. Responsibility and Accountability ............................................................................................................. 6

4. Charter of Services for Users of Statistical Information and Guaranteed Quality Standards ..................... 7

5. Next steps ..................................................................................................................................................... 8

1. Introduction

Statistical information: we can't avoid conveying it, but we can communicate it well or poorly.

The choices are many, so having an ethic for communicating is imperative.

Ethics in communication refers to the principles and guidelines that govern the responsible and fair exchange of

information between individuals or groups.

It involves the moral considerations and values that should guide all communication practices. Today,

it is often the case that disregard for rules and principles mainly dominates the sphere of

communication, in a general context that thus registers:

o low respect for the audience (as little more than a target to be reached and/or

misleading advertising cases);

o lack of attention to the specific needs of different target groups; o real abuse of the

media (often instrumental, ideological).

4

Rather, to build trust, to maintain positive relationships and reputation, as well as to contribute to a healthy and

respectful culture of communication, it is essential to adhere to ethical standards in the communication process.

2. What are the main ethical principles that govern communication?

o Transparency: ethical communication involves being transparent about motives, intentions and

conflicts of interest.

o Fairness and balance: the goal of ethical communication is the fair and objective presentation of

information. It is important to avoid bias and to present a balanced view of different points of view.

o Privacy: respecting people's privacy is fundamental. It is a basic ethical principle to avoid unauthorized

disclosure of sensitive or confidential information.

o Cultural sensitivity: awareness of and respect for cultural differences is an important part of

communication. Ethical communication involves avoiding stereotypes and understanding cultural

nuances.

o Empathy: considering the feelings and perspectives of others is part of ethical communication.

Empathy builds trust and creates a positive environment in which to communicate. o Responsibility:

communicators are responsible for their messages' consequences. This includes being aware of the

potential impact of their words and actions on individuals and society.

o Accountability: when errors occur, ethical communicators take responsibility for their actions and seek

to correct any misinformation or harm caused.

3. How does Istat respect such principles?

3.1. Transparency

The Istat website has an area dedicated to Transparent Administration (in Italian only), as required by current

Italian legislation. The web editorial team of the site, which reports to the Communication Directorate, is

responsible for here publishing all information about the Organization, as well as information about personnel,

competitions, performance, activities and procedures, tenders and contracts, budgets. A list, constantly updated,

of the annual wages of all Istat executives is also available in this section.

Furthermore, for the purposes of transparency in the way we communicate our statistical content, if an error or

typo is found in content already published on our site (e.g. press releases, publications, datasets) and therefore

needs to be replaced, as is sometimes the case, a note will always be added explaining the reasons for the

replacement and the date on which it was made. This note is published on the same page as the replaced

content.

5

3.2. Impartiality

Istat never expresses value judgements when disseminating data, analyses or publications. It also never takes

any political position.

It tries to maintain a tone as neutral as possible.

In fact, as stated in our mission statement: “The mission of the Italian National Institute of Statistics is to serve

the community by producing and communicating high-quality statistical information, analyses and forecasts in

complete independence and in accordance with the strictest ethical and professional principles and most upto-

date scientific standards…”

Moreover, in accordance with the principle of impartiality, every December the calendar of economic releases

for the following year is published. This ensures that Istat never disseminates data in ways that could serve the

needs of one political party or another.

In addition, our releases are made available to the entire community at the same time through publication on our

website.

3.3. Privacy

Istat pays particular attention to the privacy of users and respondents.

All the measures and practices adopted to this end are set out in a specific section of Istat website which

contains, in particular, information on the processing of the personal data of users of the "istat.it" domain and of

those taking part in other Institute initiatives such as events, newsletters, contests and so on.

Furthermore, in compliance with the current GDPR (General Data Protection Regulation), Istat has adopted a

cookie management policy on its websites.

For example, on the institutional website, we removed the embedding of YouTube to prevent this platform from

collecting cookies from users of the Istat website without their knowledge. The same applies to Tableau (a

platform for dashboard creation).

