The labour market across the Mediterranean region is characterised by marked heterogeneity and persistent structural disparities between its different shores. Recent data point to significant challenges, particularly with regard to youth inclusion, labour market participation, and the quality of employment. While European countries tend to exhibit more dynamic and service-oriented economic structures, other contexts continue to be affected by structural fragilities, high unemployment, and substantial levels of inactivity. The Mediterranean therefore emerges as a region shaped by shared yet differentiated challenges, calling for integrated, evidence-based policy responses aimed at fostering inclusion and sustainable development.
The labour market is analysed through the main indicators of labour force participation, employment and unemployment, providing a comparative reading of employment dynamics across the Mediterranean. The analysis highlights persistent regional and sectoral disparities, as well as gaps in labour market inclusion, identifying the key challenges and policy priorities for more sustainable and equitable growth.
Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)
Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)
Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)
Employment to population ratio, 15+, total (%) (modeled ILO estimate)
Unemployment, total (% of total labor force) (modeled ILO estimate)
Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)
Employment in agriculture (% of total employment) (modeled ILO estimate)
Employment in industry (% of total employment) (modeled ILO estimate)
Employment in services (% of total employment) (modeled ILO estimate)
Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)
area_code
ordgeo
Countries
2024
2024
2024
2024
2024
2024
2023
2023
2023
2024
Portugal
35.4
77.8
27.9
54.8
6.5
21.2
3.0
25.0
72.1
7.9
A
1
Spain
33.1
74.6
24.1
50.8
11.4
27.0
3.6
19.9
76.5
9.9
A
2
France
42.7
73.8
35.6
51.4
7.4
16.6
2.5
19.2
78.2
10.5
A
3
Italy
24.7
66.6
19.7
46.6
6.5
20.3
3.6
26.6
69.8
12.7
A
4
Slovenia
35.7
76.7
32.4
56.4
3.7
9.4
4.0
32.2
63.8
7.2
A
5
Croatia
31.1
70.0
26.0
49.4
5.0
16.6
4.7
27.5
67.8
9.8
A
6
Greece
25.2
69.3
19.0
46.4
10.0
24.7
11.5
15.9
72.6
11.4
A
7
Malta
54.9
80.9
50.6
62.0
3.1
7.8
1.1
17.6
81.3
8.2
A
8
Cyprus
43.9
79.9
37.0
64.0
4.9
15.6
2.4
16.3
81.4
12.0
A
9
Serbia
32.8
72.4
25.4
53.0
7.2
22.7
19.2
28.2
52.6
12.4
B
10
Kosovo
..
..
..
..
10.5
21.3
..
..
..
..
B
11
Bosnia and Herzegovina
25.9
62.9
18.8
44.0
10.0
27.3
17.6
30.6
51.8
16.0
B
12
Montenegro
28.7
59.8
21.3
41.2
15.0
25.9
6.0
16.1
77.8
20.0
B
13
North Macedonia
28.2
65.9
19.7
45.3
12.3
30.3
9.3
30.2
60.5
19.1
B
14
Albania
33.6
72.0
25.2
54.5
11.5
25.1
34.9
21.2
43.9
23.6
B
15
Turkiye
46.1
60.0
38.9
49.2
8.8
15.6
14.6
27.6
57.8
22.5
C
16
Syrian Arab Republic
25.2
39.7
17.3
33.0
13.3
31.5
15.0
21.8
63.1
38.8
C
17
Lebanon
35.7
50.7
27.2
40.2
11.5
23.7
3.4
20.3
76.2
25.0
C
18
Jordan
23.6
40.9
13.8
31.5
18.0
41.7
3.2
18.3
78.4
31.6
C
19
Israel
46.7
73.8
43.9
63.0
3.6
6.1
0.8
15.6
83.6
15.1
C
20
West Bank and Gaza
30.8
47.3
19.7
34.0
31.4
36.1
6.2
32.6
61.2
28.2
C
21
Egypt, Arab Rep.
