Settling In 2018

Indicators of Immigrant Integration

Please cite this publication as:
OECD/EU (2018), Settling In 2018: Indicators of Immigrant Integration, OECD Publishing, Paris/European Union, Brussels.
https://doi.org/10.1787/9789264307216-en

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DOI: https://doi.org/10.1787/9789264307216-en

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Foreword

This publication presents a comprehensive international comparison across all EU and OECD countries, as well as of selected other G20 countries, of the integration outcomes for immigrants and their children. It is the fruit of a co-operation between the European Commission (DG Migration and Home Affairs) and the OECD’s International Migration Division, as part of a regular monitoring of comparable indicators of integration across EU, OECD and G20 countries.

This publication is the third edition of an OECD series that started in 2012 with the OECD publication Settling In: Indicators of Immigrant Integration and draws on the data and information gathered in the first two editions as well as the broader work on integration issues carried out by the OECD’s International Migration Division. It also benefited from data provided by Eurostat, the EU Fundamental Rights Agency (FRA), the IOM Migration Research and Training Centre (MRTC), as well as specific data requests to EU and OECD countries. This publication would not have been possible without the support of the Delegates to the OECD Working Party on Migration and national statistics offices who provided valuable support in the data collection for this report.

Chapter 1 introduces the topics and provides a scoreboard of outcomes. It also presents a classification of countries which share similar immigrant populations. Chapter 2 presents contextual information on immigrant populations, including socio-demographic characteristics compared with those of the native-born; specific factors related to the immigrant population (such as countries of origin and length of residence) and information on the composition of immigrant households, compared to native-born households.

Against the background set out in Chapter 2, the remainder of the publication goes on to consider actual indicators of integration. Chapter 3 looks at key indicators of immigrants’ skills and labour market integration. It examines immigrants’ levels of education, language skills and participation in training, in addition to their labour market outcomes, as well as the quality of their jobs. Chapter 4 examines several aspects of living conditions: household income, housing conditions, as well as health status and access to healthcare. Chapter 5 addresses immigrants’ civic engagement and their social integration. Selected measurable aspects of social cohesion, such as sense of belonging to one’s country of residence, voting behaviour (for those naturalised), perceived discrimination, as well as host-society attitudes towards immigration are presented.

This publication also includes three large special chapters. Chapter 6 looks at gender differences. Chapter 7 examines the integration of young people with a migrant background. Chapter 8 presents a monitoring of EU “Zaragoza indicators” for third-country nationals – i.e. non-EU nationals living in an EU country.

This publication was written by Yves Breem and Cécile Thoreau together with Elisabeth Kamm, under the co-ordination of Thomas Liebig. Claire Rossi-De Vries and Jongmi Lee provided statistical assistance. The publication also benefited from contributions by Laurent Aujean, Rhea Ravenna Sohst and Elin Törnblom Duthu. Ken Kincaid provided the editing, and Véronique Gindrey, Lucy Hulett, Anna Tarutina and Joanne Dundon publication support.

It benefited from comments by Laurent Aujean (DG Migration and Home Affairs) and from Jean-Christophe Dumont, Mark Pearson and Stefano Scarpetta (all OECD) as well as from several officials from EU FRA.

Editorial

Migration has reached record highs in recent years. However, new migrants settling in the EU and the OECD every year still represent less than 0.5% of the host-country populations on average, and the current focus on new arrivals should not neglect the longstanding presence of already settled migrants and their offspring.

Migrants bring skills and a dedication to fulfil their aspirations for a better future. This has enormous potential for host countries. For these aspirations to become a reality, however, it is paramount to promote a fast and effective integration of migrants and their children. According to the recent Eurobarometer on Integration in the EU, many citizens in the EU are concerned about the economic and social integration of migrants. Providing reliable facts is therefore a prerequisite for a better-informed public debate and for better-targeted policymaking.

In this context, we are happy to present the second edition of the joint OECD-EU Settling In, which identifies both successes and areas for improvement with respect to immigrant integration. Building and extending on the “Zaragoza indicators” introduced at a ministerial conference under the Spanish presidency of the EU in 2010, this publication provides the most comprehensive international comparison of integration outcomes of immigrants and their children. It covers economic and social outcomes, both through quantitative and qualitative measurements of integration.

The good news is that many countries have made improvements in integrating immigrants and their children into the labour market and social life of their country. However, many challenges still remain, and a significant amount of the potential that migrants bring with them remains unused, hampering both economic growth and social inclusion. In many countries, some vulnerable migrant groups – such as refugees – may take 15 years or more, on average, to reach similar employment rates as the native-born and labour migrants. The inclusion of the large group of family migrants, among which many are women, is also an issue of concern. In addition, in many countries unfavourable outcomes of immigrant parents extend to their native-born children, who also often lag behind their peers with native-born parents.

At the national and European levels, the recent increase in refugee inflows has prompted new approaches and significant innovation with respect to integration in education systems, in labour markets and in society as a whole. Integration has been a priority in many OECD and EU countries, supported at EU level through different concrete measures included in the European Commission's Action Plan on the integration of third-country nationals, including the EU’s skills profiling tool, the European Integration Network, as well as through increased funding now and in the future.

Monitoring changes in integration outcomes is an important element in assessing the success of integration policies. International comparisons help, not only to provide benchmarks and to identify common challenges across countries, but also to foster peer learning on what works and what does not. The comparison between EU countries, on the one hand, and those OECD countries that were ‘settled’ by immigration, on the other hand, is particularly promising in this respect.

While domestic policies in the host countries play a key role in the integration of immigrants, international co-operation can and should support the process. This edition shows once again that a lack of integration can lead to significant economic costs in terms of lower productivity and growth. It also entails political costs and instability, and more generally negatively affects social cohesion. Moreover, integration failure in one country can negatively affect integration prospects in other countries as it may influence the overall perception of migrants. Poor integration outcomes of immigrants also constrain the political space to better manage future migration, whether it is for work, family or protection purposes.

Integration is thus a key issue for both national and international policymaking, and the present publication comes at a crucial moment for the latter: the adoption of the UN Global Compacts on Migration and on Refugees. Both compacts stress the need for better data and monitoring, which is a prerequisite for well-informed policymaking. This second edition of the joint EU and OECD monitoring of integration outcomes is an important contribution in support of that goal. We hope that this work also provides for a better understanding of both the successes that have already been achieved, and of the challenges that still need to be addressed – at national and international levels alike, and the incentive to act.

Angel Gurría

Secretary-General of the OECD

Dimitris Avramopoulos

European Commissioner for Migration, Home Affairs and Citizenship

Acronyms and abbreviations

ACS

American Community Survey (United States)

AES

Adult Education Survey (EU)

AHM

Ad Hoc Module (EU)

ASEW

Australian Survey on Education and Work

Benelux

Belgium, Netherlands, Luxembourg

CASEN

Encuesta de Caracterización Socioeconómica Nacional (Chile)

CPS

Current Population Survey (United States)

DIOC

Database on Immigrants in OECD Countries

EAPS

Economically Active Population Survey of Korean nationals

EFTA

European Free Trade Agreement (see glossary for details)

ENOE

Encuesta Nacional de Ocupación y Empleo (Mexico)

ESCS

Economic, Social and Cultural Status (see glossary for details)

ESS

European Social Survey

EU

European Union (see glossary for details)

EUR

Euros

EWCS

European Working Conditions Survey

EU-MIDIS

European Union Minorities and Discrimination Survey

G-SOEP

German Socio-Economic Panel

GSS

General Social Survey

HES

Household Economic Survey (New Zealand)

IHS

Integrated Household Survey (Israel)

ILO

International Labour Organization

IMD

International Migration Database (OECD)

IMO

International Migration Outlook (OECD)

IOM

International Organization for Migration

IPUMS

Integrated Public Use Microdata Series

ISCED

International Standard Classification of Education (see glossary for details)

ISCO

International Standard Classification of Occupations (see glossary for details)

LFS

Labour Force Survey

MRTC

Migration Research and Training Centre

NHIS

National Health Interview Survey (United States)

NPHS

National Population Health Survey (Canada)

NUTS

Nomenclature des Unités Territoriales Statistiques / Nomenclature of Territorial Units for Statistics (see glossary for details)

OECD

Organisation for Economic Co-operation and Development

PIAAC

Programme for the International Assessment of Adult Competences (OECD)

PISA

Programme of International Student Assessment (OECD)

PJSM

Participation, Job Search and Mobility survey (Australia)

SIH

Survey of Income and Housing (Australia)

SILC

Statistics on Income and Living Conditions (EU)

SILCLF

Survey on Immigrant’s Living Conditions and Labour Force (Korea)

TCN

Third-Country National(s)

WVS

World Value Survey

Executive summary

Permanent migration to EU and OECD countries has reached record highs in recent years, but this should not overshadow the longstanding presence of settled migrants, their children and their native-born descendants. Today, the OECD and the European Union are home to around 128 and 58 million immigrants, respectively, accounting for over 10% of their population. In the European Union, around two-thirds of immigrants are from non-EU countries. Over the last decade, the immigrant population has increased by 23% in the OECD and by 28% in the EU.

This publication documents the integration outcomes of immigrants and their children in all EU and OECD countries, as well as in selected non-OECD G20 countries. It focuses, in particular, on skills and labour market outcomes, living conditions and integration in the host society; it also provides comprehensive background information on immigrants and their lives.

In most domains, immigrants tend to have worse economic and social outcomes than the native-born, although these gaps tend to reduce the longer they stay and become more familiar with their host country. Education helps migrants to successfully integrate, but having a higher education does not necessarily provide them with the same returns that it does for the native-born. Immigrants in European countries tend to have lower outcomes than those in other OECD countries, particularly immigrants from outside the EU, partly driven by their lower education on average. Over the last ten years, labour market integration of immigrants has slightly improved in most OECD and EU countries, as have their qualification levels. Immigrants have generally not, however, caught up with the outcomes of the native-born. There is also still some way to go for full social integration.

Key findings

Labour market outcomes

In all OECD and EU countries, immigrants have higher unemployment rates than the native-born. The differences are particularly marked for non-EU migrants in the EU.

Over the last decade, differences in unemployment rates of immigrants and native-born have widened in OECD and EU countries, most notably in Southern Europe, due to the difficult economic situation.

When unemployed, immigrants are generally less likely to receive unemployment benefits than the native-born in the EU.

Across the EU, almost one in four economically inactive immigrants wish to work, compared to one in six among the native-born.

On average in the EU and OECD, over one in four low-skilled jobs is held by an immigrant. This figure rises to over 40% in Austria, Germany, Sweden and Norway, and over 60% in Switzerland and Luxembourg.

Among the 33.2 million immigrants in the OECD and 11 million in the EU who are considered highly educated, around 8.1 million and 2.9, respectively have jobs for which they are overqualified. About another 7 million and 2.4 million, respectively, are unemployed. Taken together in both areas, this is almost 45% of the highly educated immigrant population whose formal qualifications are not – or not fully – used, compared with 40% of the highly educated native-born OECD wide and 30% in the EU.

Almost every labour market in the OECD and the EU does not value foreign degrees as highly as native ones. In the EU, the employment rate of non-EU migrants with foreign credentials is 14 percentage points lower than that of their peers with host-country qualifications. Furthermore, those who do have a job are more likely to be overqualified.

Education and skills

In the OECD, 37% of immigrants are highly educated, 5 percentage points more than among the native-born.

In the EU, around 15% of non EU-born aged 15 to 64 went no further than primary school education. While that share has slightly declined over the last decade, it remains three times as high as among the native-born.

The highly educated proportion of immigrants has grown in virtually all OECD and EU countries, rising by 7 percentage points over the past decade in both areas.

Living conditions

Immigrants are over-represented in the lowest income decile in virtually all OECD and EU countries – 14% and 18% of immigrants, respectively. At the same time, income inequality among the foreign-born tends to be wider than among native-born.

Relative poverty is today more widespread among the foreign-born than a decade ago. The OECD- and EU-wide poverty rates among immigrants increased by 1 and 5 percentage points, respectively, over the last decade, while remaining stable among natives.

Having a job provides protection against poverty, although less so for immigrants than natives, in all countries. Over 53% of the foreign-born in the United States, Switzerland and Iceland who are poor are also working.

In a number of countries, spatial concentration is very pronounced. In the EU, 30% of non-EU migrants from the largest immigrant groups in their respective country, state that most inhabitants of their neighbourhoods share their ethnic background. This is most pronounced in Belgium and the Netherlands (where more than 50% report living in such a neighbourhood), followed by France and Portugal.

Social integration

Views of immigration have remained broadly stable in EU host countries since 2006, although in a majority of countries more people now take slightly more positive stances. In a large majority of countries, the more the native-born actually interact with the foreign-born, the more likely they are to consider immigration as an opportunity for their country rather than a problem.

In all EU and OECD countries, more than 80% of immigrants report feeling close or very close to their host country.

Around 14% of all foreign-born people in the EU report belonging to a group they think is subject to discrimination on the grounds of ethnicity, nationality or race.

An average of 74% of immigrants with host-country nationality in the OECD and the EU report that they participated in the most recent national elections – less than the native-born average, 80%.

Gender differences

In the OECD and EU, women account for 51% of both immigrants and native-born populations. This share has increased slightly during the last decade.

OECD-wide, immigrant men, 77% of whom have jobs, are slightly more likely to be employed than their native peers (74%); in the EU, the likelihood is similar. The reverse is true among women, with 59% of the foreign-born and 60% of the native-born being in work in the OECD. Rates EU-wide are 57% and 63%, respectively.

In Korea, Slovenia and Southern Europe (with the exception of Portugal), over 30% of immigrant women work in low-skilled jobs compared with less than 15% of their native peers. In the EU, immigrant women are ten times more likely to work in household services than their native peers and their proportion in these jobs exceeds 20% among the immigrant female employment in Southern European countries.

EU-wide, immigrants are more likely than natives to agree with the statement that “when jobs are scarce, men should have more right to a job than women”, although the difference is not large: 22% vs. 16%.

Youth with a migrant background

The outcomes of young people with a migrant background are often seen as the benchmark for the success or failure of integration. OECD-wide, those who immigrated as children or were born in the host country of at least one foreign-born parent account for nearly one in five 15 to 34-year-olds, or 38.7 million people (13% of the EU 15-34 population or 15.4 million). A further 9% arrived in the host country as adults (8% EU-wide).

For youth with a migrant background, on many indicators there is a disparity between European countries on the one hand and the non-European OECD countries on the other. In general, outcomes for young people with a migrant background compared with young people with native-born parents tend to be unfavourable in Europe, while the opposite is the case elsewhere. This is largely driven by differences in the socio-economic characteristics of immigrant parents.

Nevertheless, in the EU, the educational attainment levels and outcomes of youth with immigrant parents have improved over the past decade – both in absolute terms and relative to their peers with native-born parents. This is not only evident in better educational outcomes and higher resilience at age 15, but also in lower levels of school drop-out and higher educational attainment.

In spite of the progress achieved, in Europe, youth with a migrant background still lag behind their peers with no migrant background (e.g. by over half a school year for the reading score when aged 15). In non-European OECD countries in contrast, native-born with foreign-born parents perform better at school than their peers with native-born parents, except in the United States.

While there has been progress in educational outcomes, this is less evident with respect to employment. In all EU countries, except Portugal and Lithuania, young immigrants and the native-born offspring of immigrants are less likely to be in work than their peers with native-born parents. The overall employment gap between the native-born of native- vs foreign-born parentage is 6 percentage points. As for child-arrival immigrants, they are 8 points less likely to have jobs.

The relative child poverty rate in immigrant households is twice as high as in native-born households, both in the OECD and the EU, and indeed in the latter, discrepancies have grown further over the past decade. The divergent trend was most pronounced in Spain and in a number of other EU countries, such as Austria, France and the Netherlands.

In many European countries, native-born children of immigrants report higher levels of perceived discrimination than young immigrants. This is not the case in non-European OECD countries, however.

OECD- and EU-wide, close to 58% of native-born youth with immigrant parents report that they voted in the most recent national elections, 10 percentage points lower than their peers with native-born parents.

Chapter 1.

Indicators of immigrant integration: Introduction and overview

1.1. Accurate data on the integration of immigrants and their children are key for an informed policy debate

The integration of immigrants and their children has been high on the policy agenda of EU and OECD countries for the last 20 years. It has gained further attention in the aftermath of the humanitarian refugee crisis that outburst in 2015. Between 2015 and 2017, OECD countries received 5.5 million applications for asylum, not taking into account the 3.4 million Syrians who have been granted temporary protection by Turkey. Not all of these will obtain protection, but many will stay and face specific integration challenges related to their forced migration. In most countries such recent refugees make up for a relatively small part of the overall foreign-born population, which faces itself many integration challenges. Indeed, immigrants who have been in the host-countries for many years often continue to experience poorer outcomes than their native-born peers. And some of this disadvantage is passed on to their native-born children.

The integration of immigrants and of their children is vital for social cohesion and inclusive growth and the ability of migrants to become self-reliant, productive citizens. It is also a prerequisite for the host population’s acceptance of further immigration. This publication defines as integration the ability of immigrants to achieve the same social and economic outcomes as natives taking into account their characteristics.

It is crucial to provide policy makers and the public with solid facts, to assess integration outcomes, to pose the right questions, and to address the challenges. Although integration indicators are not necessarily, in themselves, gauges of integration policies, they do point to successes and failures, and thus shed light on possible policy responses. This introductory chapter first discusses the benefits of developing monitoring tools of integration at the international level, based on harmonised concepts and definitions. It then presents a tentative classification of OECD and EU countries with respect to the characteristics of their immigrant population. It summarises in a scoreboard how countries are faring on a number of core indicators, and how these integration outcomes have evolved.

1.1.1. Who is the target population?

Countries tend to define their “immigrant population” in different ways. Most settlement countries (Australia, Canada, New Zealand), the United Kingdom and OECD Latin American countries like Mexico generally refer to the foreign-born population. Other European countries use several different concepts, which include factors like current citizenship, citizenship at birth, country of birth and self-reported ethnicity. Some EU countries exclude from their national definition of the immigrant population expatriates (nationals by birth born abroad), such as France or Italy, or foreigners born abroad who belong to the same ethnic group as the majority of the population (e.g. Hungary, Greece; partly also Germany). Other may also take into account a minimum duration of stay to be included in the immigrant population, such as countries with population registers. In Japan and Korea, statistics predominantly use the notion of nationality. Canada in general excludes persons with a temporary residence permit from the “immigrants” category.

When it comes to define children of immigrants, many longstanding immigration countries considers as children of immigrants all native-born with at least one immigrant parent, or native-born with foreign nationality. Others only consider native-born with two immigrant parents. Most countries have little information on native-born descendants of immigrants because information on parents’ origin is rarely collected. This report avoids the widely used term “second generation migrant” as this term suggests that immigrant status is perpetuated across generations. It is also factually wrong, since the persons concerned are not immigrants but native-born.

This report defines immigrants as the foreign-born population. Indeed, unlike citizenship that can change over time, the place of birth cannot. In addition, conditions for obtaining host-country citizenship vary widely, hampering international comparisons. In countries that are more liberal in this respect – e.g. OECD countries that have been settled by migration – most foreign nationals may naturalise after five years of residence. Some European countries, such as Sweden, also have relatively favourable requirements for some groups. By contrast, many native-born with immigrant parents are not citizens of their country of birth in the Baltic countries, Switzerland and Germany, for instance.

There are many reasons why the outcomes of immigrants – particularly those who arrived as adults – tend to differ from those of the native-born population. They have been raised and educated in an environment – and often in a language – that may be different from that of their host country. And some elements of their foreign origin will always be part of them. Although some of these may affect their full integration, they generally become less of a hindrance the longer migrants reside in the host country.

Issues are very different when it comes to the native-born descendants of immigrants. As they have been raised and educated in the host country, they should not be facing the same obstacles as their immigrant parents and outcomes similar to those of their peers of native-born parentage may be expected. In many respects, the outcomes of the native-born offspring of immigrants are thus a better measurement for integration than the outcomes of the foreign-born. The situation of people who are foreign-born, but arrived as children when they were still of mandatory schooling age, is also different from those who came as adults. Indeed, for the latter, certain key characteristics such as educational attainment are barely influenced by integration policy (as education has been acquired abroad), and thus should not be considered indicators of integration. In contrast, educational attainment is a key indicator for those who arrived as children or are native-born descendants of immigrants.

Figure 1.1 provides an overview of the population with a migrant background that is decomposed along the lines just mentioned – i.e., the foreign-born who arrived as adults, the foreign-born who arrived as children, and the native-born offspring of immigrants. The latter are further broken down between those native-born with two foreign-born parents and those with one foreign-born parent (that is, with “mixed background”). The report examines the latter groups in more detail in Chapter 7 on youth.

According to household survey data, almost 10% of the people residing in the OECD and 11% in the EU are foreign-born – around 125 and 55 million, respectively. Among the immigrant population, one quarter arrived before the age of 15 in the OECD, a share that is slightly higher in the EU (28%). Native-born with at least one immigrant parent account for around 7% of the total population of both the OECD and the EU – around 85 and 35 million, respectively. Across the OECD, slightly more than half of the native-born with a migrant background have two foreign-born parents. That share is somewhat smaller in the EU, where native-born with a mixed background are the majority. The vast majority of native-born with a migrant background have one native- and one foreign-born parent in new destination countries where the number of descendants of immigrants is low, as well as in Sweden and in Central and Eastern European countries where the immigrant population is relatively old of age.

Overall, 17% of the total population have a migrant background in the OECD. The figure is 18% in the European Union. Three fifths of the population with a migrant background are foreign-born. Only in France, Israel, Central Europe (except Hungary) and the Baltic countries are native-born with a migrant background outnumbering immigrants. More than 40% of the population has a migrant background in the settlement countries and in those longstanding European immigration destinations that predominantly host intra-EU migrants (Luxembourg and Switzerland). That share is above 60% in Luxembourg and Israel. It is also between 25 and 35% in most European longstanding destinations, as well as in Sweden, the Baltic countries (except Lithuania) and the United States. At the other side of the spectrum, less than 1 person out of 20 is of migrant background in most Central European countries where the migrant population has been shaped by border changes and ethnic minorities, and less than 1 in 30 in the new immigration destination countries of Asia, Latin America, and Eastern Europe.

Figure 1.1. Immigrants and native-born with a migrant background

Percentage of the total population, 2017 or most recent year

StatLink  http://dx.doi.org/10.1787/888933842166

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

1.1.2. How are integration and its evolution measured?

Measuring integration requires a benchmark against which outcomes can be assessed. This report compares the outcomes of the respective target population with those of the remaining population. In other words, it compares the outcomes of immigrants with those of the native-born (Chapters 2-6), and the outcomes of the native-born with two immigrant parents with those of their peers with two native-born parents (Chapter 7). Chapter 8 takes a specific look at non-EU nationals in the European Union, as these are the focus group of EU integration policy.

The two most common ways of measuring the outcomes of a target group against those of a reference group are: i) as differences in outcomes (mainly expressed in percentage points, since most indicators are shares or rates) and ii) as a ratio between the two outcomes.

Figure 1.2 on median income shows how different measurement methods can yield different country rankings. In this example, Luxembourg and Greece are among the countries where the ratio between the median income of the natives and that of immigrants is the largest, with native-born having an income that is a third higher than that of immigrants. When it comes to the difference in EUR, the ranking of Luxembourg gets even worse, while Greece finds itself in the middle group of OECD countries. Although both measurements assess differences in median income for foreign- and native-born, ratios disregard magnitude. In fact, whereas the immigrant income in Luxembourg is one of the highest among OECD and EU countries, the immigrant income in Greece is one of the lowest. This report consequently presents indicators both as absolute values and discusses differences in percentage points, but rarely as a ratio.

Figure 1.2. Comparison of median income of foreign- and native-born

EUR 2014 constant prices, population aged 16 and more, 2015

StatLink  http://dx.doi.org/10.1787/888933842185

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

This report monitors the evolution over time of the indicators discussed, to the extent possible. The economic downturn that started in December 2007 was the most significant economic event over the past decades, often impacting disproportionately on the foreign-born population. Therefore, this report compares wherever possible the current situation with pre-crisis levels.

1.2. Compiling indicators at the international level is challenging but fruitful

In many respects, international comparisons of integration outcomes are challenging. First, because the characteristics of immigrant populations (age, gender, duration of stay, country of birth, reason of stay, education level, among others) vary widely across countries and may change over time. Second, comparing immigrant outcomes from country to country can only be adequately used to assess the success of “integration” if it takes into account country-specific economic and social contexts, which contribute to shaping these outcomes. Third, international comparisons often suffer from a lack of reliable and harmonised data across countries. National data must therefore be adapted to comply with common categories and definitions, losing some of their specificity and links with country-specific characteristics.

1.2.1. The added value of international comparisons

In exchange, international comparisons bring much added value to indicators at the national level.

a) Provide benchmarks for performance

The fact that indicators computed differently in different countries may not be fully comparable does not imply that comparing the gaps between foreign- and native-born in these countries is meaningless. International comparisons can provide benchmarks for national performance and help interpret the magnitude of differences; for example, whether or not a 5 percentage points lower employment rate for immigrants is little or a lot. International comparisons can also help to focus on the right issues and identify challenges that are not necessarily visible from evidence from individual countries.

Figure 1.3. The employment rates of the foreign-born by level of education

Differences in percentage points with native-born 15- to 64-year-olds not in education, 2016-17

StatLink  http://dx.doi.org/10.1787/888933842204

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

b) Identify common integration challenges

International comparisons also highlight common challenges across countries that are related to the nature of the migration process, rather than the host-country specific context. For example, compared with the native-born, immigrants have higher unemployment rates virtually everywhere.

Likewise, compared with their native-born peers of similar formal education levels, it is not the low-educated immigrants who tend to face the largest challenges. In almost half of all OECD and EU countries, low-educated immigrants have higher employment rates than the low-educated native-born (Figure 1.3). However, the highly educated immigrants have lower employment rates than natives in almost all countries. Virtually everywhere, they have difficulties in getting their qualifications valued, particularly those obtained abroad, highlighting issues such as employer difficulties in judging the value of foreign qualifications.

c) Identify issues that are not visible in national data

International comparisons can also help to identify issues that are not visible in national data, notably when there are strong correlations between immigrant presence and other factors of disadvantage. It is commonly claimed, for example, especially in Europe, that concentrations of immigrants in the same schools risks impairing the overall educational performance of those schools. Results based on data from the OECD Programme for International Student Assessment (PISA) show that in Europe, where immigrant parents are strongly overrepresented among the lowest-educated, pupils educational outcomes tend to be lower when they find themselves in schools with high shares of children of immigrants (Figure 1.4). However, in OECD countries such as Australia and Canada where immigrants are overrepresented among the highly educated, children perform much better when they find themselves in a school with many children of immigrants. What does emerge in contrast is that, in all countries, children’s academic performance is systematically lower in schools where there are high proportions of children with a poorly educated mother. OECD-wide, they lag almost two years behind their peers in schools with few of such students. In this instance, international comparisons help targeting the real problem to tackle: not the high concentration of children of immigrants as such, but the concentration of children with low-educated parents.

Figure 1.4. How academic performance is affected by concentrations of pupils with migrant backgrounds and low-educated mothers

Difference in PISA mean scores for 15-year-old pupils in schools above the 25% threshold and those in schools below the 25% threshold, 2015

StatLink  http://dx.doi.org/10.1787/888933842223

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

1.2.2. Integration is a multidimensional process, and some aspects are more difficult to measure than others

The effective integration of migrants is not an economic process alone. It also has numerous social, educational, spatial, and other facets. These are closely linked – disadvantage and failure to integrate in one dimension are likely to have multiple repercussions. For example, concentration of children of immigrants in disadvantaged areas affect effective integration in the education system, which in turn hampers labour market prospects.

Some outcomes are easier to measure than others. What is more, harmonised indicators relating to migrant integration across countries are easier to identify in some areas than in others. While the extent of economic integration can be well-measured using labour market outcomes from large standardised cross-country surveys, it is harder to capture social or health integration where measures often rely on surveys of attitudes, feelings, and perceptions. Such subjective indicators are prone to a number of problems. Perceptions tend to be strongly influenced not only by different national contexts in which the questions are posed, but also by the current public debate or highly mediatised incidents close to the day of the survey. What is more, cross-country comparisons often have to draw on non-harmonised data sources, due to different ways questions are posed.

Because integration is a multidimensional process, immigrants can outperform the native-born in one domain and struggle in another. And failure in any one field may severely jeopardise progress in others. Capturing multiple integration domains in different cross-country indicators, as done in this publication, inevitably involves some degree of simplification and approximation. Taken together, however, such a broad set of indicators paints a clearer picture of the success of migrant integration across OECD countries.

To interpret immigrants’ integration outcomes, the composition of the immigrant population also must be considered. In particular, category of entry matters a lot for the starting point. For example, refugees came through forced migration and are “selected” only with respect to humanitarian considerations, while labour migrants are selected on the basis of their skills and/or their job in the host-country. These and other contextual information are crucial to the proper interpretation of immigrants’ actual outcomes and observed differences with native-born populations. From one OECD country to another, the foreign-born population is made up of quite different groups of different size – depending on geographical, linguistic, and policy factors, among others. In Sweden, for example, which has taken in a large number of humanitarian migrants, the migrant population differs quite substantially from that of Switzerland, where many immigrants arrived for employment, or from the United States, where family migration makes the bulk of legal immigration flows. Table 1.1 presents an overview of the characteristics and the areas of integration included in this publication, with a detailed list of the indicators presented for each area.

Table 1.1. Contextual information and areas of integration of immigrants and their children considered in the publication

| Description | Measured by

Characteristics (chapter 2)

A number of socio-demographic factors drive integration outcomes. They include age, gender, family structure, living conditions, and geographical concentration. In addition to such factors, which also apply to the native-born, there are certain immigrant-specific determinants like category of entry, duration of stay, and region of origin. A grasp of how they differ from country to country and how immigrants fare relative to the native-born is a prerequisite for understanding integration outcomes.

Foreign-born share of population by:

- Country

- Regions

- Rural or urban area

Distribution of the immigrant population by:

- age

- gender (chapter 6)

Dependency ratio

Endogamous partnership rate

Total fertility rate

Average size of households

Composition of households

Immigration flows by category of entry

Distribution of the immigrant population by:

- Duration of stay

- Regions of origin

Skills and the labour market (Chapter 3)

Immigrants’ skills and how they integrate into the labour market are fundamental to becoming part of the host country’s economic fabric. Skills and qualifications are obviously indicators of the immigrants ability to integrate in the host society. They have a strong bearing on career paths and influence what kind of job they find.

Employment is often considered to be the single most important indicator of integration. Jobs are immigrants’ chief source of income and confers social standing in the eyes of the immigrant’s family and with respect to the host-country population. However, while employment is important per se, job quality is also a strong determinant shaping how immigrants find their place in society.

Distribution of the immigrant population by:

- Educational attainment

- Place of education

- Host-country language proficiency

- Foreign language proficiency

Language courses attendance rate

Participation in adult education and training

Participation in Early Childhood Education and Care (chapter 7)

Literacy scores (chapter 7)

Low school performers in reading (chapter 7)

Share of resilient students (chapter 7)

Share of early school leavers (chapter 7)

Employment rate

Labour market participation rate

Unemployment rate

Long-term unemployment rate

NEET rate (chapter 7)

Share of inactive who wish to work

Share of unemployed receiving benefits

Share of employees working:

- Long hours

- Part-time (chapter 6)

- Involuntary part-time (chapter 6)

Jobs distribution by:

- Types of contracts

- Physical health risks

- Job skills

Over-qualification rate

Share of self-employed

Firm size

Share of employment in the “public services” sector (chapter 7)

Living conditions (chapter 4)

Immigrants’ ability to generate sufficient income and to meet such essential needs as decent housing and healthcare is crucial if they are to take their place in the host society.

