Settling In 2018

Chapter 6. Gender differences in immigrant integration

Abstract

Comparative analysis of migrant women and men’s outcomes – and of immigrants and natives – can yield insights into integration challenges and support tailored solutions. Migrants’ past, often gendered, experience may interplay differently with host communities and so impact integration outcomes. What is more, immigrant women are less likely than men to be labour migrants. They have disproportionately often migrated for family reasons, which can reinforce gender disparities in employment and social outcomes.

However, gender gaps also exist among the native-born. Disparities between male and female immigrants do not, therefore, necessarily suggest more or less successful integration, but can also reflect persisting gender bias in the host-country labour market and society itself, as well as different choices by women and men.

This chapter examines key integration indicators to gauge whether and how outcomes differ between men and women. It begins with an overview on the size of the female immigrant population (Indicator 6.1) and a comparison of education levels (Indicator 6.2). It then turns to differences in labour market outcomes: employment, participation and unemployment rates (Indicators 6.3 and 6.4), followed by levels of involuntary inactivity (Indicator 6.5). The next section looks at the kind of work that immigrants do. It first addresses working hours with a particular focus on part-time work – a gender-specific issue in itself in many countries (Indicator 6.6) – then the skills levels of immigrants’ jobs (Indicator 6.7). The chapter next goes on to consider how well qualifications and levels of education match formal job requirements (Indicator 6.8). The last section goes beyond the workplace to examine gendered experiences of discrimination on the grounds of ethnicity, race or nationality (Indicator 6.9).

Key findings

• In the OECD and EU, women account for 51% of both immigrant and native-born populations. In most countries where foreign-born women outnumber their male peers, they do so by wider margins among EU migrants.

• The female share of immigrant populations has grown by less than 1 percentage point in the OECD and the EU.

• Immigrant women are more likely to have tertiary degrees than foreign- and native-born men. A full 38% in the OECD and 30% across the EU have completed tertiary education. They are also more likely to be highly educated than native-born women across the OECD. In the EU, they are as likely.

• OECD-wide, immigrant men, 77% of whom have jobs, are slightly more likely to be employed than their native peers, where the share is 74%. The reverse is true among women, with 59% of the foreign-born and 60% of the native-born are in work.

• In the EU, while foreign- and native-born male employment rates are similar (73%), female rates are far lower among immigrants than the native-born – 57% against 63%. Gaps between the employment rates of foreign-and native-born women are especially wide in Belgium and France, at 14 percentage points, and in the Netherlands, at almost 17 points.

• In Europe, male and female EU migrants enjoy higher employment rates than the native-born. The opposite is true of non‑EU migrants, with men and women respectively 6 and 13 percentage points less likely to be in work than their native-born peers.

• Having a host-country tertiary degree is particularly valuable in helping immigrant women find work. OECD- and EU‑wide, more than three-quarters have jobs, with an employment rate that outstrips that of their foreign-educated peers by over 14 percentage points. As for immigrant men, the country where they graduated has less of an effect on their employment rates.

• In half of all OECD countries, immigrant male participation rates are higher than those of their native peers, while immigrant women rates lag behind those of native-born women.

• Foreign-born women show slightly higher unemployment rates OECD- and EU‑wide than foreign-born men. No gender gap, by contrast, is observed among the native-born.

• Immigrant women are more prone to involuntary inactivity than native-born women. Differences between foreign- and native-born women are especially wide in the Benelux countries, Scandinavian countries (save Sweden), Poland and Southern European countries (save Spain).

• In terms of reason for inactivity, immigrant women most commonly cite family responsibilities – 30% of involuntarily inactive immigrants in the OECD and 35% in the EU do so, compared to around one-quarter of their native peers in both areas.

• Part-time contracts among employed women are especially widespread in EU countries – around 40% of immigrants (44% when they are from outside the EU) and 30% of the native-born are part-timers. Differences are particularly large in Southern Europe.

• Across the OECD and the EU, immigrant and native-born women are generally more likely than men to be in low-skilled occupations. In Southern Europe (except Portugal), as well as in Chile, Korea and Slovenia, over 30% of immigrant women work in low-skilled jobs.

• In the EU, immigrant women are ten times more likely to work in services to households than their native peers, and the proportion of those in these jobs exceeds 20% among the immigrant female workforce in Southern European countries.

• In the OECD, 36% of immigrant women and 34% of men work in jobs for which they are over-qualified, compared to 29% and 33% of their native peers. In the EU, the gender gap is more marked among immigrants with 36% of women and 31% of men over-qualified (22% and 20% among the native-born).

• EU‑wide, the immigrant female over-qualification rate is 14 percentage points higher than that of their native peers, while the male rate is 11 percentage points higher.

• EU-wide, a higher proportion of foreign-born men (15%) than women (13%) report that they belong to a group that is discriminated against on the grounds of ethnicity, nationality or race. In countries outside Europe, men and women report discrimination on these grounds in equal proportions, the exception being the United States, where a higher proportion of immigrant men than women feel discriminated against with regard to work.

6.1. Female populations

Definition

Female populations refer to the shares of women in immigrant populations.

Coverage

Population of all ages.

