The labour market in the Mediterranean countries presents a heterogeneous structure influenced by economic, demographic and cultural factors. Data show significant disparities in activity, employment and unemployment rates between different areas. In North African and Middle Eastern countries, low youth and female participation rates persist. The Mediterranean labour market also varies by sector: with agriculture leading in less industrialised countries, industry relevant in the Balkans, and services predominant in Europe.
The labour market analysed through activity, employment and unemployment rates, with a focus on regional disparities, economic sectors and labour inclusion, highlighting challenges and priorities for sustainable growth.
Labor force participation rate for ages 15-24, total (%)
Labor force participation rate, total (% of total population ages 15-64)
Employment to population ratio, ages 15-24, total (%)
Employment to population ratio, 15+, total (%)
Unemployment, total (% of total labor force)
Unemployment, youth total (% of total labor force ages 15-24)
Employment in agriculture (% of total employment)
Employment in industry (% of total employment)
Employment in services (% of total employment)
area_code
ordgeo
Countries
2023
2022
2023
2023
2023
2023
2022
2022
2022
Portugal
35.5
76.4
28.9
55.4
6.5
20.2
5.0
24.0
71.0
A
1
Spain
32.9
74.1
23.6
50.6
12.1
28.7
3.8
20.1
76.1
A
2
France
42.8
74.3
36.1
52.3
7.3
17.1
2.6
19.3
78.2
A
3
Italy
26.4
65.5
20.4
46.1
7.7
22.7
3.8
26.9
69.3
A
4
Slovenia
36.3
76.6
33.2
56.5
3.6
9.9
4.3
30.2
65.5
A
5
Croatia
30.5
69.3
24.6
49.3
6.1
19.2
5.9
28.4
65.7
A
6
Greece
24.8
68.7
18.6
46.3
11.0
26.6
11.2
15.6
73.3
A
7
Malta
54.2
79.8
49.6
63.6
3.1
9.3
0.8
17.2
82.0
A
8
Cyprus
43.6
76.8
36.2
61.1
6.0
17.5
2.4
17.2
80.5
A
9
Serbia
31.9
73.2
25.0
54.3
8.7
24.3
13.6
29.1
57.4
B
10
Kosovo
..
..
..
..
12.1
21.3
..
..
..
B
11
Bosnia and Herzegovina
28.8
61.6
20.2
44.1
10.4
26.5
16.9
33.5
49.6
B
12
Montenegro
31.4
68.2
22.9
48.3
15.3
27.9
7.2
18.5
74.4
B
13
North Macedonia
28.3
63.8
20.6
45.7
13.1
28.6
9.7
30.4
59.9
B
14
Albania
33.8
71.4
24.7
53.5
11.6
28.2
34.9
21.4
43.7
B
15
Turkiye
44.1
58.3
36.2
48.2
9.4
17.6
16.7
27.7
55.6
C
16
Syrian Arab Republic
23.7
40.3
15.8
33.4
13.5
33.5
15.5
22.7
61.8
C
17
Lebanon
35.6
50.4
27.1
40.2
11.6
23.7
3.5
20.4
76.0
C
18
Jordan
26.0
41.4
15.2
31.9
17.9
40.8
3.2
18.2
78.6
C
19
Israel
47.6
73.3
44.8
62.6
3.4
6.0
0.8
15.7
83.5
C
20
West Bank and Gaza
..
..
19.7
34.0
24.4
36.0
6.2
32.6
61.2
C
21
Egypt, Arab Rep.
24.0
46.5
20.3
41.3
7.3
19.0
18.7
28.4
53.0
D
22
Libya
17.1
50.9
8.8
39.2
18.7
49.4
9.2
22.8
68.0
D
23
Tunisia
28.5
52.3
17.0
39.0
15.1
37.5
14.0
33.4
52.6
D
24
Algeria
23.3
45.6
16.0
37.1
11.8
30.8
9.7
30.8
59.4
D
25
Morocco
26.5
47.7
20.3
39.7
9.1
22.6
30.8
24.0
45.3
D
26
Labor force participation rate for ages 15-24, total (%)
KosovoNo data available
West Bank and GazaNo data available
Labor force participation rate, total (% of total population ages 15-64)
KosovoNo data available
West Bank and GazaNo data available
Employment to population ratio, ages 15-24, total (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Employment to population ratio, 15+, total (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Unemployment, total (% of total labor force)
KosovoLatest available data: 2022
West Bank and GazaLatest available data: 2022
Unemployment, youth total (% of total labor force ages 15-24)
KosovoLatest available data: 2022
West Bank and GazaLatest available data: 2022
Employment in agriculture (% of total employment)
KosovoNo data available
Employment in industry (% of total employment)
KosovoNo data available
Employment in services (% of total employment)
KosovoNo data available
Some highlighted topics
The analysis of the labour market in the Mediterranean countries reveals a complex structure, influenced by economic, demographic and cultural variables. The study of activity and employment rates, together with the sectoral distribution of employment, provides an overview of the challenges and opportunities that characterise this area. Moreover, promoting long-term sustainable socio-economic recovery and job creation in the Southern Neighbourhood is a key shared priority and the cornerstone of the new agenda for the Mediterranean endorsed by the European Commission.