Income is a decisive factor in many socio-economic outcomes. Poverty adversely affects the well-being of immigrants in the host society in a number of ways. Housing is also a key factor in well-being. The economic situation of some immigrants, their poor knowledge of the rental market and discrimination from property owners may restrict their choice of accommodation. Lastly, health is integral to well-being, affecting the degree and manner of engagement with society as a whole.

Median income

Income distribution

Poverty rate

Overcrowding rate

Share of substandard dwellings

Perception of ethnic spatial concentration

Perception of environmental problems in the area

Share of people reporting good health status or better

Share of people who report unmet medical needs

Share of people who report unmet dental needs

Civic engagement and social indicators (chapter 5)

Becoming actively involved in the host country society is a key element in immigrant integration and has strong implications for immigrant well-being. By making their voices heard, taking an interest in how their host society works, and participating in the decisions that shape its future, immigrants become an integral part of their new country, this being the very objective of integration.

The nature of the relationship between a host society and its immigrant population is also a critical factor in integration: if social cohesion is strong, it will promote integration whereas if it is weak, immigrants will find it harder to fit in.

Naturalisation rate

National voting participation rate

Local voting participation rate

Life satisfaction

Host-country perceptions of the presence of immigrants

Perceived economic and cultural impact of immigration

Share of native-born interacting with immigrants

Agreement with the statement: "When jobs are scarce, men should have more right to a job than women"

Agreement with the statement: "Women should be prepared to cut down on paid work for the sake of the family"

Sense of belonging to the national community

Sense of belonging at school (chapter 7)

Share of pupils who report having been bullied (chapter 7)

Share of pupils who feel awkward and out of place at school (chapter 7)

Share of immigrants who feel to have been discriminated against

1.3. Classifying immigrant destination countries

Immigrant populations differ largely in their size, length of residence, age, education level, language, and predominant entry categories. On the basis of these background characteristics, eight groups of OECD and EU destination/host countries can be identified.

These peer groups of countries often face similar integration challenges related to the characteristics above. While countries can always learn from the exchange of experiences, such an exchange will be particularly fruitful with those countries whose immigrant composition is broadly similar.

Figure 1.5. Classification of OECD and EU countries as immigrant destinations according to key characteristics of the foreign-born population, 2017

StatLink  http://dx.doi.org/10.1787/888933842242

Group 1: Settlement countries (Australia, Canada, Israel, New Zealand)

In this group of countries, settlement has been a constituent element of nation-building, and immigration is considered part of the national heritage. On average, one person out of four is foreign-born in the whole population, while the native-born who have at least one immigrant parent account, on average, for another 22%.

A high proportion of immigrants have been educated to tertiary level: an average of 53% have a tertiary degree, a level well above those in other countries and higher than among the native-born (37%). In Australia, Canada and New Zealand, these high levels of educational attainment have been linked to immigration policies that have, for many years, attracted large numbers of highly skilled labour migrants. With the exception of Israel, two-thirds of permanent inflows over the last 12 years were labour or free mobility migrants and their accompanying families. Current per capita inflows are also well above the OECD and EU averages. More than one-third of migrants in settlement countries are native speakers. Israel is an exception, and proportions of both native speakers and recent migrants are relatively small.

Overall, economic and social integration of immigrants in settlement countries is relatively successful. Due to the high share of highly educated people, many of whom came as labour migrants, immigrants boast good labour market outcomes, high incomes, good access to training, and social inclusion, compared to their native peers. Low-educated migrants face, however, difficulties to access employment in Australia and Canada and their employment rate has deteriorated over the past decade. What is more, nearly a third of highly educated employed migrants are overqualified in their job in this group of countries.

Immigrants tend to be less likely to report being discriminated against than in other groups of countries. The vast majority of immigrants with more than ten years of residence have host-country citizenship. In addition, immigrants with the nationality of the country of residence tend to have the same likelihood to vote as their native counterparts. Linked with the high education levels of their immigrant parents, immigrant offspring tend to have better outcomes both at school and in the labour market than their peers with no migrant background – in stark contrast to most other host countries covered below.

Group 2: Long-standing destinations with many recent and highly educated migrants (Luxembourg, Switzerland, the United Kingdom, the United States)

These countries host significant numbers of both recent and long-settled migrants. Immigrants account for shares of the total population that range from about 14% in the United Kingdom and the United States to 29% in Switzerland and 46% in Luxembourg. Although immigration is longstanding, there have been many arrivals over the past decade, particularly in the three European countries where they make up an average of 46% of the foreign-born population of working age. For these countries, the high share of these recent immigrants stems largely from free movement within the EU / EFTA area, driven chiefly by migration for employment. Immigrants tend to be highly educated. It concerns at least 44% of those of working age and 51% among recent arrivals. The United States is an exception, however, both because recent migration has been more limited and because the vast majority of immigrants came for family reasons.

As in the settlement countries, immigrant labour market outcomes are positive and broadly similar to those of the native-born. The same trend holds for the native-born children of immigrants in comparison with their peers who have no migrant background in the United Kingdom and the United States, but not in Switzerland and Luxembourg, where they face similar issues as those in countries from group 3.

In spite of good overall outcomes, immigrants live disproportionately often in poor-quality housing, notably in the United Kingdom and in the United States.

Despite some improvement over the last ten years, the naturalisation rate is relatively low in Luxembourg and Switzerland. What is more, in Switzerland and the United States, relatively low shares of immigrants with the nationality of their country of residence participate in national elections.

Group 3: Long-standing destinations with many low-educated migrants (Austria, Belgium, France, Germany, the Netherlands)

In this group, immigration has been shaped to a large degree by flows of poorly educated so called “guest workers” during the economic boom period in the wake of World War II. They were later followed by large inflows of family migrants, also with low levels of education.

Much of that migration went into urban areas and, indeed, although the immigrant population is more heavily concentrated in densely populated areas than the natives throughout the OECD and EU, this phenomenon is particularly pronounced in this group. Immigrants are, on average, almost twice as likely to live in densely populated areas as the native-born.

While the share of migrants with less than ten years of residence remained stable since 2006 in Belgium, France and the Netherlands, it increased sharply in Austria and Germany following the recent surge of humanitarian migrants but also due to the significant intake of EU mobile migrants over the past decade. In the two latter countries, recent migrants now represent around a third of all foreign-born. In all five countries, the share of the foreign-born in the total population is above the OECD average, ranging from 12% in France to 19% in Austria. Due to the long-standing nature of immigration, the share of the native-born with at least one foreign-born parent is also relatively high, ranging from 9% of the total population in the Netherlands to 15% in France.

Partly because of their lower levels of educational attainment and partly because a significant share over the last 40 years arrived for purposes other than employment, immigrants have worse labour market outcomes than their native-born peers. Immigrants’ employment rate is, on average, 10 percentage points lower than that of the native-born, their unemployment rate is 6 points higher. Non-EU immigrant women in particular have poor labour market outcomes. Their employment rate is 22 percentage points lower than that of their native peers and it has stagnated over the past 10 years in most countries in this group. Nevertheless, non-EU migrants’ labour market outcomes in Group 3 (with the exception of France and the Netherlands) have improved, although to the same extent than the native-born and the gaps thus remained at high levels.

Immigrants also face other integration issues linked to their relatively low levels of employment and education. These include higher poverty rates (including among children) and poorer-quality housing than among the native-born. Moreover, due to the high share of older migrants – mainly early “guest worker” cohorts now reaching retirement age – health issues are more frequent among the foreign- than the native-born. In addition, in most countries of this group, living conditions have worsened over the last ten years, especially in Austria and the Netherlands.

Disadvantages related to the poor educational background of many immigrant parents have often been passed on to their native-born children, whose educational outcomes lag well behind those of their peers with no migrant background, although gaps have narrowed over the past decades. At the age of 15, the difference is still between 1 and 1.5 years of schooling. As a result, the school-to-work transition is also more difficult for immigrant offspring, who have twice as high a chance as their peers with native parents of finding themselves neither in employment, education, or training – the so called “NEETs”.

The rate of acquisition of nationality among settled immigrants has decreased over the last decade. Moreover, those with the nationality of the country of residence are far less likely to participate in national elections than their native peers. With the exception of France, the poor social integration is also noticeable given the relatively high share of immigrants (nearly one in five) who do not report a strong sense of belonging to their country of residence.

Group 4: Destination countries with significant recent and humanitarian migration (Denmark, Finland, Norway, Sweden)

Humanitarian immigrants and their families have accounted for much of the immigration into these countries, especially during the large inflow in 2015 but already before that since the beginning of the 1990s. Immigrants are overrepresented at both ends of the education spectrum. Almost half of the resident foreign-born population of working age has arrived over the past ten years, a significant share of whom are EU / EFTA free mobility migrants and, more recently, humanitarian migrants. The share of the foreign-born and their offspring remains smaller than in the long-standing destination countries (with the exception of Sweden where immigrants constitute 18% of the population), but has increased sharply over the last decade. The overwhelming majority of immigrants are non-native speakers.

Recent non-EU migrants and particularly humanitarian migrants and their families tend to struggle to catch up the high standards of the native population in terms of economic outcomes. Indeed, as elsewhere, these groups of immigrants show rather poor labour market outcomes and experience much higher levels of relative poverty and lower-standard housing than the native-born. Immigrant offspring also have lower education outcomes than their peers with no migrant background – although the differences tend to be less pronounced than in Group 3.

A high share of immigrants has taken up host-country citizenship, and more than two-thirds of those with more than ten years of residence hold the citizenship of the host country (more than 75% in Norway and Sweden). In all countries of Group 4, more than 90% of immigrants report a strong sense of belonging to their country of residence and they are more likely than in other groups of countries to report being satisfied in life.

Group 5: New destination countries with many recent, low-educated migrants (Cyprus1,2, Greece, Italy, Portugal, Spain)

This group encompasses most of the southern EU countries, which were destinations of large numbers of labour migrants who came to fill low-skilled jobs in the first half of the 2000s up to the onset of the global financial and economic crisis. These inflows are mirrored by the large share of low-educated immigrants, although many high-educated immigrants also came to fill low-skilled labour needs prior to the economic crisis. As a result, the over-qualification rate is higher than elsewhere – both in absolute terms and relatively to the native-born. In 2017, it was twice as high among the foreign- as the native-born.

With the exception of Portugal and Spain, where a significant part of migration has been associated with post-colonial ties, few settled immigrants have naturalised.

Outcomes of non-EU immigrants have not recovered from the 2007-08 downturn (with the exception of Portugal). The reason is partly that they were concentrated in sectors sorely affected by job losses and partly because many migrants arrived just before or during the crisis. Before the economic downturn, immigrants had a higher employment rate than the native-born and in spite of significant declines since, it is still roughly the same as that of the native-born in all countries of this group. Since 2006-07, the unemployment rate of the foreign-born has increased by 10 percentage points, compared with 7 points among the native-born. The situation is particularly worrisome in Greece and Spain, where immigrants’ unemployment rate increased by 20 and 13 percentage points, respectively. For the many poorly educated migrants, employability has become a critical issue. While native-born children of immigrants are still a rather small group, the number entering the labour market is growing rapidly and they show worrying outcomes in terms of employment and unemployment rates.

Again with the exception of Portugal, the poverty rate among immigrants is twice as high as among the native-born, and their housing conditions are also much worse.

Group 6: New destination countries with many recent highly educated immigrants (Iceland, Ireland, Malta)

Like Group 5, the countries in this group have seen large numbers of labour migrants arrive in the last 10 years, and two in five of the foreign-born population have lived in their host countries for less than 10 years. However, in contrast to Group 5, recent labour migration has been relatively highly educated, mostly coming from other EU countries.

Although the situation of immigrants in this group is heterogeneous, overall integration outcomes tend to be better than in Group 5. They reflect the immigrant population’s advantageous socio-economic background, especially with respect to education. However, the highly educated experience high incidence of over-qualification in the labour market, with the problem aggravating further over the last 10 years (except in Ireland).

Group 7: Countries with an immigrant population shaped by border changes and/or by national minorities (Croatia, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, the Slovak Republic, Slovenia)

The group includes most EU member countries from Central and Eastern Europe. None have experienced much immigration for many years, apart from recent labour migration to Poland which is only partly mirrored in the present data. The bulk of the foreign-born population found themselves to be foreign-born as a result of border changes or nation-building in the late 20th century, mainly related to the fall of the Iron Curtain. Consequently, the foreign-born are an ageing group (one third are more than 65 years old) and the share of nationals among the foreign-born is high. The overall size of the foreign-born population differs widely, ranging from less than 5% in Lithuania, the Slovak Republic and Poland to 16% in Slovenia.

For most indicators, the foreign-born population has outcomes that are similar to, if not better than, those of the native-born, particularly in the labour market. However, immigrants in those countries are the least likely to report being satisfied in their life and having a sense of belonging to their country of residence (in particular in the Baltic countries). The fact that many immigrants are relatively old implies that they tend to be less healthy than the native-born.

Group 8: Emerging destination countries with small immigrant populations (Bulgaria, Chile, Japan, Korea, Mexico, Romania, Turkey)

The last group of immigrant destinations includes a very diverse set of OECD countries from the Americas, Asia, and Europe. In all of them, less than 3% of the population is foreign-born. As a result, information on integration outcomes is often not available and where it is – as for employment – there are relatively wide variations. For example, immigrants have better labour market outcomes than the native-born in Chile and Korea, whereas the reverse is the case in the other countries. However, the immigration situation is changing rapidly. The proportion of foreign-born residents has more than doubled since 2000 in all countries in this group, driven either by the offspring of former emigrants “returning to the land of their parents” or by labour immigrants. In Japan and Korea, international marriages have also accounted for a non-negligible share of immigration.

Table 1.2. Scoreboard of integration outcomes of the foreign-born population and their native-born offspring

Note: 2015/17: “+”: immigrant/native-born offspring outcomes (compared with native-born/native-born with native-born parents) are more favourable than on average in the OECD; “O”: no statistically significant difference (at 1% level) from the OECD average; “-“: immigrant/native-born offspring outcomes (compared with native-born/native-born with native-born parents) are less favourable than on average in the OECD.

Evolution between 2006/08 and 2015/17: “+”: more than a 2-percentage points change to the favour of immigrants/native-born offspring, “0” between a +2-percentage points change and a -2-percentage points change, “-“: more than a 2-percentage points change to the detriment of immigrants/native-born offspring (regardless of statistical significance). The evolution refers to absolute values, not differences vis-à-vis the native-born/native-born with native-born parents. “..”: data are not available or sample size is too small.

StatLink  http://dx.doi.org/10.1787/888933842261

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.

Figure 1.1: In New-Zealand's General Social Survey it is only possible to estimate the native-born immigrant offspring as those raised by people born abroad (or a mixed couple) without specifying if one or both people were actually the biological parents. The estimate is also constrained by sample size limitations. 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. In Chile, Iceland, Japan, Korea, Mexico and Turkey, the estimates for immigrant offspring are based on the share observed from 2003 PISA (among the 15-34 native-born) and the 2015 PISA (among the less than 15 years old native-born). In Ireland, the estimates for immigrant offspring are based on the share observed from the EU-LFS AHM 2008 (among the native-born aged 15 years and over) and the 2015 PISA (among the less than 15 years old native-born). In Germany, the parental origin is based on the country of birth of parents for the native-born still living with their parents, but is based on own citizenship or the citizenship at birth of the parents for those who do not live anymore with their parents. Therefore, the so-called native-born with foreign-born parents may also include native-born with one foreign- and one native-born parent (the latter being an offspring of foreign-born parents), as well as native-born with two native-born parents who are both themselves offspring of foreign-born parents. Data differ slightly from those presented in Figure 1.5 since data sources are different.

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

Sources

Table 1.3. Sources by figures
| Figure 1.1 | Figure 1.2 | Figure 1.3 | Figure 1.4 | Figure 1.5 Native speakers
OECD/EU |  |  |  |  |
Australia | Census 2016 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | Census 2016
Austria | LFS 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2012
Belgium | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Bulgaria | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | .. | ..
Canada | Census 2016 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2012
Chile | IMO 2018: data for 2015 (foreign-born); estimates based on PISA 2003 & 2015 (native-born) | .. | Indicator 3.4 | PISA 2015 | PIAAC 2015
Croatia | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Cyprus1,2 | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | .. | EU-LFS AHM 2014
Czech Republic | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Denmark | Population register 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2012
Estonia | LFS 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Finland | Population register 2016 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | ..
France | LFS 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Germany | Mikrozensus 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Greece | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Hungary | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Iceland | IMO 2018: data for 2017 (foreign-born); estimates based on PISA 2015 (native-born) | Indicator 4.1 | Indicator 3.4 | PISA 2015 | ..
Ireland | IMO 2018: data for 2017 (foreign-born); estimates based on PISA 2015 (native-born 0-14) and one EU-LFS AHM 2008 (native-born 15+) | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2012
Israel | LFS 2016 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2015
Italy | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | ..
Japan | IMO 2018: data for 2017 (foreign-born); estimates based on PISA 2003 & 2015 (native-born) | .. | Indicator 3.4 | .. | ..
Korea | IMO 2018: data for 2016 (foreign-born); SILCLF 2017 (native-born with immigrant parents); estimates based on PISA 2003 & 2015 (native-born with mixed background) | .. | Indicator 3.4 | .. | ..
Latvia | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Lithuania | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Luxembourg | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | .. | EU-LFS AHM 2014
Malta | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Mexico | IMO 2018: data for 2016 (foreign-born); estimates based on PISA 2003 & 2015 (native-born) | .. | Indicator 3.4 | PISA 2015 | ..
Netherlands | LFS 2016 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2012
New Zealand | Census 2013 (less than 15) & GSS 2016 (15+) | .. | Indicator 3.4 | PISA 2015 | PIAAC 2015
Norway | Population register 2016 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Poland | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | .. | EU-LFS AHM 2014
Portugal | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Romania | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | .. | EU-LFS AHM 2014
Slovak Republic | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Slovenia | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Spain | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Sweden | LFS 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2012
Switzerland | LFS 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
Turkey | IMO 2018: data for 2016 (foreign-born); estimates based on PISA 2003 & 2015 (native-born) | .. | Indicator 3.4 | .. | ..
United Kingdom | EU-LFS AHM 2014 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | EU-LFS AHM 2014
United States | CPS 2017 | Indicator 4.1 | Indicator 3.4 | PISA 2015 | PIAAC 2012
Partner/G20 countries |  |  |  |  |
Argentina | .. | .. | Indicator 3.4 | .. | ..
Brazil | .. | .. | Indicator 3.4 | PISA 2015 | ..
Colombia | .. | .. | Indicator 3.4 | .. | ..
Costa Rica | .. | .. | Indicator 3.4 | PISA 2015 | ..
Indonesia | .. | .. | Indicator 3.4 | .. | ..
Russia | .. | .. | Indicator 3.4 | .. | ..
Saudi Arabia |  .. | ..  | Indicator 3.4 | .. | .. 
South Africa | .. | .. | Indicator 3.4 | .. | ..

Additional sources:

for Figure 1.5

Share of foreign-born: Indicator 2.1

Recent immigrants: Indicator 2.8

Tertiary-educated: Indicator 3.1

Educated in the host country: Indicator 3.1

Share of labour and free movement migrants: Indicator 2.7

Old immigrants: Indicator 2.3

for Table 1.2

Employment rate: Indicator 3.4

Over-qualification rate: Indicator 3.10

Poverty rate: Indicator 4.2

Overcrowding rate: Indicator 4.3

Health status: Indicator 4.5

Acquisition of nationality rate: Indicator 5.1

PISA scores: Indicator 7.4

NEET rate: Indicator 7.9

Chapter 2.

Composition of immigrant populations and households

The societies of countries in the OECD and the European Union have been shaped by successive waves of immigration. Their scale and composition vary widely across countries. A number of socio-demographic factors drive integration outcomes. They include age, gender, family structure, living conditions, and geographical concentration. In addition to such factors, which also apply to the native-born, there are certain immigrant-specific determinants like category of entry, duration of stay, and region of origin. A grasp of how they differ from country to country and how immigrants compare to the native-born is a prerequisite for understanding integration outcomes.

Reasons for emigrating have a particularly strong bearing on economic integration. Most labour migrants, for example, have a job waiting for them on arrival, which is generally not the case for family and humanitarian migrants. An immigrant’s country of origin also matters, as the standard of its education system and how its labour market operates may impact the integration outcome in the host country. Another important factor is how long immigrants have lived in the host country, since integration takes place over time. It takes time, for example, to learn the host-country language, to understand how the host country’s labour market and public services function, just as it takes time to build networks.

This chapter starts by looking at the sizes of immigrant populations (Indicator 2.1) and their geographical concentration (Indicator 2.2). It then considers their age- and gender-related composition (Indicator 2.3) as well as differences in fertility and partnership practices by country of birth (Indicator 2.4). The chapter then analyses the foreign-/ native-born balance of households (Indicator 2.5) and their family make-up (Indicator 2.6). The chapter then addresses key immigrant-specific factors, such as the composition of immigration flows by category of migration (Indicator 2.7), length of stay, and the regions of origin of the immigrant population resident in the European Union (Indicator 2.8).

Key findings

The OECD is home to around 128 million immigrants, over 10% of its population. Around 58 million foreign-born residents live in the EU – 11.5% of its population. Around two-thirds are from non-EU countries.

Over the last decade, the immigrant population has increased by 23% in the OECD and by 28% in the EU – respective rises of 1.5 and 2.5 percentage points relative to the total populations of the two areas.

Norway and Malta have seen at least a doubling in their number of foreign-born residents over the past 10 years; the foreign-born population in Poland has quadrupled over the last decade though their share in the total population remains low.

Migrant populations are not evenly distributed between regions within countries. Variations in regional distributions tend to be greater in countries where immigrants account for high shares of the total population, such as Australia, Belgium, Canada, the United Kingdom, and the United States.

Immigrants are more heavily concentrated in capital and urban regions than their native-born peers. In Europe, populations of non-EU migrants have a greater tendency than their EU peers to congregate in these areas. The increase in immigrant populations over the past decade was more pronounced in urban regions.

In both the OECD and the EU, around 80% of the foreign-born are of working age (15 to 64 years old), well above the 64% of the native-born. In Mexico and Romania, by contrast, over 40% of the immigrant population is under 15 years old – often the offspring of returning migrants.

The dependency ratio of immigrants is less than half that of the native-born in about half of countries. Differences are especially acute in Southern European countries and in Nordic countries. The sole country where dependency ratios are similar in both groups is the United States.

While almost 90% of the native-born cohabit with someone of the same origin, two-thirds of immigrants do.

The total fertility rate among immigrants is almost 1.9 children per woman in both the OECD and the EU – 0.25 more children on average than among native-born women in OECD countries and 0.35 more than in the EU.

Across the OECD, 14.5% of all households are headed by at least one immigrant.

Immigrant households are slightly larger than native-born ones in most OECD and EU countries.

Families account for one-third of immigrant households in the OECD but only a quarter of native-born ones. In the EU, however, single-person arrangements account for 38.5% of immigrant households, making them the most widespread form, particularly in longstanding immigration countries.

In 2016, OECD countries received 5 million permanent immigrants. The number was 2.8 million in the EU. In both 2015 and 2016, newly permanent immigration inflows accounted for 0.4% of the OECD’s total population and 0.6% of the EU’s.

OECD-wide, inflows over the last 12 years have been dominated by family migration (36%), free movement (28%), and labour migration, (14%). Despite recent strong increases in some countries, humanitarian migrants have accounted for less than 10% of all permanent inflows to the OECD and the EU in the last 12 years. Nevertheless, almost 30% of immigrants settled in Sweden since 2005 were humanitarian migrants.

The intra-EU movement of labour and people from other EU countries has driven almost half of all permanent flows in the EU over the last 12 years.

Compared with the average figures during the 2005-14 period, inflows as a percentage of the population tripled in Germany and doubled in Austria in 2015-16. Rates also increased significantly in Denmark, the Netherlands and Sweden. Permanent immigration to the countries of Southern Europe, by contrast, has almost halved.

Over two-thirds of immigrants in the OECD and EU have lived in their host country for at least 10 years, while 17% have been residents for up to five years.

More than half of the foreign-born in the EU originate from other European countries – over 30% from countries in the EU and around 20% from outside the EU.

In OECD countries outside Europe, the foreign-born come chiefly from Asia or countries of origin that neighbour host countries. Over 50% of the migrant population in the United States, for instance, was born in Latin America and the Caribbean. In Australia and Canada, around half of the immigrant population is Asian-born.

2.1. Size of the immigrant population

Definition

The immigrant population is taken to be all people born outside the country in which they are resident. They may also be referred to as “the foreign-born”.

Coverage

Total populations, foreign- and native-born, all ages.

The OECD is home to around 128 million immigrants, who account for over 10% of its population. Over the last decade, the immigrant population has increased by 23% in the OECD and by 28% in the EU – respective rises of 1.5 and 2.5 percentage points relative to the total populations of the two areas. Around 58 million foreign-born residents live in the EU – 11.5% of its population. Around two-thirds are from non-EU countries. Over one-third of immigrants in the OECD live in the United States, where they make up almost 14% of the population. Luxembourg is the country with the highest share of foreign-born – over 46% of its population. In numerical terms, Germany is Europe’s largest immigrant host country, being home to 22% of all the foreign-born living in the EU. Next comes the United Kingdom with 16%, France with 14%, then Italy and Spain with around 10% each.

In the settlement countries which have long operated a policy of large-scale, managed migration programmes – i.e. Australia, Canada, and New Zealand – immigrants represent more than one-fifth of their populations. Most Asian, Latin American and Central European OECD countries, by contrast, have small immigrant populations. Across eight countries in those regions, an average of less than 3% of the population is foreign-born.

The foreign-born share of populations has increased in virtually all OECD countries over the past decade. The only exceptions are Israel and the Baltic states, where the ageing of the foreign-born has not been offset by new arrivals. In the case of Israel, its fertility rate – one of the highest in the OECD – has also been a factor in the decline of the foreign-born as a share of the total population. In the five countries hosting the largest numbers of immigrants in absolute terms (the United States, Germany, the United Kingdom, France, and Canada), the foreign-born population has increased by more than 10% over the last decade – a rise of at least 1 percentage point relative to the total population of the five countries. In the United Kingdom, the increase has been as high as 60% (5 percentage points).

The free movement of people within the EU/EFTA has been a key driver of the growth in the foreign-born population, especially in the context of enlargements of the EU in the 2000s. Another, albeit lesser, factor has also been at play – recent inflows of humanitarian migrants. Norway, for instance, which has been affected by the two factors, has seen an increase of over 6 percentage points in the foreign-born share of its population and a doubling in its number over the past 10 years. As for Malta, the increase has been even steeper. Another country to have experienced a steep increase in its foreign-born population is Poland. It has quadrupled over the last decade, in recent years particularly, due to large immigrant inflows from Ukraine. Nevertheless, the foreign-born still account for only 4% of the Polish population. The trend in Spain and Italy, however, has been different. They saw sharp increases in their foreign-born population in the boom years at the turn of the century. Since the crisis, however, inflows have dwindled and a certain outflow has been observed. As a result, shares of immigrants are much the same as 10 years ago in both countries’ populations.

Figure 2.1. Foreign-born shares of populations

Shares as percentage of total populations, 2006 and 2017

StatLink  http://dx.doi.org/10.1787/888933842280

Figure 2.2. Distribution of the foreign-born population, by host country

Foreign-born populations as percentage of total populations, 2006 (inner ring of circle) and 2017 (outer ring)

StatLink  http://dx.doi.org/10.1787/888933842299

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

2.2. Regional distribution

Definition

Concentrations of immigrant populations vary from region to region within countries. Variations in their regional distribution are expressed as the range between the highest and the lowest regional share of immigrants in the population in a country. Regions are defined in accordance with Level 2 in the NUTS 2016 classification of regions.

Coverage

Total populations (all ages). Except for comparisons of 2005 and 2015, where coverage applies to populations aged 15 and over.

Migrant populations are not evenly distributed between regions within countries. In Australia, Belgium, Canada, the United Kingdom, and the United States, differences between the regions with the highest and lowest concentrations of migrants exceed 20 percentage points. Belgium has the widest gap, where 42% of the population in the Brussels-Capital region is foreign-born, compared with only 6% in Western Flanders.

Variations in regional distributions of immigrants tend to be greater in countries where immigrants account for high shares of the total population. Indeed, in the ten countries with the widest regional disparities, the foreign-born share of the total populations is above that of the OECD as a whole. The only notable exception is Ireland. Although immigrants make up a large proportion of its population, there is very little disparity between regions in concentrations of the foreign-born.

Immigrants are more heavily concentrated in capital and urban regions than their native-born peers. In Europe, the regions where they constitute the largest shares of the population are overwhelmingly capital-city regions. The only countries that are exceptions to that rule are Germany, Italy, Spain, Poland and Switzerland.

In Europe, populations of non-EU migrants have a greater tendency than their EU peers to congregate in particular areas. In other words, regional differences are generally wider among non-EU than EU mobile nationals – partly due to the heavier concentrations of non-EU migrants in capital-city areas. While such areas boast the highest shares of non-EU nationals in their populations (everywhere but Italy, Spain, Poland and Switzerland), this is less the case for migrants born in other EU countries. In the United Kingdom, for example, there is a 24-point gap between Greater London and Northern Ireland in the non-EU migrants shares of the two regions’ populations, while it is less than 10 points when it comes to EU national populations.

Across the OECD, regions with large proportions of highly educated natives usually boast similar proportions of highly educated immigrants. The inference is that the highly educated foreign-born tend to locate in the same regions as their native-born peers. The same pattern is not observed among the foreign- and native-born with low levels of education. The regions with the greatest numbers and shares of highly educated migrants, are found in Northern Europe, Australia and Canada.

The immigrant shares of most OECD regions’ populations either increased or remained stable between 2005 and 2015. They rose most steeply in regions with high levels of development and large foreign-born populations. In most countries, the increase was more pronounced in urban regions, particularly so in Norway. In Canada, France, Portugal, Spain and the United States, by contrast, the rise in shares of the foreign-born was largest in rural and intermediate regions, though not always by a large extent.

Figure 2.3. Disparities between regional foreign-born shares

Regional foreign-born shares as percentages of total regional populations, 2014-15

StatLink  http://dx.doi.org/10.1787/888933842318

Figure 2.4. How shares of immigrants in mostly rural and urban regions have evolved

Changes in percentage points in populations aged 15 and over, 2005 to 2015

StatLink  http://dx.doi.org/10.1787/888933842337

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

2.3. Age

Definition

This section considers the composition of immigrant populations by age. The dependency ratio is the number of non-working age individuals (aged under 15 and over 64) divided by the number of working-age individuals (15-64 years old).

Coverage

Total populations (all ages).

In the OECD and the EU, around 80% of the foreign-born are of working age (15 to 64 years old), well above the 64% of the native-born. They are even more present in the primary working age bracket (25 to 54 years old). In Southern Europe, which took in large numbers of labour migrants prior to the economic crisis, 90% are of working age.

Around 6% of immigrants are children under the age of 15, compared with 19% of the native-born in the OECD and 17% in the EU. The underrepresentation of immigrant children is probably attributable to the fact that immigrants are more likely to have children once they have settled. These children are thus native-born. Immigrants under 15 years of age are fewest in the longstanding migrant destinations of Europe and in Central and Eastern European countries whose foreign-born populations have been shaped by border changes.