22.1
45.3
18.0
39.5
7.2
18.7
18.7
28.6
52.7
26.9
D
22
Libya
17.4
51.6
8.8
39.3
18.2
49.5
8.7
23.2
68.1
29.3
D
23
Tunisia
23.4
51.2
14.0
38.2
15.0
40.0
12.8
33.3
53.8
22.7
D
24
Algeria
22.8
44.6
16.0
36.1
11.6
29.8
9.3
30.8
59.9
20.1
D
25
Morocco
26.8
47.7
20.8
40.1
9.0
22.1
29.6
24.1
46.3
32.9
D
26
Labor force participation rate for ages 15-24, total (%) (modeled ILO estimate)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Labor force participation rate, total (% of total population ages 15-64) (modeled ILO estimate)
KosovoNo data available
LebanonLatest available data: 2023
West Bank and GazaLatest available data: 2022
Employment to population ratio, ages 15-24, total (%) (modeled ILO estimate)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Employment to population ratio, 15+, total (%) (modeled ILO estimate)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Unemployment, total (% of total labor force) (modeled ILO estimate)
KosovoLatest available data: 2023
Unemployment, youth total (% of total labor force ages 15-24) (modeled ILO estimate)
KosovoLatest available data: 2022
West Bank and GazaLatest available data: 2022
Employment in agriculture (% of total employment) (modeled ILO estimate)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Employment in industry (% of total employment) (modeled ILO estimate)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Employment in services (% of total employment) (modeled ILO estimate)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Share of youth not in education, employment or training, total (% of youth population) (modeled ILO estimate)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Some highlighted topics
An analysis of the most recent labour market data across Mediterranean countries highlights several recurring and critical issues. First, a pronounced generational divide persists: young people (aged 15–24) consistently record lower employment rates and higher unemployment levels, with particularly severe conditions in North Africa and the Middle East, where youth unemployment exceeds 40% in countries such as Tunisia, Libya and Jordan.
Second, marked structural heterogeneity between the northern and southern shores of the Mediterranean remains evident. While EU countries and Israel display high labour force participation and a strong concentration of employment in the services sector, countries in the southern Mediterranean and the Balkans continue to show a relatively higher share of employment in agriculture and industry, indicating an economic transition that is still incomplete.
Finally, low levels of labour market participation persist in several MENA countries, often reflecting structural barriers related to gender, education and institutional frameworks. Taken together, these patterns point to shared yet differentiated challenges across the region, calling for targeted policies to promote youth employment, socio-economic inclusion and the transformation of productive models.
Labour force participation and employment rates
The updated 2024 data on labour force participation rates across the 26 Mediterranean countries reveal a highly heterogeneous picture, particularly for the youth population (aged 15–24). Significant disparities also emerge among the working-age population (15–64), reflecting persistent structural differences across the sub-regions of the basin.
With regard to youth labour force participation rates (15–24), the highest levels are recorded in Malta (54.9%), Israel (46.7%), Türkiye (46.1%) and Cyprus (43.9%), indicating a more robust integration of young people into the labour market. France (42.7%) also stands above the Mediterranean average. At the opposite end of the spectrum, Libya (17.4%), Egypt (22.1%), Tunisia (23.4%) and Jordan (23.6%) display particularly low levels, pointing to significant structural barriers to labour market access for younger generations, linked to educational, gender-related and institutional factors.
A similar pattern emerges when considering labour force participation rates for the population aged 15–64. The highest levels are concentrated in Mediterranean European countries, notably Malta (80.9%), Cyprus (79.9%), Portugal (77.8%) and Slovenia (76.7%), followed by Israel and France (both at 73.8%). At the lower end of the distribution are mainly North African and Middle Eastern countries, such as Syria (39.7%), Algeria (44.6%), Egypt (45.3%) and Morocco (47.7%), which are characterised by substantially lower levels of labour market participation.
Overall, labour force participation rate data highlight a clear structural divide between the northern and southern shores of the Mediterranean. While European countries and Israel exhibit high and relatively stable participation levels, labour market inclusion in North African and Middle Eastern countries—particularly among young people—remains weak, with large shares of inactive population. This pattern underscores the need for integrated policies aimed at strengthening school-to-work transitions, increasing female labour force participation, and activating NEETs (Not in Education, Employment or Training).