In the OECD and the EU, women account for 51% of both immigrant and native-born populations of all ages. While they form a similar share of the native-born in virtually all countries, proportions vary widely from one country to another in immigrant populations. In about half of all countries, at least 52% of immigrants are women. They are, for example, overrepresented in the foreign-born populations of Southern European countries. And in those where the foreign-born population is relatively old – such as Israel, Poland and the Baltic countries – women are in a 55% majority or more. They also make up a slight majority in longstanding European immigration destinations, the settlement countries, and in Turkey and Japan. In Germany and the Nordic countries with large intakes of humanitarian migrants, men slightly outnumber women. The share of women in immigrant populations is below 48% in six countries only, falling as low as 43% in Korea and 40% in the Czech Republic.

Overall, though, the female share of immigrant populations has grown by less than 1 percentage point in the OECD and in the EU. However, there are wide variations between countries. In fact, the proportion of women among the foreign-born climbed in one-quarter of countries only – particularly those which previously experienced large-scale male labour migration, as in Southern Europe and Ireland. In Iceland and Spain, the increase was as high as 4 percentage points. In Ireland, men outnumbered women in the immigrant population 10 years ago, while today women are in the majority. By contrast, the share of immigrant women actually dropped in half of all countries, only slightly in most cases but by up to 7 percentage points in Chile. Falls also came in Bulgaria, in countries where male humanitarian migrants accounted for a large part of recent migration (e.g. the Nordic countries) and in those where recent immigrants were mostly men as in Hungary, and the Czech Republic.

EU‑wide, immigrants born in an EU country are slightly more likely than those born in a third country to be women. Indeed, in most countries where foreign-born women outnumber their male peers, they do so by wider margins among EU migrants. However, in most Nordic countries, Spain, and in countries with significant numbers of intra‑EU mobile workers (e.g. Switzerland and Luxembourg), there are more EU‑born men than women, but more non‑EU women than men. By contrast, EU migrants in Austria, Hungary, Slovenia and Sweden are mainly women and non‑EU migrants mainly men.

Figure 6.1. Shares of women among immigrants

Shares as percentage, all ages, 2017

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Figure 6.2. How shares of women in the immigrant population have evolved

Changes in percentage points, all ages, between 2007 and 2017

Notes and sources are to be found at the end of the chapter.

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6.2. Educational attainment

Definition

This section measures educational attainment against the International Standard Classification of Educational Degrees (ISCED). It considers three levels: i) low, no higher than lower secondary education (ISCED Levels 0‑2); ii) very low, no higher than completed primary education (ISCED Levels 0‑1); iii) high, tertiary education (ISCED Levels 5‑8).

Coverage

People not in education aged 15 to 64 years old.

Across the OECD and the EU, women are overrepresented among the highly educated. The trend is also true of immigrant women, who are more likely to have tertiary degrees than foreign- and native-born men. Indeed, a full 38% of immigrant women in the OECD and 30% across the EU have completed tertiary education. Immigrant women thus make up 15% and 13%, respectively, of all highly educated women in the two areas. They outdo their male peers in educational attainment in virtually all countries. They are also more likely to be highly educated than native-born women across the OECD. In the EU, they are as likely, thanks chiefly to EU migrant women who boast high levels of educational attainment. Levels among non‑EU female and male migrants are similar. Overall, women are also slightly overrepresented among the poorly educated in the OECD, but slightly underrepresented in the EU. That trend is also true of immigrant women in the OECD, but not in the EU, where foreign-born women (both EU and non-EU born) are overrepresented among the low-educated. The 22% of low-educated natives compares with 25% of EU-born immigrants and 39% of non-EU-born.

Regardless of gender, the highly educated account for the largest shares of immigrant populations in the settlement countries, whose large-scale labour migration policies are geared towards them. In those countries, foreign-born women are more likely to be highly educated than their male peers. In Southern Europe, where only less than 30% of immigrants are highly educated, there is also a pro-women gender gap among the foreign-born. In some countries (Korea, the Netherlands, Turkey and Austria), immigrant women are more often highly educated than men, whereas the reverse is true among the native-born. The only countries where female immigrants are more likely than men to be poorly educated are the Latin American OECD countries. In the small immigrant populations of those countries, foreign-born men are better educated than both immigrant women and the native-born.

Over the past decade, the highly educated have accounted for growing shares of immigrant populations in most countries (+6 percentage points both for men and women in the OECD). In the EU, the increase has been stronger for immigrant women (+8 percentage points both among EU- and non-EU-born although with smaller shares among the latter) compared to their male counterparts (+6 percentage points with again similar trends among EU and non-EU born immigrant men). In two countries in five, the share rose even more among female immigrants than natives. This is especially true in Poland, the United Kingdom and Denmark.

Figure 6.3. The highly educated, by gender

Percentages, 15- to 64-year-olds, 2017

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Figure 6.4. How shares of highly educated women have evolved

Changes in percentage points, 15- to 64-year-old women, between 2006‑07 and 2017

Notes and sources are to be found at the end of the chapter.

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6.3. Employment and labour market participation

Definition

The employment rate denotes people in employment as a percentage of the population of working age, aged between 15 and 64 years old. The International Labour Organization (ILO) defines an employed person as one who, in the course of the reference week, worked at least one hour or who had a job but was absent from work.

Participation denotes the economically active population (employed and unemployed) as a share of the working age population.

Coverage

Working age population, 15 to 64 years old.