Activity and employment rates
In 2023, regional disparities in activity rates among Mediterranean countries are evident. In the European Union, Malta stands out with a youth activity rate (15-24 years) of 54.2%, followed by Cyprus and France, while Greece and Italy show lower values, at 24.8% and 26.4% respectively. In the Western Balkans, Serbia and Albania record moderate rates (32-34%), while in the Middle East, Israel records 47.6%, which is significantly higher than Syria and Jordan. In North Africa, rates are low overall, with Tunisia at 28.5%, Algeria at 23.3% and Libya at 17.1%.
Considering the entire working age group (15-64 years) in 2022 Malta and Slovenia record the highest values, 79.8% and 76.6% respectively, reflecting the stability of the labour market for adults of working age. At 65.5 per cent, Italy records the lowest value among the Mediterranean European countries. In the Balkans, Serbia maintains an activity rate of 73.2%, while Bosnia and Herzegovina and North Macedonia are below 65%. Among Middle Eastern countries, Israel shows high participation (73.3%), while Lebanon and Jordan have significantly lower values of 50.4% and 41.4%, respectively. In North Africa, the activity rates of the working-age population are generally low, with Algeria and Libya recording 45.6% and 50.9% respectively, and only Tunisia exceeding 52%, signalling a persistent difficulty for labour market inclusion. These data highlight the need for targeted interventions to improve labour force integration, especially among young people and in areas with particularly low activity rates, and considering that the low rates depend significantly on the very limited labour market participation of women.
Figure 1 - Activity rate. Years 2022 (%)
...
With regard to the employment rate among young people (15-24 years), Malta has the highest rate (49.6%) in 2023, followed by Israel (44.8%) and Turkey (36.2%), highlighting a greater integration of young people into the labour market than other countries. In contrast, Libya (8.8 per cent) and Jordan (15.2 per cent) show extremely low youth employment rates, signalling significant difficulties for young people to access employment in these contexts.
For the overall population (15 years and over), Malta continues to stand out with the highest employment rate (63.6 per cent), followed by Cyprus (61.1 per cent) and Israel (62.6 per cent), values that indicate a relatively stable and active labour market. In contrast, countries such as Syria (33.4%) and Jordan (31.9%) show very low overall employment levels, reflecting substantial economic challenges. In North Africa, employment rates remain particularly low: Algeria, Tunisia and Libya do not exceed 40 per cent, highlighting an urgent need for action to promote labour inclusion in these areas.
The analysis shows notable heterogeneity in participation and employment in the Mediterranean countries. In Europe, activity and employment rates are high among adults, but youth participation remains a challenge. In the Western Balkans, lower rates require interventions to stimulate the economy and support youth employment. In the Middle East, Israel emerges for high participation rates, while other countries have limited employment opportunities. Finally, in North Africa, low youth participation and employment rates indicate an urgent need to expand employment opportunities.
Figure 2 - Employment rate. Year 2023 (%)
...
Unemployment rate
Youth unemployment is a crucial issue in Mediterranean countries. It is particularly high in North African countries and in some European countries, such as Spain and Italy. The lack of job opportunities for young people is an issue of concern, as it fuels social instability and drives many to seek opportunities abroad.
Analysing in particular the data on unemployment rates for 2023, in the youth group (15-24 years old) unemployment rates are particularly high in Libya (49.4%), Jordan (40.8%) and Tunisia (37.5%), but Palestine and Algeria also register high rates, at 36% and 30.8% respectively. In the Middle East, Israel represents a case apart for the area, recording a rate of 6%. In Europe, Spain (28.7 per cent) and Italy (22.7 per cent) have the highest youth unemployment rates, while Malta records 9.3 per cent.
Considering the overall population, Malta and Israel have the lowest unemployment rates in the entire Mediterranean area, at 3.1% and 3.4% respectively, reflecting relatively stable economies. By contrast, Middle Eastern and North African countries show high rates: unemployment in Libya reaches 18.7%, followed by Palestine (24.4%) and Jordan (17.9%). In the Balkans, Montenegro and North Macedonia have overall unemployment rates of 15.3% and 13.1%, while among the European countries bordering the Mediterranean, Spain has the highest value at 12.1%.
Figure 3 - Unemployment rates in ages 15-24 and 15 years and over. Year 2023 (%)
...