Figure 1 - Labour force participation rate. Years 2024 (%)
...
In 2024, employment rates across the 27 countries of the Mediterranean region continue to reveal a pronounced divide between young people (aged 15–24) and the adult population (aged 15 and over), alongside substantial differences across Mediterranean Europe, the Western Balkans, the Middle East and North Africa.
Within Mediterranean Europe, Malta and Cyprus stand out for recording the highest employment levels both among young people (50.6% and 37%, respectively) and in the overall population (61.97% and 63.95%). France also displays relatively strong youth labour market integration (35.6%). By contrast, Italy (19.7%) and Greece (18.9%) remain at significantly lower levels, underscoring persistent structural weaknesses in the labour market integration of younger cohorts.
In the Middle East, Israel emerges as a positive outlier, with a youth employment rate of 43.9% and an overall employment rate of 63%. In contrast, youth labour market integration in other countries of the region remains highly constrained: Jordan (13.8%), Syria (17.3%) and Lebanon (27.2%) record very low employment levels, mirrored by weak outcomes for the total population.
The situation is even more critical in North Africa. Libya (8.8%) and Tunisia (14%) report the lowest youth employment rates, while Algeria (16%) and Morocco (20.8%) fail to reach 40% in overall employment. These figures point to a structural marginalisation of young people and a limited capacity of economic systems to generate sufficient employment opportunities.
Overall, the 2024 evidence confirms that youth inclusion remains the central challenge of Mediterranean labour markets, particularly in the Middle East and North Africa. Beyond job creation, the data highlight the need for targeted policies addressing education, vocational training and school-to-work transitions in order to achieve a durable reduction in the generational divide.
Figure 2 - Employment to population ratio. Year 2024 (%)
...
Unemployment rate
The 2024 unemployment data for the Mediterranean countries reveal substantial disparities both across regions and between age groups, confirming the pronounced heterogeneity of labour markets in the area. Overall unemployment rates (aged 15 and over) vary widely. Relatively low levels are observed in Mediterranean European countries such as Malta (3.1%), Israel (3.6%), Slovenia (3.7%) and Cyprus (4.9%). By contrast, Montenegro and Tunisia (both at 15%), and particularly Libya (18.2%), record markedly higher rates, signalling persistent structural fragilities. Within Mediterranean EU countries, Spain (11.4%), Greece (10%) and France (7.4%) stand out among those facing the greatest employment challenges.
The situation deteriorates sharply when youth unemployment (aged 15–24) is taken into account, which emerges as the most critical dimension of Mediterranean labour markets. In several countries, youth unemployment reaches extremely high levels: Libya (49.5%), Tunisia (40.1%), Jordan (41.7%) and Syria (31.5%) point to a genuine generational emergency, with a large share of young people excluded from employment. Youth unemployment also remains elevated in the Western Balkans, with significant levels in North Macedonia (30.3%), Bosnia and Herzegovina (27.3%) and Montenegro (25.9%). At the opposite end of the distribution, Israel (6.1%), Malta (7.8%) and Slovenia (9.4%) record the lowest rates, reflecting more effective school-to-work transitions and youth labour market integration mechanisms.
Overall, the Mediterranean region appears highly polarised. Labour markets in Mediterranean Europe and Israel display a greater capacity to absorb labour supply, whereas in many Middle Eastern and North African countries—and, to a lesser extent, in the Balkans—youth unemployment constitutes a structural constraint on economic and social development. These findings reinforce the urgency of targeted active labour market policies focused on education and training, school-to-work transitions, and inclusive economic growth, with particular attention to younger generations.
Figure 3 - Unemployment rates in ages 15-24 and 15 years and over. Year 2024 (%)
...
Sectoral distribution of employment
In 2023, the sectoral distribution of employment across the 27 countries of the Mediterranean region confirms the pronounced structural heterogeneity of labour markets in the area. The services sector clearly dominates employment across the region, particularly in Mediterranean European countries and Israel, where it accounts for well over 75% of total employment. Especially high shares are observed in Israel (83.6%), Cyprus (81.4%), Malta (81.3%), France (78.2%), Spain (76.5%) and Jordan (78.4%), reflecting an advanced process of economic tertiarisation.