OECD-wide, immigrant men, 77% of whom have jobs, are more likely to be employed than their native peers, where the share is 74%. The reverse is true among women, with 59% of the foreign-born and 60% of the native-born are in work. In the EU, while foreign- and native-born male employment rates are similar (73%), female rates are far lower among immigrants than the native-born – 57% against 63%. Those trends hold true in three-quarters of EU countries. Gaps between the employment rates of foreign-and native-born women are especially wide in Belgium and France, at 14 percentage points, and in the Netherlands, at almost 17 points. The divide is attributable to relatively low proportions of immigrant women in work in those countries. Similarly, in the Nordic countries, where native women have high employment rates, employment divides between native and foreign populations are much wider among women – as much as 15 points in Sweden, Denmark, and Finland – than men. Immigrant women are more likely to be in work than their native-born peers only in the few countries where immigrants, male and female alike, are more widely employed than the native-born. Examples are most Central European countries, Portugal, Luxembourg and such emerging immigrant destinations as Chile.

High levels of education improve prospects of entering the workplace. Yet, immigrants with degrees, especially women, still struggle more than their native counterparts. Gaps in employment rates between native- and foreign-born women are as wide as 7 percentage points in the OECD and 10 percentage points EU-wide. Having a host-country tertiary degree is particularly valuable in helping immigrant women find work. OECD- and EU‑wide, more than three-quarters of female immigrants with host-country tertiary degree have jobs, with an employment rate that outstrips that of their foreign-educated peers by over 14 percentage points, but is slightly lower than that of the native-born. As for immigrant men, the country where they graduated has less of an effect on their employment rates. Those with host-country credentials are as likely as their native peers to be employed.

The gender gap in employment has narrowed in the bulk of OECD and EU countries among the foreign- and native-born over the past decade. The trend stems mostly from the disproportionate impact of the global economic crisis on the male workforce. Indeed, OECD- and EU‑wide, employment among immigrant women is now above pre-crisis levels, while among their male peers it is still slightly lower. The gender gap among foreign-born has narrowed most sharply in Southern Europe, due primarily to the steep decline in employment among immigrant men – double that of their female peers. Employment among immigrant women has not improved, though, as it has among native-born women, who are now back at work in the same proportions as before the crisis (save in Greece). Foreign-born male employment rates, however, are still at least 10 percentage points below pre-crisis levels, 7 percentage points more than those for the native-born men. As for Australia and Belgium, while foreign- and native-born women and immigrant men enjoy higher employment rates, job levels among native-born males have still not recovered from the crisis.

Figure 6.5. Employment rates, by gender

Percentages, 15- to 64-year-olds, 2017

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Figure 6.6. How female employment rates have evolved

Changes in percentage points, 15- to 64-year-olds, between 2006‑07 and 2017

Notes and sources are to be found at the end of the chapter.

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In Europe, male and female EU migrants enjoy higher employment rates than the native-born. The opposite is true of non‑EU migrants, with men and women respectively 6 and 13 percentage points less likely to be in work than their native-born peers. In about half of EU countries, however, rates of male employment among the non‑EU foreign-born men exceed those of the native-born. Examples are the countries of Central and Southern Europe (barring Spain), where many non‑EU migrants came as labour immigrants.

Regardless of place of birth and whether employed or unemployed, men are more likely than women to participate in the labour market across the board. The gender gap in participation is wide among the foreign-born in most countries. The EU‑wide participation rate of foreign-born men is 82% – 17 percentage points above that of their female peers and 3 points higher than among native men. Rates are higher by even greater margins with respect to foreign-born women in Mexico, Korea, Turkey, the United States, Italy and Greece. Indeed, in half of all OECD countries, immigrant male participation rates are higher than those of their native peers, while immigrant women rates lag behind those of native-born women. That trend is especially true of the settlement countries and most long-standing destinations. In Germany, the Netherlands and Sweden, though, immigrants of both genders are less likely to be part of the labour market than the native-born. Non‑EU migrant men show a slightly higher EU‑wide participation rate than native-born males with 79% against 78%, while EU migrants outstrip them both with 85 %. Non‑EU migrant women, however, with an EU‑wide rate of 60%, are outperformed by both female natives (66%) and EU migrant women (72%).

Over the last decade, women’s participation has increased in the OECD and EU, the only exception being the United States. Rises have been relatively more robust among foreign-born females in non-European countries (especially New Zealand). At the same time, participation rates have dropped among native-born men in most non-European OECD countries and have only slightly risen among male immigrants. In the EU, participation rates have increased for both foreign- and native-born women (slightly more among the native-born). By contrast, rates have remained the same among foreign-born men in the EU. As a result, the participation-related gender gap for both foreign- and native-born has narrowed in virtually all countries.

Figure 6.7. Labour market participation rates, by gender

Percentages, 15- to 64-year-olds, 2017

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Figure 6.8. How female participation rates have evolved

Changes in percentage points, 15- to 64-year-olds, between 2006‑07 and 2017

Notes and sources are to be found at the end of the chapter.

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6.4. Unemployment

Definition

The International Labour Organization (ILO) defines the unemployed as people without, but available for, work, and who have been seeking work in the course of the reference week. The unemployment rate is the percentage of unemployed people in the labour force (the sum of employed and unemployed individuals).

Coverage

The economically active population of working age (15 to 64 years old).

Foreign-born women show slightly higher unemployment rates OECD- and EU‑wide than foreign-born men. No gender gap, by contrast, is observed among the native-born. In the OECD and EU, joblessness respectively affects 8.7% and 12.5% of immigrant women, 7.1% and 10.9% of foreign-born men, and 6% and 7.4% of the native-born. Immigrant women are more likely to be unemployed than their male peers in most countries. Gender disparities are the widest in Southern European countries, but among the foreign- and native-born alike. There are no such gaps, by contrast, in European destinations like Austria, Germany and Sweden, even though foreign-born unemployment rates are double or triple those of the native-born.

