The analysis of gender gaps in the Mediterranean labour market shows significant disparities in participation, employment and unemployment rates between men and women. Strong sectoral divisions are observed, with men predominating in industry and agriculture and women in services. Women are often under-represented in leadership and entrepreneurial roles, hampered by socio-cultural barriers. The promotion of inclusive policies is essential to improve gender equality in the region.
Labour market analysis explored through gender inequalities, participation and employment rates, cultural barriers and sectoral disparities, offering a detailed geographical overview, to identify solutions aimed at sustainable labour equality.
Labor force participation rate for ages 15-24, female (%)
Labor force participation rate for ages 15-24, male (%)
Labor force participation rate, female (% of female population ages 15-64)
Labor force participation rate, male (% of male population ages 15-64)
Employment to population ratio, ages 15-24, female (%)
Employment to population ratio, ages 15-24, male (%)
Employment to population ratio, 15+, female (%)
Employment to population ratio, 15+, male (%)
Contributing family workers, female (% of female employment)
Contributing family workers, male (% of male employment)
Self-employed, female (% of female employment)
Self-employed, male (% of male employment)
Wage and salaried workers, female (% of female employment)
Wage and salaried workers, male (% of male employment)
Employers, female (% of female employment)
Employers, male (% of male employment)
Unemployment, female (% of female labor force)
Unemployment, male (% of male labor force)
Employment in agriculture, female (% of female employment)
Employment in agriculture, male (% of male employment)
Employment in industry, female (% of female employment)
Employment in industry, male (% of male employment)
Employment in services, female (% of female employment)
Employment in services, male (% of male employment)
area_code
ordgeo
Countries
2023
2023
2022
2022
2023
2023
2023
2023
2022
2022
2022
2022
2022
2022
2022
2022
2023
2023
2022
2022
2022
2022
2022
2022
Portugal
33.6
37.2
74.4
78.5
27.3
30.3
51.5
59.8
0.6
0.7
11.5
18.7
88.5
81.3
2.9
7.3
6.9
6.1
3.2
6.8
15.5
32.3
81.3
60.8
A
1
Spain
30.9
34.8
69.9
78.3
21.7
25.3
45.3
56.2
0.4
0.3
11.7
18.4
88.3
81.6
3.3
6.1
13.9
10.6
1.9
5.4
9.4
29.1
88.7
65.4
A
2
France
40.9
44.6
71.8
76.9
35.1
37.0
49.2
55.7
0.4
0.3
10.0
16.0
90.0
84.0
3.0
7.0
7.2
7.5
1.6
3.6
9.8
28.3
88.6
68.1
A
3
Italy
21.6
30.8
56.4
74.6
16.2
24.3
37.9
54.8
1.2
0.7
16.1
25.5
83.9
74.5
3.8
8.0
8.8
6.8
2.3
4.8
14.0
36.2
83.6
58.9
A
4
Slovenia
32.2
40.2
74.0
79.0
29.6
36.5
51.9
61.0
2.3
1.7
9.8
17.5
90.2
82.5
1.7
4.6
3.7
3.6
3.9
4.7
17.1
41.2
79.0
54.1
A
5
Croatia
24.2
36.4
65.4
73.1
18.5
30.3
44.5
54.5
1.4
1.0
9.4
16.9
90.6
83.1
3.5
7.2
6.6
5.6
4.0
7.5
15.7
39.1
80.3
53.4
A
6
Greece
22.9
26.6
61.2
76.3
16.1
20.8
38.8
54.2
3.7
1.8
31.0
41.7
69.0
58.3
4.8
8.8
14.2
8.4
9.8
12.2
7.8
21.3
82.4
66.5
A
7
Malta
53.1
55.2
72.7
85.9
50.9
48.3
55.5
70.9
0.1
0.0
9.0
19.4
91.0
80.6
2.0
5.6
3.0
3.2
0.3
1.2
7.6
24.0
92.1
74.8
A
8
Cyprus
42.0
45.2
72.1
81.3
35.8
36.5
56.3
66.0
0.5
0.8
9.1
12.4
90.9
87.6
1.2
2.2
5.9
6.0
0.9
3.6
7.2
25.4
91.9
71.0
A
9
Serbia
24.8
38.5
66.9
79.6
20.3
29.4
47.0
62.5
8.4
2.9
23.4
29.9
76.6
70.1
2.0
4.1
9.0
8.4
11.5
15.3
19.3
37.3
69.2
47.4
B
10
Kosovo
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
..