By contrast, industry plays a relatively more prominent role in the Western Balkans and in selected North African countries. Slovenia (32.2%), Tunisia (33.3%) and Algeria (30.8%) record the highest industrial employment shares, pointing to productive structures that remain closely linked to manufacturing and more traditional industrial activities.
Although agriculture has become marginal in the more advanced economies—accounting for less than 5% of total employment in Israel, France, Spain, Portugal and Italy—it continues to represent a key pillar of employment in several lower-income countries. In Albania (34.9%), Morocco (29.6%), Egypt (18.7%), Bosnia and Herzegovina (17.6%) and Serbia (19.2%), the primary sector still absorbs a substantial share of the labour force, indicating an incomplete process of structural transformation.
Overall, the sectoral composition of employment reveals a clear geographical and development gradient across the Mediterranean. EU countries and Israel exhibit a strong specialisation in services, while more balanced employment structures—or a continued reliance on agriculture and industry—persist in the southern and eastern Mediterranean. This asymmetry underscores the need for differentiated policy strategies aimed at economic diversification, productive upgrading and labour market inclusion, particularly in contexts where traditional sectors remain dominant.
Figure 4 - Distribution of employment in sectors by macro-region. Year 2023 (% total employment)
...
Young people not in education, employment or training (NEETs)
The indicator of young people not in education, employment or training (NEET), based on modelled estimates produced by the International Labour Organization (ILO), provides a crucial lens through which to assess the structural fragilities and socio-economic asymmetries of the Mediterranean region. Its evolution over time captures not only the effects of cyclical economic, financial and health-related shocks, but, more fundamentally, the capacity of education systems and labour markets to support younger generations in their transition to adulthood. In a context marked by deep territorial, demographic and institutional divides, the NEET phenomenon assumes a systemic relevance, emerging as an advanced indicator of social exclusion, development sustainability and potential future instability, with markedly differentiated dynamics across the northern, southern and eastern shores of the basin.
In 2024, the indicator confirms a pronounced territorial polarisation across the Mediterranean, reflecting persistent structural inequalities in education systems, labour markets and opportunities for youth inclusion.
In Mediterranean Europe, NEET levels remain relatively contained but heterogeneous. Portugal and Slovenia record values below 8%, while Spain and France stand at around 10%. Italy displays one of the highest levels within the Mediterranean EU area (approximately 12.7%), pointing to enduring weaknesses in school-to-work transitions and to labour market segmentation that disproportionately affects young people.
In the Western Balkans, NEET rates are on average higher than in Mediterranean EU countries, reflecting more fragile labour markets and a limited capacity to absorb youth employment, albeit with notable cross-country variation. In this context, youth emigration plays a significant role: in some cases, relatively stable or declining NEET levels may reflect migratory selection processes rather than genuine improvements in domestic labour market integration.
In North Africa (Western MENA), the most critical levels are observed across the entire Mediterranean basin. Tunisia and Algeria exceed 20%, while Morocco reaches extremely high levels (above 30%), pointing to a structural condition of youth exclusion that combines unemployment, inactivity and a pronounced mismatch between education and labour demand. In these contexts, rising educational attainment has not translated into adequate employment absorption, fuelling over-education and frustrated expectations and contributing to the expansion of the NEET population.
In the Middle East, the NEET phenomenon is characterised by widespread criticalities and strong heterogeneity. The highest rates are recorded in Syria (38.8%) and Jordan (31.6%), reflecting the combined effects of prolonged instability, economic crises and the limited capacity of productive systems to generate qualified employment. Lebanon (25.0%) and Türkiye (22.5%) also register elevated levels, partly attributable to a persistent mismatch between acquired skills and labour demand. Israel, while recording more moderate values (15.1%), nevertheless exhibits a non-negligible share of young people facing exclusion.
Overall, the analysis confirms that NEETs in the Mediterranean represent not merely a cyclical phenomenon, but a structural signal of generational vulnerability. In several contexts—particularly in the Balkans and the southern Mediterranean—the dynamics of the indicator are shaped both by migration processes and by the misalignment between education systems and models of productive development. This reinforces the interpretation of NEETs as a synthetic measure of the difficulties faced by younger generations in integrating into labour markets and society, and as a key indicator for assessing the medium- to long-term economic and social sustainability of the region.