Sectoral distribution of employment
In the less industrialised countries, agriculture still accounts for an important share of employment, particularly in rural areas, while its incidence is declining in the more advanced countries of the region. The service sector, on the other hand, is booming, thanks mainly to tourism and trade, which play a central role in the Mediterranean economy.
The 2022 data reveal a varied employment distribution in the three sectors in the Mediterranean countries. In the agricultural sector, Albania (34.9 per cent) and Morocco (30.8 per cent) record the highest percentages of employment, indicating a strong dependence on agriculture. In contrast, more advanced nations such as France and Malta have minimal shares of agricultural employment, 2.6% and 0.8% respectively, reflecting a lower relevance of this sector in their labour markets.
As regards the industrial sector, Bosnia and Herzegovina and Slovenia stand out as having the highest levels, with 33.5% and 30.2% of total employment respectively, highlighting a significant manufacturing and industrial base. In Italy, industry employs 26.9% of the workforce, while in countries such as France and Jordan, industry plays a less significant role, with a share of less than 20%.
The service sector dominates in most European and Middle Eastern countries. Malta (82%), Israel (83.5%) and France (78.2%) record the highest percentages, indicators of advanced, service-oriented economies typical of high-income countries. In North African countries and the Balkans, the service sector is less dominant but still remains significant, as shown by the figures for Algeria (59.4%) and Tunisia (52.6%).
In conclusion, the European and Middle Eastern Mediterranean countries tend to focus more on services, while in the Balkan and North African countries agriculture and industry continue to play a significant role in the employment structure, reflecting the different economic and social characteristics of the region.
Figure 4 - Distribution of employment in sectors by macro-region. Year 2022 (% total employment)
...
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Metadata
Indicators
Definition
Percentage of the population ages 15-64 that is economically active: all people who supply labor for the production of goods and services during a specified period.
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with corresponding labour force survey data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
Percentage of the population ages 15-24 that is economically active: all people who supply labor for the production of goods and services during a specified period.
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
National data on labour force participation rates may not be comparable owing to differences in concepts and methodologies. The single most important factor affecting data comparability is the data source. Labour force data obtained from population censuses are often based on a restricted number of questions on the economic characteristics of individuals, with little possibility of probing. The resulting data, therefore, are generally not consistent with corresponding labour force survey data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Establishment censuses and surveys can – by their nature – only provide data on the employed population, leaving out the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who fall outside the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
Percentage of a country's population ages 15 years and over that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15 and older are generally considered the working-age population.
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
Percentage of a country's population ages 15-24 that is employed. Employment is defined as persons of working age who, during a short reference period, were engaged in any activity to produce goods or provide services for pay or profit, whether at work during the reference period (i.e. who worked in a job for at least one hour) or not at work due to temporary absence from a job, or to working-time arrangements. Ages 15-24 are generally considered the youth population.
Sources
ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. Nevertheless, despite their strength, labour force survey data may contain non-comparable elements in terms of scope and coverage, mainly because of differences in the inclusion or exclusion of certain geographic areas, and the incorporation or non-incorporation of military conscripts. Also, there are variations in national definitions of the labour force concept, particularly with respect to the statistical treatment of some specific groups, such as “contributing family workers” and “persons not employed, available for work but not looking for work”. Non-comparability may also arise from differences in the age limits used in measuring the labour force (formerly known as the economically active population). Some countries have adopted nonstandard upper-age limits for inclusion in the labour force, with a cut-off point of 65 or 70 years, which will affect broad comparisons, and especially comparisons of those at the higher age levels. Finally, differences in the dates to which the data refer, as well as the method of averaging over the year, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of the ILO modelled estimates of labour force participation rates included in ILOSTAT. Only household labour force survey and population census data that are representative of the whole country (with no geographic limitation) were used in the construction of the estimates. In countries with more than one survey source, only one type of source was used. If a labour force survey was available for the country, labour force participation rates derived from this source were chosen in favour of those derived from population censuses. imputed observations are not based on national data, are subject to high uncertainty and should not be used for country comparisons or rankings. This series is based on the 13th ICLS definitions.
Persons of working age engaged in the agricoltural sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The agriculture sector consists of activities in agriculture, hunting, forestry and fishing, in accordance with division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
Data presented by branch of economic activity is based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. A country may continue to use the previous version even after starting a new data series according to the most recent version. Although these different classification systems can have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
Persons of working age engaged in the industrial sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The industry sector consists of mining and quarrying, manufacturing, construction, and public utilities (electricity, gas, and water), in accordance with divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) or categories B-F (ISIC 4).