Unemployment rates still exceed the pre-crisis levels in most OECD and EU countries among both men and women, and more markedly so among the foreign-born. However, gender-related differences in unemployment levels have actually narrowed slightly across the OECD and EU among native- and foreign-born alike. The narrowing has been more pronounced among immigrants in half of all countries due to a greater increase in male unemployment (as in Southern Europe) or to a drop among women (as in Mexico and Chile). By contrast, the unemployment gender gap has widened among immigrants in Poland, while remaining unchanged among natives.

Non‑EU immigrants are more prone to unemployment than the native-born across the EU, while rates among EU immigrants and the native-born are similar. Around 15% of non‑EU immigrant men and 16.3% of their female peers are unemployed. Differences between non-EU foreign- and native-born are more pronounced among women in most countries. Not, though, in Southern Europe, where there were heavy concentrations of non‑EU male migrants in the sectors worst hit by the economic crisis (as in Greece and Spain), or in Austria, Luxembourg and the Slovak Republic. Over the last decade, the unemployment gap between non‑EU immigrants and natives, both men and women, has widened by at least 2 percentage points.

Figure 6.9. Unemployment rates, by gender

Percentages, 15- to 64-year-olds, 2017

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Figure 6.10. How female unemployment rates have evolved

Changes in percentage points, 15- to 64-year-olds, between 2006‑07 and 2017

Notes and sources are to be found at the end of the chapter.

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6.5. Involuntary inactivity

Definition

Involuntarily inactive people are those who are not seeking work though willing to take up work. They include among others, discouraged workers, who are not seeking work because they believe no suitable jobs are available.

Coverage

The 15- to 64-year-old economically inactive.

Women are more likely than men to be inactive in most OECD countries, particularly when they are foreign-born. Immigrant women are also more prone to involuntary inactivity, with about one in six inactive foreign-born women willing to work compared to one in seven native-born women. In the EU, the proportions are one in five versus one in six. Differences between foreign- and native-born women are especially wide in the Benelux countries, Scandinavian countries (save Sweden), Poland and Southern European countries (save Spain). As for genders, foreign- (especially those born outside the EU) and native-born men across the OECD and EU, although less affected by inactivity, are more likely to be involuntarily inactive than their female peers.

As a reason to be economically inactive, women most commonly cite family responsibilities – 30% of involuntarily inactive immigrants in the OECD and 35% in the EU do so, compared to around one-quarter of their native peers in both areas. A further 13% in the OECD and 19% in the EU cite discouragement, as do 18% and 24% of native-born women. Native- and foreign-born mothers of children under the age of six are more likely to be involuntarily inactive than other women although this is not true in the United States. Among those mothers, the native-born more frequently report being trapped at home in the EU: a full 25% of native-born women with small children are involuntarily inactive, compared to 23% of foreign-born mothers.

In the OECD and EU, rates of involuntary inactivity among men and women have increased from the pre-crisis levels, more steeply among the foreign- than the native-born. The biggest rises have come in Southern Europe and the longstanding immigration destinations of Europe. In Spain and Switzerland, by contrast, the increase has been greater in the native populations. The Nordic countries show contrasting trends. In Denmark, involuntary inactivity has grown among both foreign- and native-born women. Norway has also seen a rise in the share of involuntary inactive immigrant women but a decline among their native-born peers, while the opposite is true in Finland. In Sweden, it remained broadly unchanged for both groups, at low levels. There has also been very little change in levels of involuntary inactivity in the United States.

Figure 6.11. Reasons for involuntary inactivity among women

Percentages among economically inactive, 15- to 64-year-olds, 2015‑16

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Figure 6.12. How shares of involuntarily inactive women have evolved

Changes in percentage points, 15- to 64-year-old inactive women, between 2006‑07 and 2015‑16

Notes and sources are to be found at the end of the chapter.

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6.6. Working hours

Definition

Part-time work denotes a working week of less than 30 hours. This section considers the share of part-time workers and the share of part-time workers who would like to work longer hours (involuntary part-time).

Coverage

People aged 15 to 64 who are in employment, not including the self-employed or those still in education.

Across the OECD, 30% of immigrant women work part-time compared to 9% of their male counterparts. While similar shares of foreign- and native-born women work part-time, almost one third of the foreign- and one quarter of the native-born would like longer hours. Part-time contracts among employed women are especially widespread in EU countries – around 40% of immigrants (44% when they are from a third country) and 30% of the native-born are part-timers. Immigrant women are more likely than native women to work part-time in 6 countries out of 10 – particularly in Southern Europe and, albeit to a lesser extent, in France and Germany. The labour markets in Sweden and Norway have a relatively high propensity for part-time female workers, who make up similar shares of the foreign- and native-born female employed populations. In the countries with the highest incidence of part-time work, i.e. the Netherlands and Switzerland, it is more widespread among the native-born women than among foreign-born women. A similar finding also holds for most non-European countries, as well as the United Kingdom, Luxembourg and to a lesser degree Ireland.

In most countries, a majority of part-time workers (both immigrant and native-born) declare they would not want to work more hours. Only in Chile, Finland and some countries of Southern Europe, a majority of part-time employment is involuntary, although only for the foreign-born in Finland. One in three part-time working immigrant woman wishes to work more hours, against one quarter of the native-born, OECD- and EU-wide. However, in most countries, involuntary part-time is disproportionately high among foreign-born women, although this is not true in Oceanian OECD countries, Central Europe, Israel and Portugal.