Figure 5 - Share of youth not in education, employment or training, total. Year 2024 (% of youth population)
...
NEETs in the Mediterranean between 2005 and 2024: a long-term perspective
A diachronic analysis of the NEET indicator in the Mediterranean reveals divergent trajectories and persistent territorial asymmetries, confirming the structural nature of youth exclusion in several parts of the basin. Over the long term, changes in the indicator cannot be interpreted simply as responses to cyclical economic fluctuations; rather, they reflect different institutional regimes governing labour markets, education systems and school-to-work transition mechanisms.
Over the period 2005–2024, Mediterranean Europe displays an overall cyclical but broadly stable pattern. The global financial crisis of 2008–2013 represents a clear turning point, with a sharp increase in NEET rates—particularly in Italy, Spain and Greece—linked to the collapse of youth employment and the disruption of labour market entry pathways. From 2015 onwards, against a backdrop of gradual economic recovery and a strengthening of active labour market policies, the indicator shows a progressive decline, only temporarily interrupted by the COVID-19 pandemic in 2020. By 2024, NEET levels are lower than those observed on the southern and eastern shores of the Mediterranean, but remain higher than in north-central Europe, signalling persistent structural weaknesses in school-to-work transitions.
In the Western Balkans, NEET rates remain consistently high throughout the entire period. Unlike Mediterranean Europe, major international crises do not generate sharp breaks, but rather reinforce an already structural condition of labour market fragility. The modest reductions observed in some countries after 2015 are slow and uneven, and are accompanied by strong youth emigration flows, which contribute to making the NEET indicator only partially responsive to domestic economic cycles. In 2024, levels remain medium to high, reflecting weak labour markets and vocational education and training systems that are poorly integrated with labour demand.
In North Africa (Western MENA), the indicator displays the greatest persistence over time. Between 2005 and 2024, NEET levels remain structurally high, with only marginal declines in a few countries. The political and economic disruptions following 2011 mark a significant breakpoint, which does not translate into a reduction of the indicator but rather into its stabilisation at elevated levels. In this context, NEET rates capture not only the effects of repeated crises, but above all the limited capacity of productive systems to absorb a growing youth labour force, despite rising levels of educational attainment.
The Middle East exhibits more volatile dynamics. In countries affected by conflict or prolonged instability, such as Syria, the time series shows a sharp and lasting increase in NEET rates, signalling a regime shift in labour market functioning. In relatively more stable contexts, such as Israel and Türkiye, trends are more contained; nevertheless, NEET levels remain high by international standards, pointing to a form of structural generational vulnerability.
Figure 6 - Share of youth not in education, employment or training, total. Year 2005-2024 (% of youth population)
...
Overall, the long-term evidence confirms that the NEET indicator in the Mediterranean is structural in nature and characterised by slow-moving dynamics, responding more to changes in development models, institutional frameworks and mechanisms of inclusion than to short-term cyclical fluctuations. The data reveal a threefold divide: between the northern and southern shores of the Mediterranean; between relatively stable contexts and regions affected by prolonged crises; and between settings where youth exclusion tends to decline cyclically (Mediterranean Europe) and those where it remains structurally persistent (North Africa and the Middle East).
Looking ahead, the NEET indicator emerges as a key analytical tool for assessing the social and demographic sustainability of the Mediterranean region and for guiding integrated policies on education, employment and youth inclusion, tailored to the specific characteristics of different territorial contexts.
Quality of employment and economic inclusion
Alongside traditional quantitative indicators—employment, unemployment and inactivity—an assessment of labour markets in the Mediterranean requires closer attention to the quality of employment. In contexts characterised by strong structural dualisms, being employed does not necessarily imply economic inclusion, income stability or access to social protection. This issue is particularly pronounced in the economies of North Africa and the Middle East, where a significant share of employment is concentrated in informal or semi-formal segments, often lacking contractual safeguards, job continuity and access to welfare systems. In such settings, low unemployment rates may coexist with high levels of employment vulnerability, unpaid family work and underemployment, especially among young people and women.