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
Data presented by branch of economic activity is based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. A country may continue to use the previous version even after starting a new data series according to the most recent version. Although these different classification systems can have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
Persons of working age engaged in the tertiary sector to produce goods or provide services for pay or profit, whether at work during the reference period or not at work due to temporary absence from a job, or to working-time arrangement. The services sector consists of wholesale and retail trade and restaurants and hotels; transport, storage, and communications; financing, insurance, real estate, and business services; and community, social, and personal services, in accordance with divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. This procedure produces accurate estimates of low variance, which is not surprising, given that the indicator is a very persistent variable. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
Data presented by branch of economic activity is based on the International Standard Industrial Classification of All Economic Activities (ISIC). Its main purpose is to provide a set of activity categories that can be utilized for the collection and reporting of statistics according to such activities. The original version of ISIC was adopted in 1948, and it has been revised four times since : in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of the ISIC Rev.3 was introduced in 2002 to account for substantial changes in many countries’ economic structure (ISIC Rev. 3.1). It is important to note that different versions of the ISIC can be used across countries, with countries moving to adopting the most recent version at different paces. A country may continue to use the previous version even after starting a new data series according to the most recent version. Although these different classification systems can have an impact on comparability at detailed levels of economic activity, changes from one ISIC to another should not have a significant impact on the information for the three broad sectors presented in ILOSTAT. A number of factors can limit the comparability of statistics on employment by economic activity between countries or over time. Comparability of employment statistics across countries is affected most significantly by variations in the definitions used for the employment figures. Differences may result from age coverage, such as the lower and upper age bounds for labour force activity. Estimates of employment are also likely to vary according to whether members of the armed forces are included. When the armed forces are included in the measure of employment they are usually allocated to the services sector. Therefore, in countries that do not include armed forces, the services sector tends to be understated in comparison with countries where they are included. Another area with scope for measurement differences has to do with the national treatment of particular groups of workers. The international definition of employment calls for inclusion of all persons who worked for at least one hour during the reference period. Workers could be in paid employment or in self-employment, including in less obvious forms of work, some of which are dealt with in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeship or non-market production. The majority of exceptions to coverage of all persons employed in a labour force survey have to do with slight national variations to the international recommendation applicable to the alternate employment statuses. For example, some countries measure persons employed in paid employment only and some countries measure “all persons engaged”, meaning paid employees plus working proprietors who receive some remuneration based on corporate shares. Other possible variations to the norms pertaining to measurement of total employment include hours limits (beyond one hour) placed on contributing family members before for inclusion in employment. Comparisons can also be problematic when the frequency of data collection varies. The range of information collection can run from one month to 12 months in a year. Given the fact that seasonality of various kinds is undoubtedly present in all countries, employment figures can vary for this reason alone. Also, changes in the level of employment can occur throughout the year, but this can be obscured when fewer observations are available.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
Share of the labor force that is without work but available for and seeking employment.
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labour market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programmes, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave. The overall unemployment rate for a country is a widely used measure of its unutilized labour supply. Unemployment rates by specific groups, defined by age, sex, occupation or industry, are also useful in identifying groups of workers and sectors most vulnerable to joblessness.
Presence in policy-oriented statistical systems
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
Share of the labor force ages 15-24 without work but available for and seeking employment.
Sources
a) ILO Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. Linear interpolation is used to fill in the missing data for countries for which such a procedure is possible. In all other cases, weighted multivariate estimation is carried out. Countries are divided into nine estimation groups, chosen on the combined basis of broad economic similarity and geographical proximity. Based on the data structure and the heterogeneity among the countries covered by the input data, the model was specified using panel data with country fixed effects. The regressions are weighted by the inverse of the likelihood of a labour force survey’s availability. The explanatory variables used include economic and demographic variables. To produce estimates for 2020, a cross-validation approach is used to select the model that minimizes prediction error in that specific year. b) Istat (for Italy) - Data are collected through the Labour Force Survey, a sample survey conducted by means of household interviews; the main objective is to estimate the aggregates that make up the labour supply: employed persons and jobseekers.
Notes
The unemployed comprise all persons of working age who were: a) without work during the reference period, i.e. were not in paid employment or self-employment; b) currently available for work, i.e. were available for paid employment or self-employment during the reference period; and c) seeking work, i.e. had taken specific steps in a specified recent period to seek paid employment or self-employment. Future starters, that is, persons who did not look for work but have a future labour market stake (made arrangements for a future job start) are also counted as unemployed, as are participants in skills training or retraining schemes within employment promotion programmes, who on that basis, were “not in employment”, not “currently available” and did not “seek employment” because they had a job offer to start within a short subsequent period generally not greater than three months. The unemployed also include persons “not in employment” who carried out activities to migrate abroad in order to work for pay or profit but who were still waiting for the opportunity to leave. The overall unemployment rate for a country is a widely used measure of its unutilized labour supply. Unemployment rates by specific groups, defined by age, sex, occupation or industry, are also useful in identifying groups of workers and sectors most vulnerable to joblessness.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Area 'Population and Social conditions'