Overall, there are more people aged 65 and over among native- than foreign-born populations – the proportions in the OECD are 17% and 15%. Indeed, this is the case in two-thirds of EU and OECD countries, and particularly so in the EU taken on its own. Longstanding European immigration destinations and Central and Eastern European countries have larger shares of older foreign-born populations than other OECD and EU countries. In France and Germany, for example, over 20% of migrants are aged 65 or older. Shares are even higher in many Central European and Baltic countries, such as Poland and Estonia, where over 40% of the foreign-born population is over 65.

In some emerging destination countries, recent migrant inflows include relatively large shares of children. In Mexico and Romania, for example, over 40% of the immigrant population is under 15 years old – often the offspring of returning migrants. In the wake of the 2008 economic crisis, many emigrants chose to return to their home country, taking with them their children born in the host country where they had settled.

The dependency ratio of immigrants is lower than that of the native-born, even when it includes their native-born children. In about half of countries, immigrant dependency ratios are less than half those of the native-born. Differences are especially acute in the Southern European countries that experienced large labour migrant inflows prior to the economic crisis. They are also wide in Nordic countries, such as Finland and Denmark. Only in a handful of countries with a high incidence of older immigrants, as in the Baltic countries, do foreign-born populations have significantly higher dependency ratios than their native peers. The sole country where dependency ratios are similar in both groups is the United States. While old-age dependency is greater among the native-born, the child-related dependency ratio is higher among the foreign-born.

Figure 2.5. Age composition

Age groups as percentage of total populations, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842356

Figure 2.6. Dependency ratios

Ratios as percentage, 2016

StatLink  http://dx.doi.org/10.1787/888933842375

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

2.4. Endogamous partnership and fertility

Definition

The endogamous partnership rate is the share of individuals cohabiting with a person of the same region of origin. A region of origin is a geographical grouping of countries of birth or, in the case of the native-born, the parents’ country of birth. A person born in a given group of countries, and living with a partner of whom at least one parent was born in the same group of countries, is considered endogamous.

The total fertility rate (TFR) is the number of births per woman. It is calculated as the number of children that would be born alive to a woman during her lifetime if she were to spend her childbearing years bearing children in accordance with the age- and group-specific fertility rates of a given year. The TFR is estimated from the number of under-fives declared by respondents in the course of household surveys, then matched with the official TFR drawn from birth registers. The TFR presented here may include children under five born abroad. It may, therefore, not be fully consistent with administrative data.

Coverage

For endogamous partnerships: all persons over 15 years old who report that they are cohabiting. For fertility rates: all women aged 15 to 49 years old, the “childbearing” years.

Most cohabiting individuals – immigrants and natives alike – are endogamous EU- and OECD-wide. Almost 90% of the native-born cohabit with someone of the same origin. The respective share among immigrants is two-third. Native-born are most likely to live with persons of the same origin in countries of Southern Europe, where many foreign-born are recently arrived, as well as in Central Europe, where the foreign-born population is relatively small and old. By contrast, with an endogamy rate below 80%, native-born couples are more diverse in countries where many children are the native-born offspring of immigrants, such as Latvia, Estonia, and longstanding immigration countries, especially in France, Israel and Luxembourg. In the latter two countries, immigrants are actually more endogamous than the native-born. Greece, Italy, Spain and, to a lesser extent, Canada, are the countries with the highest endogamy rates among both the native- and foreign-born.

The total fertility rate (TFR) among immigrants is almost 1.9 children per woman in both the OECD and the EU – 0.25 more children on average than among native-born woman in OECD countries and 0.35 more in the EU. Foreign-born women have more children on average than their native-born peers in three out of five countries. Belgium, France and Lithuania have the highest estimated immigrant TFRs (at least 2.2 children per woman) – 0.6 children more than the native-born. The gap is also wide in a number of countries where native-born fertility is very low, such as in Spain and Croatia. Total fertility rates among the foreign- and native-born, by contrast, are very similar in Ireland, the United Kingdom and the Netherlands. The native-born have actually more children in parts of Central and Eastern Europe and the Oceanian OECD countries. In Israel, they have twice as many children as the foreign-born.

Figure 2.7. Endogamous partnership rates

Rates as percentage of populations aged 15 and above, 2016

StatLink  http://dx.doi.org/10.1787/888933842394

Figure 2.8. Total fertility rates

Number of births per woman, 15- to 49-year-olds, 2012-16

StatLink  http://dx.doi.org/10.1787/888933842413

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

2.5. Immigrant households

Definition

An immigrant household is defined as a group of persons who usually share the same dwelling, where – looser definition – at least one head of household (also called responsible person) is an immigrant or – strict definition – all the heads of the household are immigrants. Up to two people can be household heads, but definitions thereof may vary from one country to another. The stricter definition applies in this publication, unless otherwise stated. The average size of households includes all occupants in the dwelling and is calculated for entirely immigrant and entirely native-born households. It thus excludes mixed households. There are no data on immigrant households in Japan or Turkey.

Coverage

Households with at least one head of household over the age of 15.

Across the OECD, 14.5% of all households are headed by at least one immigrant. In three-quarters of such households (which account for 10.5% of the total number), all heads are immigrants. The share of immigrant households in the EU is somewhat lower: 13% of all households are headed by at least one immigrant and immigrants are the sole heads of 9%. Among the latter, two-thirds are made up by non-EU foreign-born and one-third by EU migrants. There are very few households headed by one EU and one non-EU migrant. In Australia, Israel and New Zealand, up to 40% are headed by at least one immigrant. Luxembourg and Switzerland – both longstanding immigration destinations that do host many intra-EU migrants – have the highest shares of immigrant households in Europe (mainly from EU countries). At least one immigrant heads half of all households in Luxembourg and one-third in Switzerland. As for Austria, Ireland and Sweden, the rate is one in five. Estonia and Latvia, too, have high shares of immigrant households, especially ones where immigrants are the sole heads. Immigrant households account for less than 5% of the total number, however, in most Central European countries (e.g. Poland, Hungary and the Czech Republic), in Latin American OECD countries like Mexico and Chile, and in Korea.

Mixed households – where one head is foreign-born and the other native – make up 4% of households in both the OECD and the EU. In half of them in the EU, the immigrant head is born in a third country. Mixed households are most widespread in the settlement countries, particularly Australia and Israel, where around one household in seven is mixed. The figure exceeds 6% in Ireland, Sweden and longstanding European immigration countries like Germany, the Netherlands and Switzerland. As for the United States, 5% of households are mixed. The share is, however, particularly low in emerging destination countries such as Mexico, Chile, Poland, as well as in Denmark.

Immigrant households are slightly larger than native-born ones in most OECD and EU countries. The OECD foreign-born household size is 2.7 people, compared with 2.4 in native-born households. In the EU, the difference is smaller with figures being 2.4 members in foreign- and 2.3 in native-born households (notably due to large share of single-person migrant household, see Indicator 2.6). Immigrant households are larger in Greece, Luxembourg, Canada and the United States, by no less than 0.5 persons. However, native-born households are larger in two-fifths of countries, such as Israel, Latin American OECD countries, and most Central and Eastern European countries. As the presence of children widely determines the size of a household, households tend to be smaller in countries where their members are older. Most striking examples are immigrant households in Poland and the Baltic countries.

Figure 2.9. Households headed by immigrants

Percentages of households, 2016

StatLink  http://dx.doi.org/10.1787/888933842432

Figure 2.10. Household sizes

Average number of persons in solely immigrant and native-born households, 2016

StatLink  http://dx.doi.org/10.1787/888933842451

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

2.6. Household composition

Definition

This indicator identifies four types of households depending on whether or not children under the age of 18 are present and whether one or more adults live in the household. Households may thus be divided into four broad categories: single-person households – one adult, no children; adults without children – living as a couple or not; single-parent households with at least one child – referred to as “single-parent families”; and two or more adults with at least one child – referred to as “families” for the sake of simplicity.

Coverage

Households with at least one responsible person, or head of household, over the age of 15.

Families constitute the most common form of household among immigrants in the OECD. They account for one-third of immigrant households (32.5%) but only a quarter of native-born ones. A further 31% are single-person households, 30.5% are made up of adults without children, and 6% are single-parent families. In the EU, however, single-person arrangements account for 38.5% of immigrant households, making them the most widespread form. Next come families (29%), adults without children (27%), and single-parent families (6%). Overall, children are present in 38% of immigrant households OECD-wide, compared with 30% of native-born households. There are children in at least half of immigrant households in predominantly recent immigration destinations like Chile, Greece and Ireland. That share falls to only 10%, however, in countries with high shares of older immigrants, such as the Baltic countries, Poland and the Slovak Republic. In almost three-quarters of countries, the incidence of households with children is greater among the foreign- than the native-born. The gap is particularly wide – by at least 14 percentage points – in the United States, Southern European countries, and European countries, like Luxembourg and Ireland, which have recently attracted highly educated immigrants from other EU countries.

Immigrants are less likely to live in multiple-adult households without children than the native-born. Such living arrangements include couples without children, parents living with their adult children, and flat shares. About 40% of native households comprise adults living together without children in the OECD and EU, an arrangement that is respectively 9 and 14 percentage points less widespread in immigrant households. In Southern European countries, many households are made up of elderly couples, while large numbers of young adults live longer at home with their parents. As a result, the incidence of multiple-adult households is much greater among the native-born than among immigrants. The reverse is true, however, in some countries with relatively old immigrant populations, such as Israel, Estonia and Latvia.

Single-person households are more common among immigrants in three out of five countries, particularly in Europe. They account for over 40% of immigrant households in longstanding destinations with many settled, poorly educated foreign residents (e.g. France, Germany and the Netherlands), in countries with ageing foreign-born populations (like the Baltic countries and Poland), and in Italy and Norway. That share is at least 8 percentage points higher than among natives. The foreign-born are also more likely than the native-born to live alone in Israel and Latin American OECD countries, where the incidence of single-person households among the native-born is lowest. In Switzerland, Australia, Canada and the United States, by contrast, the native-born are more likely to live alone than immigrants. Lastly, single-parent households are slightly more widespread among the foreign- than the native-born in both the OECD and the EU.

Table 2.1. Composition of households
Percentages (left) and differences in percentage points (right), 2016
| Immigrant households | Difference (+/-) with the native-born households +: higher than the native-born -: lower than the native-born
| No child in the household | Child(ren) in the household | No child in the household | Child(ren) in the household
| Single person | More than one adult | Single person | More than one adult | Single person | More than one adult | Single person | More than one adult
| Total=100 | Difference in percentage points
Australia | 26.8 | 24.3 | 13.4 | 35.5 | -4.9 | -0.7 | -0.4 | +6.0
Austria | 36.9 | 28.1 | 3.8 | 31.2 | -1.8 | -12.8 | +1.7 | +12.8
Belgium | 40.6 | 27.1 | 5.8 | 26.5 | +5.2 | -14.9 | +2.9 | +6.8
Canada | 36.8 | 30.0 | 6.6 | 26.6 | -3.8 | -4.3 | +0.1 | +8.0
Chile | 21.6 | 29.3 | 7.9 | 41.2 | +8.0 | -9.7 | +2.4 | -0.8
Croatia | 21.7 | 48.8 | 1.4 | 28.1 | -3.8 | +2.7 | +0.5 | +0.6
Cyprus1,2 | 26.1 | 38.8 | 5.4 | 29.7 | +4.8 | -9.3 | +3.3 | +1.2
Czech Republic | 39.1 | 34.7 | 2.1 | 24.0 | +10.5 | -9.5 | -0.7 | -0.3
Denmark | 43.6 | 22.0 | 8.5 | 25.8 | -2.2 | -9.5 | +4.7 | +7.0
Estonia | 46.1 | 42.4 | 1.0 | 10.5 | +8.3 | +8.4 | -2.4 | -14.3
Finland | 44.4 | 23.3 | 5.6 | 26.7 | +2.4 | -13.9 | +2.5 | +9.0
France | 42.6 | 26.4 | 6.8 | 24.3 | +5.3 | -10.4 | +2.9 | +2.1
Germany | 50.7 | 22.1 | 5.9 | 21.3 | +7.9 | -12.2 | +1.5 | +2.8
Greece | 18.9 | 31.0 | 1.8 | 48.2 | -7.9 | -18.3 | +0.8 | +25.3
Hungary | 32.0 | 33.2 | 3.9 | 30.9 | -2.1 | -9.2 | +1.3 | +10.0
Iceland | 39.4 | 18.4 | 9.1 | 33.2 | +8.4 | -18.4 | +3.3 | +6.7
Ireland | 18.2 | 27.5 | 9.3 | 45.0 | -8.8 | -12.0 | +4.4 | +16.4
Israel | 33.2 | 44.4 | 0.5 | 21.9 | +18.0 | +15.6 | -2.2 | -31.4
Italy | 40.5 | 23.4 | 4.1 | 32.0 | +8.6 | -20.8 | +1.9 | +10.3
Latvia | 42.6 | 45.0 | 2.2 | 10.2 | +13.3 | +5.1 | -1.7 | -16.6
Lithuania | 60.2 | 29.2 | 2.7 | 7.9 | +22.0 | -7.0 | -1.0 | -14.0
Luxembourg | 24.1 | 34.8 | 2.8 | 38.3 | -10.0 | -9.5 | +0.4 | +19.2
Malta | 39.6 | 34.2 | 6.1 | 20.1 | +14.4 | -12.6 | +3.5 | -5.4
Mexico | 42.4 | 26.7 | 5.4 | 25.4 | +32.9 | -5.0 | +0.7 | -28.5
Netherlands | 53.5 | 20.4 | 7.2 | 18.8 | +14.8 | -18.7 | +4.5 | -0.5
Norway | 57.7 | 16.4 | 9.6 | 16.3 | +12.4 | -15.4 | +4.4 | -1.5
Poland | 60.8 | 32.2 | 0.4 | 6.6 | +34.5 | -11.2 | -0.9 | -22.4
Portugal | 25.5 | 31.9 | 9.7 | 33.0 | +2.8 | -17.6 | +7.0 | +7.7
Slovak Republic | 67.0 | 22.0 | 0.0 | 11.0 | +44.8 | -23.8 | -1.6 | -19.4
Slovenia | 36.7 | 39.5 | 0.6 | 23.1 | +6.2 | -1.0 | -2.1 | -3.1
Spain | 23.2 | 34.0 | 5.2 | 37.6 | -3.8 | -12.9 | +3.2 | +13.4
Sweden | 40.8 | 25.5 | 8.6 | 25.0 | -4.9 | -8.0 | +4.8 | +8.1
Switzerland | 36.7 | 31.3 | 3.2 | 28.8 | -5.4 | -8.9 | +1.0 | +13.4
United Kingdom | 30.3 | 29.3 | 5.9 | 34.5 | -0.8 | -13.9 | +0.8 | +13.9
United States | 21.7 | 35.0 | 5.1 | 38.2 | -9.1 | -6.2 | -0.3 | +15.6
OECD total (31) | 31.2 | 30.5 | 5.8 | 32.5 | +0.4 | -8.8 | +1.4 | +7.0
EU total (27) | 38.5 | 27.1 | 5.6 | 28.8 | +4.3 | -13.6 | +2.4 | +6.8
Argentina | 25.6 | 33.1 | 6.0 | 35.4 | +7.7 | +0.4 | +0.3 | -8.5
Brazil | 34.8 | 44.1 | 2.8 | 18.3 | +22.9 | +10.0 | -2.8 | -30.0
Colombia | 38.6 | 32.8 | 6.2 | 22.4 | +27.2 | +10.0 | -1.8 | -35.4
Costa Rica | 14.5 | 23.4 | 8.4 | 53.7 | +2.8 | -9.7 | +1.7 | +5.1
Indonesia | 29.7 | 24.0 | 5.8 | 40.5 | +22.2 | +1.5 | +2.4 | -26.1
South Africa | 43.3 | 30.5 | 4.6 | 21.6 | +17.1 | +8.7 | -4.3 | -21.4
StatLink  http://dx.doi.org/10.1787/888933842546
Notes and sources are to be found at the end of the chapter.

2.7. Immigration flows by category

Definition

The OECD collects data by category of residence permit from most EU and OECD countries. These administrative data are standardised by the OECD for 24 countries. This section considers: i) permanent immigration flows as a percentage of the total population; ii) the composition of permanent immigration flows by legal category of entry.

Coverage

Permanent immigrants are foreign nationals of any age who received in a given year a residence permit that, under normal circumstances, grants them the right to stay permanently in the host country. They include foreigners who obtain a permanent residence permit upon entry, those who have an initial temporary residence permit which is routinely and indefinitely renewed or transformed into permanent residence, and free mobility migrants (excluding those on short-term stays). To these are added temporary immigrants who become permanent-type residents following a change in their status, such as students taking up employment after completing their studies.

In 2016, OECD countries received 5 million permanent immigrants. The number was 2.8 million in the 15 EU countries considered. In both 2015 and 2016, newly permanent immigration inflows accounted for 0.4% of the OECD’s total population and 0.6% of the EU’s. They comprised less than 0.5% of populations in Asian OECD countries, Southern Europe, France and the United States, and less than one-thousandth in Mexico and Japan. In Australia and Canada, permanent immigration inflows made up between 0.8 and 1% of the total population in both years. The share of newly permanent residents in New Zealand was even higher. In the EU, in countries that are home to large numbers of intra-EU migrants and those with high recent refugee intakes, inflows accounted for more than 1% of the population. These countries include Austria, Germany and the Nordic countries (except Finland). Newly permanent foreign residents account for 1.5% of Switzerland’s population and more than 3% of Luxembourg's, two countries that attract a significant number of intra-EU labour migrants. Indeed, the free intra-EU movement of labour and people has driven almost half of all permanent flows in the EU over the last 12 years. That share is twice that of flows related to family migration and three times greater than those of labour migration from non-EU countries. Free mobility is behind the bulk of inflows into three out of five European countries and three-quarters of permanent arrivals in Luxembourg, Ireland and Switzerland.

OECD-wide, inflows over the last 12 years have been dominated by family migration (36%), free movement (28%), and labour migration, which makes up 14% of flows, or 21% if their accompanying families are included. Family migration is the driving force behind two-thirds of immigration to the United States, to Korea (60%) and to France (43%). Labour migration that includes accompanying family members makes up one-third of all permanent inflows into Japan and one-half in the settlement countries with their large-scale, carefully managed labour migration programmes. Despite recent strong increases in some countries, humanitarian migrants have accounted for less than 10% of all permanent inflows to the OECD and the EU in the last 12 years. Nevertheless, they have represented since 2015 more than 13% of flows in Austria, Canada, Germany, the Nordic countries and the United States. Almost 30% of immigrants settled in Sweden since 2005 have benefited from international protection.

Compared with the average figures during the 2005-14 period, inflows as a percentage of the population tripled in Germany and doubled in Austria in 2015-16. Rates also increased significantly in Denmark, the Netherlands and Sweden. Permanent immigration to the countries of Southern Europe, by contrast, has almost halved. It is also much lower in Ireland than in the decade prior to 2015. It has remained broadly constant in Australia, the United States and the United Kingdom.

Figure 2.11. Inflows of permanent migrants

Percentages of the population in 2005-14 and 2015-16

StatLink  http://dx.doi.org/10.1787/888933842470

Figure 2.12. Categories of entry

Percentages, 2005-16

StatLink  http://dx.doi.org/10.1787/888933842489

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

2.8. Duration of stay and regions of origin

Definition

The duration of stay refers to the length of time that has elapsed since an immigrant’s year of arrival. Region of origin denotes five broad regions, namely Asia, Africa, Europe (including Turkey), Latin America and the Caribbean, and Canada-United States-Oceania. This indicator considers as long-term or settled immigrants those foreign-born with 10 or more years of residence. It considers immigrants with under five years of residence as recent arrivals.

Coverage

Immigrants aged between 15 and 64 years old, excluding those whose country of origin is not reported

Over two-thirds of immigrants in the OECD and EU have lived in their host country for at least 10 years, while 17% have been residents for up to five years. In the Baltic countries and Croatia, for example, where immigration has been shaped by border changes, more than 90% of the foreign-born have been settled for 10 years or more. Settled immigrants also account for over three-quarters of migrants in longstanding immigration countries with relatively few recent arrivals, such as the United States, France and the Netherlands. By contrast, they make up only around half of the foreign-born population in other countries with a long and significant immigrant presence, like Luxembourg, the United Kingdom, Norway and Denmark. Their share is even lower in such new destination countries as Romania and Chile, where over half of the foreign-born population have lived for less than five years. The share of recent arrivals climbs to 60% in Korea. The Southern European countries that drew large numbers of low-educated labour migrants prior to the crisis have seen relatively few new arrivals – doubtless because of struggling labour markets.

More than half of the foreign-born in the EU originate from another European country – over 30% from countries in the EU and around 20% from outside. Those levels represent a slight fall over previous years. The immigrant population from Europe accounts for over two-thirds of the immigrant population in half of European countries, and the immigrant population from the EU (intra-EU mobility) for more than a half in one quarter of European countries. In Luxembourg and Austria, and in most European countries where the immigrant population has been shaped by border changes, over 80% of migrants are European-born (from inside or outside the EU).

Much less European is the make-up of the immigrant population in countries with recent intakes of humanitarian and poorly educated labour migrants. In most Nordic countries, for example, over half of the immigrant population was born outside Europe, chiefly in Asia. The immigrant populations of a number of European countries are shaped by post-colonial ties and the legacy of the recruitment of so-called “guest workers” in the wake of World War II. Some 40% of immigrants in the EU were born in Africa or Asia. Belgium, France and the Netherlands, for example, are all home to large numbers of African-born migrants, while in the United Kingdom, one in three immigrants originates from Asia, particularly South Asia. One-third of Spain’s migrant population was born in Latin America and one-fifth in Africa, mainly Morocco. As for Portugal, its largest migrant group – over 40% of its foreign-born residents – is African-born and comes mainly from its former colonies. Outside Europe, the foreign-born come chiefly from Asia or countries of origin that neighbour host countries. Over 50% of the migrant population in the United States, for instance, was born in Latin America and the Caribbean. And in Mexico, Chile, Japan and Korea, more than 85% originate from neighbouring countries. In Australia and Canada, around half of the immigrant population is Asian-born.

Figure 2.13. Duration of stay among immigrants

Percentages staying up to 5 years and over 10 years, 15- to 64-year-olds, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842508

Figure 2.14. Regions of birth

Percentages of the population, 15- to 64-year-olds, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842527

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

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.

Korea, Japan and Saudi Arabia determine who is an immigrant on the basis of nationality, not on the basis of country of birth.

New Zealand determines the migration status of the household according to the country of birth of the main head of household only.

Figure 2.3: Regions in bold refer to capital-regions.

Figure 2.8: As children’s country of birth is not available in Israel, all young children in the family are deemed to be born in the country.

Figure 2.13: Korea includes in the immigrant population all foreigners and immigrants who have been naturalised in the past 5 years. Shares shown are for the 15-59 population.

Figure 2.14: In Mexico, immigrants born in Canada are included in the “Latin America and Caribbean” region of origin, because they cannot be distinguished from “Other American countries”. In Finland and Sweden, immigrants born in Northern Africa are included in the “Asia” region of origin because they cannot be distinguished from “Near and Middle-East”.

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

For further detailed data, see Annex A.

Table 2.2. Sources by indicator
| 2.1 Size of the immigrant population | 2.2 Regional distribution | 2.3 Age | 2.4 Endogamous partnership and fertility | 2.5 Immigrant households | 2.6 Household composition | 2.7 Immigration flows by category | 2.8 Duration of stay and regions of origin
OECD/EU |  |  |  |  |  |  |  |
Australia | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | Census 2016 (F2.5 only) | Census 2016 | Census 2016 | Census 2016 | IMD 2005-2016 | Census 2016
Austria | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Belgium | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Bulgaria | Eurostat 2017 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Canada | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | Census 2016 (F2.5 only) | Census 2016 | Census 2016 | Census 2016 | IMD 2005-2016 | Census 2016
Chile | IMO 2018: data for 2007 & 2015 | .. | CASEN 2015 | .. | CASEN 2015 | CASEN 2015 | .. | CASEN 2015
Croatia | Eurostat 2017 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Cyprus1,2 | Eurostat 2009 & 2017 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Czech Republic | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Denmark | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | .. | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Estonia | IMO 2018: data for 2007 & 2017 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Finland | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | .. | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
France | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Germany | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | Mikrozensus 2016 (F2.5 only) | Mikrozensus 2016 (F2.8 only) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | Mikrozensus 2016
Greece | IMO 2018: data for 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Hungary | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Iceland | IMO 2018: data for 2007 & 2017 | .. | .. | .. | EU-SILC 2015 | EU-SILC 2015 | .. | EU-LFS 2015-16
Ireland | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS 2016 (F2.8 only) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Israel* | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | .. | LFS 2011 | IHS 2015 | IHS 2015 | IMD 2014-2016 | LFS 2016
Italy | IMO 2018: data for 2009 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Japan | IMO 2018: data for 2007 & 2017 | .. | Census 2015 (F2.5 only) | .. | .. | .. | IMD 2005-2016 | ..
Korea | IMO 2018: data for 2007 & 2016 | .. | Census 2015 (F2.5 only) | .. | Census 2015 (F2.8 only) | .. | IMD 2005-2016 | SILCLF 2017
Latvia | IMO 2018: data for 2007 & 2016 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Lithuania | Eurostat 2007 & 2017 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Luxembourg | IMO 2018: data for 2007 & 2017 | .. | .. | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2012-2016 | EU-LFS 2015-16
Malta | Eurostat 2009 & 2017 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Mexico | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | ENOE 2016; ENOE 2017 Q3 (F2.3) | .. | ENOE 2017 Q3 | ENOE 2017 Q3 | IMD 2005-2016 | ENOE 2016
Netherlands | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS 2016 (F2.8 only) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
New Zealand | IMO 2018: data for 2007 & 2016 | .. | .. | LFS Q2-Q4/2016-Q1/2017 (F2.8 only) | HES 2015/16 | .. | IMD 2005-2016 | LFS 2017
Norway | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | .. | EU-LFS AHM 2014 (F2.7 only) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Poland | Eurostat 2008 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Portugal | IMO 2018: data for 2007 & 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Romania | Eurostat 2017 | .. | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Slovak Republic | IMO 2018: data for 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Slovenia | Eurostat 2009, IMO 2018: data for 2016 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | .. | EU-LFS 2015-16
Spain | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2006-2016 | EU-LFS 2015-16
Sweden | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | .. | .. | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Switzerland | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | .. | .. | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
Turkey | IMO 2018: data for 2016 | .. | .. | .. | .. | .. | .. | ..
United Kingdom | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | EU-LFS 2015-16 | EU-LFS AHM 2014 (F2.7); EU-LFS 2016 (F2.8) | EU-SILC 2016 | EU-SILC 2016 | IMD 2005-2016 | EU-LFS 2015-16
United States | IMO 2018: data for 2007 & 2017 | OECD database on immigrant integration at the regional level, 2005 & 2014-15 | CPS 2016-17, ACS 2016 (F2.6) | ACS 2016 (F2.8 only) | ACS 2016 | ACS 2016 | IMD 2005-2016 | CPS 2016-17
Partner/G20 countries |  |  |  |  |  |  |  |
Argentina | IPUMS Census 2010 | .. | IPUMS Census 2010 | .. | IPUMS Census 2010 | IPUMS Census 2010 | .. | IPUMS Census 2010 (F2.14 only)
Brazil | IPUMS Census 2010 | .. | IPUMS Census 2010 | .. | IPUMS Census 2010 | IPUMS Census 2010 | .. | IPUMS Census 2010
Colombia | IPUMS Census 2005 | .. | 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
Indonesia | IPUMS Census 2010 | .. | IPUMS Census 2010 | .. | IPUMS Census 2010 | IPUMS Census 2010 | .. | IPUMS Census 2010 (F2.14 only)
Russia | IMO 2018: data for 2007 & 2017 | .. | Census 2010 | .. | .. | .. | .. | Census 2010 (F2.13 only)
Saudi Arabia | Population Characteristics Survey 2017 | ..  | ..  | .. | .. | ..   |  .. | .. 
South Africa | IPUMS Census 2011 | .. | IPUMS Census 2011 | .. | IPUMS Census 2011 | IPUMS Census 2011 | .. | IPUMS Census 2011

Chapter 3.

Immigrant skills and labour market integration

Immigrants’ skills and how they integrate into the labour market are fundamental to becoming part of the host country’s economic fabric. Although skills and qualifications are obviously decisive determinants in immigrants’ economic and social integration, they do not necessarily indicate how well immigrants actually integrate or fare in the labour market, but rather their ability to do so. Skills have indeed a strong bearing on career paths, and influence what kind of job they find.

Employment is often considered to be the single most important indicator of integration. Jobs are immigrants’ chief source of income and also help them – though there is no guarantee – to take their place in society by, for example, finding decent accommodation, interacting with others in the workplace, and learning the host-country language. Work further confers social standing in the eyes of the immigrant’s family, particularly children, and with respect to the host-country population. However, while employment is important per se, so is its quality.

This chapter begins by considering immigrants’ skills. It compares their levels of educational attainment with those of the native-born (Indicator 3.1), assesses their proficiency in the host-country language (Indicator 3.2) and their access to adult education and training (Indicator 3.3). It then examines immigrants’ labour market outcomes, analysing their employment, participation and unemployment rates (Indicators 3.4 and 3.5) and looking at indicators on labour market exclusion – long-term unemployment and involuntary inactivity (Indicator 3.6). The chapter goes on to look at the characteristics of the jobs that immigrants hold: types of contracts (Indicator 3.7), working conditions (Indicator 3.8) and the skill levels of jobs (Indicator 3.9). It also considers the match between workers’ educational attainment and the requirements of their occupations (Indicator 3.10). The chapter concludes with a look at the incidence of self-employment (Indicator 3.11).

Key findings

In the OECD, 27% of immigrants are educated to low levels and 11% to very low levels, compared with 26% and 7% of the native-born. The immigrant population is even less well educated in Europe: one-third are low-educated EU-wide, rising to 39% among non-EU migrants, compared with 23% of the native-born.

Of the foreign-born, 37% are highly educated, a larger share than among the native-born (32%). With the exception of Iceland and the Latin American OECD countries, the proportion of highly educated immigrants has grown in all OECD and EU countries, rising by 7 percentage points over the past decade in both areas.

Around half of all the highly educated immigrants in the EU and Canada, and a full 55% in the United States, graduated abroad. In the EU, that share has dropped in over the last decade among both EU-born and non-EU migrants.

Across the EU, 56% of recently arrived non-native speakers in need of language training have attended classes since their arrival. The share is 70% in the Nordic and German-speaking countries.

Attending a language course in the host country is associated with an 8 percentage points higher probability of advanced proficiency in an EU host-country language.

Sixty-eight million immigrants have a job in the OECD, and 28 million in the EU. Across the OECD, native- and foreign-born employment rates are on average very similar, at around two-thirds of the working age population. In the EU, however, immigrants are less likely to be employed than the native-born, a trend attributable to the wide employment gap between the native-born and non-EU migrants. It is as high as 10 percentage points in most Nordic countries and in longstanding European immigrant destinations.

The employment rate fell in virtually all OECD and EU countries with the onset of the global economic crisis in 2008. However, it is now just slightly lower than it was 10 years ago OECD-wide among both the foreign- and native-born. In the EU, the employment rate of non-EU immigrants has dropped by 3 percentage points over the past decade, while rising by 3 points among both natives and EU-born migrants.

Education improves the employment prospects of both immigrants and the native-born, though generally less for the former. The employment rate of the highly educated foreign-born is 79%, against 84% among the native-born.

Almost every labour market in the OECD discounts foreign degrees. In the EU, the employment rate of non-EU migrants with foreign qualifications is 14 percentage points lower than that of immigrants with host-country qualifications.