15.9
10.5
..
..
..
..
..
..
B
11
Bosnia and Herzegovina
20.4
36.8
50.4
72.5
13.0
27.1
34.7
53.9
5.0
1.3
25.7
24.2
74.3
75.8
4.2
7.0
12.5
9.0
19.3
15.3
17.7
43.9
63.0
40.8
B
12
Montenegro
26.3
36.2
61.5
74.9
19.6
26.0
42.5
54.7
3.2
1.6
15.3
25.9
84.7
74.1
2.6
5.1
14.7
15.7
5.8
8.3
7.5
27.8
86.7
63.9
B
13
North Macedonia
20.4
35.6
52.2
75.0
14.0
26.7
37.3
54.3
4.8
1.9
12.3
19.5
87.7
80.5
2.4
5.2
11.7
14.0
8.0
10.9
25.5
33.8
66.5
55.3
B
14
Albania
27.5
40.0
63.4
79.3
20.4
28.9
47.0
60.1
27.1
15.6
50.4
54.7
49.6
45.3
1.2
4.5
11.4
11.7
39.8
31.0
16.8
25.1
43.4
43.9
B
15
Turkiye
31.3
56.1
39.4
76.6
23.9
47.8
30.9
65.7
19.4
4.2
30.3
29.4
69.7
70.6
1.9
5.8
12.1
8.1
21.8
14.2
17.6
32.5
60.6
53.2
C
16
Syrian Arab Republic
7.9
39.1
14.9
65.7
3.7
27.6
10.6
56.7
2.2
0.7
8.7
41.9
91.3
58.1
0.4
1.5
25.3
10.9
10.0
16.5
6.0
25.8
84.0
57.7
C
17
Lebanon
25.7
44.6
31.3
71.1
19.9
33.6
23.4
58.8
1.7
0.5
17.1
35.4
82.9
64.6
3.8
10.8
14.7
10.1
1.4
4.4
6.4
26.6
92.2
69.0
C
18
Jordan
10.1
41.5
14.5
65.8
5.0
25.1
10.4
51.7
0.2
0.4
3.6
13.1
96.4
86.9
0.9
3.5
26.6
16.2
0.6
3.6
7.2
20.2
92.2
76.1
C
19
Israel
47.3
47.9
71.2
75.5
44.6
45.1
58.9
66.4
0.0
0.0
8.6
14.0
91.4
86.0
1.3
4.7
3.3
3.4
0.4
1.1
7.4
23.4
92.2
75.5
C
20
West Bank and Gaza
10.8
50.7
..
..
4.7
34.7
11.3
57.4
7.7
2.9
22.9
24.0
77.1
76.0
2.0
6.1
40.0
20.2
6.3
6.2
9.6
37.3
84.2
56.5
C
21
Egypt, Arab Rep.