Even in Mediterranean European countries, where employment is largely formal, job quality remains a critical concern. Labour market segmentation between insiders and outsiders, the widespread use of temporary contracts and the prevalence of involuntary part-time work contribute to fragmented employment trajectories, particularly for younger generations. In this context, the presence of working poor and low-paid workers highlights that access to employment does not automatically translate into adequate economic well-being or prospects for social mobility. The combination of contractual precariousness, low wages and limited opportunities for career progression generates persistent forms of insecurity, reflected in elevated NEET rates, youth emigration and delayed transitions to economic autonomy.
Overall, the analysis suggests that the fragilities of Mediterranean labour markets extend beyond a simple shortage of jobs and increasingly concern the structure and quality of available employment. Integrating quantitative indicators with a qualitative perspective allows for a more nuanced understanding of asymmetries across the Mediterranean and supports an interpretation of employment not merely as a statistical outcome, but as a central component of economic inclusion, social cohesion and long-term development sustainability.
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Metadata
Indicators
Definition
Percentage of the population aged 15-24 who are economically active: all people who offer labour on the market for the production of goods and services in a given period.
Sources
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting the comparability of data is the source of the data itself. Labour force data obtained from population censuses are often based on a limited number of questions about individuals' economic characteristics, with little scope for sampling. The resulting data are therefore generally not consistent with the 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. Censuses and surveys of local units can, by their nature, only provide data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small production units or in the informal economy who do not fall within the scope of the survey or census. For international comparisons of labour force data, the most comprehensive source is undoubtedly labour force surveys. However, despite their strength, labour force survey data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and the inclusion or exclusion of conscripted military personnel. In addition, there are variations in national definitions of the labour force concept, particularly with regard to the statistical treatment of certain specific groups, such as 'contributing family workers' and 'unemployed persons available for work but not seeking employment'. Non-comparability may also arise from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the labour force, with a cut-off point at 65 or 70 years, which affects broad comparisons, particularly those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, 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's modelled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the entire country (without geographical limitations) 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. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the 13th ICLS.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Percentage of the population aged 15-64 economically active: all people who offer labour on the market for the production of goods and services in a given period.
Sources
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
National data on labour force participation rates may not be comparable due to differences in concepts and methodologies. The most important factor affecting data comparability is the source of the data. Labour force data obtained from population censuses are often based on a limited number of questions about the economic characteristics of individuals, with little chance of survey. The resulting data, therefore, are generally not consistent with the corresponding LFS data and may vary considerably from one country to another, depending on the number and type of questions included in the census. Censuses and surveys of establishments can, by their nature, provide only data on the employed population, excluding the unemployed and, in many countries, also excluding workers engaged in small establishments or in the informal economy who are not covered by the survey or census.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Persons of working age engaged in the agricultural sector in any activity of production of goods or provision of services for consideration or profit, whether working during the reference period or not working due to a temporary absence from work or an agreement on working time. The agricultural sector consists of agriculture, hunting, forestry and fishing, according to division 1 (ISIC 2) or categories A-B (ISIC 3) or category A (ISIC 4).
Sources
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
The data presented by economic activity branch are 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 used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. 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 may 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 may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Persons of working age engaged in the industrial sector in any activity of production of goods or provision of services for consideration or profit, whether they are working during the reference period or not working because of a temporary absence from work or an agreement on working time. The industrial sector includes mineral extraction, manufacturing, construction and utilities (electricity, gas and water), according to divisions 2-5 (ISIC 2) or categories C-F (ISIC 3) orcategories B-F (ISIC 4).
Sources
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
The data presented by economic activity branch are 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 used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may 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 may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Persons of working age engaged in the Services sector in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reference period, or not at work due to a temporary absence from a workplace or an agreement on working time. The sectorof services includes wholesale and retail trade, restaurants and hotels, transport, warehousing and communications, financing, insurance, real estate and business services, as well as social and personal services, according to divisions 6-9 (ISIC 2) or categories G-Q (ISIC 3) or categories G-U (ISIC 4).