If highly educated immigrants had the same employment rate as their native peers, there would be 1.5 million more immigrants in employment in the OECD and 850 000 in the EU.

In almost half of OECD and EU countries, low-educated immigrants have higher employment rates than their native-born peers – particularly in Southern and Central Europe, Chile and the United States.

Over 5.8 million immigrants are unemployed in the OECD, and 3.7 million in the EU. The OECD-wide immigrant unemployment rate is 8%, compared to 6% among the native-born. In the EU, the rates are 11.5% and 7.5%, respectively.

Across the EU, almost one-quarter of economically inactive immigrants and one-sixth of inactive native-born wish to work. In all countries – except Iceland, Australia, the United Kingdom and the Slovak Republic – immigrants are more likely than the native-born to be involuntarily inactive.

On average 48% of the foreign-born fear losing their jobs, compared to 42% of the native-born.

Unemployed immigrants are generally less likely to receive unemployment benefits than the native-born in the EU.

Immigrants are more likely to work on temporary contracts in most European countries, though not, generally, outside Europe and Asia. Comparisons of settled migrants only with the native-born reveal that, over time, the temporary contract gap between them narrows in most countries and even vanishes in one-third.

Across the OECD, 16% of the native-born in employment work over 50 hours a week, compared to 11% of the foreign-born. In the EU, equal proportions of the two groups work long hours (11%). Among the highly educated, the foreign-born are generally more likely to work longer hours than their native-born peers.

In all European countries, immigrants, regardless of their educational attainment, are more likely than the native-born to have jobs that put their physical health at risk.

Over one in four low-skilled jobs is held by an immigrant in the EU, the United States and in the settlement countries. The level rises to over 40% in Austria, Germany, Sweden and Norway, and over 60% in Switzerland and Luxembourg.

The share of immigrants employed in highly skilled jobs has risen by over 2 percentage points in the EU and 3 points in the OECD in the last decade. In most countries over the same period, however, the gap between the share of immigrants and the native-born in highly skilled jobs widened.

Among highly educated immigrants, almost 16 million in the OECD and 5.5 million in the EU are either not in employment or in jobs for which they are over-qualified – i.e. almost 45% of the highly educated immigrant population in both areas, compared with 40% of the highly educated native-born in the OECD and 30% in the EU.

Over one-third of highly educated immigrants in employment are over-qualified for their jobs across the OECD and the EU. Over-qualification rates are higher among non-EU migrants than EU-born in all European countries, with the exceptions of Ireland and the United Kingdom.

EU-wide, over-qualification affects 42% of foreign-educated immigrants. The figure drops to 28% for those who graduated in the host country. To a lesser extent, the same pattern is true for the United States and Australia. In Southern European countries, Nordic countries, France, Germany and the Netherlands, over-qualification rates are twice as high among immigrants who graduated abroad as among those with host-country tertiary degrees.

Although the share of the foreign-born with host-country degrees has gone up over the last decade in the EU, immigrant over-qualification rates have risen slightly. They dropped in the United States, however, despite an increase in the share of foreign-educated immigrants.

Around 12% of employed immigrants are self-employed – the same rate as among the native-born. Immigrant businesses tend to be smaller than native ones.

3.1. Educational attainment

Definition

This section measures educational attainment against the International Standard Classification of Education (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, around one-quarter of both the foreign- and native-born of working age are poorly educated – 27% and 26%, respectively, to a low level and 11% and 7% to a very low one. However, 37% of the foreign-born are highly educated, a larger share than among the native-born (32%). The share of the immigrant population with low levels of education is higher in Europe. It stands at one-third in the EU altogether – 39% of non-EU migrants and some 26% of those who are EU-born – against 23% of the native-born. There are 13 million poorly educated immigrants in the EU. They outnumber their 11 million highly educated peers, who account for 29% of immigrants.

OECD-wide, there are 24.5 million low-educated and 33.5 million highly educated immigrants. The largest shares of those who are highly educated are in settlement countries like Canada and Australia, where they account for more than half of the immigrant population. High proportions are also to be found in EU countries that have recently attracted a large number of highly educated migrants, such as Poland, Ireland and the United Kingdom. In longstanding European destinations, by contrast, as well as in Southern Europe, Korea and Sweden, immigrants are largely overrepresented among the poorly educated, accounting for over 35% in the countries of Southern Europe, Belgium and France. In the EU, 12% of foreign-born people have very low levels of education (15% of non-EU migrants), compared to 5% among the native-born.

With the exception of Iceland and the Latin American OECD countries, the share of highly educated individuals among immigrants has grown throughout the OECD and the EU, rising by 7 percentage points over the past decade. In half of countries, however, the rise was slower than for the native-born. It was at its steepest in countries like Poland, the United Kingdom, Luxembourg and Australia. The increases are due mostly to the fact that recent migrants are better educated than their predecessors virtually everywhere. The pattern is particularly true of the United Kingdom, Denmark and some Baltic countries, where the highly educated share of recent migrants has climbed by at least 20 percentage points over the past decade. It is worth noting that, in two-thirds of European countries, the rise was greater among EU migrants than among non-EU migrants.

Three-fifths of the OECD and EU immigrant populations – 44 and 21 million people, respectively – obtained their highest degrees abroad. The proportion exceeds 70% in Southern Europe, Austria and Luxembourg, and is almost 90% in Korea. Among the highly educated foreign-born, only 42% in Australia obtained their qualifications abroad, around 50% in the EU and Canada, and 55% in the United States. In Canada, this share has dropped by 4 percentage points over the last decade, as it has in the EU for both EU-born and non-EU migrants. Among highly educated non-EU immigrants, the share is also 50% in the EU. It is below 40% only in countries that attract many immigrant students, such as France, the United Kingdom and the Netherlands.

Figure 3.1. Low- and highly educated

Percentages of 15- to 64-year-olds not in education, 2017

StatLink  http://dx.doi.org/10.1787/888933842584

Figure 3.2. How shares of the highly educated have evolved

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

StatLink  http://dx.doi.org/10.1787/888933842603

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

3.2. Language proficiency

Definition

Share of the foreign-born who report advanced skills in the host country’s main language or who state that it is their mother tongue.

Coverage

The foreign-born aged 15 to 64 years old. Data on language class attendance refer to those who have lived in the host country for less than 10 years, declare language training needs, and for whom the host country’s main language is not their mother tongue.

Mastering the host-country’s language is the most important skill immigrants need if they are to find their place in its labour market and society at large. Two-thirds of the foreign-born in the EU state they have at least advanced language proficiency. Almost 30% of immigrants have the host country’s language as their mother tongue. In Australia, at 70%, the share of the foreign-born who report advanced proficiency in English is even higher and two out of five have English as their mother tongue. More than 90% of the foreign-born report advanced language skills in countries with an immigrant population shaped by national minorities (such as Croatia or Hungary), as well as in Portugal and Luxembourg. In contrast, less than half of the immigrant population in Estonia, Malta, Latvia and Norway is fluent in the host-country’s main language.

In all countries, longer residence is associated with better knowledge of the host-country language. Among settled immigrants in the EU who are not native speakers, six out of ten report advanced proficiency in the host-language – 20 percentage points more than among recent migrants. The difference is most pronounced in the Slovak Republic, Greece and Germany.

If migrants with limited resources who struggle with the host-country language are to learn it, publicly funded language training is a requirement. Most OECD and EU countries now provide such training. Across the EU, 56% of recently arrived non-native speakers in need of language training have attended courses. The figure exceeds 60% among non-EU migrants – from over 70% in the Nordic and German-speaking countries to less than 40% in Southern Europe, the Slovak Republic and Hungary. In the EU, among recently arrived non-native speakers (not counting those who claim not to need language training) attending a language course in the host country is associated with an 8 percentage point greater likelihood of proficiency in the host language. The difference in the likelihood of advanced host-language proficiency between those who have attended courses and those who have not is particularly wide in Greece, Slovenia, Italy and Belgium, where it exceeds 25 percentage points. In Spain, France, Switzerland and the Nordic countries, by contrast, the share of advanced speakers among recent immigrants is similar whether or not they have attended language classes.

Language skills go beyond mastering the host-country language. Immigrants use more languages in their daily lives than the native-born. Across the EU, over four in five foreign-born people use at least one language that is not their mother tongue, compared to less than two-thirds of the natives. Over one in six foreign-born person uses more than two languages, against only one in 12 among the native-born. 76% of immigrants in the EU speak at least one foreign language fairly fluently, while only 52% of the native-born do. However, the share of immigrants who report good command of English is lower than among the native-born in two-thirds of European countries (excluding English-speaking ones).

Figure 3.3. Advanced host-country language proficiency

Percentages of the foreign-born, 15- to 64-year-olds, 2014

StatLink  http://dx.doi.org/10.1787/888933842622

Figure 3.4. Shares of advanced host-country language speakers among settled immigrants

Differences in percentage points with recent migrants, foreign-born population who are not native speakers, 15- to 64-year-olds, 2014

StatLink  http://dx.doi.org/10.1787/888933842641

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

3.3. Access to adult education and training

Definition

This section looks at the share of foreign- and native-born adults who have participated in education programmes, training or language courses, classes, workshops and seminars, on-the-job learning and private lessons over the last 12 months. It also considers the share of adults who receive guidance and counselling on learning opportunities from institutions and organisations.

Coverage

Adults aged 25 to 64 years old.

Immigrant adults are slightly less likely to participate in formal and non-formal education and training than the native-born in three-quarters of OECD and EU countries. In the EU, 42% attend a course or training, against 45% of their native peers. OECD-wide, shares are 5 percentage points higher in both groups.

Immigrants lag behind the native-born by over 10 percentage points in the Baltic countries and most longstanding European immigration destinations, where many foreign-born are educated to low levels. In Estonia, France, Latvia and Slovenia, the gap exceeds 15 percentage points. Underrepresentation is also observed in most non-European OECD countries, with the exception of New Zealand and Chile. In the United States, the share of the foreign-born attending adult education is 10 percentage points lower than among the native-born. The foreign-born are more likely than their domestically born peers to take part in adult education and training in only 8 OECD and EU countries, most notably in Poland, Portugal and Malta.

Over the last five years, the share of both the foreign- and native-born participating in adult education and training has increased by 4 percentage points in the EU. There is, however, wide variations from country to country, with the participation gap widening in two-thirds of the countries. It narrowed considerably, by contrast, in Germany, Poland and Turkey.

Immigrants’ lower rates of participation in adult education may be associated with a lack of guidance and counselling on learning opportunities. Across the EU, about a quarter of the foreign-born enjoy such support, against one-third of the native-born. Indeed, immigrants receive less guidance on learning opportunities than natives in virtually all EU countries. The gaps are widest in Sweden, Denmark, Norway, Estonia, the Netherlands and Austria. The sole exceptions are Lithuania, Portugal and Finland. In Finland, for example, almost half of the foreign-born benefit from guidance and counselling, against two-fifths of their native peers.

Figure 3.5. Participation in adult education and training among the foreign- and native-born

Percentages of adults, 25- to 64-year-olds, 2016

StatLink  http://dx.doi.org/10.1787/888933842660

Figure 3.6. How foreign- and native-born participation in education and training has evolved

Changes in percentage points among 25- to 64-year-olds, 2011 to 2016

StatLink  http://dx.doi.org/10.1787/888933842679

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

3.4. 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.

The participation rate (or activity rate) 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.

Across the OECD, native- and foreign-born employment rates are very similar – around two-thirds in both groups. In the EU, however, it is lower, at 64%, among immigrants than among native-born (68%). Most immigrants are in employment in all countries except Turkey, where the native-born employment rate is also among the lowest. In total, 68 million immigrants have a job in the OECD, and 28 million in the EU. The foreign-born account for 12% of the employed population in both areas.

Immigrant employment rates exceed 70% in countries where immigration is mostly labour-driven and highly skilled, as in settlement destinations (like Canada, Israel and New Zealand) and in longstanding European destination countries with many recent labour immigrants (e.g. Switzerland and the United Kingdom). In all these countries, however, with the exception of Israel, the native-born are still more likely than the foreign-born to be employed. The opposite is true, however, in a dozen countries, such as the United States, Luxembourg, Italy, Portugal and Central European countries, and new destinations outside Europe, like Korea and Chile.

The immigrant participation rate in OECD countries is 74%, slightly higher than that of the native-born (71%). In the EU, participation is 73% among both groups. In the Southern European countries, Korea, Chile, Israel, Hungary and Luxembourg, immigrants are more likely to participate in the labour market than the native-born. In most of Europe’s longstanding immigration countries, by contrast, they are less likely, particularly among women (see Chapter 6).

The employment rate fell in all OECD and EU countries in the wake of the global economic downturn. It has since recovered, however, and is now only slightly lower than 10 years ago in the OECD as a whole, among both the foreign- and native-born. In the EU, however, immigrants have benefitted less from the recovery than their native-born counterparts, although the effect of the crisis is visible only among migrants from outside the EU. Over the last decade, their employment rate has dropped by 3 percentage points, while increasing by the same amount among both the EU- and native-born. Southern European countries with many recent and less well educated immigrants – such as Spain, Greece and Italy – were worst affected by the crisis, along with Ireland. In those countries, the employment rates of the foreign-born fell by between 5 and 13 percentage points, at least twice as much as for the native-born. Conversely, in half of countries, they increased – even more steeply than among the native-born in most countries. In several Eastern European countries, in contrast, native-born employment rates rose but fell among immigrants, partly due to the ageing of the foreign-born population. In Poland, however, which recently attracted large numbers of foreign workers, the immigrant employment rate increased by a full 34 percentage points.

Figure 3.7. Employment and participation rates

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

StatLink  http://dx.doi.org/10.1787/888933842698

Figure 3.8. How employment rates have evolved

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

StatLink  http://dx.doi.org/10.1787/888933842717

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

In the EU, EU-born immigrants show a higher employment rate (71%) than the native-born (66.5%). Among non-EU migrants, however, it is significantly lower (58.5%). In only Italy, Portugal, as well as in a few Central and Eastern European countries (the Czech and Slovak Republics, Hungary) are non-EU migrants, mostly from neighbouring countries, more likely to be in employment than the native-born. By contrast, native-born employment rates are as much as 10 percentage points higher in most Nordic countries that are home to large numbers of refugees and in long-standing European immigration destinations. In Belgium, for instance, only 46% of the non-EU foreign-born are in work.

Education improves the labour prospects of immigrants, though less than those of the native-born. Across the OECD, the gap between the employment rates of highly and low-educated immigrants is 21 percentage points, against 29 points among the native-born. Indeed, in virtually all countries, immigrants educated to tertiary degree level struggle more than their native peers to find jobs: 79% versus 84% are in employment, OECD wide. In the EU, too, the average employment rate of the highly educated is lower among immigrants than among native born – by 7 percentage points. And the difference climbs to at least 9 percentage points in long-standing immigration destinations and Southern European countries (except for Portugal). The gap is narrower in OECD countries where many highly educated immigrants came as labour migrants, such as Luxembourg, the United Kingdom and non-European settlement countries. If highly educated immigrants had the same employment rates as their native peers, there would be 1.5 million more immigrants in employment in the OECD and 850 000 more in the EU.

Virtually every labour market in the OECD discounts foreign tertiary degrees, the only significant exceptions being Korea, Finland and the Slovak Republic. The employment gap between immigrants educated in the host country and those educated abroad is 8 percentage points in the OECD. It rises to 10 points in the EU, where the differences are particularly stark for non-EU migrants with foreign qualifications. Their employment rate is 14 percentage points lower than for their peers with host-country qualifications, and at least 20 points lower in Latvia, Portugal, Sweden and the Netherlands.

Employment rates among low-educated immigrants paint an entirely different picture. In almost half of OECD and EU countries, they outstrip those of their native-born peers – particularly in Southern and Central European countries, Israel and Chile. As for the United States, the employment rate of low-educated foreign-born is a full 29 percentage points higher than among their native peers. By contrast, immigrants with little education are less likely to have a job than their native peers in many longstanding European immigration destinations and the Nordic countries. In the Netherlands, Sweden and Denmark, the gap is as wide as 10 percentage points. These poor outcomes are attributable chiefly to the lower employment rates of non-EU migrants. Indeed, EU-wide, gaps in employment rates between non-EU and EU migrants are wider among the low-educated (11 percentage points) than among the highly educated (8 percentage points). However, in some Central and Southern European countries (such as the Czech Republic, Hungary, Greece and Italy), non-EU migrants with low levels of education (mostly labour migrants who arrived prior to the global economic crisis) are more likely to be in employment than natives.

The OECD-wide employment rate of recent immigrants – resident in the host country for less than five years – is 10 percentage points lower than that of the native-born and as much as 15 points lower in the EU. Less than half of recent immigrants are in employment in Southern European countries, as well as in many Nordic and longstanding immigration countries. These rates are below 40% in Greece, France and Italy.

Figure 3.9. Employment rates of the foreign-born by level of education

Differences in percentage points with native-born, 15- to 64-year-olds not in education, 2017

StatLink  http://dx.doi.org/10.1787/888933842736

Figure 3.10. Employment rates of the highly educated foreign-born, by place of education

Percentages of 15- to 64-year-olds not in education, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842755

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

3.5. 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).

The OECD-wide immigrant unemployment rate is 8% and that of the native-born is 6%. It rises to 11.5% in the EU, against the native-born’s 7.5%. In total, 5.8 million immigrant workers are unemployed in the OECD, and 3.7 million in the EU. Indeed, immigrants are more likely to be unemployed than their native counterparts in the vast majority of countries, except for the United States, Chile, Latvia, Hungary, Bulgaria and Israel. The gap is over 5 percentage points in most Southern European countries (such as Spain and Greece), in the longstanding European immigration destinations (like Belgium and France) and in the Nordic countries (where it exceeds 10 points in Sweden). In the latter group of countries, as well as in Switzerland, Austria and the Benelux, the unemployment rates of the foreign-born are at least twice as high as among the native-born. This is observed even in countries where the overall employment situation is good, such as in Switzerland and Austria.

The effects of the economic crisis have started to fade in the OECD and the EU and both foreign- and native-born unemployment rates are now similar to pre-crisis levels. However, that broad picture encompasses very different country-specific situations. The gap in unemployment rates between the foreign- and the native-born has widened in a dozen countries, especially in Poland and Southern Europe. It remained stable in most countries, however, in the last ten years, actually narrowing in a few, particularly the Czech Republic and Germany.

In most countries, low-educated immigrants are more likely to be unemployed than their native-born peers – by over 10 percentage points in Sweden and Belgium. The situation is the reverse in Canada, the United States, and in Central and Eastern European countries like the Czech Republic. Although unemployment is generally more widespread among people with low levels of education, the gap between the foreign- and native-born turns out to be wider among highly educated in two thirds of OECD and EU countries. The only countries where the unemployment rates of highly educated immigrants are lower than that of the native-born are Mexico, Chile, Turkey and most Central and Eastern European countries.

As for the EU, finding a job is particularly difficult for non-EU migrants, whose unemployment rate (all levels of education included) is almost twice that of the native-born. The gap reaches at least 8 percentage points in most Nordic countries, Belgium, Luxembourg and Switzerland. Non-EU migrants are, in fact, the group most affected by the economic crisis, particularly in Greece and Spain, where their unemployment rate rose by 22 and 18 percentage points, respectively.

Figure 3.11. Unemployment rates

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

StatLink  http://dx.doi.org/10.1787/888933842774

Figure 3.12. How unemployment rates have evolved

Changes in percentage points, 15- to 64-year-olds not in education, 2006-07 to 2017

StatLink  http://dx.doi.org/10.1787/888933842793

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

3.6. Risks of labour market exclusion

Definition

The long-term unemployment rate is the share of job seekers who have been without a job for at least 12 months among all the unemployed. Involuntarily inactive people are those who are not seeking work but are 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

Unemployed and economically inactive persons aged 15 to 64.

Over one-third of unemployed immigrants in the OECD – 2.2 million people – have been looking for a job for at least one year. The long-term unemployed account for almost half of the unemployed foreign-born population in the EU (almost 2 million) – a full 50% among non-EU migrants and 44% of EU migrants.

Ten years ago, immigrants were less likely to be long-term unemployed than the native-born across the OECD and the EU. Rates are now similar after rising by 7 percentage points among the foreign-born in the EU and by 8 points in the OECD, three times as much as among the native-born. Over the same period, the share of long-term unemployed immigrants among those unemployed increased by more than 20 points in countries hard-hit by the global economic crisis, like Ireland, Latvia, Greece and Spain. Shares significantly dropped in only a few countries, such as Estonia and the Czech Republic. Long-term unemployment is more widespread among the foreign-born in two-thirds of OECD and EU countries, particularly in the Nordic countries and most longstanding European immigration destinations. In Denmark, Sweden, Switzerland and Lithuania, rates are more than 10 percentage points higher than those for natives. In contrast, the foreign-born unemployed suffer less from long-term unemployment than their native peers in Southern Europe, Ireland, the United Kingdom and Oceania.

Among the economically inactive, one-quarter of those who are immigrants in the EU wish to work, against one-sixth of the inactive native-born. Involuntary inactivity is less widespread in the United States, where less than 10% of the inactive wish to work, regardless of country of birth. Altogether, 3.4 million foreign-born across the OECD and 2 million in the EU are involuntarily outside the labour force. They are more likely to be involuntarily inactive than the natives everywhere except in Iceland, Australia, the United Kingdom and the Slovak Republic. The involuntary share of the inactive foreign-born is 10 percentage points higher than among the native-born in Luxembourg, Norway, Poland and Austria. In the EU, only 4% of inactive immigrants are not actively looking for a job because they are discouraged, a share similar to that observed among the native-born. Other reasons for involuntary inactivity are family commitments (more widespread among immigrants), health issues and non-specified causes. The share of discouraged workers exceeds 10% only in Italy – among both native- and foreign-born. In the Netherlands, by contrast, it is twice as high among the foreign-born.

Immigrants are also more likely to fear exclusion from the labour market everywhere except Estonia. Between 2010 and 2014, on average 48% of the foreign-born were worried about losing their jobs, compared to 42% of the native-born. Differences were particularly stark in the United States at 20 percentage points, the Netherlands and Sweden (10 points both). Immigrants who had been unemployed for at least two months were also less likely to receive unemployment benefits than their native peers – 36% versus 40% on average in the EU. In the Netherlands, they were three times less likely in 2016. However, proportions were similar among the foreign- and native-born in the Nordic countries, France and the United States.

Table 3.1. Long-term unemployment rate
Percentages of total unemployed, 2006-07 and 2015-16
| Long-term unemployment of the foreign-born population (% of total unemployment) | Differences with the native-born (% points) +: higher than natives -: lower than natives
| 2006-07 | 2015-16 | 2006-07 | 2015-16
Australia | 17.9 | 24.3 | +1.4 | -0.3
Austria | 30.4 | 32.5 | +4.1 | +2.7
Belgium | 57.2 | 57.1 | +8.5 | +8.3
Canada | 10.4 | 13.5 | +3.2 | +2.8
Croatia | 60.4 | 58.4 | +0.3 | +0.9
Cyprus1,2 | 19.6 | 39.2 | +0.9 | -7.7
Czech Republic | 69.9 | 48.8 | +17.0 | +7.8
Denmark | 20.1 | 33.8 | +1.8 | +12.2
Estonia | 58.8 | 38.3 | +12.1 | +3.9
Finland | 32.0 | 28.1 | +10.1 | +1.8
France | 45.7 | 49.6 | +7.1 | +7.7
Germany | 56.7 | 57.7 | -0.1 | -0.3
Greece | 44.5 | 71.1 | -8.2 | -1.6
Hungary | 41.9 | 53.8 | -4.2 | +8.1
Iceland | .. | 13.1 | -0.1 | +3.5
Ireland | 24.5 | 52.3 | -9.6 | -5.5
Israel | .. | 12.5 | .. | -0.7
Italy | 41.2 | 55.2 | -8.3 | -4.1
Korea | .. | 2.1 | .. | +0.8
Latvia | 28.2 | 50.5 | -2.8 | +7.8
Lithuania | .. | 54.3 | +2.8 | +14.3
Luxembourg | 29.8 | 30.6 | +1.7 | +2.1
Malta | .. | 48.1 | -7.7 | +6.4
Netherlands | 50.2 | 50.3 | +10.8 | +9.9
New Zealand | 10.4 | 9.5 | -0.8 | -2.4
Norway | 31.1 | 34.4 | +13.1 | +7.9
Portugal | 42.2 | 51.9 | -7.2 | -5.0
Slovenia | 54.8 | 57.7 | +7.9 | +5.7
Spain | 11.9 | 48.2 | -11.1 | -0.3
Sweden | 18.7 | 27.6 | +6.6 | +13.0
Switzerland | 46.3 | 43.6 | +16.2 | +14.3
Turkey | .. | 21.9 | +0.0 | -2.6
United Kingdom | 24.0 | 24.1 | +1.0 | -5.0
United States | 6.6 | 11.8 | +0.2 | +0.4
OECD total (29) | 29.2 | 37.3 | -2.1 | +4.6
EU total (28) | 41.3 | 48.4 | -3.7 | +0.1
StatLink  http://dx.doi.org/10.1787/888933843021

Figure 3.13. Involuntary inactivity due to discouragement or other reasons

Percentages among the inactive foreign-born (F) and native-born (N), 15- to 64-year-olds, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842812

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

3.7. Types of contracts

Definition

In most countries, temporary work denotes any kind of wage-earning employment governed by a fixed-term contract, including apprenticeships, temporary employment agency work, and remunerated training courses. In Australia, temporary work is defined as work without paid leave. No such definition of temporary work exists in the United States.

Coverage

People aged 15 to 64 years old who are in employment but not self-employed or in education.

In the OECD and the EU, the proportion of foreign-born in work with temporary contracts is 15% in both, and 16% and 12% among native workers, respectively. At 18%, the share of temporary workers EU-wide is even higher among non-EU migrants. In most Central and Eastern European countries, the United Kingdom, Austria and Italy, however, temporary contracts are slightly more prevalent among EU-born migrants. In total, 5 million foreign-born workers have temporary contracts in the OECD and 3.4 million in the EU. Immigrants are more likely to work on such contracts in all European countries, though not, generally, in non-European OECD countries.

Shares of temporary workers among immigrants are similar to those of the native-born in Canada, Australia and New Zealand, and around 5 percentage points lower in Latin American OECD countries, where the foreign-born are more likely to be highly educated. The only exceptions among non-European countries are Japan and Korea, where more than half of all immigrants are temporary workers, against one-third of the native-born in Japan and one-tenth in Korea.

Similarly, the share of temporary workers is at least 5 points higher among the foreign- than the native-born in about half of EU countries, and even more in the Nordic countries (with the exception of Norway) and in longstanding immigrant destinations with large numbers of low-educated immigrants. The gap is also wide in Spain, Greece and Poland. By contrast, temporary work accounts for less than 10% of immigrant employment in most Central and Eastern European countries, as well as in those European countries with a significant recent intake of highly educated migrants.

A temporary contract is often the first step into the labour market. Recent arrivals are thus more likely to work in temporary jobs, the proportion of which shrinks as residence lengthens. Across the EU, only 13% of settled immigrants (i.e. the foreign-born with at least 10 years of residence) work on temporary contracts, almost half as many as among their peers with less than 10 years of residence. Comparisons of settled migrants with the native-born reveal that the temporary contract gap between them narrows in most countries with the increase of the duration of stay and even vanishes in one-third, e.g. in Nordic countries (Sweden in particular), Slovenia and Germany.

There was no significant change in temporary contracts as a share of employment arrangements in the past decade. Most countries showed rises or falls of 2 percentage points, irrespective of the place of birth. Among the few exceptions, Spain saw a steep drop in the share of temporary contracts, especially among the foreign-born, from over 50% before the crisis to less than 30% now. The drop was attributable chiefly to job losses that primarily affected temporary positions. The share of temporary immigrant workers fell by a further 5 percentage points in both Portugal and the Czech Republic, while it rose slightly among the native-born. By contrast, in Poland, Slovenia and, to a lesser extent, France, the foreign-born are now much more likely than the native-born to work on temporary contracts.

Figure 3.14. Workers on temporary contracts

Percentages of all wage-earners, 15- to 64-year-olds, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842831

Figure 3.15. How shares of temporary contracts among workers have evolved

Changes in percentage points among wage-earners, 15- to 64-year-olds, 2006-07 to 2015-16

StatLink  http://dx.doi.org/10.1787/888933842850

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

3.8. Working conditions

Definition

This indicator relates to the proportion of employed persons who report working long hours (over 50 per week) and the share of employees who state that their employment generates risk to physical health.

Coverage

All 15- to 64-year-olds in employment. The self-employed are not included in the physical health risk indicator.

Working conditions are strongly related to a person’s overall wellbeing. Overwork, for example, may exert a negative impact on the work-life balance, physical health and social integration. OECD-wide, 16% of the native- and 11% of the foreign-born in employment work over 50 hours. In the EU, native and immigrants workers (whether born inside or outside the EU) are, at 11%, as likely to work long hours. In two-thirds of countries, higher shares of the native-born work more than 50 hours per week. The gap is especially wide in Austria, the United States and Australia. Conversely, in Latin American OECD countries, the United Kingdom, and all Central and Eastern European countries (with the exception of Estonia), the foreign-born work long hours more frequently than the native-born. In the Czech Republic in particular, they are twice as likely to work long hours.

Working hours are determined by the occupational and sectoral distribution of jobs held by the native- and foreign-born, as well as by educational attainment. In three-fifths of countries, the native-born with little education are more likely to work long hours than their foreign-born peers, though generally not by much (except in Greece, Ireland, Iceland, Slovenia, and Switzerland). Among the highly educated, however, it is the foreign-born who are more likely to work longer hours than the native-born in three-fifths of countries. Notable exceptions are Germany and Austria. In the latter, highly educated immigrants are less likely to work long hours than their native-born peers (12% versus 18%).

Certain types of jobs generate physical health risks, which can affect workers’ long-term wellbeing. Employed immigrants in all European countries are more likely to have such jobs – 46% on average against 35% among the native-born. In Germany, Slovenia, Estonia and Sweden, the gap in the shares of foreign- and native-born in occupations that put their physical health at risk is at least 20 percentage points. The only countries where immigrants are not at significantly greater occupational risk than the native-born are Denmark and Norway.

Most occupations that generate physical health risks are low-skilled. Indeed, three in five low-educated immigrants had jobs that put their physical health at risk in 2015, compared to half of their native-born peers. The shares of immigrants at occupational health risk are higher in all countries except France and Spain (where there is no difference between the foreign- and native-born). Even highly educated immigrants are more likely to work in jobs that generate physical health risk.

Figure 3.16. Working long hours

Percentages of 15- to 64-year-olds in employment and not in education, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842869

Figure 3.17. Shares of the foreign- and native-born in occupations that put their physical health at risk

Percentages of 15- to 64-year-olds in employment, 2015-16

StatLink  http://dx.doi.org/10.1787/888933842888

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

3.9. Job skills

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]).

Coverage

People in employment aged between 15 and 64 years old.

Across the OECD, 18% of immigrant workers hold low-skilled jobs, or “elementary occupations”, against 11% of the native-born. At the EU level, these figures are 20% and 8%, respectively. Indeed, immigrant workers are more heavily concentrated in low-skilled occupations in virtually all countries. In Southern Europe (with the exception of Portugal), at least 30% of immigrants work in such jobs, three times more than their native-born counterparts. In Greece, they are as much as six times more likely than the native-born to be in elementary occupations and around four times more in the Nordic countries and some longstanding European immigrant destinations, such as Austria or Germany. Over one in four low-skilled jobs is held by an immigrant in the EU, the United States and in the settlement countries, a level that exceeds 40% in Austria, Germany, Sweden and Norway, and exceeds 60% in Switzerland and Luxembourg. Non-EU migrants are more likely to hold an elementary occupation than their EU peers in all European countries, with the exception of the United Kingdom, Ireland, and Hungary. They are at least three times as likely as the native-born to work in low-skilled jobs in three countries out of five, including longstanding European immigrant destinations, and Nordic and Southern European countries.