8.6
38.8
17.5
74.4
5.4
34.5
14.2
68.0
17.7
2.4
29.0
27.1
71.0
72.9
1.2
4.0
17.9
4.9
18.1
18.8
8.0
32.6
73.9
48.7
D
22
Libya
10.8
23.2
37.0
64.3
3.5
13.8
26.2
52.1
1.4
0.8
10.0
17.2
90.0
82.8
1.2
2.1
24.7
15.4
5.4
11.1
11.3
28.4
83.2
60.5
D
23
Tunisia
18.4
38.2
29.9
75.6
10.9
22.8
19.8
59.2
2.1
1.4
14.0
29.3
86.0
70.7
2.1
5.6
20.5
12.9
8.8
15.8
31.7
34.0
59.5
50.2
D
24
Algeria
8.6
37.3
18.0
72.0
4.6
26.9
13.2
60.3
2.0
1.6
26.0
32.1
74.0
67.9
1.8
4.4
20.7
9.6
3.4
11.0
23.6
32.4
73.0
56.6
D
25
Morocco
12.6
40.0
21.2
73.8
9.6
30.7
17.6
61.9
35.0
8.7
54.7
46.3
45.3
53.7
0.8
2.6
10.7
8.6
48.2
25.9
13.8
26.8
38.0
47.3
D
26
Labor force participation rate for ages 15-24, female (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Labor force participation rate for ages 15-24, male (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Labor force participation rate, female (% of female population ages 15-64)
KosovoNo data available
West Bank and GazaNo data available
Labor force participation rate, male (% of male population ages 15-64)
KosovoNo data available
West Bank and GazaNo data available
Employment to population ratio, ages 15-24, female (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Employment to population ratio, ages 15-24, male (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Employment to population ratio, 15+, female (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Employment to population ratio, 15+, male (%)
KosovoNo data available
West Bank and GazaLatest available data: 2022
Contributing family workers, female (% of female employment)
KosovoNo data available
Contributing family workers, male (% of male employment)
KosovoNo data available
Self-employed, female (% of female employment)
KosovoNo data available
Self-employed, male (% of male employment)
KosovoNo data available
Wage and salaried workers, female (% of female employment)
KosovoNo data available
Wage and salaried workers, male (% of male employment)
KosovoNo data available
Employers, female (% of female employment)
KosovoNo data available
Employers, male (% of male employment)
KosovoNo data available
Unemployment, female (% of female labor force)
KosovoLatest available data: 2022
West Bank and GazaLatest available data: 2022
Unemployment, male (% of male labor force)
KosovoLatest available data: 2022
West Bank and GazaLatest available data: 2022
Employment in agriculture, female (% of female employment)
KosovoNo data available
Employment in agriculture, male (% of male employment)
KosovoNo data available
Employment in industry, female (% of female employment)
KosovoNo data available
Employment in industry, male (% of male employment)
KosovoNo data available
Employment in services, female (% of female employment)
KosovoNo data available
Employment in services, male (% of male employment)
KosovoNo data available
Some highlighted topics
Gender issues in the labour market are a key challenge for equality and sustainable growth in the Mediterranean countries. Despite inclusive legislation and policies, inequalities between men and women persist in terms of participation, wages, career advancement and working conditions.
Labour Market Participation
In the Mediterranean countries, gender differences in female activity rates are marked and vary significantly. In the European Union, female participation is relatively high but remains lower than that of men. In Spain, for example, the activity rate for adult women is 69.9%, compared to 78.3% for men, while in Italy the gap is more pronounced, with 56.4% of women active compared to 74.6% of men.
In the Western Balkans, the gaps are also evident. In Serbia, only 24.8 per cent of young women (15-24 years) are active compared to 38.5 per cent of men, and in the entire working-age population (15-64 years), the female rate is 66.9 per cent compared to 79.6 per cent for men. In Montenegro and Albania, the rates show a similar situation, with women less represented in the labour market.
In the Middle East, the gaps are among the widest: in Jordan and Lebanon, the female activity rate is drastically low compared to the male rate, with 14.5% of active adult women in Jordan compared to 65.8% of men. Israel is an exception, with female participation closer to that of men.
In North Africa, female activity rates are the lowest in the region. In Algeria and Tunisia, adult women show rates of around 18-30%, compared to over 70% for men. These gaps, strongly linked to cultural barriers and the lack of structural supports, limit women's access to the labour market.
Figure 1 - Activity rates in ages 15-24 and 15-64 by gender. Year 2023 (%)
...
The 2023 data also show significant gender disparities in employment rates, with the gaps most marked in North Africa and the Middle East. In Algeria and Egypt, for example, the employment rate among young women is extremely low, at 4.6 per cent and 5.4 per cent respectively, compared to 26.9 per cent and 34.5 per cent for men. Across all working age groups, only 13.2% of Algerian women and 14.2% of Egyptian women are employed, compared to over 60% of men in both countries.
In Jordan, the employment rate of young women is just 5%, compared to 25.1% of young men, while overall the is 10.4% compared to 51.7% of men. In Palestine and Lebanon, employed women also remain significantly less than men. In the Western Balkans, the situation is similar but less extreme: in Serbia, the female employment rate is 47% in the 15-64 age group, against 62.5% for men.