Sources
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
The data presented by economic activity branch are 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 used for the collection and reporting of statistics. The original version of ISIC was adopted in 1948 and has since been revised four times: in 1968 (ISIC Rev.2), in 1990 (ISIC Rev.3) and in 2008 (ISIC Rev.4). An updated version of ISIC Rev. 3 was introduced in 2002 to take account of substantial changes in the economic structure of many countries (ISIC Rev. 3.1). It is important to note that countries may use different versions of ISIC, and that countries move to the adoption of the latest version at different times. A country may continue to use the previous version even after starting a new data series according to the latest version. Although these different classification systems may 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 may limit the comparability of employment statistics by economic activity between countries or over time. The comparability of employment statistics between countries is significantly affected by variations in the definitions used for employment data. Differences may arise from age coverage, such as lower and upper age limits for labour force activity. Employment estimates may also vary depending on whether members of the armed forces are included. When the armed forces are included in the measurement of employment, they are usually assigned to the service sector. Therefore, in countries that do not include the armed forces, the service sector tends to be underestimated compared to countries where they are included. Another area of measurement difference concerns the national treatment of particular groups of workers. The international definition of employment includes all persons who worked for at least one hour during the reference period. Workers may be paid or self-employed, even in less obvious forms of work, some of which are discussed in detail in the resolution adopted by the 19th ICLS, such as unpaid family work, apprenticeships or non-market production. Most exceptions to the coverage of all employed persons in a labour force survey have to do with minor national variations from the international recommendation applicable to alternative employment statuses. For example, some countries measure only paid employees, while others measure 'all employed persons', i.e. paid workers and business owners who receive remuneration based on company shares. Other possible variations to the rules for measuring total employment include hour limits (over one hour) imposed on family members who contribute before being included in employment. Comparisons can also be problematic when the frequency of data collection varies. The interval for collecting information can range from one month to 12 months in a year. Since seasonality of various kinds is undoubtedly present in all countries, employment data may vary for this reason alone. In addition, changes in the level of employment may occur during the year, but this may be obscured when fewer observations are available.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Percentage of a country's employed population in the 15-24 age group. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age between 15 and 24 is generally considered the reference for the young population.
Sources
a) International Labour Organization Modelled Estimates (ILOEST)
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for the countries for which this procedure is possible; This produces accurate and low-variance estimates, which is not surprising, given that such a indicator is a very persistent variable. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Share of the workforce that does not have a job but is available and looking for a job.
Sources
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
The unemployed include all persons of working age who: (a) were not in employment during the reference period, i.e. were not in paid employment or self-employment; (b) were currently available for employment, i.e. were available for paid employment or self-employment during the reference period; (c) were looking for a job, i.e. had taken specific actions in a certain recent period to seek paid employment or self-employment. Future start-ups, i.e. people who have not looked for work but have a future interest in the labour market (have made arrangements for a future start of work), as well as participants in vocational training or retraining programmes as part of employment promotion programmes, who were not "employed" on this basis, are also counted as unemployed. they were not "currently available" and did not "look for work" because they had a job offer to start within a short subsequent period, generally no longer than three months. The unemployed also include "unemployed" people who have migrated abroad to work for pay or profit, but who were still waiting for the opportunity to leave. A country's overall unemployment rate is a widely used measure of unused labor supply. Unemployment rates for specific groups, defined by age, gender, occupation or industry, are also useful for identifying the groups of workers and sectors most vulnerable to unemployment.
Presence in policy-oriented statistical systems
SDG Goal 8, indicator 8.5.2; ENP-South Eurostat Data Browser: Population and Social Conditions Area
Share of the labour force aged 15-24 who are unemployed but available and looking for work.