Since the economic crisis, the share of employed immigrant women working part-time has grown by 3 percentage points in the OECD and by 4 points in the EU (by 7 points among third-country immigrants). Meanwhile, it has remained steady among their native-born peers. The steepest rises in part-time work have come in the countries of Southern Europe, as well as in Austria and Ireland where, together with Greece, changes have been twice as high among foreign-born female workers as among their native-born peers. In North America (save Mexico) and the United Kingdom, part-time work has grown in the foreign-born but fallen in the native-born female workforce. A few countries, though, have reported the opposite. In Australia, for instance, part-time work has risen twice as much among the native- as among foreign-born women. And in Luxembourg and the Slovak Republic, shares of immigrant women working part-time have even dramatically dropped, while they have remained stable or increased among native-born females in employment.

The share of immigrant women wishing to work longer hours has generally grown. The rise has been greater among immigrants than natives in most countries, with the exceptions of Australia, Portugal and the Netherlands. Shares have also increased among foreign-born women in Sweden and Switzerland, while these countries show significant falls in levels of involuntary part-time female workers among the native-born. These falls are also significant in Malta and Germany, particularly among native-born women in the latter.

Figure 6.13. Shares of employed women working part-time

Percentages of employed, 15- to 64-year-olds, 2015‑16

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Figure 6.14. How shares of women working part-time have evolved

Changes in percentage points, 15- to 64-year-olds, between 2006‑07 and 2015‑16

Notes and sources are to be found at the end of the chapter.

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6.7. Job skills and economic activities

Definition

Job skills are measured by the International Standard Classification of Occupations (ISCO). The job skills indicator compares the share of workers in low-skilled jobs (i.e. elementary occupations that require simple, routine tasks and, often, physical effort [ISCO 9]) with the share of workers in highly skilled jobs (e.g. senior managers, professionals, technicians and associate professionals [ISCO 1‑3]). The composition by economic activities denotes 4 broad sectors: manufacturing, energy and construction; trade, accommodation and food services; public services (including public administration, education, health and social work activities); and other sectors.

Coverage

People in employment aged between 15 and 64 years old.

Across the OECD and the EU, immigrant and native-born women are generally more likely than men to be in low-skilled occupations. Only in four countries – Australia, Ireland, Malta and New Zealand – are both foreign- and native-born men more likely than women to work in menial jobs. In the EU, 25% of immigrant women work in menial jobs, compared to 9% of native-born women and 15% of immigrant men. EU-wide, 27% of all low-skilled positions are held by immigrants – 29% and 25% among female and male employment, respectively. Immigrant women and men are especially heavily concentrated in low-skilled jobs in Chile, Korea and in the Southern European countries (except for Portugal). In these countries, as well as in most other OECD and EU countries, gender differences are also wider among the foreign- than the native-born. In Southern Europe (again except for Portugal), as well as in Chile, Korea and Slovenia, over 30% of immigrant women work in low-skilled jobs – about 20 percentage points more than their male peers (and 5 points higher in Italy and Korea). In most longstanding destinations, too, particularly Belgium, France, and Luxembourg, there are at least twice as many foreign-born women in low-skilled occupations as foreign-born men, which translates into wider gender disparities than among the native-born. The gender gap among the native-born, by contrast, is less than 10 percentage points in those countries.

At the other end of the labour market, immigrant workers are underrepresented among those who hold highly skilled positions, but shares are higher for women than for men. As a result, relative to men, immigrant women are much less likely to be in medium-skilled occupations. Around 35% of employed immigrant women in the OECD hold highly skilled positions, as do 33% in the EU. The respective percentages among their native peers are 39% and 45% and among immigrant men, they are 32% and 31%. Overall, the gender gap in shares of immigrants with highly skilled jobs has been reduced over the last decade OECD- and EU‑wide as the share of employed men who work in highly skilled jobs has risen faster than among women. The reverse is true among the native-born.

In all countries, compared with their native-born peers, immigrant women are underrepresented in public services. In contrast, immigrant women are more likely to work in manufacturing in the United States, while they are underrepresented among manufacturing in the EU. In the EU, they are slightly overrepresented in the trade/accommodation and food services sector, and strongly overrepresented in services to households. EU-wide, immigrant women are ten times more likely to work in that sector than their native peers (i.e. 11.5% of immigrant women employment compared with 1% of native-born employment), although this result is largely driven by Southern European countries, where the proportion often exceeds 20% among the immigrant women in employment. In the United States, the corresponding figure is a mere 2%. When it comes to men, immigrants are overrepresented in both the United States and the EU in the construction sector but underrepresented in manufacturing.

Figure 6.15. Shares in low-skilled employment, by gender

Percentages of employed population, 15- to 64-year-olds, 2017

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Figure 6.16. Composition of the female workforce by economic activities

Percentages, 15- to 64-year-olds, 2015‑16, inner circle: native-born, outer circle: foreign-born

Notes and sources are to be found at the end of the chapter.

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6.8. Over-qualification

Definition

The over-qualification rate is the share of the highly educated, i.e. educated to ISCED Levels 5‑8 (see Indicator 6.2), but work in a job that is ISCO-classified as low- or medium-skilled, i.e. ISCO Levels 4‑9 (see Indicator 6.7).

Coverage

People not in education aged 15 to 64 years old who are in employment and highly educated (not including military occupations [ISCO 0], where data on skills levels are not referenced.