3.4. Cultural sensitivity

We never use language or images that invoke cultural stereotypes in our communication campaigns.

As a general rule, we do not represent families with images of only the traditional family, nor do we represent

the population with only people of Caucasian ethnicity.

But even the best intentions can sometimes be misrepresented. In the communication campaign for the 2018

Permanent Population Census, the choice of an image of a dynamic woman standing with a baby and busy on

the phone, used with another image of a man with two children sitting on a couch, did not go down well with

the feminist community in Italy. From their point of view, the image of the woman in this way was that of a

mother who was not very concerned about the safety of her children, in contrast to the image of the man who

suggested the idea of a father who was very comfortable with his children. A lesson in learning from mistakes.

6

We also pay close attention to the choice of colors we use in our web products and applications and in the

creation of cards for their social promotion. As much as possible, we try to use colors that are different from

those that have always identified males and females in cultural gender stereotypes (blue and pink). An

example of this is the interactive content "How many babies are named...?", one of the most visited web

applications on our site, for which we use orange for females and green for males. This year there was also a

change in the title of this product: from "Quanti bambini si chiamano" (How many babies are named) to

"Quante bambine e bambini si chiamano" (How many female babies and male babies are named). In fact, the

Italian language, unlike the English language, has a different term for identifying male and female children.

Finally, in line with the care we take to avoid any kind of discrimination, we have been paying attention for

years to the way in which we write greetings on the occasion of traditional religious holidays. For example,

we know that not everyone celebrates Christmas, even though many public and private companies in Italy

close for the holidays in this period of the year. For this reason, our greetings always contain the text "Happy

Holidays" and never "Merry Christmas".

3.5. Empathy

Our mission, as mentioned, is to serve the community not only by producing high-quality statistical

information, but also by communicating it in the best possible way so that anyone, even non-expert users, can

easily understand and use it. This also applies to the tone with which we interact with and listen to our users

and it turns into empathy when, through this listening, we are sometimes able to improve our statistical

offerings.

A recent and striking case was when in 2020 we published a dashboard on our website showing Covid deaths in

the Italian municipalities. The visualization was set up so that the first municipality displayed by default was

the one with the most Covid deaths in Italy, Bergamo. One woman from Bergamo later wrote us to say that

while she understood the need to inform the community with this figure, it was very painful for her to see a

graph with such dramatic data about her community at the top of the dashboard, especially since Covid had just

killed her father.

We took what she wrote to us very seriously. In fact, the very next morning, we changed the setting of the

graph to use as the default the municipality that is always the first municipality in Italy (Agliè). We

communicated this change to the woman, who was very pleased and impressed by the attention shown to her by

Istat (Humanizing the data).

3.6. Responsibility and Accountability

Two sides of the same coin: responsibility and accountability.

As stated above, we take great care in choosing the words and images we use to communicate our data.

We are aware of the potential impact they can have on our goals.

7

Nevertheless, sometimes we fail to achieve this goal, as in the case of the campaign for the Permanent

Population Census (see above).

Two other examples of missed targets: an Instagram post where we talked about birth dates with a baby bottle

and were accused of promoting artificial breastfeeding and a Facebook post celebrating World Homeopathic

Day where we were accused of promoting palliative products for real cures. In the first case we decided not to

respond (the issue was too sensitive, and it was deemed best not to fuel the controversy that had inevitably

already arisen; any response could have made things worse rather than the other way around, better to wait for

it to die down on its own). In the second case, on the contrary, we intervened by clarifying and explaining that

homeopathic products are recognized as medicinal products in the European Union, as provided for by

Directive 2001/83/EC of the European Parliament and of the Council of 6 November 2001.

4. Charter of Services for Users of Statistical Information and Guaranteed

Quality Standards

To reaffirm the Istat commitment to ethical communication with users, for more than 15 years the

Communication Directorate has been drafting and regularly updating a Charter of services for users of

statistical information. It defines the quality standards of the provided communication and dissemination

services, their use, the commitments towards the users and the quality standards guaranteed.