Sources
a) International Labour Organization Modelled Estimates (ILOEST); b) Istat for Italy
Methodology
a) ILO Modelled Estimates (ILOEST) - Labour market indicators are estimated using a set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
The unemployed include all persons of working age who: (a) were not in employment during the reference period, i.e. were not in paid employment or self-employment; (b) were currently available for employment, i.e. were available for paid employment or self-employment during the reference period; (c) were looking for a job, i.e. had taken specific actions in a certain recent period to seek paid employment or self-employment. Future start-ups, i.e. people who have not looked for work but have a future interest in the labour market (have made arrangements for a future start of work), as well as participants in vocational training or retraining programmes as part of employment promotion programmes, who were not "employed" on this basis, are also counted as unemployed. they were not "currently available" and did not "look for work" because they had a job offer to start within a short subsequent period, generally no longer than three months. The unemployed also include "unemployed" people who have migrated abroad to work for pay or profit, but who were still waiting for the opportunity to leave. A country's overall unemployment rate is a widely used measure of unused labor supply. Unemployment rates for specific groups, defined by age, gender, occupation or industry, are also useful for identifying the groups of workers and sectors most vulnerable to unemployment.
Presence in policy-oriented statistical systems
ENP-South Eurostat Data Browser: Population and Social Conditions Area
Percentage of a country's employed population in the age group 15 years and over. Employment is defined as persons of working age who, during a short reporting period, have been engaged in any activity of producing goods or providing services for remuneration or profit, whether they were at work during the reporting period (i.e. worked at a workplace for at least one hour) or were not at work due to temporary absence from a post or agreements on working time. The age of 15 and above is generally considered the reference for 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 set of models that establish statistical relationships between the observed labour market indicators and the explanatory variables. To fill in the missing data, linear interpolation is used for countries for which this procedure is possible. In all other cases, a weighted multivariate estimate is made. The countries are divided into nine esteem groups, chosen on the basis of broad economic similarity and geographical proximity. Given the structure of the data and the heterogeneity between the countries covered by the input data, the model was specified using panel data with fixed effects per country. Regressions are weighted by the inverse of the probability of availability of a labour force survey. The explanatory variables used include economic and demographic variables. To produce the estimates for 2020, a cross-validation approach is used to select the model that minimizes the forecast error in that specific year. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers. b) Istat (for Italy) - The data are collected with the Labour Force Survey, a sample survey conducted through interviews with households; The main objective is to estimate the aggregates that make up the labour supply: employed and jobseekers.
Notes
For international comparisons of labour force data, the most comprehensive source is undoubtedly the LFS. However, despite their strength, LFS data may contain elements that are not comparable in terms of scope and coverage, mainly due to differences in the inclusion or exclusion of certain geographical areas and whether or not conscripts are included. In addition, there are variations in national definitions of the concept of labour force, in particular with regard to the statistical treatment of certain specific groups, such as 'family contributors' and 'persons not in employment, available for work but not seeking employment'. Non-comparability may also result from differences in the age limits used to measure the labour force (formerly known as the economically active population). Some countries have adopted non-standard upper age limits for inclusion in the workforce, with a cut-off point at 65 or 70, which affects broad comparisons, and in particular those of higher age levels. Finally, differences in the dates to which the data refer, as well as the method of calculating the annual average, may contribute to the non-comparability of the resulting statistics. To a large extent, these comparability issues have been addressed in the construction of ILO-modeled estimates of labour force participation rates included in ILOSTAT. Only data from household labour force surveys and population censuses representative of the whole country (without geographical limitations) were used to construct 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 from this source were chosen in favour of those from population censuses. The imputed observations are not based on national data, are subject to high uncertainty and should not be used for comparisons or rankings between countries. This series is based on the definitions of the thirteenth ICLS.
Young people not in employment, employment or training, total (% of youth population) (ILO modelled estimate)
Sources
World Bank elaborations on International Labour Organization (ILO) data
Methodology
The NEET rate = (young people – young people in employment – young people not in employment but in education or training) / young people x 100. It is important to note that young people who are simultaneously employed and in education or training should not be counted twice when subtracted from the total number of young people. The formula can also be expressed as: NEET rate = [(Young unemployed + Young people out of the labour force) – (Young unemployed people in education or training + Young people out of the labour force in education or training)] / Young people x 100. Young people who are not in education are those who are not enrolled in school or in a formal training programme (e.g. vocational training). For the purposes of this indicator, young people are defined as all people aged between 15 and 24 (inclusive).