Only in Australia, New Zealand, Portugal, Malta, Turkey and some Central European countries (such as Bulgaria and the Slovak Republic) are immigrants not significantly overrepresented in elementary occupations. In these countries, immigrants are more likely than the native-born to work in highly skilled jobs and by as much as 10-plus percentage points in Poland. Otherwise, though, the share of immigrants in highly skilled occupations is lower than that of the native-born in all OECD and EU countries. One-third of employed immigrants, EU-wide, work in highly skilled positions – 11 percentage points fewer than their native peers. The share is slightly lower among non-EU migrants.

Nevertheless, the share of employed immigrants in highly skilled jobs has increased in the last decade by over 2 percentage points in the EU (for both EU and non-EU born) and 4 points in the OECD, a trend similar to that observed among the native-born. The rise was particularly steeper among immigrants than among native-born in the United States, the Baltic countries and Ireland. Yet in most countries, the gap between immigrants and the native-born occupying highly skilled positions widened over this period. The widening was especially pronounced in the long-standing European immigration destinations and the Nordic countries (except Sweden). The share of immigrants in highly skilled positions even fell in about one-quarter of countries (e.g. Norway, Iceland, and Belgium), while increasing among their native peers. Overall, though, there was a general rise in the share of skilled employment among all workers (native- and foreign-born) – with the exception of Greece, Italy, the Netherlands and the Slovak Republic.

Figure 3.18. Low-skilled and highly skilled employment

Percentage of 15- to 64-year-olds in employment, 2017

StatLink  http://dx.doi.org/10.1787/888933842907

Figure 3.19. How shares of workers in highly skilled occupations have evolved

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

StatLink  http://dx.doi.org/10.1787/888933842926

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

3.10. Over-qualification

Definition

The over-qualification rate is the share of the highly educated, i.e. educated to ISCED Levels 5-8 (see Indicator 3.1), who work in a job that is ISCO-classified as low- or medium-skilled, i.e. ISCO Levels 4-9 (see Indicator 3.9).

Coverage

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

Over one-third of highly educated immigrants in employment in the OECD and the EU are over-qualified for their jobs – a rate 13 percentage points above that of the native-born in the EU and 4 points in the OECD area. Immigrant over-qualification is a particular issue in Southern Europe (except in Portugal) where many highly educated labour migrants have taken up low- and medium-skilled jobs. Indeed, over half of all highly educated immigrants work in jobs for which they are formally over-qualified. The issue is also pronounced in Korea, Israel, Germany and the Scandinavian countries, where immigrant over-qualification rates are at least 15 percentage points higher than those of their native peers. In Italy, Norway and Iceland, the foreign-born are three times more likely to be over-qualified. In only a handful of countries, such as Switzerland and the United States, are they as likely or less likely. Overall, over 8 million foreign-born workers are over-qualified in the OECD, and 3 million in the EU. Among the highly educated not in education, almost 15 million immigrants in the OECD and 5.5 million in the EU are either in work for which they are over-qualified or not in employment – i.e. almost 45% of the highly educated immigrant population, compared with 40% of their native peers in the OECD and 30% in the EU.

Non-EU migrants have higher over-qualification rates than the natives in all European countries. The gap with the native-born is greater than 15 percentage points in half of all EU/EFTA countries. Recent immigrants are particularly affected by over-qualification, with a rate 7 percentage points higher than that of settled immigrants in the EU. Yet, even settled immigrants who have been in a host country for 10 or more years show over-qualification rates that are 6 points higher than those of the native-born. Another highly over-qualified group comprises the foreign-born who graduated abroad. EU-wide, over-qualification affects 42% of foreign-educated immigrants and 46% if born outside the EU. By contrast, it affects 28% of immigrants with host-country qualifications. To a lesser extent, the same also holds for the United States and Australia, where the over-qualification rate is 7 percentage points higher among foreign degree-holders. Over-qualification rates are twice as high among immigrants who graduated abroad as among their peers with host-country degrees in Southern Europe, Nordic countries, France, Germany and the Netherlands. In the latter three countries, as well as in Portugal and Slovenia, immigrants with host-country education are no more likely than the native-born to be over-qualified, while in all other EU and OECD countries they are.

The immigrant over-qualification rate has risen slightly over the last decade in the EU, but it has dropped in the United States. In most Southern European and Baltic countries, the native-born are more likely to be over-qualified than they were before the crisis, while there is a downward trend among immigrants. The explanation may be that over-qualified immigrants lost their jobs during the crisis, which reduced the over-qualification rate but increased the unemployment rate. That notwithstanding, the incidence of over-qualification rose faster among immigrants than native-born in most European countries, especially in Norway, the United Kingdom, Poland and Italy.

Figure 3.20. Over-qualification rates

Percentage of highly educated, 15- to 64-year-olds, 2017

StatLink  http://dx.doi.org/10.1787/888933842945

Figure 3.21. How over-qualification rates have evolved

Changes in percentage points among highly educated, 15- to 64-year-olds, 2006-07 to 2017

StatLink  http://dx.doi.org/10.1787/888933842964

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

3.11. Self-employment

Definition

The self-employed are people who work in their own firms or create their own businesses, sometimes hiring employees. Self-employment includes entrepreneurs, liberal professions, artisans, traders, and many other freelance activities.

Coverage

Population aged between 15 and 64 who are in employment, excluding the agricultural sector.

Across the OECD and the EU, around 12% of immigrants in employment are self-employed – the same rate as for the native-born. There are more than 7.5 million foreign-born self-employed workers in the OECD, and more than 3 million in the EU. Immigrants are more likely to be self-employed than the native-born in over two-thirds of the countries, although only slightly in the vast majority of them. They are, however, considerably more likely to be self-employed in Central and Eastern Europe, especially in Poland, where the proportion is twice that of their native peers. When it comes to countries where, on the one hand, self-employment is widespread and, on the other hand, labour migrants account for the bulk of immigration, the foreign-born are less likely to be self-employed than the native-born. That pattern is found in Southern Europe, Japan, Korea and the Latin American OECD countries. In Greece, Italy and Iceland, for example, twice as many native- as foreign-born are self-employed and four times as many in Korea.

Although self-employment is widespread in many countries of origin, immigrants may struggle to adjust to the business environment and rules governing self-employment in the host country. Many new arrivals need time to adapt and build up the necessary capital stock. Lower rates of self-employment are thus to be expected among more recent than settled immigrants, which is indeed the case. The differences between the two groups are particularly marked in the settlement countries, as well as in Chile, Korea and Ireland.

The share of immigrants in self-employment has risen over the last decade in one half of all countries, and dropped in the other half. In countries worst hit by the economic crisis (Spain, Portugal, Greece, and Ireland), the share of employed immigrants in self-employment rose, while a significant proportion of the native-born left self-employment. Across the EU, self-employment increased more markedly among non-EU migrants than among their EU-born peers.

Immigrant businesses in OECD countries tend to be smaller than those of the native-born, with the exceptions of Australia, New Zealand, Central Europe and the Baltic countries. In the EU, three-quarters of immigrant businesses have no employee, while seven natives in ten do. The share of one-person businesses is 10 percentage points higher among foreign-born in Luxembourg, Ireland and Iceland. Throughout EU countries, apart from Central and Eastern Europe, there are relatively more native than immigrant-owned businesses with over 10 employees, particularly in Luxembourg, Denmark and Switzerland, where there are twice as many.

Figure 3.22. Self-employed workers

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

StatLink  http://dx.doi.org/10.1787/888933842983

Figure 3.23. How shares of self-employed workers have evolved

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

StatLink  http://dx.doi.org/10.1787/888933843002

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

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.

Data for New Zealand and Saudi Arabia include people still in education. Data for Australia and the United States include people aged over 24 who are still in education. The United States calculates rates for the 16- to 64-year-old age group. Korea calculates rates for the 15-59.

Japan and Saudi Arabia determine 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 3.1, 3.4, 3.5 and 3.10: The level of education for Korea includes ISCED 4 in the highly educated. The level of education in South American countries (Argentina, Brazil, Colombia) is based on the IPUMS standardised data and may not be consistent with official data.

Figure 3.1: Japan is not included in the OECD total.

Figure 3.2, Figure 3.21: Due to a break in series from 2014 in the definition of the highly educated, Austria is not included in the OECD and EU totals.

Table 3.1: Turkey is not included in the OECD total.

Figure 3.18: The United States’ Standard Occupational Classification (SOC) system precludes distinguishing between low- and medium-skilled occupations. The low-skilled section does not therefore consider the United States.

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

For further detailed data, see Annex B.

Table 3.2. Sources by indicator
| 3.1 Educational attainment | 3.2 Language proficiency | 3.3 Access to adult education and training | 3.4 Employment and labour market participation | 3.5 Unemployment | 3.6 Risks of labour market exclusion | 3.7 Types of contracts | 3.8 Working conditions | 3.9 Job skills | 3.10 Over-qualification | 3.11 Self-employment
OECD/EU |  |  |  |  |  |  |  |  |  |  |
Australia | ASEW 2007 & LFS 2017 | Census 2016 | PIAAC 2012 | ASEW 2007 & LFS 2017, ASEW 2016 (F3.9 & F3.10) | ASEW 2007 & LFS 2017, ASEW 2016 (by education) | ASEW 2007 & 2016; PJSM 2016 (F3.13) | Charac. of employ. 2006 & 2015 | ASEW 2016 | ASEW 2007 & 2016 | ASEW 2007 & 2016 | LFS 2006-07 & 2015-16
Austria | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Belgium | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Bulgaria | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Canada | LFS 2006-07 & 2017 | .. | PIAAC 2012 | LFS 2006-07 & 2017, 2015-16 (F3.9 & F3.10) | LFS 2006-07 & 2017, 2015 (by education) | LFS 2006-07 & 2015-16 | LFS 2006-07 & 2015-16 | LFS 2006-07 & 2015-16 | .. | .. | LFS 2006-07 & 2015-16
Chile | CASEN 2015 | .. | PIAAC 2015 | CASEN 2015 | CASEN 2015 | .. | CASEN 2015 | CASEN 2015 | CASEN 2015 | CASEN 2015 | CASEN 2015
Croatia | EU-LFS 2015-16 | EU-LFS AHM 2014 | AES 2016 | EU-LFS 2015-16 | EU-LFS 2015-16 | EU-LFS 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2015-16 | EU-LFS 2015-16; EWCS 2015 (F3.17) | EU-LFS 2015-16 | EU-LFS 2015-16 | EU-LFS 2015-16
Cyprus1,2 | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; WVS 2010-14 (fears); EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Czech Republic | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Denmark | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.9 & F3.10 & non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Estonia | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; WVS 2010-14 (fears); EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Finland | EU-LFS AHM 2014 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants); EU-LFS AHM 2014 (F3.9 & F3.10 & non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants); EU-LFS AHM 2014 (by education) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | EU-LFS 2006-07 & 2015-16
France | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU mig.), 2015-16 (F3.10 and non-EU mig.) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Germany | EU-LFS 2006-07 & Mikrozensus 2016 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & Mikrozensus 2016 | EU-LFS 2006-07 & Mikrozensus 2016 | EU-LFS 2006-07 & Mikrozensus 2016; WVS 2010-14 (fears); EU-SILC 2016 (benefits) | EU-LFS 2006-07 & Mikrozensus 2016 | EU-LFS 2006-07 & Mikrozensus 2016; EWCS 2015 (F3.17) | EU-LFS 2006-07 & Mikrozensus 2016 | EU-LFS 2006-07 & Mikrozensus 2016 | EU-LFS 2006-07 & Mikrozensus 2016
Greece | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Hungary | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Iceland | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | .. | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | 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
Ireland | EU-LFS 2006-07 & 2015-16 | .. | PIAAC 2012 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Israel* | LFS 2017 | .. | PIAAC 2015 | LFS 2017 | LFS 2017 | LFS 2016 | LFS 2016 | LFS 2016 | LFS 2017 | LFS 2017 | LFS 2016
Italy | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Japan | Census 2010 | .. | PIAAC 2012 | Census 2015 | Census 2015 | .. | Census 2015 | .. | Census 2015 | .. | Census 2015
Korea | SILCLF 2017 & EAPS 2017 (provided by MRTC) | .. | PIAAC 2012 | 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) | SILCLF 2017 & EAPS 2017 (provided by MRTC)
Latvia | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Lithuania | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Luxembourg | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Malta | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Mexico | ENOE 2017 | .. | .. | ENOE 2017 | ENOE 2017 | .. | ENOE 2016 | ENOE 2016 | .. | .. | ENOE 2016
Netherlands | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | .. | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; WVS 2010-14 (fears); EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
New Zealand | LFS 2006-07 & Q2-4/2015-Q1/2016 | .. | PIAAC 2015 | LFS 2006-07 & Q2-4/2015-Q1/2016 | LFS 2006-07 & Q2-4/2015-Q1/2016 | LFS 2006-07 & Q2-4/2015-Q1/2016; WVS 2010-14 (fears) | LFS 2006-07 & LFS 2017 | .. | LFS 2006-07 & LFS 2017 | LFS 2006-07 & LFS 2017 | LFS 2006-07 & Q2-4/2015-Q1/2016
Norway | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Poland | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Portugal | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Romania | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Slovak Republic | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16
Slovenia | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; WVS 2010-14 (fears); EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Spain | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Sweden | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; WVS 2010-14 (fears); EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Switzerland | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2011 & 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.9 & F3.10 & non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
Turkey | LFS 2017 | .. | AES 2011 & 2016 | LFS 2017 | LFS 2017 | LFS 2015 | LFS 2015 | LFS 2015; EWCS 2015 (F3.17) | LFS 2015 | LFS 2015 | LFS 2015
United Kingdom | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS AHM 2014 | AES 2016 | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants), 2015-16 (F3.10 and non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16; EU-SILC 2016 (benefits) | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16; EWCS 2015 (F3.17) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2017, 2015-16 (non-EU migrants) | EU-LFS 2006-07 & 2015-16
United States | CPS 2006-07 & 2016-17 | .. | PIAAC 2012 | CPS 2006-07 & 2016-17 | CPS 2006-07 & 2016-17 | CPS 2006-07 & 2016-17; WVS 2010-14 (fears) | .. | CPS 2006-07 & 2016-17 | CPS 2006-07 & 2016-17 (highly skilled only) | CPS 2006-07 & 2016-17 | CPS 2006-07 & 2016-17
Partner/G20 countries |  |  |  |  |  |  |  |  |  |  |
Argentina | IPUMS Census 2010 | .. | .. | IPUMS Census 2010 | IPUMS Census 2010 | .. | .. | .. | .. | .. | ..
Brazil | IPUMS Census 2010 | .. | .. | IPUMS Census 2010 | IPUMS Census 2010 | .. | .. | .. | IPUMS Census 2010 | IPUMS Census 2011 | IPUMS Census 2010
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 2012 | IPUMS Census 2011
Indonesia | IPUMS Census 2010 | .. | PIAAC 2015 | IPUMS Census 2010 | IPUMS Census 2010 | .. | .. | .. | .. | .. | IPUMS Census 2010
Russia | Census 2010 | .. | PIAAC 2012 | Census 2010 | Census 2010 | .. | .. | .. | .. | .. | ..
Saudi Arabia | Census 2010 | .. | .. | LFS 2016 | .. | .. | .. | LFS 2016 | LFS 2016 | LFS 2016 | ..
South Africa | IPUMS Census 2011 | .. | .. | IPUMS Census 2011 | IPUMS Census 2011 | .. | .. | .. | .. | .. | ..
StatLink  http://dx.doi.org/10.1787/888933843040

Chapter 4.

Living conditions of immigrants

Immigrants are not integrated solely through the labour market or the education system. Integration also has economic, spatial and social facets. Immigrants’ ability to generate sufficient income and to meet such essential needs as decent housing and healthcare is crucial if they are to take their place in the host society. Employment status and job quality largely shape living conditions in the OECD and EU, as earnings account for the bulk of family incomes and higher income is associated with better health and housing conditions. Moreover, decent living conditions can, in turn, trigger a virtuous circle leading to improved general well-being, which includes brighter employment prospects.

This chapter focuses on three major determinants of living conditions: income, housing, and health. Income is a decisive factor in many socio-economic outcomes. Poverty adversely affects the well-being of immigrants in the host society in a number of ways, such as poor housing and inhibited skills development. Beyond poverty itself, the inequitable distribution of income can lead to marginalisation and damage social cohesion.

Housing is also a key factor in well-being. The economic situation of some immigrants and their poor knowledge of the rental market may restrict their choice of accommodation. They may also be prone to discrimination from property owners. Lastly, health is integral to well-being, affecting the degree and manner of engagement with society as a whole. Healthier immigrants are able to work and earn more, and to build broader social networks.

This chapter looks first at disposable household income (Indicator 4.1) and the overall risk of poverty (Indicator 4.2). It then considers housing indicators: the incidence of overcrowding (Indicator 4.3), and general housing conditions (Indicator 4.4). Finally, it analyses self-reported health (Indicator 4.5) and the lack of medical treatment (Indicator 4.6).

Key findings

The annual median immigrant household income is around EUR 20 000 in the OECD and EUR 16 000 in the EU – some 10% lower than that of natives in both areas. The gap between native- and foreign-born is largest in Austria and Southern Europe. By contrast, the gap is narrower in Central European countries, Portugal and the United Kingdom.

Immigrants are over-represented in the lowest income decile in virtually all OECD and EU countries – 14% and 18% of immigrants are in this decile, respectively.

Income inequality among the foreign-born tends to be greater than among host-country natives.

Around 30% of immigrants live in relative poverty in both the OECD and the EU. Poverty rates are at least twice those of natives in the longstanding immigration destinations in Europe that host large numbers of low-educated foreign-born, as well as in the Scandinavian and Southern European countries (except Portugal).

Relative poverty among the foreign-born is today more widespread than a decade ago in about two-thirds of countries. The OECD- and EU-wide poverty rates among immigrants increased by 1 and 5 percentage points, while remaining stable among natives.

Having a job affords protection against poverty in all countries, although less so for immigrants. The immigrant in-work poverty rate is about 19% in the OECD and the EU, twice that of natives. Gaps are particularly wide in Denmark, Benelux, Austria and the Southern European countries. Over 53% of the foreign-born poor in the United States, Switzerland and Iceland are in employment.

The immigrant housing overcrowding rate is 17% in the OECD and the EU, against 8% and 11% among the native-born, respectively. The widest differences between the foreign- and native-born occur in Austria, Greece and Italy, the United States and Sweden.

One in four of the foreign-born lives in substandard housing in the EU against one in five of the native-born. Gaps between the two are particularly marked in Southern Europe and in some longstanding European destinations, such as Belgium, the Netherlands, the United Kingdom and Austria.

Few people live in housing that is both overcrowded and substandard. 6% of foreign-born and 3% of native-born live in such housing in the EU. The share is below 1% in non-European OECD countries for both groups.

In the EU, one-third of the foreign-born from the largest ethnic minorities stated that most inhabitants of their neighbourhoods were of the same ethnic background as them. Perceptions of ethnic spatial concentration were felt most acutely in Belgium and the Netherlands (where more than 50% of respondents reported living in such neighbourhoods) and, to a lesser extent, in France and Portugal.

Immigrants are more likely than the native-born to say they are in good health in the OECD: 79% against 76% (shares adjusted by age). Although the shares are similar in the EU at around 67% for both groups, in fact immigrants are more likely than native-born to report good health only in one-quarter of countries, including Poland, the United Kingdom, Italy and Hungary.

A similar share of foreign- and native-born (5.5%) report unmet medical needs across both the OECD and the EU. The incidence is higher than among the native-born in the Nordic countries and Italy, as well as in Greece and Estonia.

Differences in access to care are wider with respect to dental health. Across the EU, the share of immigrants reporting unmet dental needs is 11.5%, against 8.5% for the native-born. Gaps are greatest in the Baltic and Nordic countries, as well as in longstanding European immigration countries and Greece.

4.1. Household income

Definition

A household’s annual equivalised disposable income is the income per capita adjusted by the square root of household size. Income is expressed in euros (EUR) at constant prices based on purchasing power parity (PPP) for 2014. It includes earnings from labour and capital. The median income divides households into two halves: one-half receives less and the other more than the median income. One-tenth of the population has an income lower than the first decile (D1) and one-tenth higher than the ninth decile (D9).

Coverage

People aged 16 years old and over who live in ordinary housing (see glossary). The household's annual equivalised income is attributed to each individual member.

The median immigrant household income is around EUR 15 500 in the OECD and EUR 12 500 in the EU – some 10% lower than that of natives in both areas. The median income is even lower among non-EU migrants, with EUR 11 500 in the EU. By contrast, the median income of EU migrants (EUR 13 200) is similar to that of the natives. With the three exceptions of Malta, Hungary and Bulgaria, immigrants’ incomes are lower than those of the native-born in all countries. In Austria and Southern Europe (though not in Portugal), they are up to one-third lower. By contrast, the gap is narrower in Central European countries, Portugal and the United Kingdom. Immigrants are over-represented in the lowest income decile in virtually all OECD and EU countries –14% and 18% of immigrants (20.5% of non-EU migrants) are in this decile, respectively (compared to 9% of the native-born in both areas. At the other end of the spectrum, only 8% of immigrants (6% of non-EU migrants) belong to the top income group in both areas and only as 5% in Austria, Estonia and the Southern European countries (excluding Portugal).

Over the last decade, the share of those immigrants themselves in the lowest income decile increased by 1 percentage point across the OECD and 3 points EU-wide. That rise mainly affected immigrants born outside the EU. While the foreign-born are particularly at risk to be in the lowest income decile in Southern European countries and Austria, they are less so than 10 years earlier in about a quarter of countries. The largest declines occurred in Finland, Luxembourg and the Czech Republic. However, the proportion of immigrants in the highest income decile also declined over the same 10-year period in about three-quarters of the countries, with the sharpest drops happening in Norway and the Slovak Republic. In some Central and Eastern European countries, as well as in Greece and the United States, the share of immigrants increased in both the lowest and the highest deciles.

Income inequality among the foreign-born tends to be greater than among natives. Across the OECD, those in the top income decile boast 5.6 times the income of their peers in the lowest. The figure is 4.8 among the native-born. In the United States, the OECD country with the highest level of income inequality, the top decile outstrips the bottom by a factor of 7.4 among the foreign- and 6.6 among the native-born. As for the EU, the income gap among immigrants is again more pronounced than among the native-born, and particularly so in Spain, Italy and Sweden. It is, by contrast, similar between the two groups in a quarter of the countries and narrower among immigrants than natives in Israel, Iceland, Ireland, and Estonia.

Figure 4.1. Median income

EUR in constant prices (based on 2014 PPP), population aged 16 and over, 2015

StatLink  http://dx.doi.org/10.1787/888933843059

Figure 4.2. How shares of foreign-born in the lowest and highest income decile have evolved

Changes in percentage points, aged 16 and above, between 2006 and 2015

StatLink  http://dx.doi.org/10.1787/888933843078

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

4.2. Relative poverty

Definition

The relative poverty rate is the proportion of individuals living below the poverty threshold. The Eurostat definition of the poverty threshold used here is 60% of the median equivalised disposable income in each country.

Coverage

All people aged 16 years old and over living in ordinary housing (see glossary). The annual equivalised household income is attributed to each individual.

Around 30% of immigrants live in relative poverty in both the OECD and the EU. They are more likely to be poor than the native-born in all countries, with the exceptions of Poland, Bulgaria and Israel. The ratios of foreign- to native-born poverty rates are lowest in Central European countries (except for the Czech Republic), Germany, Australia and the United Kingdom. Rates are, however, at least twice those of natives in the longstanding immigration destinations in Europe that host large numbers of low-educated foreign-born, as well as in the Scandinavian and Southern European countries (except Portugal). In Spain and Greece, more than 40% of immigrants live below the poverty threshold. Among the immigrant population, non-EU migrants are particularly affected, with a EU-wide poverty rate of 31%, and are more likely to be poor in all countries but the Czech Republic. Rates are three times those of the native-born in Austria and the Benelux.

Over the last decade, the OECD- and EU-wide immigrant poverty rates increased by 1 and 5 percentage points, respectively, while remaining stable among natives. Poverty is today more widespread among the foreign-born than before the economic crisis in about two-thirds of countries. Changes in immigrant poverty rates were generally more pronounced than among natives. In Southern Europe and Austria, for instance, native-born poverty rates fell (apart from Greece), while rising among immigrants – by as high as 17 percentage points in Spain. In addition, in countries like Sweden and those of Central and Eastern Europe which saw native-born poverty levels increase, the increase was twice as high among the foreign-born. At the same time, in a quarter of the countries where poverty levels dropped – e.g. Denmark, Australia, Ireland and the United Kingdom – the drop was steeper for the foreign-born. In the United States, France and Germany, changes in poverty rates between the foreign- and native-born were not significant.

Having a job affords protection against poverty in all countries, although less so for immigrants, particularly where they are over-represented in low-skilled, low-paid occupations – e.g. in the Southern European countries, Austria, Benelux, and Denmark. About 11.7 million migrant workers aged 15 to 64 live in poverty in the OECD and more than 4.4 million in the EU – an in-work poverty rate of about 19% in each area, twice that of natives. Over 53% of the foreign-born poor in the United States, Switzerland and Iceland are in employment, 10 percentage points more than natives.

Figure 4.3. Relative poverty rates

Percentages of the population, aged 16 and above, 2015

StatLink  http://dx.doi.org/10.1787/888933843097

Figure 4.4. How foreign-and native-born relative poverty rates have evolved

Changes in percentage points, aged 16 and above, between 2006 and 2015

StatLink  http://dx.doi.org/10.1787/888933843116

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

4.3. Overcrowded housing

Definition

A dwelling is considered to be overcrowded if the number of rooms is less than the sum of one living room for the household, plus one room for the single person or the couple responsible for the dwelling (or two rooms if they do not form a couple), plus one room for every two additional adults, plus one room for every two children.

Coverage

People aged 16 years and over living in ordinary housing (see glossary).

Almost 17 million immigrants in the OECD and over 7 million in the EU live in overcrowded accommodation – a rate of 17% in both areas, against 8% and 11% among the native-born, respectively. Foreign-born overcrowding rates are lowest where they are also low among natives, as in Malta, Canada, and Ireland. However, they exceed one immigrant in three in Bulgaria, Italy and Greece. In two-thirds of countries, the foreign-born are more than twice as likely to live in overcrowded conditions as the native-born. They are less likely, however, in the Baltic countries, Poland and Hungary. The widest differences between the foreign- and native-born occur in Austria, Greece and Italy, where they exceed 20 percentage points, and in the United States and Sweden with over 13 points. Overcrowding is much more an issue among non-EU migrants in all countries, with the exceptions of Denmark and the United Kingdom. Indeed, one non-EU foreign-born in five lives in an overcrowded dwelling EU-wide, against only one EU migrant in seven.

Over the last decade, the foreign-born overcrowding rate rose in half of all OECD countries, particularly in longstanding European destinations such as Germany, the Netherlands and the United Kingdom. Even more marked, however, were the rises in Sweden and Iceland among the foreign-born, while the share of the native-born living in overcrowded conditions climbed only slightly. The other half of OECD countries saw overcrowding among the foreign-born decline over the same period and, with the exception of Hungary and the Czech Republic, more markedly than among the native-born. In Baltic countries and Slovenia, the proportion of immigrants in overcrowded conditions in 2016 was at least 15 percentage points down compared to ten years earlier. In Denmark, too, it dropped 7 points, while rising slightly by 2 points among the native-born. Similar trends occurred in Greece and, to a lesser extent, in the United States.

Among both the foreign- and native-born, overcrowding is more common in rented than in owned accommodation. OECD-wide, it is 3 times higher among immigrant tenants than home-owners. As for the native-born, it is around 2.5 times greater. In the EU, too, immigrant tenants are almost 3 times as likely to live in overcrowded conditions as those who own their homes – a gap of 16 percentage points. Native-born tenants are only a little more likely, however, to live in overcrowded accommodation. In Austria, where the gap is widest, almost four in ten immigrant tenants live in overcrowded housing, compared to only 1 in 20 immigrant home-owners.

Figure 4.5. Overcrowding rates

Percentages, aged 16 and above, 2016

StatLink  http://dx.doi.org/10.1787/888933843135

Figure 4.6. How overcrowding rates among the foreign- and native-born have evolved

Changes in percentage points, aged 16 and above, between 2008 and 2016

StatLink  http://dx.doi.org/10.1787/888933843154

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

4.4. Housing conditions

Definition

Housing is considered substandard or deprived if it is too dark, does not provide exclusive access to a bathroom, or if the roof leaks. No comparable information on housing quality is available for the United States.

Coverage

People aged 16 years and over living in ordinary housing (see glossary).

In the EU, one foreign-born in four (whether from inside or outside the EU) lives in substandard housing against one in five native-born. Differences between the two are particularly marked in Southern Europe and in some longstanding European destinations, such as Belgium, the Netherlands, the UK and Austria. 38% of all foreign-born residing in Belgium live in deprived housing, in contrast to 22% of the native-born. The respective shares in the Netherlands are 31% and 19%. Gaps are narrowest in Central and Eastern Europe (except in Hungary), Portugal and Germany. In one-quarter of countries only, there is less chance that immigrants live in substandard housing than natives, notably in settlement destinations (e.g. Canada and Australia) and the Baltic countries. Indeed, foreign-born in Latvia and Canada are at least 6 percentage points less likely to live in substandard housing than the native-born.

Housing conditions are better for home-owners OECD- and EU-wide. For the immigrant population in both areas, the share of tenants living in substandard housing exceeds that of home-owners by over 10 percentage points. The same pattern also arises among the native-born in 3 countries out of 5. Housing conditions in both the OECD and EU are slightly better in accommodation rented at market rates than in housing at reduced rates.

Few people live in housing that is both overcrowded and substandard: 6% of foreign-born and 3% of native-born in the EU. The share is below 1% in non-European OECD countries for both groups. The widest gaps between immigrants and natives living in such accommodation are to be found in Southern Europe (with the exception of Spain), Austria, and the United Kingdom – over 4 percentage points to the detriment of the foreign-born. In Central and Eastern European countries, non-European OECD countries and Denmark, by contrast, shares are not significantly different. Overall, though, over one-third of all immigrants (and two in five among non-EU migrants) occupy an accommodation that is either overcrowded or deprived in the EU, against one-fourth of the native-born. Gaps are at least 20 percentage points in Greece, Austria and Italy.

In addition to actual housing conditions, the characteristics and composition of neighbourhoods are also an important factor in integration. In the second wave of the European Union Minorities and Discrimination Survey (EU-MIDIS II), one-third (31%) of the non-EU migrants in the largest ethnic minorities stated that most inhabitants of their neighbourhoods were of the same ethnic background as them. Perceptions of ethnic spatial concentration were most widespread among immigrants from Turkey and North Africa. They were felt most acutely in Belgium and the Netherlands (where more than 50% of respondents stated that they live in such area) and, to a lesser extent, in France and Portugal. One immigrant respondent in seven also lived in an area with environmental problems (e.g. air or water pollution, offensive smells), especially in France and the Netherlands.