Across the OECD and EU, over-qualification is more widespread among immigrants than natives and most prevalent among immigrant women. In the OECD, 36% of immigrant women and 34% of men work in jobs for which they are over-qualified, compared to 29 and 33% of their native peers. In the EU, the gender gap is more marked among immigrants with 36% of women and 31% of men over-qualified (22 and 20% among the native-born). Over-qualification rates are higher, however, among native-born men than among their foreign-born and female peers in Latin American OECD countries, Lithuania, Turkey, Switzerland and the United States. Native- and foreign-born over-qualification rates differ most widely, and particularly between men, in the Nordic countries, home to many humanitarian migrants who tend to have high incidence of over-qualification. Gaps are also wide in Southern Europe (bar Portugal), but between female populations. More than half of all highly educated immigrant women in Italy, Spain, and Greece are over-qualified for their jobs, while the proportion among their male counterparts is 8 to 15 percentage points lower.

In all EU countries, non-EU immigrants are more frequently over-qualified for their jobs than the native-born, with an EU‑wide female over-qualification level that is 17 percentage points higher than that of their native peers and a male rate that is 13 percentage points higher. Over-qualification rates among EU migrants, both male and female, are also significantly higher than those of the native-born, though by smaller margins. EU migrant women in Latvia, Portugal and Ireland, however, have higher over-qualification rates than their native and non‑EU peers, as do EU migrants of both sexes in the United Kingdom.

Having a host-country tertiary degree limit the risk for highly educated women to be over-qualified in their job. EU-wide, 46% of female immigrants trained abroad are over-qualified in their job compared with 37% of their male counterparts and 30% of immigrant women trained in their country of residence. EU-wide, the gender gap in over-qualification rates is smaller among immigrants trained in the country of residence.

Female over-qualification has grown slightly over the past decade EU-wide (+2 percentage points both among foreign- and native-born) as well as in Australia while it has decreased slightly in the United States. In Greece, the over-qualification rate of native-born women has climbed 13 percentage points, while remaining relatively unchanged among their immigrant peers but at a much higher level.

As for immigrant men, over-qualification is similar to pre-crisis levels in the EU and even declined slightly in the United States. Native-born men, by contrast, are slightly more likely than before the economic downturn to be working in jobs for which they are over-qualified. While differences between male and female native-born over-qualification rates have diminished in longstanding destinations like Austria and Switzerland, they have remained the same among immigrants. In Hungary however, they have narrowed between both foreign- and native-born men and women. As a result, highly educated immigrant women in those countries are now less likely than before the crisis to be over-qualified for their jobs. The opposite is true in Italy, where immigrant women are now more likely to be over-qualified.

Figure 6.17. Over-qualification rates, by gender

Percentages, 15- to 64-year-olds, 2017

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Figure 6.18. How female over-qualification rates have evolved

Changes in percentage points, 15- to 64-year-olds, between 2006‑07 and 2017

Notes and sources are to be found at the end of the chapter.

Statlink

6.9. Perceived discrimination

Definition

This section considers shares of immigrants who report having undergone discrimination. In the EU, perceived discrimination among immigrants is the sentiment of belonging to a group that is discriminated against on grounds of ethnicity, nationality, or race. In Australia and Canada, perceived discrimination relates to reported personal experience of discrimination. In the United States, only work-related discrimination is covered, people who feel they have been discriminated against with regard to work over the past five years.

Coverage

Foreign-born people aged 15 to 64 years old.

EU‑wide, a higher proportion of foreign-born men (15%) than women (13%) report that they belong to a group that is discriminated against on the grounds of ethnicity, nationality or race. In Greece, almost one-quarter of all male immigrants feel discriminated against, as do one in five in most longstanding immigration destinations, especially France, Belgium and the Netherlands. Slovenia and Croatia, by contrast, are the countries where foreign-born men and women alike report the lowest levels of discrimination.

However, male and female perceptions of discrimination vary widely from country to country. In Germany, for example, foreign-born men are almost twice as likely as women to feel discriminated against. In Belgium, Denmark and Ireland, too, the sentiment is much more widespread among foreign-born men than women. The opposite is true of Greece, however, where 31% of foreign-born women report instances of discrimination, compared to 24% of their male peers. In Portugal and Sweden, immigrant women also feel more discriminated against, and in the Czech Republic almost three times more. In countries outside Europe, men and women report discrimination in equal proportions, the exception being the United States, where a higher proportion of immigrant men than women (8% versus 6%) feel discriminated against with regard to work.

Perceptions of discrimination have changed considerably over the past decade, diverging widely between men and women in some countries. Comparisons between the periods 2002‑08 and 2010‑16 reveal that levels of perceived discrimination among foreign-born men fell substantially in Austria and Spain, more so than among immigrant women. They also declined in the United Kingdom, Portugal and the Nordic countries (save for Finland), but rose among foreign-born women. The opposite trend was observed between the two time periods in Germany, France and the Netherlands, where foreign-born women reported fewer instances of discrimination and their male peers more (except for Germany). In Belgium the incidence of perceived discrimination increased, but less so among foreign-born women than men. The feeling of being discriminated against has declined in Canada among both immigrant men and women, while in other non-European countries it has not changed significantly for either gender over the last decade.

Recent arrivals – immigrants who came to their OECD host country less than 10 years ago – tend to claim more frequently than the long-settled that they are discriminated against. This is particularly true among immigrant women: 15% of recent female migrants (16% of men) feel discriminated against versus 11% of those settled (14% of men).