The document is available on the institutional website and it identifies four different quality aspects for each

user service: timeliness, accessibility, transparency and efficiency. Morover, it sets out indicators and goals for

each of them.

The document mainly aims to ensure that services comply with the following principles:

o Equality: the guarantee of equal conditions of use and equal treatment in the provision of services to all

users, without distinction as to sex, race, religion, language or political opinions and the prohibition of

any unjustified discrimination.

o Impartiality: the service is guaranteed to be objective, fair, equitable and impartial to all of its users.

o Continuity: The service is continuous, regular and uninterrupted. Should they occur, disruptions are

minimised.

o Clarity and transparency: the user is guaranteed to receive clear, complete and timely information on

the procedures, times and criteria for the provision of the service.

o Efficiency and effectiveness: the performance will be achieved through continuous improvement of

efficiency, effectiveness and quality, adopting the most functional managerial, organizational,

procedural and technological solutions.

o Accessibility: Ensure access for all without discrimination, regardless of physical, technological or

environmental barriers.

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o Participation: continuous dialogue with users through reports and suggestions, complaints and

satisfaction surveys (User relations).

5. Next steps

By 2024, Istat will have a new area on its website that will focus exclusively on ethics, as part of its

communication strategy.

In addition, two working groups have been formed, one to develop inclusive education guidelines and another

to develop gender policies. Representatives from the Communication Directorate participate in both teams on

the competency aspects.

Finally, the work of the “Comitato Unico di Garanzia per le pari opportunità” (Unique Guarantee Committee

for Equal Opportunities), active at Istat since 2011, will be pursued with the aim of improving the well-being of

employees and combating any discrimination within the Institute.

However, the most important challenge in the coming years, even in the field of communication, will be to

respect ethical principles in a world that is increasingly based on artificial intelligence (AI). Statistical agencies

cannot escape this challenge, as they too, including Istat, are beginning to implement AI in their user services

and data management.

The first principle at risk is privacy. How to combine privacy and artificial intelligence? AI has developed at an

exponential rate in recent years. It is now part of everyone's daily life. A lot of questions arise when we

consider that the machine learning (ML) on which intelligent systems are based is precisely designed to process

huge amounts of data. Where does this information come from? Is it handled ethically? Does it respect

personal, social and individual rights? In a world dominated by AI, how can we ensure privacy?

In our Countries and at the European level - see the AI Act - we are all following closely what the relevant

legislative bodies are doing in this regard.

As stated, this is a challenge we cannot afford to miss, because it is how we face it that will allow us, as official

statistical agencies, to maintain our brand reputation, keeping up with the most innovative technological

developments and thus improving the quality of the services we provide to the community.

  • 1. Introduction
  • 2. What are the main ethical principles that govern communication?
  • 3. How does Istat respect such principles?
  • 3.1. Transparency
  • 3.2. Impartiality
  • 3.3. Privacy
  • 3.4. Cultural sensitivity
  • 3.5. Empathy
  • 3.6. Responsibility and Accountability
    • 4. Charter of Services for Users of Statistical Information and Guaranteed Quality Standards
    • 5. Next steps

Ethics and proactive communication: the Istat case

Workshop on Ethics in Modern

Statistical Organisations

Geneva, 26 - 27 March 2024

Istat | Communication Directorate

Giulia Peci - Michela Troia

Outline

o Introduction – the context

o What are the main ethical principles that govern communication?

o How does Istat respect such principles?

o Charter of Services for Users of statistical information and guaranteed

quality standards

o Next steps

2 ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

About us

3

After graduation and a Master's degree in economics with a subsequent Master's degree in communication, she has been working at Istat since 1998. Web Content Specialist, usability and accessibility expert since 2003. Since 2001 to 2019 she dealt with social media and with users’ satisfaction. Since 2016 to 2019 she was responsible for the relationships with users. Member of WP1 ‐ User analysis of project for Digital communication, User analytics and Innovative products (DIGICOM) of the European statistical system. She also is a member of the working group on the usability of the Public Administration. In 2019 she became member of the Unece Strategic Communication Framework and actually she is member of the UNECE Capabilities and Communication Group. She is currently responsible for the Istat website.