Figure 4.7. Substandard accommodation

Percentages, aged 16 and above, 2016

StatLink  http://dx.doi.org/10.1787/888933843173

Figure 4.8. Shares of tenants who live in substandard and overcrowded dwellings rented at market rates

Differences in percentage points between the foreign- and native-born, aged 16 and above, 2016

StatLink  http://dx.doi.org/10.1787/888933843192

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

4.5. Self-reported health status

Definition

Self-reported health status denotes how people perceive their physiological and psychological health. Individuals who rate their health as “good” or better are considered as in good health. As health status is strongly age-dependent, and immigrants tend to be younger in most countries, health status of immigrants is adjusted to estimate what outcomes would be if immigrants had the same age structure as the native-born.

Coverage

People aged 16 years and over.

Immigrants are more likely than the native-born to say they are in good health in the OECD and the EU: 81% against 76%, and 71% against 67%, respectively. After controlling for age, immigrants have still a better health status in the OECD, with 79% reporting to be in good health. Although shares for both groups are similar in the EU – at around 67% after controlling by age – the native-born in fact claim good health more often than their immigrant counterparts in half of all countries. Self-reported health is especially poor in the Baltic countries, Portugal and in the Czech and Slovak Republics. In these countries, overall health is low in international comparison. Accordingly, so is the share of immigrants in good health.

Immigrants have similar or better reported health than the native-born in about half of countries. These include countries that are host to highly educated recent arrivals, such as the United States, the settlement countries and some new destinations like Ireland. They are more likely to report good health than native-born in seven countries, including Poland, the United Kingdom, Italy and Hungary. In the other countries, where native-born reported better health than their immigrant peers, the differences are largest in the Netherlands, Switzerland and Denmark, where the incidence of good health status among the foreign-born is 10 percentage points less than among the native-born.

Over the past decade, the share of the foreign-born reporting good health status rose in about half of all countries. The steepest increases were in Latvia and certain Central European countries – as high as 13 percentage points in Latvia, compared to 5 points among the native-born. Norway and Germany also saw a sharper increase among the foreign-than the native-born. Portugal and most longstanding European immigration destinations, by contrast, recorded declines in the incidence of immigrants reporting good health. It was particularly noteworthy in Portugal, where it fell by 5 percentage points, while rising by 4 points among the native-born. The opposite was observed in Sweden, where the share of those with good health fell among the native-born but rose among immigrants. In a quarter of countries, good health rates dropped more strongly among the native- than the foreign-born – especially in Denmark, Luxembourg and the United Kingdom. In the latter, the share of the native-born who described themselves as in good health fell by 10 percentage points, against 3 points among immigrants.

Figure 4.9. Good health status

Percentage, aged 16 and above, 2016

StatLink  http://dx.doi.org/10.1787/888933843211

Figure 4.10. How shares of foreign- and native-born in good health have evolved

Changes in percentage points, aged 16 and above, between 2007 and 2016

StatLink  http://dx.doi.org/10.1787/888933843230

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

4.6. Unmet health care needs

Definition

Share of people who reported needing but not receiving medical healthcare or dental care in the previous 12 months. The indicator is adjusted to estimate what outcomes would be if immigrants had the same age structure as the native-born.

Coverage

People aged 16 years and over.

Similar shares of foreign- and native-born (5.5%) report unmet medical needs across both the OECD and the EU (whether the share is age-adjusted or not). However, while the share for the native-born is not significantly different from that of EU migrants in most countries, differences between native-born and immigrants from third countries are particularly large in Sweden, Estonia, Italy, and Greece, where one in four immigrants claim unmet healthcare needs against one in six among their native peers.

Differences in access to care are wider with respect to dental health. Across the EU, the share of immigrants reporting unmet dental needs is 11.5%, against 8.5% for the native-born. Gaps are greatest in the Baltic and Nordic countries, as well as longstanding European immigration countries and Greece. As with medical health, immigrants are less likely to report unmet dental needs in only three countries: Portugal, Poland and the Slovak Republic.

Across the OECD and the EU, both the foreign- and native-born were only slightly less likely to report unmet medical needs than before the economic crisis. In Latvia, Portugal and Germany, however, they were at least 7 percentage points less likely. By contrast, the situation worsened in Greece, Denmark, Estonia, Italy and Belgium, particularly among immigrants. In Greece, for instance, the increase in the share of immigrants reporting unmet medical needs was twice that of the native-born over the last decade.

Immigrants’ higher tendency to have unmet medical needs could be attributable to individual socio-economic factors such as poorer education, incomes, working conditions, and social integration – all of which tend to adversely affect access to health care services.

In the EU, the EU-MIDIS II survey found that 6% of non-EU migrants from the largest immigrant groups did not have a medical examination or treatment in the previous 12 months each time they really needed it. Among that group:

39% could not afford it (too expensive or not covered by the insurance);

16% preferred to wait until they got better;

11% thought the waiting list was too long.

Figure 4.11. Unmet medical needs

Percentages, aged 16 and above, 2016

StatLink  http://dx.doi.org/10.1787/888933843249

Figure 4.12. How shares of individuals reporting unmet medical needs have evolved

Changes in percentage points, aged 16 and above, between 2007 and 2016

StatLink  http://dx.doi.org/10.1787/888933843268

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

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.

Figure 4.2 and Figure 4.4 do not include Estonia and Switzerland in changes between 2007 and 2016 due to a break in series data.

Indicators 4.3 and 4.4: Data for Germany cover the population aged 18 years and over.

Indicator 4.3: The overcrowding rate for the United States uses the number of bedrooms, instead of the number of rooms. A dwelling is therefore considered overcrowded if the number of bedrooms is less than one bedroom for the single person or the couple responsible for the dwelling (or two bedrooms if they do not form a couple), plus one bedroom for every two additional adults, plus one bedroom for every two children.

Indicators 4.5 and 4.6: Data for Australia and Germany are not age-adjusted.

Indicator 4.6: Data from the United States refer only to medical needs that go unmet for reasons of cost. Data for Australia refer to people who could not obtain healthcare of either type when it was needed.

Data for Australia and Canada cover populations aged 15 years and over.

All panel survey designs tend to under-represent recent arrivals. EU Statistics on Income and Living Conditions (EU-SILC) update one quarter of the panel every year. Newly arrived immigrants are included if they appear in an updated quarter or join a resident household in the other three quarters, e.g. through family reunification or formation.

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

For further detailed data, see Annex C.

Table 4.1. Sources by indicator
| 4.1 Household income | 4.2 Relative poverty | 4.3 Overcrowded housing | 4.4 Housing conditions | 4.5 Reported health status | 4.6 Unmet health care needs
OECD/EU |  |  |  |  |  |
Australia | SIH 2005-06 & 2015-16 | SIH 2005-06 & 2015-16 | SIH 2005-06 & 2015-16 | SIH 2015-16 | GSS 2014 | GSS 2014 (medical care only)
Austria | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Belgium | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Bulgaria | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Canada | Census 2006 & 2016 | Census 2006 & 2016 | Census 2006 & 2016 | Census 2016 | NPHS 2007-08 & 2013-14 | NPHS 2013-14 (medical only)
Chile | .. | .. | .. | .. | .. | ..
Croatia | EU-SILC 2016 | EU-SILC 2016 | EU-SILC 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2016 | EU-SILC 2016, EU-MIDIS II 2016 (reasons)
Cyprus1,2 | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Czech Republic | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Denmark | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Estonia | EU-SILC 2016 | EU-SILC 2016 | EU-SILC 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2016 | EU-SILC 2016, EU-MIDIS II 2016 (reasons)
Finland | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
France | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Germany | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Greece | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Hungary | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Iceland | EU-SILC 2007 & 2015 | EU-SILC 2007 & 2015 | EU-SILC 2008 & 2015 | EU-SILC 2015 | EU-SILC 2007 & 2015 | EU-SILC 2007 & 2015
Ireland | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Israel* | IHS 2015 | IHS 2015 | .. | .. | .. | ..
Italy | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Japan | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | ..
Latvia | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Lithuania | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Luxembourg | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Malta | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2008 & 2016 | EU-SILC 2008 & 2016, EU-MIDIS II 2016 (reasons)
Mexico | .. | .. | .. | .. | .. | ..
Netherlands | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
New Zealand | .. | .. | .. | .. | .. | ..
Norway | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016 | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016
Poland | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Portugal | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Romania | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Slovak Republic | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Slovenia | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Spain | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Sweden | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
Switzerland | EU-SILC 2016 | EU-SILC 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016 | EU-SILC 2008 & 2016 | EU-SILC 2008 & 2016
Turkey | .. | .. | .. | .. | .. | ..
United Kingdom | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016 | EU-SILC 2008 & 2016 | EU-SILC 2016, EU-MIDIS II 2016 (neighbourhood) | EU-SILC 2007 & 2016 | EU-SILC 2007 & 2016, EU-MIDIS II 2016 (reasons)
United States | CPS 2007 & 2017 | CPS 2007 & 2017 | ACS 2008 & 2016 | .. | NHIS 2007 & 2016 | NHIS 2007 & 2016 (medical only)
StatLink  https://doi.org/10.1787/888933843287

Chapter 5.

Immigrant civic engagement and social integration

Becoming actively involved in the host-country society is a key element in immigrant integration and has strong implications for immigrant well-being. By making their voices heard, taking an interest in how their host society works, and participating in the decisions that shape its future, immigrants become an integral part of their new country, this being the very objective of integration.

The nature of the relationship between a host society and its immigrant population is also a critical factor in integration: if social cohesion is strong, it will promote integration whereas if it is weak, immigrants will find it harder to fit in.

This chapter starts by looking at two fundamental elements of immigrants’ civic engagement: acquisition of nationality (Indicator 5.1) and voter participation (Indicator 5.2). Although it does not necessarily mark the end of the integration process, obtaining host-country nationality certainly represents a key step in that process. From the viewpoint of the host country, conferring nationality on an immigrant is also a way of welcoming him or her into the community of citizens. One fundamental right of citizens is the right to vote. Participating in elections is a sign of integration – a desire to influence society by getting involved in the selection of those who govern it.

The chapter continues by exploring key aspects of social cohesion, represented by the five following indicators: host-country degree of acceptance of immigration (Indicators 5.3 and 5.4); attitudes of immigrants – compared to those of the native-born – towards gender equality (Indicator 5.5); the extent to which immigrants feel part of the host society or their resultant sense of belonging (Indicator 5.6); the perceived incidence of discrimination against immigrants on the grounds of ethnicity, race or nationality (Indicator 5.7); and, finally, overall life satisfaction (Indicator 5.8) or the extent to which immigrants are satisfied with their life in the host society.

Key findings

About two-thirds of long-settled immigrants (i.e. more than ten years of residence) in the OECD and 59% in the EU have host-country citizenship, over 74 million and 34 million immigrants, respectively.

While there was no change in the shares of the settled foreign-born who have host-country citizenship in non-European countries over the last decade, there was an average drop of almost 10 percentage points in the EU that concerned both EU-born and other migrants.

An average of 74% of immigrants with host-country nationality in the OECD and the EU report that they participated in the most recent national elections – less than the native-born rate of around 80%.

EU-wide, about half of the native-born hold no particular view on whether immigrants make their country a better or a worse place to live in. The other half, however, believe in equal proportions that immigrants exert either a positive or a negative overall effect on their country.

Host-country society views of immigration have remained broadly stable in the EU since 2006, although in a majority of countries slightly more people now take positive stances.

The more the native-born interact with the foreign-born, the more likely they are to consider immigration as an opportunity.

EU-wide, immigrants are slightly more likely than natives to agree with the statement that “when jobs are scarce, men should have more right to a job than women” (22% vs. 16%).

A majority of immigrants in the EU (52%) share the view that “a woman should be prepared to cut down on her paid work for the sake of her family”, compared with 44% among the native-born.

In all EU and OECD countries, more than 80% of immigrants report feeling close or very close to their country of residence. The rate ranges from 80% in the Baltic States and Austria to around 95% in France and Switzerland.

Around 14% of all foreign-born people in the EU claim to belong to a group subject to discrimination on the grounds of ethnicity, nationality or race. In the United States, less than 10% of immigrants say they have experienced discrimination with regard to work because of their race, ethnicity or national origin in the past five years. As for Australia and Canada, 16% and 12% of immigrants, respectively, report that they personally experienced discrimination.

Over the past decade, the overall level of perceived discrimination on the grounds of ethnicity, nationality or race has slightly declined in the EU, falling by 2 percentage points among both men and women.

In most EU countries, immigrants are less satisfied with their life than the native-born whereas no significant differences appear between those two groups in non-EU OECD countries.

Immigrant women are happier than their male counterparts in the few countries where the gender gap is significant (Austria, Denmark, Greece, Ireland, Italy, the Netherlands and the United Kingdom).

5.1. Acquisition of nationality

Definition

The acquisition of nationality is the process through which immigrants become citizens of the host country in which they reside. In addition to other requirements, immigrants must have lived for a certain time in the host country before they can apply for nationality. Required durations vary according to the host country and the immigrant group. After 10 years of residence, most immigrants are eligible for citizenship in all countries. This section uses the term “acquisition rate” to denote the share of immigrants who have resided in the host country for at least 10 years and hold the host-country nationality.

Coverage

Immigrants aged 15 years and older who have resided in the host country for at least 10 years. Immigrants who have acquired the nationality of the host country at birth (e.g. expatriates) are also included since they cannot be separately identified.

About two-thirds of long-settled immigrants (i.e. more than ten years of residence) in the OECD and 59% in the EU have host-country citizenship – over 74 million and 34 million immigrants, respectively.

The countries where the largest shares – up to 90% – of the foreign-born are host-country citizens are those, like the Slovak Republic, Poland, Lithuania and Croatia, where border changes shaped the national make-up. Host-country nationals also account for considerable proportions of immigrant populations in countries such as Canada, Sweden, Portugal and Australia, which encourage the acquisition of citizenship. However, in countries where the process of acquiring nationality is (or has been) more difficult, naturalisation is not encouraged to the same extent, or dual citizenship not legally possible (or has not been until recently), substantially fewer immigrants are host-country nationals. Such countries include those of Southern Europe (with the exception of Portugal) and Luxembourg.

The EU-wide acquisition rate is lower amongst European immigrants than among those from other regions. This trend is attributable to the facilitation of freedom of movement between EU countries, thus lowering the value-added of host-country citizenship. As a result, only 45% of European immigrants in EU countries have sought to acquire their host-country’s nationality compared to around two-thirds of those originating from non-European countries. By comparison, in countries that are not part of mobility agreements with European countries, such as Australia and the United States, acquisition rates are high among Europeans (more than 80%). At 46%, they are much lower, however, among Latin American and Caribbean immigrants in the United States – and even lower in Chile, where less than one-third had acquired nationality in 2015. In Norway and Belgium, the acquisition rate among immigrants from Africa and Asia is 30 percentage points higher than among their European peers. Remarkably cultural and historical ties may affect acquisition rates. For example, the Netherlands’ ties with countries in Asia (Indonesia) and in Latin America and the Caribbean (Suriname) is reflected in the high rates of acquisition of Dutch nationality among immigrants from the two regions. The same is equally true of Portugal with regard to immigrants from Africa, who tend to come from lusophone countries.

While there was no change in the shares of the settled foreign-born who have host-country citizenship in non-European countries over the last decade, there was an average drop of almost 10 percentage points in the EU that concerned both EU-born and other migrants. In some countries, the fall was much steeper – up to 20 percentage points in the Czech Republic, Denmark, Estonia, and Italy. By contrast, other countries, like Portugal and Switzerland, which had streamlined naturalisation procedures in the previous decade, saw rises in the shares of settled foreign-born residents acquiring nationality.

Figure 5.1. How the acquisition of nationality among immigrants has evolved

Percentages of host-country nationals among settled immigrants, aged 15 and above, 2006-07 and 2017

StatLink  http://dx.doi.org/10.1787/888933843306

Figure 5.2. Acquisition of nationality by region of birth

Percentages of host-country nationals among settled immigrants, aged 15 and above, 2015-16

StatLink  http://dx.doi.org/10.1787/888933843325

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

5.2. Voter participation

Definition

Voter participation is measured as the share of citizens who report that they have casted a ballot in the most recent national parliamentary election in the country of residence. A comparison between participation in local/municipal and national/general elections is drawn from the EU MIDIS II.

Coverage

All nationals of the country of residence aged 18 and older who are eligible to vote in national elections.

An average of 74% of immigrants with host-country nationality in the OECD and the EU report that they participated in the most recent national elections – less than the native-born rate of 79%. The gap in voter participation with the native-born remains constant after accounting for age and education. The few countries in which immigrants are significantly more likely than natives to vote are some Eastern and Central European countries and Israel. In absolute terms, immigrants’ turnout is highest in Denmark and Belgium (where there is a formal obligation for all citizens to vote), and lowest in the Czech Republic, Switzerland and Ireland. These rates are similar to native-born participation in several longstanding destinations, such as France, the United Kingdom and Canada. Gaps are widest, ranging from 12 to 20 points, in the Nordic countries, Southern Europe (excluding Italy), Ireland and Switzerland.

In almost all countries, immigrant citizens who have been residents for over 10 years generally boast higher rates of participation in national elections than newer arrivals already naturalised. Turnout among the long-settled foreign-born citizens is, however, still on average 4 percentage points lower than among their native-born peers. That being said, in the United Kingdom, Poland and several other Central and Eastern European countries, long-resident immigrants are actually more likely to vote than the native-born. EU and non-EU migrants with host-country nationality show similar turnout EU-wide, after accounting for age and level of education. However, there are wide variations from country to country. In Switzerland, Germany and Ireland, EU immigrants are more likely to take part in national elections than their non-EU counterparts. The reverse is true in Austria and the United Kingdom. In the latter country, citizens of Commonwealth countries enjoy full voting rights, regardless of how long they have been residents.

EU- and OECD-wide, immigrant voter participation has hardly changed over time although the overall gap with natives has narrowed slightly. However, different trends are at play across countries. While the gap has decreased in Denmark, Austria, Sweden, Spain, France and the United Kingdom, it has widened significantly in Iceland, Greece, Ireland, Finland and Switzerland. In the EU, while the voter participation of long-settled immigrants has hardly changed, it decreased by 5 percentage points among more recent immigrants with host-country citizenship, to 51%.

According to the second European Union Minorities and Discrimination Survey (EU-MIDIS II) among communities of 16 years old and over, the levels of immigrants’ participation in local and national elections tend to be similar. Notable exceptions are North Africans in the Netherlands, who tend to participate more in national than in local elections, while the reverse is true for these groups in Southern Europe. Overall, immigrants from Asia are more likely to vote. Sub-Saharan African immigrants tend to cast their ballots more often in Nordic countries and the United Kingdom than in Southern Europe, Ireland or France.

Figure 5.3. Self-reported participation in most recent election

Percentages of the population with the host-country’s nationality, aged 18 and above, 2008-16

StatLink  http://dx.doi.org/10.1787/888933843344

Figure 5.4. How self-reported participation rates in most recent elections have evolved

Changes in percentage points between the native- and the foreign-born with the nationality of the country of residence, aged 18 and above, between 2002-08 and 2010-16

StatLink  http://dx.doi.org/10.1787/888933843363

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

5.3. Host-society attitudes towards immigration

Definition

This indicator seeks to assess the integration of immigrants from the point of view of the host country, as positive attitudes make integration easier and tend to be associated with better social integration. Various questions have been analysed for the EU, Australia and the United States (see notes at the end of the Chapter).

Coverage

The native-born aged 15 and older.

EU-wide, about half of the native-born hold no particular view on whether “immigrants make their country a better or a worse place to live in”. The other half, however, believe in equal proportions that immigrants exert either a good or bad overall effect on their country. Nordic countries and Ireland harbour the most positive opinions, and Hungary, Italy and the Czech Republic the most negative. Since 2006, native-born views of immigrants have remained broadly stable in Europe (with a mean score close to 5) although, in a majority of countries, more people now take slightly more positive stances. The strongest swings to more favourable opinions came in Portugal, the United Kingdom and Norway, while in Hungary, Italy and the Czech Republic public opinion became less favourable, creating a host-country divide in Europe.

The native-born tend, on the whole, to report slightly more positive views when asked about specific impacts that immigrants have on their country, notably when it comes to their contribution to the national cultural life. The picture is more mixed with respect to the economic impact of migration. More than 80% of natives in Australia see themselves tolerant of society being comprised of different cultures. In Sweden and Denmark, the vast majority of native-born think that immigrants enrich their country’s culture while they are slightly more sceptical about the economic impact. In Europe, native-born in Germany and Switzerland have the most positive views regarding the economic impact of migration, while the native-born in Central and Eastern European countries are among the most negative, together with Italy, Austria and France. As for Greece, two-thirds of native-born believe that the foreign-born make Greece a worse place to live in, are bad for the economy, and undermine their culture.

In the United States, while almost half of the native-born believe that immigrants are good for the economy, the other half also think that inflows should be cut. Similarly, while 39% of Australian-born consider that the number of immigrants accepted into Australia at present is “too high”, around 60% agree with the statement that “accepting immigrants from many different countries makes Australia stronger”.

When it comes to the impact on the labour market, half of the EU native population hold no particular view on whether immigrants take or create jobs. More than a quarter, however, are inclined to think that they take jobs and a minority (18%) that they create them. Overall, opinions in this regard have become more positive since 2006, particularly in Germany, Norway and Switzerland.

Within countries, a clear age- and education-related divide emerges, with the younger and better educated showing more positive attitudes towards immigrants. Differences between age groups are, however, less marked in the most positive countries and wider in the most negative (except Hungary). Attitudes differ the most between young adults and the elderly in the United Kingdom, Austria, France, Estonia and Spain.

Figure 5.5. How host-country perceptions of the presence of immigrants have evolved

Mean scores on a scale from 0 to 10 for question: “Is [country] made a worse or a better place to live by people coming to live here from other countries?”, 2006 and 2016

StatLink  http://dx.doi.org/10.1787/888933843382

Figure 5.6. The age divide in host-country perceptions of immigrants

Mean score on a scale from 0 to 10 for question: “Is [country] made a worse or a better place to live by people coming to live here from other countries?”, 2016

StatLink  http://dx.doi.org/10.1787/888933843401

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

5.4. Interactions with immigrants

Definition

This indicator, which is only available for EU countries, seeks to assess the frequency of interactions of the natives with immigrants born in a third country (“On average, how often do you interact with immigrants?”, “Interaction can mean anything from exchanging a few words to doing an activity together”), and its association with attitudes towards immigration, based on the question: “Do you see immigration more of a problem, an opportunity, neither a problem nor an opportunity, both of a problem and an opportunity?” Two types of interaction are considered in this section: in the workplace and in the neighbourhood. Interactions are considered frequent when they occur at least once a week; rare when they occur once a year or less frequently.

Coverage

The native-born aged 15 and older.

Interaction is most widespread in neighbourhoods and in the workplace, where 44% and 28% of the native-born population, respectively, report interacting at least once a week with immigrants from non-EU countries. Countries where the native-born interact most with the non-EU-born in their neighbourhood are Southern European countries, Ireland and Austria. Interaction while working with immigrant colleagues is most common in Sweden, Denmark and the Netherlands.

EU-wide, around 32% of the native-born consider that third-country immigration is both a problem and an opportunity and 8% that it is neither a problem nor an opportunity. Around 40% think that it is more of a problem, while a 20% consider it an opportunity. In about half of all EU countries, the dominant view is that immigration is both a problem and an opportunity or neither a problem nor an opportunity.

Native-born who interact with the foreign-born are more likely to consider immigration as an opportunity, particularly so when interactions occur in the workplace. More than 26% of native-born who interact once a week or more with immigrants in their workplace view immigration as an opportunity. This share falls to 14% among those who report little interaction. Notable exceptions are Portugal and Luxembourg, where people having seldom interactions with immigrants are more likely to report that immigration is more of an opportunity than those who have frequent interactions. The association between interaction with immigrants in the workplace and positive attitudes towards immigration is particularly strong in the Baltic countries and Southern European countries (bar Latvia and Portugal), the United Kingdom and Finland.

Figure 5.7. How frequently native-born populations interact with immigrants

Percentages of the native-born who interact at least once a week with immigrants, 2018

StatLink  http://dx.doi.org/10.1787/888933843420

Figure 5.8. The extent to which interactions with immigrants shape the likelihood to see immigration as more of an opportunity

Percentages of the native-born who consider immigration as more of an opportunity, 2018

StatLink  http://dx.doi.org/10.1787/888933843439

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

5.5. Attitudes towards gender equality

Definition

This indicator is based on self-reported views on two statements: “When jobs are scarce, men should have more right to a job than women” and “A woman should be prepared to cut down on her paid work for the sake of her family.” Data are only available for EU countries.

Coverage

Persons aged 15 and older.

Across the EU, 22% of the foreign-born population and 16% of the native-born population agree with the statement that “when jobs are scarce, men should have more right to a job than women”. Women are generally less inclined to agree with this statement but gender gaps are wider among immigrants. It concerns around 20% of foreign- and 15% of native-born women EU-wide, compared with 24% among foreign- and 16% among native-born men, respectively.

In general, in those countries where native approval rates are very low (very high) among the native-born, they are also low (high) among immigrants. Immigrants are more likely than natives to agree with the above statement in all countries, with the exceptions of Hungary, Portugal and Israel. In Greece, over half of the foreign-born population agree (62% of men and 45% of women), compared to 44% of the native-born population (52% of men and 37% of women). Gaps between the native- and foreign-born views are particularly wide in the Southern European countries (save Portugal), but also in Germany and Denmark, ranging between 10 and 17 percentage points.

As for the view that “a woman should be prepared to cut down on her paid work for the sake of her family”, it is shared by 52% of the foreign-born and 44% of the native-born EU-wide. In the vast majority of countries, foreign-born populations are more likely to report this view than their native peers. Unlike the statement on men having greater entitlement to jobs, which addresses men’s and women’s relative right to work, this one considers the tension between work and family life for women only. It elicits much higher average approval rates, which indicates that the view that a woman’s chief responsibility is to care for her children and family is widespread, among both native- and foreign-born. The lowest approval rates among foreign-born populations (both EU and non-EU-born) come in the Nordic countries and in the Netherlands. In the Baltic countries, by contrast, overall approval rates are high (around 70%), and gaps between native- and foreign-born respondents are minor, save in Estonia.

Figure 5.9. Attitudes towards gender equality in job access

Percentages who agree with the statement: “When jobs are scarce, men should have more right to a job than women”, 2008-16

StatLink  http://dx.doi.org/10.1787/888933843458

Figure 5.10. Attitudes towards women’s responsibility to care for the family

Percentages who agree with the statement: “Women should be prepared to cut down on paid work for the sake of the family”, 2004-10

StatLink  http://dx.doi.org/10.1787/888933843477

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

5.6. Sense of belonging

Definition

This indicator shows the shares of foreign- and native-born who feel part of their national community. In the EU, this indicator is the share of individuals who report that they feel close or very close to their respective country of residence on a scale from 1 to 5; in Australia, it is based on the extent to which individuals “have a sense of belonging in Australia” and is measured as the share who report such sense of belonging to a “great” or a “moderate” extent (versus “only slightly” and “not at all”). In New Zealand, it is the share who report having a sense of belonging to the country higher than 6 (on a scale from 0 to 10). In all other countries, it is the share of persons who self-report that they agree or strongly agree with the statement that they see themselves as part of the “nation”.

Coverage

Population aged 15 and older.

Across all EU and OECD countries, more than 80% of immigrants report feeling close or very close to their country of residence. The rate ranges from 80% in the Baltic countries and Austria to around 95% in France and Switzerland. The gap with natives is generally small, except in countries where immigrants’ sense of belonging is the lowest, as well as in Ireland, Norway and the United States, where particularly high shares of native-born report a strong sense of belonging to their country of birth.

However, natives are generally more prone to “strongly agree” with the statement that they are part of their national community while immigrants tend to more often say that they simply “agree”. This is particularly the case in some European countries where the host-country attitude towards immigration is relatively unfavourable (Austria, Lithuania) or where immigration is fairly recent (Ireland), as well as in Belgium and the Netherlands, Australia and New Zealand.

At the same time, it seems that many immigrants maintain personal, cultural, economic or political ties with their country of origin, although only few countries have data on this. In New Zealand, for example, while around 85% of immigrants reported a sense of belonging to the host country, three-quarters also reported feeling an attachment to their origin country.

Figure 5.11. How close individuals feel to their country of residence

Percentages who feel part of their national community, 2014

StatLink  http://dx.doi.org/10.1787/888933843496

Figure 5.12. How close foreign-born feel to their country of residence

Percentages, aged 15 and above, 2014

StatLink  http://dx.doi.org/10.1787/888933843515

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

5.7. Perceived discrimination

Definition

This indicator considers shares of immigrants who report having experienced discrimination. In the EU, perceived discrimination among immigrants is measured as 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 discrimination with regard to work is covered.

Coverage

Foreign-born people aged 15 to 64 years old.

Around 14% of all the foreign-born in the EU claim to belong to a group subject to discrimination on the grounds of ethnicity, nationality or race. Levels are particularly high in Greece and Latvia, where over a quarter of the foreign-born population feel part of a discriminated group. They are high, too, at around one-sixth, in Portugal and in several longstanding countries of immigration in Europe, such as the Netherlands, France and Belgium. In the United States, less than 10% of immigrants say they have experienced discrimination with regard to work because of their race, ethnicity or national origin in the past five years. As for Australia and Canada, respectively 16% and 12% of immigrants personally experienced discrimination.

Across the EU, immigrants who have lived in the host country for 10 years or more are slightly less likely to report discrimination than those who arrived during the last 10 years. These long-settled immigrants are markedly less likely than recent ones – by 6 to 8 points – in Southern European countries with above-average levels of overall discrimination. The same holds true, although to a lesser extent, of Hungary and Ireland. However, in a number of countries the reverse applies. In Austria, Norway, Sweden, the United Kingdom, and a number of Central and Eastern European countries, long-settled immigrants are more likely to feel discriminated against than recent ones.

Over the past decade, the overall level of perceived discrimination has declined, falling in the EU by 2 percentage points among both men and women. The steepest drops, however, were observed among people from certain regions of origin. Although the foreign-born from North Africa, Sub-Saharan Africa and other European countries (which includes Turkey) report some of the highest discrimination levels in absolute terms, they stand out with declines of 4 to 6 points. Among Sub-Saharan foreign-born, for example, the share who felt that they belong to a group that is discriminated against dropped from 27% to 23%. Similarly, among the unemployed foreign-born, it dropped from 20% to 16%. Only among older immigrants, aged 55 to 64, perceived discrimination rose slightly.

The second European Union Minorities and Discrimination Survey (EU-MIDIS II) affords unique insights into levels of discrimination against different ethnic minority groups in the EU. Among the largest groups of non-EU immigrants aged 16 and over, immigrants from Africa are the most likely to feel discriminated against, and those from Asia the least. More than 40% of Sub-Saharan Africans report to encounter discrimination in Austria, Denmark, Finland, Luxembourg and the Netherlands in the past 12 months. The perception of discrimination is most frequent during the use of services such as public transport, medical care and restaurants (14%), followed by job search (11% report being discriminated while looking for work). At 7% and 4%, respectively, immigrants reported the lowest incidence of perceived discrimination when looking for accommodation and in education, be it in the schools that their children attend or in the establishments where they themselves study.

Figure 5.13. Self-reported discrimination, by length of residence

Percentages of immigrants, 15- to 64-year-olds, 2008-16

StatLink  http://dx.doi.org/10.1787/888933843534

Figure 5.14. Socio-economic characteristics in immigrants’ perceptions of discrimination

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

StatLink  http://dx.doi.org/10.1787/888933843553

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

5.8. Life satisfaction

Definition

Self-reported life satisfaction denotes respondents’ perceptions and assessments of their lives at the time of the interview. Survey respondents were asked to rate, on a scale from 0 to 10, their overall life satisfaction (respondents with a score of 10 being the most satisfied).