In EU countries, the gaps are less pronounced but still present. In Italy, for example, only 37.9 per cent of adult women are employed compared to 54.8 per cent of men, while in Spain the rates are closer, with 21.7 per cent of young women employed compared to 25.3 per cent of men. These figures reflect cultural barriers and a lack of support structures for female employment, particularly in the North African and Middle Eastern regions.
Figure 2 - Employment rates in age 15-24 and 15 years and over by gender. Year 2023 (%)
...
The analysis of 2023 unemployment rates in the Mediterranean countries reveals marked gender differences, with female unemployment levels generally higher than male unemployment levels. In Middle Eastern and North African countries, these inequalities are particularly pronounced due to socio-cultural barriers and limited job opportunities for women. In Jordan, for example, the unemployment rate for women is 26.6 per cent compared to 16.2 per cent for men, while in Palestine the gap is even wider: 40 per cent of women are unemployed, compared to 20.2 per cent of men.
In North Africa, women face very high unemployment rates. In Egypt, the unemployment rate for women is 17.9%, compared to only 4.9% for men. In Algeria and Tunisia, female unemployment exceeds 20%, while male unemployment remains below 13%.
In European countries, the gaps are less extreme but still present. In Spain, the female unemployment rate is 13.9 per cent compared to 10.6 per cent for men, while in Italy, women record a rate of 8.8 per cent compared to 6.8 per cent for men. France is an exception, with male unemployment (7.5%) slightly higher than female unemployment (7.2%).
In the Western Balkans, disparities vary. In Montenegro, the male unemployment rate (15.7 per cent) exceeds that of women (14.7 per cent), while in Bosnia and Herzegovina and Serbia, women have higher unemployment rates. These figures reflect the gender segmentation in the labour market and women's difficulties in gaining access to stable, well-paid positions.
Figure 3 - Unemployment rate by gender. Year 2023 (%)
...
Occupational segmentation and occupational segregation
Men and women often concentrate in different sectors and occupations. Women tend to be more present in low-paid sectors (e.g. social and care services), while men are over-represented in areas such as technology, engineering and finance.
The 2022 analysis shows a gender gap between salaried and self-employed workers in the Mediterranean. Women are predominantly employed in salaried jobs, especially in Southern Europe. In Italy, 83.9 per cent of women are wage-earners compared to 74.5 per cent of men, while in Spain and Portugal more than 88 per cent of women work as employees, compared to about 81 per cent of men.
In the Western Balkans, the difference is less pronounced: in Bosnia and Herzegovina, self-employment is almost equal between the genders, while in Serbia and Montenegro, self-employment rates are higher among men.
In the Middle East, the disparity is more pronounced. In Lebanon, 35.4 per cent of men are self-employed compared to 17.1 per cent of women, while in Jordan, 96.4 per cent of women are salaried, underlining the concentration of women in salaried work.
In North Africa, the differences are pronounced in Tunisia and Algeria. In Morocco, however, 54.7% of women are self-employed, one of the highest percentages in the region, indicating their role in the informal sector. These data reflect cultural and structural barriers that hinder women's access to self-employment, underlining the need for inclusive policies to foster female entrepreneurship.
Figure 4a - Self-employed and wage-earners by gender. Year 2022 (% of female employment)
...
Figure 4b - Self-employed and wage-earners by gender. Year 2022 (% of male employment)
...
The analysis of 2022 data on employers in the Mediterranean countries reveals strong gender disparities in entrepreneurial positions, with a marked male dominance. In EU countries, such as Italy and Greece, women occupy a significantly lower percentage of leadership roles than men: in Italy, 3.8% of employed women are entrepreneurs compared to 8% of men, while in Greece the disparity is similar (4.8% versus 8.8%).
In the Western Balkans, women account for less than half as many employers as men. In Bosnia and Herzegovina, for example, only 4.2% of women are entrepreneurs compared to 7% of men.
The disparity is even greater in the Middle East and North Africa: in Lebanon, Jordan and Palestine, women entrepreneurs are under 4%, while in Morocco, Algeria and Tunisia they are a small minority (up to 0.8% in Morocco). These figures highlight deep-seated cultural and social barriers that limit women's access to leadership and entrepreneurship roles in the region.