Figure 6.19. Self-reported discrimination, by gender

Percentages, 15- to 64-year-olds, 2008‑16

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Figure 6.20. How self-reported discrimination rates have evolved, by gender

Changes in percentage points, 15- to 64-year-olds, between 2002‑08 and 2010‑16

Notes and sources are to be found at the end of the chapter.

Statlink

Notes and sources

Notes on Cyprus

1. Note by Turkey: The information in this document with reference to “Cyprus” relates to the southern part of the Island. There is no single authority representing both Turkish and Greek Cypriot people on the Island. Turkey recognises the Turkish Republic of Northern Cyprus (TRNC). Until a lasting and equitable solution is found within the context of the United Nations, Turkey shall preserve its position concerning the “Cyprus issue”.

2. Note by all the European Union Member States of the OECD and the European Union: The Republic of Cyprus is recognised by all members of the United Nations with the exception of Turkey. The information in this document relates to the area under the effective control of the Government of the Republic of Cyprus.

Note on Israel

The statistical data for Israel are supplied by and under the responsibility of the relevant Israeli authorities. The use of such data by the OECD is without prejudice to the status of the Golan Heights, East Jerusalem and Israeli settlements in the West Bank under the terms of international law.

Notes on figures and tables

Lithuania was not an OECD Member at the time of preparation of this publication. Accordingly, Lithuania does not appear in the list of OECD Members and is not included in the zone aggregates.

On 25 May 2018, the OECD Council invited Colombia to become a Member. At the time of publication the deposit of Colombia’s instrument of accession to the OECD Convention was pending and therefore Colombia does not appear in the list of OECD Members and is not included in the OECD zone aggregates.

New Zealand, Japan and Canada data include people still in education. Australian data include people aged over 24 who are still in education. The United States includes people over 55 who are still in education and calculates rates for the 16‑64 age group.

Japan determines who is an immigrant on the basis of nationality, not on the basis of country of birth. Korea includes in the immigrant population all foreigners and immigrants who have been naturalised in the past 5 years.

Indicators 6.2, 6.3, 6.4 and 6.8: The level of education for Korea includes ISCED 4 in the highly educated.

Figure 6.3: Japan is not included in OECD total.

Indicator 6.6: Part-time work in Mexico denotes a working week of less than 35 hours.

Figure 6.13: Japan, Korea and Mexico cannot distinguish involuntary from voluntary part-time.

Indicator 6.9: Data on European countries refer to the sense of belonging to a group that is discriminated against on the grounds of race, ethnicity, or nationality. Australian data refer to immigrants who report having experienced discrimination or been treated unfairly because of their skin colour, nationality, race, ethnic group or language they speak. Canadian data refer to immigrants who have experienced discrimination or have been treated unfairly in the past five years because of their ethnicity, culture, race, or colour. There are two set of data for the United States. The first set of data (for the year 2016) refers to respondents who feel they have been discriminated against with regard to work (for instance, when applying for a job, or when being considered for a pay increase or promotion at work) over the past five years because of their race, ethnicity or nationality. The second set of data (for the year 2014 and before) refers to respondents in employment who feel, in one way or another, discriminated against at work because of their race or ethnicity.

Averages factor in rates that cannot be published individually because sample sizes are too small.

For further detailed data, see Annexes A, B, C and D.