Coverage

All populations aged 15 and older.

While across non-EU OECD countries, there appear to be no significant differences between foreign- and native-born life satisfaction scores, in most EU countries, immigrants are less satisfied than natives. OECD- and EU-wide, the highest levels of self-reported life satisfaction among the foreign-born are found in countries with high overall life satisfaction levels, such as the Nordic and settlement countries. At the opposite side of the spectrum lie Greece, Hungary and the Baltic countries. Satisfaction gaps with the native-born are particularly wide in the Baltic countries as well as in Belgium, the Netherlands and Switzerland. Conversely, in Japan and Portugal, the foreign-born report greater overall life satisfaction than natives.

The extent to which migration shapes how people born abroad perceive their lives is contingent on a number of factors. They include the extent to which their pre-migration expectations are met upon arrival, as well as how circumstances and aspirations evolve over time. Variations in migrants’ self-reported life satisfaction from country to country also likely reflect education levels, countries of origin, employment status, reasons for migrating, and living conditions in the country of residence.

In most countries, no or limited difference is observed between immigrant men and women. In the few countries where the gap is significant, though, immigrant women are generally happier with their life than men. This is the case in Austria, Denmark, Greece, Ireland, Italy, the Netherlands and the United Kingdom. In a majority of countries, native women are overall happier with their life than men.

In all European countries but the Czech Republic, life satisfaction among EU immigrants is higher than for non-EU immigrants and on a par with the native-born. In Hungary and Portugal, it is even higher – despite a context of low overall life satisfaction levels.

As for the native-born in the EU, migrants’ levels of satisfaction are strongly associated with their financial situation and accommodation. While personal relationships are less of a determinant among immigrants than among the native-born, the reverse is true of jobs. Satisfaction with one’s job is a more important factor in satisfaction with life among the foreign- than the native-born. In both groups, however, having a job is always associated with higher degrees of life satisfaction.

Figure 5.15. Self-reported life satisfaction

Mean score on a scale from 0 to 10, aged 16 and above, 2008-15

StatLink  http://dx.doi.org/10.1787/888933843572

Figure 5.16. Self-reported life satisfaction, by country of birth

Mean score on a scale from 0 to 10, aged 16 and above, 2013

StatLink  http://dx.doi.org/10.1787/888933843591

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

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.

Indicator 5.3: In the EU, host country opinions of immigration have been assessed by asking the following questions: “Is [country] made a worse or a better place to live by people coming to live here from other countries?”; “Would you say it is generally bad or good for [country]’s economy that people come to live here from other countries?”; “Would you say that [country]’s cultural life is generally undermined or enriched by people coming to live here from other countries?”; “Would you say that people who come to live here generally take jobs away from workers in [country], or generally help to create new jobs?” Answers yield scores on a scale from 0 to 10, from which mean scores and frequencies are calculated. How frequently respondents give positive, negative or neutral answers is determined by dividing responses into three groups: 0 to 3, negative; 4 to 6, neutral; 7 to 10, positive.

In Australia, it is assessed through two questions: “What do you think of the number of immigrants accepted into Australia at present?” (too high; about right; too low); “Accepting immigrants from many different countries makes Australia stronger” (strongly agree; agree; neither agree or disagree; disagree; strongly disagree). In the United States, two statements are considered: “Immigrants are generally good for America's economy” (strongly agree; agree; neither agree or disagree; disagree; strongly disagree); “Do you think the number of immigrants to America nowadays should: be increased/remain the same/be reduced?”.

Indicator 5.7: 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. The United States 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.

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

For further detailed data, see Annex D.

Table 5.1. Sources by indicator
| 5.1 Acquisition of nationality | 5.2. Voter participation | 5.3. Host-society attitudes towards immigration | 5.4. Interactions with immigrants | 5.5. Attitudes towards gender equality | 5.6. Sense of belonging | 5.7. Perceived discrimination | 5.8. Life satisfaction
OECD/EU |  |  |  |  |  |  |  |
Australia | Census 2016 | .. | Scanlon Foundation Surveys 2016-2017 |  .. | ..  | Scanlon Foundation Surveys 2016-2017 | GSS 2014 | Gallup, 2008-15
Austria | EU-LFS 2006-07 & 2017 | ESS 2002-06 & 2014-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2014-16 (F5.9), ESS 2004-06 (F5.10) | ESS 2014 round | ESS 2002-06 & 2014-16 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Belgium | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9), ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Bulgaria | EU-LFS 2006-07 & 2015-16 | ESS 2008-12 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-10 (F5.9) |  .. | ESS 2008-12 | Gallup, 2008-15
Canada | Census 2016 | GSS, 2014 |  .. | ..  | ..  | GSS, 2013  | GSS, 2014 | Gallup, 2008-15
Chile |  .. |  .. |  .. |  .. | .. |  .. |  .. | Gallup, 2008-15
Croatia | EU-LFS 2006-07 & 2015-16 | ESS 2008-10 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-10 (F5.9), ESS 2008-10 (F5.10) |  .. | ESS 2008-10 | Gallup, 2008-15
Cyprus1,2 | EU-LFS 2006-07 & 2015-16 | ESS 2008-12, 2006-08 & 2010-12 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-12 (F5.9), ESS 2006-10 (F5.10) |  .. | ESS 2008-12, 2006-08 & 2010-12 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Czech Republic | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-04+2008 & 2010-16 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9), ESS 2004+2008-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-04+2008 & 2010-16 | Gallup, 2008-15 and SILC 2013
Denmark | EU-LFS 2006-07 & 2017 | ESS 2008-14, 2002-08 & 2010-14 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-14 (F5.9), ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-14, 2002-08 & 2010-14 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Estonia | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2004-08 & 2010-16 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9), ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2004-08 & 2010-16 | Gallup, 2008-15
| 5.1 | 5.2 | 5.3 | 5.4 | 5.5 | 5.6 | 5.7 | 5.8
Finland | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9), ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15
France | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9), ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Germany | EU-LFS 2006-07 & Mikrozensus 2016 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9), ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15
Greece | EU-LFS 2006-07 & 2017 | ESS 2008-10 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-10 (F5.9), ESS 2004+2008-10 (F5.10) |  .. | ESS 2008-10 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Hungary | EU-LFS 2006-07 & 2017 | ESS 2008-14, 2002-08 & 2010-14 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-14 (F5.9), ESS 2004-10 (F5.10) |  .. | ESS 2008-14, 2002-08 & 2010-14 | Gallup, 2008-15 and SILC 2013
Iceland | EU-LFS 2006-07 & 2015-16 | ESS 2012+2016 | ESS 2006 and 2016 waves |  .. | ESS 2012+2016 (F5.9) |  .. | ESS 2012+2016 | Gallup, 2008-15
Ireland | EU-LFS 2006-07 & 2015-16 | ESS 2008-16, 2002-08 & 2010-16 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 | Gallup, 2008-15 and SILC 2013
Israel | .. | ESS 2008-16, 2002+2008 & 2010-16 | ESS 2006 and 2016 waves |  .. | ESS 2008-16 (F5.9) ESS 2008-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002+2008 & 2010-16 | Gallup, 2008-15
Italy | EU-LFS 2006-07 & 2017 | ESS 2012 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2012 (F5.9) |  .. | ESS 2012 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Japan | .. | ..  | ..  | ..  | ..  | ..  | ..  | Gallup, 2008-15
Korea | .. |  .. | .. | ..  | ..  | .. |  .. | Gallup, 2008-15
Latvia | EU-LFS 2006-07 & 2017 | ESS 2010-14 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2010-14 (F5.9), ESS 2010 (F5.10) |  .. | ESS 2010-14 | Gallup, 2008-15
Lithuania | EU-LFS 2006-07 & 2017 | ESS 2010-14 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2010-14 (F5.9) ESS 2010 (F5.10) | ESS 2014 round | ESS 2010-14 | Gallup, 2008-15
Luxembourg | EU-LFS 2006-07 & 2017 | ESS 2004 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2004 (F5.10) |  .. | ESS 2004 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Malta | EU-LFS 2006-07 & 2015-16 | .. | ESS 2006 and 2016 waves | Eurobarometer 2017 | .. |  .. | .. | Gallup, 2008-15
Mexico | .. |  .. | ..  | ..  | ..  | ..  | ..  | Gallup, 2008-15
Netherlands | EU-LFS 2006-07 & 2015-16 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
New Zealand | .. | GSS, 2016 |  .. |  .. |  .. | GSS, 2016-2017 | GSS, 2016 | Gallup, 2008-15
Norway | EU-LFS 2006-07 & 2015-16 | ESS 2008-16, 2002-08 & 2010-16 | ESS 2006 and 2016 waves |  .. | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 | Gallup, 2008-15 and SILC 2013
Poland | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) |  .. | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Portugal | EU-LFS 2006-07 & 2017 | ESS 2008-14, 2002-08 & 2010-14 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-14 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-14, 2002-08 & 2010-14 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Romania | EU-LFS 2006-07 & 2015-16 | ESS 2008 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008 |  .. | ESS 2008 | Gallup, 2008-15
Slovak Republic | EU-LFS 2006-07 & 2017 | ESS 2008-12, 2004-08 & 2010-12 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-12 (F5.9) ESS 2004-10 (F5.10) |  .. | ESS 2008-12, 2004-08 & 2010-12 | Gallup, 2008-15
Slovenia | EU-LFS 2006-07 & 2015-16 | ESS 2008-16, 2002-08 & 2010-16 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 | Gallup, 2008-15
Spain | EU-LFS 2006-07 & 2017 | ESS 2008-14, 2002-08 & 2010-14 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-14 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-14, 2002-08 & 2010-14 | Gallup, 2008-15 and SILC 2013
Sweden | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
Switzerland | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-08 & 2010-16 | ESS 2006 and 2016 waves |  .. | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 | Gallup, 2008-15 and SILC 2013
Turkey | .. | ESS 2004+2008 | ESS 2006 and 2016 waves |  .. | ESS 2004+2008 (F5.10) |  | ESS 2004 & 2008 | Gallup, 2008-15
United Kingdom | EU-LFS 2006-07 & 2017 | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | ESS 2006 and 2016 waves | Eurobarometer 2017 | ESS 2008-16 (F5.9) ESS 2004-10 (F5.10) | ESS 2014 round | ESS 2008-16, 2002-08 & 2010-16 EU-MIDIS II 2016 | Gallup, 2008-15 and SILC 2013
United States | ACS 2006 & 2016 | CPS November Supplement, 2008 and 2016 | USGSS 2014  |  .. | ..  | WVS, wave 6 (2014) | USGSS 2016 (with regard to work) | Gallup, 2008-15
Partner/G20 countries |  |  |  |  |  |  |  |
Argentina |  .. |  .. |  .. | ..  |  .. |  .. | .. |  ..
Brazil |  .. |  .. |  .. |  .. |  .. |  .. | .. |  ..
Colombia | .. | .. | .. | .. | .. | .. | .. | ..
Costa Rica |  .. |  .. |  .. |  .. |  .. |  .. | .. | Gallup, 2008-2015
Indonesia |  .. |  .. |  .. |  .. |  .. |  .. | .. |  ..
Russia |  .. | ESS, various years |  .. | ..  |  .. | WVS, wave 6 (2014) |  .. | Gallup, 2008-15
Saudi Arabia |  .. | ..  |  .. |  .. |  .. |  .. | .. | Gallup, 2008-15
South Africa |  .. |  .. |  .. |  .. |  .. | WVS, wave 6 (2014) | .. | Gallup, 2008-15
StatLink  http://dx.doi.org/10.1787/888933843610

Chapter 6.

Gender differences in immigrant integration

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

StatLink  http://dx.doi.org/10.1787/888933843629

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

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

StatLink  http://dx.doi.org/10.1787/888933843648

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

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

StatLink  https://dx.doi.org/10.1787/888933843667

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

StatLink  http://dx.doi.org/10.1787/888933843686

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

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

StatLink  https://doi.org/10.1787/888933843705

Figure 6.6. How female employment rates have evolved

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

StatLink  https://doi.org/10.1787/888933843724

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

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

StatLink  https://doi.org/10.1787/888933843743

Figure 6.8. How female participation rates have evolved

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

StatLink  https://doi.org/10.1787/888933843762

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

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

StatLink  https://doi.org/10.1787/888933843781

Figure 6.10. How female unemployment rates have evolved

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

StatLink  https://doi.org/10.1787/888933843800

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

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

StatLink  https://doi.org/10.1787/888933843819

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

StatLink  https://doi.org/10.1787/888933843838

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

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

StatLink  https://doi.org/10.1787/888933843857

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

StatLink  https://doi.org/10.1787/888933843876

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

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

StatLink  https://doi.org/10.1787/888933843895

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

StatLink  http://dx.doi.org/10.1787/888933843990

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

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

StatLink  http://doi.org/10.1787/888933843914

Figure 6.18. How female over-qualification rates have evolved

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

StatLink  http://dx.doi.org/10.1787/888933843933

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

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

StatLink  https://doi.org/10.1787/888933843952

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

StatLink  http://dx.doi.org/10.1787/888933843971

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

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  https://doi.org/10.1787/888933844009

Chapter 7.

Integration of young people with a migrant background

How well they integrate children with foreign-born parents can be considered a yardstick of host countries’ success in integration. Because they were schooled in their parents’ host country, the children of immigrants – both those who are native-born and those who arrived at a very young age – should not, in theory, encounter the same difficulties as adults who arrive from a foreign country. Ultimately, their outcomes should be much the same as those of young people with no migrant background. Yet that is not what happens in many host countries, particularly in Europe.

The chapter begins by considering some basic demographic and immigrant-specific pointers that help situate young people with a migrant background (Indicators 7.1 and 7.2). It then describes their access to early childhood education and care (7.3), and to what extent they are concentrated in some schools (7.4). It then goes on to analyse their educational outcomes: their school performance (7.5 and 7.6), their sense of belonging and well-being (7.7), their levels of education (7.8), and their drop-out rates (7.9). The chapter then looks at labour market integration, considering the proportions of immigrant offspring who are NEETs (7.10), their labour market outcomes (7.11 and 7.12) and the quality of the jobs they hold (7.13 and 7.14). The last area of focus, social inclusion and civic engagement, examines child poverty (7.15), voter participation (7.16) and, finally, perceived discrimination (7.17).

Key findings

OECD-wide, 27% (59 million) of people aged 15-34 have a migrant background (i.e. are either foreign-born or have at least one foreign-born parent). Around 7% of these youth are native-born to immigrant parents and 5% to one native- and one foreign-born parent. A further 5% are foreign-born who arrived as children under the age of 15 and 9% arrived after this age.

EU-wide, 21% of this age group have a migrant background (25.5 million), of whom a little over 4% are the native-born offspring of immigrants, with the same number arriving as children under 15; 5% are natives of mixed parentage and a further 8% of the EU youth population immigrated as adults.

Main countries of residence of youth with a migrant background are the United States (17.1 million), Germany and France (3.4 million each), the United Kingdom and Canada (2.4 million each).

Over the last decade, the steepest increase OECD-wide came in the share of native-born with two foreign-born parents, driven chiefly by the United States. Total numbers of native-born with immigrant parents quadrupled in Italy and doubled in Spain, Hungary and Greece.

EU-wide, the reading score of the 15-year-old native-born with foreign-born parents lags behind that of their peers with no migrant background by 25 points – over half a school year. The gap exceeds one year of schooling in the Nordic countries and most longstanding European destinations (save the United Kingdom). In non-European OECD countries, the reverse is true, except in the United States.

Reading literacy gaps between 15-year-olds native pupils with and without migrant background shrank in most countries over the last decade. Not, however, in Southern Europe (excluding Portugal), France, Sweden and Switzerland.

School performance improves the longer pupils reside in the host country, with the native offspring of foreign-born parentage outperforming immigrants who arrived in childhood.

Despite noticeable progress over the last decade, a significant share of pupils with a migrant background lack basic skills at the age of 15. In the EU, 24% of them are low-school performers, against 16% of their peers with native-born parents while native-born immigrant offspring are less likely to lack basic skills than their peers with no migrant background in most non-European countries (except in the United States).

The share of resilient students (top performers despite a disadvantaged socio-economic background) among the native-born children of immigrants has risen by 6 percentage points in the OECD over the last decade and by 3 points in the EU, while it remained stable for the children of natives in both regions. As a result, the disadvantage of children of immigrants in this respect that was observed a decade ago has disappeared – in the OECD it even turned into an advantage.

OECD-wide, native-born immigrant offspring aged 15 to 34 years old are more likely to be highly educated than their peers of native-born parentage – 46% versus 42%. The reverse is true in the EU, where the respective shares are 35% and 37%. Similarly, in the EU, immigrant offspring are more frequently poorly educated than native-born with native-born parents (20.5% versus 16%), while the low-educated account for about 11% of both groups in the OECD.

Over the last decade, the share of highly educated young adults has increased throughout the EU and the OECD by 6 percentage points among both native-born with foreign- and native-born parents. The rise has been greater among immigrant offspring than those with native-born parents in two-fifths of countries.

Across the OECD, 7% of native-born pupils with immigrant parents leave the education system prematurely (600 000 young people per year). The proportion in the EU is 9%, or 250 000 pupils. These percentages are similar among young people of native-born parentage. As for foreign-born young people who arrived as children in the OECD, 600 000, or 11%, leave school early, while the share of drop-outs in the EU is 15%, or 240 000 pupils.

In the EU, the share of early-school leavers among pupils native-born to non-EU migrants, is higher, in particular in longstanding immigration destinations, save the United Kingdom.

In most countries, with the exceptions of Canada and the United Kingdom, drop-out rates have declined more among the native-born of immigrant parentage than among their peers with native-born parents.

In three countries in five, native-born immigrant offspring are more likely to be NEET than their peers with native-born parents while the reverse is true in the United States and Southern Europe (except Spain).

In all European OECD countries with the exception of Portugal and Lithuania, immigrants and the native-born offspring of immigrants are less likely to be in work than their peers with native-born parents – by 3 percentage points OECD-wide. Across the EU, the employment gap between the native-born of native- and foreign-born parentage is 6 points. As for child-arrival immigrants, they are 8 points less likely to have a job.

OECD-wide, the current employment rates of native-born young adults with immigrant parents are comparable to their levels ten years ago, while falling slightly among their peers with native parents. However, in the EU, the situation for native-born youth with immigrant parents has worsened. The greatest deterioration for immigrant offspring has occurred in countries that suffered most from the economic downturn, such as Greece and Italy, as well as in France and the Netherlands.

Unemployment rates have increased since the onset of the economic downturn in most OECD and EU countries. And in many of these countries, unemployment has risen more steeply among youth of foreign-born parentage. In the United States, Belgium and Sweden, however, the native-born offspring of immigrants have actually seen a drop in unemployment.

EU-wide, 25% of native-born with immigrant parents born outside the EU have a level of education that exceeds the requirements of the job that they hold. That share is slightly higher than among the native-born with native-born parents, but 7 points lower than among the native-born with EU background.

In Europe, the share of public sector employment among employed native-born young adults of immigrant parentage has generally increased over the last decade. However, they still remain strongly underrepresented in a number of European OECD countries such as Austria, Belgium, France, Germany, and the Netherlands.

Across the OECD, half of all children in immigrant households live below the relative poverty line, compared to over a quarter in native-born households. Although the share is lower in the EU, it is still 40% – twice the level of children in native households. Immigrant offspring in Spain, Greece and the United States are the most at risk of poverty.

Over the last decade, the relative child poverty rate in immigrant households has slightly increased by 1 percentage point across the OECD but remained stable across the EU. The steepest rises – over 10 points – are to be found in Spain, Slovenia, Estonia and France.

In the EU, discrepancies in relative poverty between children in immigrant and in native-born households have grown further over the last decade. The divergence trend was most pronounced in Spain and a number of other EU countries such as Austria and France.

OECD- and EU-wide, close to 58% of native-born with immigrant parents report that they voted in the most recent national elections (10 percentage points less than among their peers with native-born parents, and 5 points below turnout among immigrants who arrived as children in the host country). This compares with about half of immigrants who arrived after the age of 15.

In all EU and OECD countries (except in Canada, Sweden and Israel), the native-born with two immigrant parents are markedly more likely to feel discriminated against than immigrants who arrived as children.

7.1. Youth with a migrant background

Definition

The youth with a migrant background is divided into four categories: a) native-born with two foreign-born parents (also referred to as “immigrant offspring” or native-born with foreign-born parents); b) native-born with mixed background (i.e. one native- and one foreign-born parent); c) foreign-born who immigrated as children (arrived in the host country before the age of 15); d) foreign-born who immigrated as adults (who were 15 or older at the time). The foreign-born who immigrated as adults are not a focus of this chapter, and are only covered in this indicator, unless stated otherwise.

Coverage

Population aged 15 to 34 years old.

OECD-wide, 27% of young people are either foreign-born themselves or have foreign-born parents (59 million). Of those, 7% are native-born to immigrant parents and 5% to one native- and one foreign-born parent. A further 5% are foreign-born who arrived as children under the age of 15 and 9% arrived after this age. Across the EU, shares are lower. Around one in five have a migrant background (25.5 million), of whom a little over 4% are the native-born offspring of immigrants, with the same number arriving as children under 15. A slightly higher share, 5%, are of mixed parentage. A further 8% of the EU youth population immigrated as adults.

Of the 38.9 million young people who came to an OECD country as children or were native-born to at least one immigrant parent, 17.1 million reside in the United States, about 3.4 million in Germany and France each, and 2.4 million in both the United Kingdom and Canada. New Zealand and Southern and Northern Europe host more child-arrivals than young natives with two foreign-born parents. By contrast, in half of countries, especially such longstanding destinations as the United States, Germany, France and Benelux, immigrant offspring outnumber the foreign-born who arrived as children. Young immigrants who arrived as adults (between 15 and 34) in most EU countries and Australia also outnumber those who arrived when children by two to one. And they do so by three to one in the United Kingdom. By contrast, young people of migrant background in Israel and Norway are more likely to have immigrated as children than as young adults.

Unlike the other groups, the native-born of mixed parentage are more numerous in the European Union than the United States. They form a diverse group – which includes persons whose native-born parent has immigrant parents – and account for over half of all young people with a migrant background in most of Eastern Europe. In Israel, Canada, Germany and the United States, by contrast, there are fewer native-born of mixed than of immigrant parentage only.

In the OECD and EU countries for which comparable data are available, the share of young people with a migrant background has increased by 4 percentage points over the last decade. The steepest increase came in the share of native-born with two foreign-born parents – 1.8 percentage points OECD-wide and driven chiefly by the United States where the rise was 3 percentage points, or close to 3 million young people. Total numbers of native-born with immigrant parents quadrupled in Italy and doubled in Spain, Hungary and Greece. However, the largest relative increase in the EU was among native-born youth of mixed parentage.

Figure 7.1. Young people with a migrant background

Percentages, 15- to 34-year-olds, around 2017

StatLink  http://dx.doi.org/10.1787/888933844028

Figure 7.2. How the native-born youth population has evolved

Changes in percentage points, 15- to 34-year-olds, between 2008 and 2017

StatLink  http://dx.doi.org/10.1787/888933844047

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

7.2. Regions of parental origin

Indicator

Countries of origin are grouped as follows: EU-28, other Europe (including Turkey), Africa, Asia, Latin America (including the Caribbean), and United States, Canada and Oceania. The father’s region of birth is considered for those with two foreign-born parents and the immigrant parent’s region of birth for the native-born with mixed background. The share of native-born with an EU background is calculated differently. Every native-born immigrant offspring with an EU-migrant parent (father or mother) is considered as having an EU background.

Coverage

Population aged 15 to 34 years old.

EU-wide, around 45% of native-born with two immigrant parents are of European parentage, 27% African, and 24% Asian. Shares within countries reflect past migration flows which in turn were shaped by migration policies and historic connections to other parts of the world. In Benelux and German-speaking countries, for example, most are born to parents from Europe, in France over two-thirds to parents from Africa, and in the United Kingdom more than three-fifths to parents from Asia. Nearly half of all foreign-born who arrived in an EU country under the age of 15 come from elsewhere in Europe, roughly 30% from Africa, and 15% from Asia. While only 3% of the native-born of immigrants EU-wide are of Latin American or Caribbean origin, four times that share (13%) arrived from the sub-continent as children.

Throughout the EU and in Norway, most immigrant parents of native-born offspring were themselves born outside the EU, as were the youth who arrived before they were 15 years old. The foreign-born parent of the native offspring of mixed native-born and immigrant parentage, by contrast, is most likely to be EU-born. In some longstanding immigration countries with core immigrant regions of non-EU origin – like France and Africa, the United Kingdom and South Asia, and the Baltic States and Russia – the shares of immigrant offspring native-born to at least one EU-born parent are below 20%.

In the United States, the parents of 66% of immigrant offspring come from Latin America and the Caribbean and 26% from Asia. As for migrants entered before 15, 57% arrived from Latin America, 23% from Asia, and 20% from other parts of the world.

When it comes to regions of parental origin, there have been great changes over the last decade among the native-born children of immigrants in the EU. Relatively more children are now native-born to parents who immigrated from Asia, fewer to parents from Europe, and much the same to those who originate from the rest of the world (Africa and Americas). Overall, the share of native-born with two immigrant parents of whom at least one was born in the EU decreased from 26 to 21% of the immigrant offspring population. By contrast, the share of native offspring of mixed native-born and EU parentage has increased by 3 percentage points to nearly half of the offspring of mixed background.

As for the United States, the parental origins of the native-born children of immigrants have also changed, with a slight rise of 3 percentage points in the proportion of parents of Latin American origin, and a decline of 4 percentage points in those from Europe.

Figure 7.3. Regions of birth of the father of young people with a migrant background

Percentages, 15- to 34-year-olds, inner circle 2008 and outer circle around 2016

StatLink  http://dx.doi.org/10.1787/888933844598

Figure 7.4. Youth with an EU background

Percentages among the youth with a migrant background, 15- to 34-year-olds, around 2016

StatLink  http://dx.doi.org/10.1787/888933844066

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

7.3. Early childhood education and care

Definition

Attendance rates in formal childcare and pre-school services, defined as paid care services provided either through organised structures or through direct arrangements between the parents and care provider, even for a few hours per week. This includes centre-based services (e.g. nursery or childcare centres and pre-schools, both public and private), organised family day care, and 'unstructured' care services provided by private childminders.

Coverage

Children aged 2 to 5 years old.

Across the EU, 77% of all children in immigrant households attend some type of preschool education and care against 81% among children in native households. Shares are similar among native households in the OECD (82%), where attendance among immigrant offspring is lower at 70%. Attendance rates among the children of immigrants are highest in Portugal, Belgium and Luxembourg at over 90%. By contrast, in Australia and the United States they are only 47% and 59%, respectively. In all five countries, however, rates among children from immigrant and native households vary by less than 5 percentage points. Gaps are much wider in the United Kingdom, Slovenia and France, where the children of immigrants are at least 10 points less likely to attend early education. Finland stands out as the only country where they are in fact more likely – by a full 10 percentage points – to go to preschool than the children of the native-born.

Attendance rates across the OECD remained on average at similar levels over the ensuing 10 years among children from native households, while increasing by 5 percentage points among their peers in immigrant households. However, they fell particularly steeply in the United Kingdom – by 4 percentage points among the children with native-born parents and by twice as much among those with immigrant parents. Rates climbed around 20 percentage points, by contrast, among children from immigrant households in Ireland and Luxembourg, and by 15 points in Austria. While the increases in Ireland and Austria were of equal magnitude among the children of the foreign- and native-born, it was greater among the former in Luxembourg.

Children of immigrants especially profit from attending formal childcare and pre-school services and continue to reap the benefits far beyond early childhood. Comparisons of the PISA reading scores of 15-year-old students with immigrant parents and similar socio-economic backgrounds show that those who attended preschool consistently achieve higher scores. Across the EU, the benefit of preschool is 55 points among the native-born children of immigrants – roughly equivalent to 1.5 school years. The corresponding benefit among native-born children of natives is 23 points (half a year of schooling). In Germany, it is as high as two years among children of immigrants and 1.5 school years among their peers with native-born parents. Preschool generally yields less pronounced advantages among the native-born children of immigrants in non-European OECD countries. In the United States, Israel and Australia, for example, the difference between those who attend early childhood school and those who do not is less than 10 points.

Figure 7.5. Early childhood education attendance rates, by place of birth of parents or guardians

Percentages, 2- to 5-year-olds, 2016

StatLink  http://dx.doi.org/10.1787/888933844085

Figure 7.6. How attendance rates in early childhood education have evolved

Changes in percentage points, 2- to 5-year-olds, between 2006 and 2016

StatLink  http://dx.doi.org/10.1787/888933844104

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

7.4. Concentration of students with a migrant background in schools

Definition

This section considers the overall share of students with one or two foreign-born parents in schools where at least 25%, 50%, and 75% of pupils are from such backgrounds.

Coverage

Students aged 15 years old at the time of the survey (with three-month margin).

OECD-wide, almost three in four 15-year-old students with migrant backgrounds go to schools where at least a quarter of their classmates also have a migrant background, and more than one in five where over three-quarters do. Across the EU, concentrations are less marked than in non-European OECD countries. Nevertheless, 66% of students of foreign-born parentage attend schools where at least one-quarter of students are also of immigrant parentage and a further 13% where they make up more than three-quarters of pupils. In the settlement countries, more than one in two pupils with a migrant background go to schools where the majority of their classmates also have immigrant parents. In Luxembourg, the proportion rises to 93% in line with the overall large proportion of the population with a migrant background. Among pupils with foreign-born parentage in Ireland, Israel and Belgium, over 80% find themselves in schools where more than a quarter of their classmates also have immigrant parents. In the United Kingdom and Belgium, they are more likely to be schooled in establishments where over three-quarters of students have some migrant background than in ones where less than a quarter do.

In more than half of OECD countries, students who have migrant parentage are more likely than they were 10 years ago to attend schools where at least a quarter of their classmates also do. However, this is partly driven by an increase in overall numbers of pupils with migrant backgrounds. In fact, the steepest rises in numbers of pupils in schools where at least 25% of their schoolmates are of migrant parentage have come in Southern Europe and Ireland, where significant immigration is a recent development. However, in the United Kingdom, the United States and New Zealand – all countries of longstanding immigration – there has been more than a two-fold increase in proportions of pupils with migrant backgrounds attending schools where over three-quarters of their fellow pupils have similar backgrounds.

Figure 7.7. Concentration of pupils with a migrant background in schools

Percentages of 15-year-old pupils with at least one immigrant parent in schools, by overall share of pupils with at least one immigrant parent in schools, 2015

StatLink  http://dx.doi.org/10.1787/888933844123

Figure 7.8. How the concentration of pupils with a migrant background in schools has evolved

Percentages of 15-year-old pupils with foreign-born parents in schools where more than 75% of pupils have at least one immigrant parent, 2006 and 2015

StatLink  http://dx.doi.org/10.1787/888933844142

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

7.5. Reading literacy

Definition

Reading literacy results are drawn from the OECD Programmes of International Student Assessment (PISA) tests. A 40-point gap is equivalent to roughly a year of school.

Coverage

Pupils aged 15 years old at the time of the survey (with three-month margin).