Figure 5 - Employers by gender. Year 2022 (% of female and male employment)
...
The analysis of data on family workers in the Mediterranean confirms a marked gender segregation, with women more present in less remunerative and subordinate roles. In European countries, such as Spain and France, there are few family workers for both sexes. In Italy and Greece, the gap widens: in Italy, 1.2% of women hold this role, compared to 0.7% of men, and in Greece, 3.7% compared to 1.8% respectively.
In the Western Balkans, the disparities are more pronounced. In Serbia, 8.4% of women are family workers, compared to 2.9% of men. Similar percentages are observed in Bosnia-Herzegovina and North Macedonia.
In the Middle East and North Africa, the percentages of women in these roles are even higher. In Palestine and Egypt, women family workers account for 7.7 per cent and 17.7 per cent respectively, while in Morocco, the percentage reaches 35 per cent, compared to 8.7 per cent for men. These figures reflect socio-cultural barriers that limit women's access to better paid and secure positions, highlighting the need for policies that promote gender equity in employment.
Figure 6 - Family workers by gender. Year 2022 (% of female and male employment)
...
Gender distribution in sectoral employment
The 2022 analysis of employment distribution in the agriculture, industry and services sectors in the Mediterranean countries shows strong gender disparities, with women predominating in services and men more in agriculture and industry. This gap reflects gender stereotypes and socio-cultural constraints that influence women's access to certain sectors.
In services, women make up the majority of employment in almost all Mediterranean countries. In France, 88.6% of women work in services, compared to 68.1% of men, while in Israel and Jordan more than 92% of women are employed in the sector. A similar concentration of women can also be observed in Lebanon and Cyprus, where women are often relegated to traditional roles with fewer opportunities for growth.
In agriculture, employment is predominantly male, but in some countries such as Morocco, Albania and Turkey, a significant percentage of women are employed in this sector (48.2%, 39.8% and 21.8% respectively). This reflects the weight of informal and rural work, where women often work without social protection.
Furthermore, the industrial sector shows a strong male predominance. In Italy, 36.2% of men are employed in industry, while only 14% of women work there. Similar situations are observed in Serbia and Tunisia, where men employed in industry represent 37.3% and 34%, respectively, while women are 19.3% and 31.7%. The under-representation of women in industry is linked to barriers in access to technical roles and male dominance in these areas. These data underline the importance of policies that facilitate women's equal access to all areas of work.
Figure 7a - Distribution of employment in sectors by gender and macro-region. Year 2022 (% of female employment)
...
Figure 7b - Distribution of employment in sectors by gender and macro-region. Year 2022 (% of male employment)
...
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Metadata
Indicators
Definition
Percentage of the female 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 male 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.
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Percentage of the female 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.
Percentage of the male 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.
Percentage of a country's female 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 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 female population 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
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 male population 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
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.
Women 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.
Men 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.
Women of working age who 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.
Men of working age who 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.
Women 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.
Men 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.
Persons who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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 who hold the type of jobs defined as 'paid employment jobs,' where the incumbents hold explicit (written or oral) or implicit employment contracts that give them a basic remuneration that is not directly dependent upon the revenue of the unit for which they work.
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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 who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a 'self-employment jobs.' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers.
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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 who, working on their own account or with one or a few partners or in cooperative, hold the type of jobs defined as a 'self-employment jobs.' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced. Self-employed workers include four sub-categories of employers, own-account workers, members of producers' cooperatives, and contributing family workers.
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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 who, working on their own account or with one or a few partners, hold the type of jobs defined as a 'self-employment jobs' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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 who, working on their own account or with one or a few partners, hold the type of jobs defined as a 'self-employment jobs' i.e. jobs where the remuneration is directly dependent upon the profits derived from the goods and services produced), and, in this capacity, have engaged, on a continuous basis, one or more persons to work for them as employee(s).
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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 who hold 'self-employment jobs' as own-account workers in a market-oriented establishment operated by a related person living in the same household.
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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 who hold 'self-employment jobs' as own-account workers in a market-oriented establishment operated by a related person living in the same household.
Sources
ILO Modelled Estimates (ILOEST)
Methodology
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. 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.
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.
Share of the female 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 male 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'