She has been working in the field of Communication for over 30 years. Graduated in Languages, she worked for about 15 years at 3M Italy S.p.A. as Public Affairs Specialist in the Legal Affairs and Public Relations Department. She moved to the Public Administration in 2001 (Istat, Central Communication Directorate). Since then, she has worked as Internal communication and Web communication expert for about 10 years. In the last years she has been mainly dealing with social media and relations with external users. Actually, member of the UNECE Capabilities and Communication Group and former member for two years of the Unece Strategic Communication Framework project. For one academic year, she also taught Corporate Communication at the European Institute of Design, based in Rome.

Michela Troìa

Giulia Peci

ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

Introduction

4

Statistical information:

NSIs can’t avoid conveying it, but they can communicate it well or poorly

Today, Communication often fails to respect rules and principles

Result

o Low respect for the target groups

o Failure to address specific audience needs

o True media abuse

Building trust and preserving a positive reputation requires ethical communication

ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

The main ethical principles that govern communication

5

o Transparency – being transparent about motives, intentions and conflicts of interest

o Fairness and balance – to present information avoiding bias and offering a balanced view of

different point of view

o Privacy – respecting people’s privacy to avoid unauthorized disclosure of sensitive or confidential

information

o Cultural sensitivity – awareness and respect for cultural differences

o Empathy – considering the feelings and perspectives of others

o Responsibility – being aware of the potential impact of one’s words and actions on individual and

society

o Accountability - correcting any misinformation or harm caused when errors occur

ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

How does Istat respect such principles?

Transparency

o Dedicated website area

o Transparent communication as content is replaced

Impartiality

o Istat never expresses value judgements when

disseminating statistical information

o All content is disseminated simultaneously to everyone.

o The calendar of the economic data is announced every

December for the following year to avoid the risk of

thinking that the release is instrumental for the public

debate (e.g. in view of the elections)

6 ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

How does Istat respect such principles?

Privacy

o Dedicated website area

o Istat has adopted a cookie management policy on its

websites in compliance with the current GDPR (General

Data Protection Regulation)

Cultural sensitivity

o Istat never uses language, images or colors that invoke

cultural sterotypes

o Attention is also paid to religious differences, in line with the

focus on avoiding any form of discrimination

7 ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

How does Istat respect such principles?

Empathy

o We listen and interact with our users in an empathetic way. This allows us

to adapt our tone to the person we're talking to and thus improve our

services

Responsability and Accountability

o Two sides of the same coin involving awareness of the potential impact on

achieving our goals of the words and images we choose to communicate

o Attention is always high. However, this does not always prevent us from

making mistakes. Taking responsibility for such failures and acting to adjust

our focus, learning from the lessons, is crucial

8 ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

Charter of services for Users of statistical information and guaranteed quality standards

9 ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

For more than 15 years, the Communication Directorate has developed a service charter for users. It is

regularly updated and defines the quality standards of communication and dissemination services.

These standards are based on ethical communication with users and are embodied in the following principles:

o Equality

o Impartiality

o Continuity

o Clarity and Transparency

o Efficiency and Effectiveness

o Accessibility

o Participation

Next steps

10

o 2024: A new section of the website dedicated to ethics

o Two newly formed task teams:

• To develop guidelines for an inclusive internal training

program

• To develop a gender policy for employees

o To continue the work of the Unique Committee for Equal

Opportunities within the Institute

o New challenge: respecting ethical principles when using AI in

communication

ETHICS AND PROACTIVE COMMUNICATION:: THE ISTAT CASE | GIULIA PECI – MICHELA TROIA

Thanks! GIULIA PECI – MICHELA TROIA

[email protected] - [email protected]