Table 6.1. Sources by indicator

	6.1 Female populations	6.2 Educational attainment	6.3 Employment and labour market participation	6.4 Unemployment	6.5 Involuntary inactivity	6.6 Working hours	6.7 Job skills and economic activities	6.8 Over-qualification	6.9 Perceived discrimination
OECD/EU
Australia	IMD 2007 & 2017	ASEW 2007 & LFS 2017	ASEW 2007 & LFS 2017, ASEW 2016 (by education)	ASEW 2007 & LFS 2017, ASEW 2016 (by education)	PJSM 2016	LFS 2006‑07 & 2015‑16	ASEW 2016	ASEW 2007 & 2016	GSS 2014
Austria	IMD 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2002‑06 & 2014‑16
Belgium	IMD 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑08 & 2010‑16
Bulgaria	Eurostat 2011 & 2017	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	ESS 2008‑12
Canada	Census 2006 & 2016	LFS 2006‑07 & 2017	LFS 2006‑07 & 2017, 2015‑16 (by education)	LFS 2006‑07 & 2017, 2015 (by education)	..	LFS 2006‑07 & 2015‑16	..	..	GSS 2004 & 2014
Chile	IMD 2009 & 2015	CASEN 2015	CASEN 2015	CASEN 2015	..	CASEN 2015	CASEN 2015	CASEN 2015	..
Croatia	Eurostat 2017	EU-LFS 2015‑16	EU-LFS 2015‑16	EU-LFS 2015‑16	EU-LFS 2015‑16	EU-LFS 2015‑16	EU-LFS 2015‑16	EU-LFS 2015‑16	ESS 2008‑10
Cyprus1,2	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	ESS 2008‑12, 2006‑08 & 2010‑12
Czech Republic	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑04+2008 & 2010‑16
Denmark	IMD 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	ESS 2008‑14, 2002‑08 & 2010‑14
Estonia	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2004‑08 & 2010‑16
Finland	IMD 2007 & 2017	EU-LFS AHM 2014	EU-LFS 2006‑07 & 2017; EU-LFS AHM 2014 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants); EU-LFS AHM 2014 (by education)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS AHM 2014	ESS 2008‑16, 2002‑08 & 2010‑16
France	IMD 2007 & 2014	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education & F6.7)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑08 & 2010‑16
Germany	IMD 2007 & 2017	EU-LFS 2006‑07 & Mikrozensus 2016	EU-LFS 2006‑07 & Mikrozensus 2016	EU-LFS 2006‑07 & Mikrozensus 2016	..	EU-LFS 2006‑07 & Mikrozensus 2016	Mikrozensus 2016	EU-LFS 2006‑07 & Mikrozensus 2016	ESS 2008‑16, 2002‑08 & 2010‑16
Greece	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑10
Hungary	IMD 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑14, 2002‑08 & 2010‑14
Iceland	IMD 2007 & 2017	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	ESS 2012+2016
Ireland	IMD 2007 & 2016	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	ESS 2008‑16, 2002‑08 & 2010‑16
Israel*	IMD 2007 & 2016	LFS 2017	LFS 2017	LFS 2017	LFS 2016	LFS 2016	LFS 2017	LFS 2017	ESS 2008‑16, 2002+2008 & 2010‑16
Italy	IMD 2008 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	..
Japan	IMD 2007 & 2017	Census 2010	Census 2015	Census 2015	..	Census 2015	Census 2015	..	..
Korea	IMD 2007 & 2016	SILCLF 2017 & EAPS 2017 (provided by MRTC)	SILCLF 2017 & EAPS 2017 (provided by MRTC)	SILCLF 2017 & EAPS 2017 (provided by MRTC)	..	SILCLF 2017 & EAPS 2017 (provided by MRTC)	SILCLF 2017 & EAPS 2017 (provided by MRTC)	SILCLF 2017 & EAPS 2017 (provided by MRTC)	..
Latvia	Eurostat 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	..
Lithuania	Eurostat 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2010‑14
Luxembourg	IMD 2010 & 2017	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	..
Malta	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	..
Mexico	IMD 2007 & 2016	ENOE 2017	ENOE 2017	ENOE 2017	..	ENOE 2016	ENOE 2017	ENOE 2017	..
Netherlands	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑08 & 2010‑16
New Zealand	IMD 2007 & 2014	LFS 2006‑07 & Q2‑4/2015-Q1/2016	LFS 2006‑07 & Q2‑4/2015-Q1/2016	LFS 2006‑07 & Q2‑4/2015-Q1/2016	..	LFS 2017	LFS 2017	LFS 2006‑07 & LFS 2017	..
Norway	IMD 2007 & 2016	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	ESS 2008‑16, 2002‑08 & 2010‑16
Poland	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑08 & 2010‑16
Portugal	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑14, 2002‑08 & 2010‑14
Romania	Eurostat 2017	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2015‑16	EU-LFS 2006‑07 & 2015‑16	..
Slovak Republic	IMD 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2015‑16	ESS 2008‑12, 2004‑08 & 2010‑12
Slovenia	Eurostat 2009 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU mig.)	ESS 2008‑16, 2002‑08 & 2010‑16
Spain	IMD 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU mig.)	ESS 2008‑14, 2002‑08 & 2010‑14
Sweden	IMD 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑08 & 2010‑16
Switzerland	IMD 2010 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑08 & 2010‑16
Turkey	IMD 2016 & DIOC 2010/11	LFS 2015	LFS 2015	LFS 2015	..	LFS 2015	LFS 2015	LFS 2015	..
United Kingdom	IMD 2007 & 2017	EU-LFS 2006‑07 & 2017	EU-LFS 2006‑07 & 2017, 2015‑16 (by education)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2006‑07 & 2015‑16	EU-LFS 2017, 2015‑16 (F6.16)	EU-LFS 2006‑07 & 2017, 2015‑16 (non-EU migrants)	ESS 2008‑16, 2002‑08 & 2010‑16
United States	IMD 2007 & 2015	CPS 2006‑07 & 2016‑17	CPS 2006‑07 & 2016‑17	CPS 2006‑07 & 2016‑17	CPS 2006‑07 & 2016‑17	CPS 2006‑07 & 2016‑17	CPS 2016‑17 (highly skilled only)	CPS 2006‑07 & 2016‑17	USGSS 2006‑10 & 2012‑14 (employed); USGSS 2016 (with regard to work)
Partner/G20 countries
Argentina	IPUMS Census 2010	IPUMS Census 2010	IPUMS Census 2010	IPUMS Census 2010	..	..	..	..	..
Brazil	IPUMS Census 2010	IPUMS Census 2010	IPUMS Census 2010	IPUMS Census 2010	..	..	IPUMS Census 2010	IPUMS Census 2011	..
Colombia	IPUMS Census 2005	IPUMS Census 2005	IPUMS Census 2005	IPUMS Census 2005	..	..	..	..	..
Costa Rica	IPUMS Census 2011	IPUMS Census 2011	IPUMS Census 2011	IPUMS Census 2011	..	..	IPUMS Census 2011	IPUMS Census 2012	..
Indonesia	IPUMS Census 2010	IPUMS Census 2010	IPUMS Census 2010	IPUMS Census 2010	..	..	..	..	..
Russia	Census 2010	Census 2010	Census 2010	Census 2010	..	..	..	..	ESS 2008‑12+2016, 2006‑08 & 2010‑12+2016
Saudi Arabia	Population Characteristics Survey 2017	..	Census 2010	..	..	..	LFS 2016	LFS 2016	..
South Africa	IPUMS Census 2011	IPUMS Census 2011	IPUMS Census 2011	IPUMS Census 2011	..	..	..	..	..

Statlink