The OECD-wide level of reading literacy among the native-born children of immigrants is similar to that of their peers with native-born parents. However, that overall similarity obscures the fact that the European and non-European OECD countries paint two different pictures. EU-wide, the reading score of the native-born with foreign-born parents lags behind that of their peers with no migrant background by 25 points – over half a school year. The gap exceeds one year of schooling in the Nordic countries and most longstanding European destinations (save the United Kingdom). In most non-European OECD countries, the reverse is true. In the settlement countries and Turkey, for example, the native-born children of immigrants outperform their peers with native-born parents. Not, though, in the United States, where reading scores are 15 points lower among native-born immigrant offspring than among their peers with native-born parents. When it comes to 15-year-olds born abroad, they lag behind those with no migrant background in both the OECD and EU. The EU-wide gap, however, is 46 points, much wider than the 27 points across the OECD, where Turkey and the settlement countries (except Israel) show no disparity.

Over the last decade, the reading literacy scores of the native-born children of immigrants have improved in four OECD countries out of five. Indeed, their scores increased by over 20 points OECD- and EU-wide – more so than among the native-born with native-born parents. In the settlement countries and Turkey, Belgium and the Netherlands, literacy improved among children with a migrant background while dropping among their peers with none. As a result, performance gaps between those with and without migrant backgrounds shrank in most countries – particularly in some longstanding European countries such as Austria, Belgium, Germany and the Netherlands as well as in Norway. Not, however, in Southern Europe (excluding Portugal), France, Sweden and Switzerland, where the gap widened.

Families’ socioeconomic backgrounds are a key element in school performance. Given the same socioeconomic background, the gap between the native-born children of foreign and native parents narrows in virtually all countries, albeit unevenly from one to another. While it vanishes after controlling for socioeconomic backgrounds in the United States and Norway, it is only partly reduced across the EU, where it still stands at 19 points. Literacy gaps also remain wide between foreign-born pupils and their native-born peers with native-born parents – 41 points across the EU and 32 points OECD-wide.

Across the OECD, students rated as most disadvantaged by the PISA index of Economic, Social, and Cultural Status (ESCS) perform worse than their privileged peers, irrespective of migrant background. OECD-wide, they lag two years behind. Although the gap is slightly narrower among native-born pupils with immigrant parents, it is still 1.5 years. A deprived social and economic background thus seems to affect the literacy skills of the foreign-born and the native-born with no migrant background somewhat more than the native-born with immigrant parents.

Figure 7.9. Mean PISA reading scores

15-year-old pupils, 2015

StatLink  http://dx.doi.org/10.1787/888933844161

Figure 7.10. How mean PISA reading scores have evolved

Changes in PISA points, 15-year-old pupils, between 2006 and 2015

StatLink  http://dx.doi.org/10.1787/888933844180

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

7.6. Proportions of pupils who lack basic reading skills at 15

Definition

Pupils who lack basic reading skills at 15 years old (i.e. low-school performers) are those who score no higher than Level 1 (or 407 points) in PISA assessments of reading proficiency. Also considered is the share of resilient students – those from backgrounds rated as most deprived by the PISA index of Economic, Social and Cultural Status (ESCS), but whose reading scores are in the top quartile of pupils in their host countries.

Coverage

Pupils aged 15 years old at the time of the survey (with three-month margin).

Across the OECD, 20% of native-born 15-year-olds lack basic reading skills, whether or not they have a migrant background. Among their foreign-born peers, the share is 30%. While native-born immigrant offspring are less likely to lack basic reading skills than their peers with no migrant background in most non-European countries, they are more likely to do so in Europe and the United States. In the EU, 24% of them are low-school performers, against 16% of their peers with native-born parents. Furthermore, foreign-born students are more likely to perform poorly in school than the native-born children of immigrants in virtually all countries.

The share of native-born children of foreign-born parents who perform poorly at school has dropped by 6 percentage points OECD-wide over the past decade and 8 points across the EU. In two-thirds of countries, the fall has been greater among immigrant offspring than among their peers with native-born parents. Whatever the migrant background, the share of low-school performers is higher among boys in all OECD and EU countries. This gender gap is widest among the native-born with immigrant parents in virtually all European countries (except the Netherlands) and among those with no migrant background in Australia, Canada and the United States.

Across the OECD, 15% of the most underprivileged native-born children of immigrants are in the top quartile of reading scores in their host country against 12% of their peers with no migrant background. Underprivileged children of migrants are especially better off than their peers with native-born parents in Australia, Canada and the United Kingdom. However, in the EU, there is no difference in the resilience rates of pupils with foreign-born parents compared with their peers with no migrant background – it is even 6 points lower in Switzerland and Denmark. The share of resilient students among the native-born children of immigrants has risen by 6 percentage points in the OECD over the last decade and by 3 points in the EU, while it remained stable for the children of native-born in both areas.

Socioeconomic background of the families in schools that pupils attend, whatever their origin, influences reading literacy. In a school whose socioeconomic intake is homogeneous, native-born pupils with a migrant background and those with none show similar levels of literacy in virtually all countries. In fact, the children of immigrants in schools that serve disadvantaged areas slightly outperform those with native-born parents in the United States, the United Kingdom, Ireland and Greece.

Across both the OECD and the EU, native-born pupils with immigrant parents who speak a foreign language at home lag half a year behind their peers whose immigrant parents speak the host-country language at home. And the foreign-born who speak a foreign language at home trail by a whole year. Arriving young also improves reading scores. In two-thirds of countries, the foreign-born who come to the host country before they are 6 years old read at least as well as the native-born children of immigrants. However, those who arrive between the ages of 11 and 16 lag one school year behind young arrivals.

Figure 7.11. Low reading performance

Percentages, 15-year-old pupils, 2015

StatLink  http://dx.doi.org/10.1787/888933844199

Figure 7.12. How shares of low reading performance have evolved

Changes in percentage points, 15-year-old pupils, between 2006 and 2015

StatLink  http://dx.doi.org/10.1787/888933844218

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

7.7. Sense of belonging and well-being at school

Definition

Share of pupils who, at least a few times a month, report any of the following statements: “Teachers disciplined me more harshly than other students”; “Teachers ridiculed me in front of others”; or “Teachers said something insulting to me in front of others”. Also considered is the share of pupils who report having been bullied by other students (see Notes on figures and tables) and those who feel awkward and out of place at school.

Coverage

Pupils aged 15 years old at the time of the survey (with three-month margin).

Across Europe, the native-born children of immigrants are more likely to feel unfairly treated by teachers than their peers who have native-born parents. The reverse is true in many non-European countries, where relatively more children with no migrant background share the sentiment. EU-wide, 29% of native-born pupils with a migrant background report unfair treatment from their teachers, against 24% among their foreign-born peers and 20% among those whose parents are native-born. Shares are especially high in long-standing immigration destinations with large intakes of poorly educated foreign-born parents and in some Central European countries.

Responses to questions about relationships with other pupils paint a more diverse picture. Again, greater proportions of native-born pupils with native-born parents experience bullying in non-European countries, while in six EU countries in ten, native-born pupils with a migrant background report more frequently to be victims. Furthermore, the perceived bullying of foreign-born pupils seems to be more widespread in European than non-European countries.

OECD-wide, over 20% of native-born pupils with immigrant parents feel awkward and out of place at school. In most European countries, too, pupils with a migrant background are slightly more likely than their peers with native-born parents to feel that way – by as much as 9 percentage points in Estonia and Italy. By contrast, in the settlement countries and the United Kingdom, the sentiment is more widespread among pupils with no migrant background than among those native-born to immigrant parents. However, in virtually all countries, foreign-born pupils who arrived as children are even more prone to feeling awkward and out of place at school: more than 25% report a sense of not belonging in Portugal, Sweden and the United States.

Socioeconomic intake of school influences well-being. In schools that serve deprived areas, feelings of unfair treatment and not belonging are generally more widespread among pupils. In such schools, the native-born with native-born parents are more likely to be affected than those with immigrant parents in non-European countries and Denmark, the Netherlands and the United Kingdom. The weaker sense of belonging that prevails overall among pupils with a migrant background in the EU is not significant, neither in schools that serve disadvantaged areas (save in Estonia and Luxembourg) nor in those that serve better-off districts (except in Estonia and Italy). Controlling for the socioeconomic levels of schools’ pupil intakes, the reported frequency of being bullied is not significantly different between children of immigrants and those with native-born parents in most countries. However, the native-born with foreign-born parents are less likely to be bullied in schools with disadvantaged pupil intakes in non-European countries, Belgium and the Netherlands. For such pupils in the EU, however, perceived unfair treatment by other students is much more of an issue. It appears worst in schools in deprived areas in Estonia, Switzerland and France, in schools with socioeconomically advantaged intakes in the Netherlands and the United Kingdom, and in both kinds of schools in Germany.

Figure 7.13. Pupils who feel unfairly treated by their teacher

Percentages, 15-year-olds, 2015

StatLink  http://dx.doi.org/10.1787/888933844237

Figure 7.14. Pupils who feel awkward and out of place at school

Percentages, 15-year-olds, 2015

StatLink  http://dx.doi.org/10.1787/888933844256

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

7.8. Young adults’ educational attainment levels

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 aged 25-34 years old who are not in education.

There are over 600 000 highly educated native-born 25- to 34-year-olds with foreign-born parents in the EU, and 2.4 million in the OECD. As for those who are low-educated, the figures are respectively 370 000 and 600 000. OECD-wide, native-born immigrant offspring are more likely to be highly educated than their peers of native-born parentage – 46% versus 42%. The reverse is true in the EU, where the respective shares are 35% and 37%. Similarly, immigrant offspring are more frequently poorly educated than native-born with native-born parents in the EU (20.5% versus 16%), while the low-educated account for about 11% of both groups in the OECD. Differences are particularly large in Austria, Belgium, Denmark, Germany and Greece. Native-born young adults of immigrant parentage have higher levels of education than their peers with native-born parents in the settlement countries, the United Kingdom and the Baltic countries, bar Estonia. They are, however, underrepresented among the highly educated in all other countries, particularly so in Belgium, Greece and Luxembourg.

When it comes to young adult immigrants who arrived as children, their levels of education are generally lower than those of the native-born with native-born parents – except in the settlement countries, the United Kingdom and Portugal. They are also lower than those of immigrant offspring, except in countries like Luxembourg and Belgium, where the native-born children with immigrant parents are particularly underrepresented in higher education.

Across the EU, native-born 25- to 34-year-olds with foreign-born parents from outside the EU generally boast similar levels of educational attainment to their peers with an EU background. In France, Germany and (in particular) Spain, however, they lag behind, while in the United Kingdom they perform better.

Over the last decade, the share of highly educated young adults has increased throughout the EU and the OECD by 6 percentage points among both native-born with foreign- and native-born parents. The rise has been greater among immigrant offspring than among those with native-born parents in two-fifths of countries.

Women aged 25 to 34 are more likely than men to be highly educated in all OECD and EU countries, with the exception of Switzerland. Women who are native-born to immigrant parents are no exception. The gender gap in educational attainment is narrower among young adults with a migrant background than among their native-born peers with native-born parents in all countries bar Germany, the Netherlands and the United Kingdom. Immigrant women who arrived as children are again more likely to be highly educated than immigrant men in all countries but Switzerland. Unlike their male peers, they appear to enjoy a higher chance of going on to higher education if they attend school in the host country.

Figure 7.15. Low- and highly educated, by migrant background

Percentages, 25- to 34-year-olds not in education, around 2017

StatLink  http://dx.doi.org/10.1787/888933844636

Figure 7.16. Gender differences in the rates of highly educated, by migrant background

Difference in percentage points between women and men, 25- to 34-year-olds not in education, around 2017

StatLink  http://dx.doi.org/10.1787/888933844275

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

7.9. Early school leaving

Definition

The proportion of young people who are neither in education nor training and have gone no further than lower-secondary school.

Coverage

Young people aged 15 to 24 years old.

Across the OECD, 7% of native-born pupils with immigrant parents leave the education system prematurely. That percentage translates into 600 000 young people. The proportion in the EU is 9%, or 250 000 pupils. OECD- and EU-wide, drop-out levels of immigrant offspring are similar to that of young people of native-born parentage. As for foreign-born young people who arrived as children in the OECD, 600 000, or 11%, leave school early, while the share of drop-outs in the EU is 15%, or 240 000 pupils.

The native-born children of immigrants are more likely than their peers with no migrant background to drop out early in two-fifths of countries, particularly in longstanding European destinations and the Nordic countries. Shares exceed 13% in Sweden, Austria and Spain. The widest gaps in drop-out rates between pupils of foreign- and native-born parents are in Austria and Slovenia – at least 8 percentage points. In Switzerland, Italy and the United States, however, rates are similar in the two groups. They are actually lower among immigrant offspring and the foreign-born who arrived as children in the settlement countries, the Baltic States, the United Kingdom and Portugal. By contrast, foreign-born pupils who arrived in the host country before they were 15 are more likely than any other group to leave school early in all other countries. More than 12% dropped out in the Nordic countries, Spain, Austria and Switzerland, and almost 20% in Germany.

At 10% in the EU, the share of early-school leavers among pupils native-born to non-EU migrants is particularly high. It is more than 50% higher than among their peers with native-born parents in all longstanding immigration destinations, save the United Kingdom. In Switzerland, Austria and Slovenia, young people with a non-EU background are more than twice as likely to drop out as their peers of native-born parentage. By contrast, the drop-out rate of native-born with an EU background is lower than any other group in virtually all countries. Non-EU migrants who arrived as children are also more likely to drop out than their EU migrant peers in six countries in ten. In Norway and Portugal, their drop-out rates are at least twice as high as those of natives with an EU background. In contrast, rates of EU migrants arrived as children in the United Kingdom and Greece are at least 10 percentage points higher than those from their peers born outside the EU.

Proportions of early-school leavers among native-born young people of immigrant parents have dropped over the last decade – by 5 percentage points in the EU and by 3 points in Canada and the United States. They have also fallen in Southern Europe. In most countries, with the exceptions of Canada and the United Kingdom, the decline was steeper among the native-born of immigrant parentage than among their peers with native-born parents.

Figure 7.17. Early school leavers

Percentages, 15- to 24-year-olds, around 2016

StatLink  http://dx.doi.org/10.1787/888933844294

Figure 7.18. Early school leavers, by migrant background

Percentages, 15- to 24-year-olds, around 2016

StatLink  http://dx.doi.org/10.1787/888933844313

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

7.10. Not in employment, education or training

Definition

Proportions of young adults who are not in employment, formal education or training (NEET).

Coverage

The young adult population aged 15 to 34 years old.

In the OECD, over 2 million native-born 15- to 34-year-olds of immigrant parentage are NEETs – a 14% NEET rate. In the EU, they number almost 850 000 – a 17% rate. In three OECD countries in five, native-born immigrant offspring are more likely to be NEET than their peers with native-born parents. Their NEET rates are twice as high in Slovenia, Austria, Belgium, France and the Netherlands, where immigrant parents tend to be poorly educated. However, young adults with no migrant background are more likely to be NEET in the settlement countries, the United States and Southern Europe (except for Spain). When it comes to the foreign-born who arrived as children, they are even more estranged from the labour market – 1.9 million of whom are NEET in the OECD and almost 1 million in the EU. NEET rates are higher among the foreign-born who arrived as children than among native-born with two immigrant parents in virtually all EU countries, but not in the United States or the settlement countries.

Overall NEET rates have risen slightly over the last decade OECD- and EU-wide. Among native-born immigrant offspring, however, they have dropped a little. Gaps in NEET rates between the native-born with native- and foreign-born parents have significantly narrowed in two-thirds of countries with available data.

Some population groups are more prone to be NEET than others. Young women are more at risk than young men OECD-wide, regardless of migrant background, although the gender gap is narrower among native-born with foreign-born parents in two-thirds of countries. However, in all countries where overall NEET rates are higher among young people with a migrant background, both male and female immigrants and immigrant offspring are more likely to be NEET than their counterparts with no migrant background. The only exception is Spain, where young men with foreign-born parents are in fact less likely to be NEET than their peers of native parentage, while the reverse is true among women.

In two-thirds of countries, native-born with foreign-born parents are more likely than their peers with native parentage to be both NEET and low-educated. This is especially true in Spain, Denmark, and most European longstanding destinations. Indeed, the poorly educated are another group at high risk of being NEET. Among all native-born young adults in the OECD and EU, NEET rates are higher among the low-educated than the highly educated, particularly among those with no migrant background. Indeed, among the low-educated, NEET rates of youth with native parentage are 5 percentage points higher than those of the native-born with foreign-born parents, both OECD- and EU-wide. The countries where poorly educated immigrant offspring are more likely to be NEET are Slovenia and the long-standing European immigration destinations (with the exceptions of the United Kingdom and Germany). In particular, they are up to 10 points more likely to be NEET in France and Belgium. Finally, parents’ country of birth also influences the likelihood of being NEET. EU-wide, the native-born with non-EU background show a slightly higher NEET rate than those with EU background. At the country level, differences are greatest in Spain, Austria and France.

Figure 7.19. NEET rates

Percentages, 15- to 34-year-olds, around 2017

StatLink  http://dx.doi.org/10.1787/888933844332

Figure 7.20. NEET rates among low-educated

Percentages, 15- to 34-year-olds, around 2017

StatLink  http://dx.doi.org/10.1787/888933844351

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

7.11. Employment

Definition

The employment rate denotes people in employment as a percentage of the young adult population, aged between 15 and 34 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.

Coverage

The population aged 15 to 34 years old not in education.

Almost 7.3 million native-born 15- to 34-year-olds with foreign-born parents are employed in the OECD and 1.9 million EU-wide. Those numbers respectively represent employment rates of 72% and 69% in the 15-34 age group (excluding students). A further 5.9 million immigrants who arrived in the OECD as children also have jobs – a 73% employment rate. The corresponding figures for the EU are 2.1 million and 66%. In most countries, immigrants and the native-born offspring of immigrants are less likely to be in work than their peers with native-born parents – by 3 percentage points OECD-wide. Across the EU, the employment gap between the native-born of native- and foreign-born parentage is higher, at 6 percentage points. As for child-arrival immigrants, they are 8 points less likely to have a job. In the EU, young adults of non-EU origin generally struggle more to find work than their counterparts with EU background. In Italy and Spain, less than one-third of the native-born with parents born outside the EU are in employment.

In all OECD and EU countries, young men are generally more likely to be in employment than young women, though such is not always the case among native-born young adults of immigrant parentage. In Italy and Portugal, native-born men with immigrant parents lag far behind their female peers, while the same gender gap (albeit narrower) is also observed in Switzerland, Norway and Canada. Conversely, native-born women with immigrant parents are particularly disadvantaged with regard to their male peers in the Baltic countries and Spain. As for child-arrival immigrants, women are over 10 points less likely to be employed than their male peers in France, Germany, the Netherlands and the United States.

Being highly educated helps when it comes to getting a job. In OECD settlement countries, highly educated native-born young adults with immigrant parents are as likely as their peers with native-born parents to be employed. That pattern is not, however, true of most EU countries. Even when native-born of immigrant parentage are highly educated, they are still less likely to have work than their peers with native parents, by 2 percentage points EU-wide, and over 10 percentage points in most EU longstanding destination countries (4 points only in Germany). As regards the low-educated, native-born of immigrant parentage are 3 percentage points less likely than their peers of native parentage to be in work EU-wide. The employment gap is over 15 percentage points in Southern European countries, Sweden and the Netherlands, far worse than the gap among the highly educated. The employment gap among low-educated is also wide in the OECD at 7 points. The only two exceptions are Australia and Israel, where low-educated native-born with immigrant parents are more likely to be at work than their peers with no migrant background.

OECD-wide, the employment rates of native-born young adults with immigrant parents have remained stable over the last decade, while falling by 1 percentage point among their peers with native parents. The situation has worsened across the EU, however, with both groups showing 5-point declines in employment. The greatest deterioration for immigrant offspring has come in countries that suffered most from the economic downturn, such as Greece and Italy, as well as France and the Netherlands. By contrast, Israel, Sweden, the United States, the Czech Republic and Belgium have seen significant increases in their employment rates for immigrant offspring.

Figure 7.21. Employment rates, by migrant background

Percentages, 15- to 34-year-olds not in education, around 2017

StatLink  http://dx.doi.org/10.1787/888933844370

Figure 7.22. Employment rates of native-born with foreign-born parents, by level of education

Differences in percentage points with native-born with native-born parents, 15- to 34-year-olds not in education, around 2017

StatLink  http://dx.doi.org/10.1787/888933844389

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

7.12. 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 labour force (whether employed or unemployed) aged 15 to 34 years old and not in education.

While the unemployment rates of 15- to 34-year-olds who are native-born to immigrant parents are similar to those of their peers with native-born parents in non-European countries, they are higher in virtually all European countries. In the EU as a whole, 17.5% are unemployed, against 14% among the native-born with no migrant background. OECD- and EU-wide, young immigrants who arrived as children are worst affected by unemployment – 12% are jobless in the OECD and 20% in the EU.

More than 40% of native-born of immigrant parentage are unemployed in Southern European countries (save Portugal). In most Nordic and longstanding immigration countries, unemployment rates are at least twice as high among the native-born with migrant backgrounds as among those without. By contrast, gaps in youth unemployment rates between the two groups are narrower in countries with low unemployment rates (bar Switzerland and Denmark) as well as in Portugal and Lithuania. It is worth noting, though, that in most rather recent immigrant destinations – such as those in Northern and Southern Europe – significant shares of young people of migrant parentage are still in education.

Lack of work experience partly explains why the young are proportionally worse affected by unemployment. And native-born 15- to 24-year-olds with immigrant parents are even harder hit than their older peers: their EU-wide unemployment rate is three times that of their 25- to 34-year-old peers and more than twice as high in the OECD. Although unemployment among 15- to 24-year-olds is high in absolute and relative terms in Sweden, Luxembourg, the United Kingdom and France, it should again be taken into account that considerable proportions of young people of migrant background in that age group are still in education. Among native-born youth with immigrant parents, those with non-EU origin are worst affected by unemployment. Over half are unemployed in Italy and Spain and more than a quarter in Sweden and France, a rate which substantially exceeds that of their peers with EU background.

Wide gender differences are observed in unemployment rates of native-born with foreign-born parents, while gender gaps are small among those with no migrant background in virtually all countries. In half of EU countries, native-born women with foreign-born parents are more likely to be unemployed than men, especially in Greece, Spain and the Baltic countries. The opposite, though, is true in Italy, the United Kingdom, Austria and France. Gender gaps are not generally as large among immigrants who arrived as children. Exceptions are Sweden and Switzerland, where unemployment rates of foreign-born men who arrived as children are twice those of their female peers.

Unemployment rates have increased since the onset of the economic downturn in almost all OECD and EU countries. In most countries, unemployment has risen among the native-born with native-born parents, but more steeply among their peers of foreign-born parentage. In the United States, Belgium and Sweden, however, the native-born offspring of immigrants have actually seen a drop in unemployment.

Figure 7.23. Unemployment rates, by migrant background

Percentages, 15- to 34-year-olds not in education, around 2017

StatLink  http://dx.doi.org/10.1787/888933844408

Figure 7.24. Gender differences in unemployment rates, by migrant background

Difference in percentage points between women and men, 15- to 34-year-olds not in education, around 2017

StatLink  http://dx.doi.org/10.1787/888933844427

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

7.13. Over-qualification

Definition

The over-qualification rate is the share of the highly educated, i.e. educated to ISCED Levels 5-8 (see Indicator 7.8), who work in a job that is ISCO-classified as low- or medium-skilled, i.e. ISCO Levels 4-9 (see Indicator 3.9).

Coverage

Young adults in employment aged between 25 and 34 years old who are highly educated (excluding those in the armed forces [ISCO 0]).

OECD-wide, 30% of highly educated employed native-born 25- to 34-year-olds of immigrant parentage (a total of 700 000 individuals) are formally over-qualified for the jobs they hold. In the EU, the share is 23%, corresponding to 125 000 young people. OECD- and EU-wide, the native-born with foreign-born parents are not more likely than their peers with native-born parents to be over-qualified. From country to country, however, the situation varies widely. In Estonia, Belgium and Germany, they are more likely to be over-qualified (up to twice as likely in Estonia), but less so in Switzerland, Israel and Canada. As for highly educated child-arrival immigrants, their over-qualification rates are higher than those of the native-born in Sweden, the Netherlands and, by over 11 points, in Belgium and France. However, they are broadly similar in most other countries.

The proportion of women who are not in jobs that match their levels of education is higher than that of men in the EU and Australia, regardless of migrant background. The gender gap to the detriment of women is as wide as 9 percentage points in the EU among the native-born with migrant backgrounds against only 2 points among those without. In the United States and Canada, however, young men are more likely than women to be over-qualified in all groups, with the gender gap widest among those with no migrant background. Finally, over-qualification is a slightly bigger issue for the native-born with immigrant parents born outside the EU. EU-wide, 25% are in low- or medium-skilled jobs despite high levels of education. That share is slightly higher than among the native-born with native-born parents, but 7 points lower than among the native-born with EU background.

Figure 7.25. Over-qualification rates, by migrant background

Percentages of employed highly educated, 25- to 34-year-olds, around 2016

StatLink  http://dx.doi.org/10.1787/888933844446

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

7.14. Employment in the public service sector

Definition

Share of the employed population working in the public service sector. This sector encompasses public administration, healthcare, the social services, and education.

Coverage

Population in employment aged 15 to 34 years old.

Almost 23% of native-born young adults with immigrant parents are employed in the public service sector OECD-wide. That corresponds to 1.8 million workers in the OECD – and almost 1.1 million in the United States alone. The share of immigrant offspring working in the public service sector is similar to that of other native-born, whether they are of native-born or mixed parentage. In the EU, 400 000 native-born young adults of migrant parentage are public service employees. In other words, 18% of them work in the public sector, compared with 20% of the native-born with native-born parents and 23% of the native-born with mixed background.

In fact, the native-born of immigrant parentage are over-represented in the public services only in the United Kingdom and Latvia. In all non-European and Nordic countries, though, they are as likely as their peers with no migrant background to be public service employees. But they are less likely in longstanding European immigration destinations and Southern Europe (by at least 10 percentage points in Spain, Portugal and Greece). In Germany and Luxembourg they are 9 and 20 points less likely to work in public service. The proportions of child-arrival immigrants employed in the public sector (some of whom have not naturalised), are even lower in all countries, save the United Kingdom, Australia and Sweden. In total, they number 1.2 million in the OECD and 350 000 in the EU.

In the United Kingdom, Sweden and Belgium, native-born young people with non-EU immigrant parents are more likely than those with EU backgrounds to have a job in public service. By contrast, they are less likely in Austria, France and the Netherlands. In the vast majority of countries, the share of the public sector among the total employment of native-born young adults of immigrant parentage has increased over the last decade, thereby partly compensating for the decline observed in private sector employment over that period. The increase has generally been more noticeable than for their peers with no migrant background, especially in Luxembourg, Belgium, the United Kingdom and Italy.

Figure 7.26. Shares working in the public service sector

Percentages of employed, 15- to 34-year-olds, around 2016

StatLink  http://dx.doi.org/10.1787/888933844484

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

7.15. Relative child poverty

Definition

The relative child poverty rate, in accordance with the Eurostat definition, is the share of children living in a household whose equivalent annual income lies below the poverty threshold which is set at 60% of a country’s median equivalised disposable income.

Coverage

Any person aged less than 16 years old living in a household with at least one head over 15. The household’s annual equivalised income is attributed to each child.

Across the OECD, almost half of all children in immigrant households live below the relative poverty line, compared to over a quarter in native-born households. Although the share is lower in the EU, it is still 40% – twice the level of children in native households. The countries with the highest shares of immigrant offspring living in relative poverty are Spain, Greece and the United States. Over half do so in Greece and Spain, compared with a quarter among children in native households. Proportionately, the fewest immigrants’ children in poverty are to be found in Latvia, Germany, and Israel, where levels are nevertheless still around 20%. The poverty gap between children in native and foreign-born households is generally wide, reaching almost 40 points in Spain and the Netherlands and around 30 in Belgium and France. It is comparatively narrower at close to 10 points in Portugal, the United Kingdom, Croatia and Estonia, and only 4 points in Germany. The only countries in which children in native-born households are more likely than immigrant offspring to live in relative poverty are Latvia and Israel.

Over the last decade, the relative child poverty rate in immigrant households has only slightly increased by 1 percentage point across the OECD. In the EU, the rate stayed roughly the same among both the foreign- and the native-born households. The steepest rises – over 10 points – have come in Iceland, Spain, Slovenia, Estonia and France. In all these countries, the rise was also much stronger than for the native-born who generally experienced little increase or even a slight decline. In only one-third of countries has relative poverty among the children of immigrants declined rather than grown. The sharpest falls have been in the Czech Republic, the United Kingdom, Germany and Denmark, where poverty rates among children in native-born households have, at the same time, changed only marginally.

Comparisons of the poverty rates of immigrants aged 16 or older with those of children living in immigrant households show that children are clearly more likely to be poor – by 11 points in the EU. In the United States, the poverty gap is 23 points, with 56% of children in immigrant households living in poverty. After the United States, gaps are widest in France, Spain and the Netherlands. In the Baltic countries, by contrast, whose foreign-born populations are shaped by national minorities and border changes, adult immigrants are more likely to live in poverty than children in immigrant households.

Figure 7.27. Relative child poverty rates, by migrant background

Percentages, children up to 16 years old, 2015

StatLink  http://dx.doi.org/10.1787/888933844522

Figure 7.28. How relative child poverty rates have evolved, by migrant background

Changes in percentage points, children up to 16 years old, between 2007 and 2015

StatLink  http://dx.doi.org/10.1787/888933844617

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

7.16. Voter participation

Definition

Self-reported voter participation is measured here through public polls in which respondents are asked if they voted in the last national parliamentary elections in their country of residence.

Coverage

All 18-34-year-olds entitled to vote in national elections. Apart from few exceptions for certain nationalities in countries such as the United Kingdom and Portugal, foreigners do not have the right to vote in national parliamentary elections. This indicator therefore applies only to people with the nationality of the country in which they live.

Across the OECD and the EU, close to 60% of native-born with immigrant parents report that they voted in the most recent national elections. That turnout is almost 10 percentage points lower than among their peers with native-born parents, and 5 points below turnout among immigrants who arrived as children in the host country. However, it is 10 points higher than among immigrants with host-country nationality who arrived after the age of 15.

The children of native-born parents are generally more likely to vote than those born to immigrant parents. The gap is particularly wide in Switzerland, where turnout is very low among the eligible native-born of immigrants who are almost two times less likely than the offspring of the native-born to report that they voted in the most recent national election. Similarly, the Netherlands, Germany and Sweden also show wide disparities in excess of 15 percentage points. In Estonia, Israel and Belgium, by contrast, there is little or no turnout gap between native-born with of foreign- and native-born parents. As for Belgium, where voting is compulsory, high voter turnout comes as no surprise.

In both the EU and OECD, young adults of mixed parentage and those who arrived as children in the host country are generally more likely to participate in elections than the native-born with two foreign-born parents. Their participation in voting is similar to that of the bulk of the population.

In Germany, EU-born young people who arrived before they were 15 years old are 14 percentage points more likely to vote in elections than their peers born outside the EU. In fact, their 77% turnout is very much the same as among the native-born children of native parents.

Figure 7.29. Self-reported participation in most recent election, by migrant background

Percentages, 18- to 34-year-olds, 2008-16

StatLink  http://dx.doi.org/10.1787/888933844541

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

7.17. Perceived discrimination

Definition

This section considers shares of immigrants who report having experienced discrimination (refer to Indicator 5.7 for definitions).

Coverage

Foreign-born 15 to 34-year-olds and people born in the host country to two immigrant parents.

Among young people born to immigrants in EU countries, almost one in five feels part of a group that is discriminated against on the grounds of ethnicity, nationality or race. One in seven experience discrimination because of their ethnicity, culture, race, or colour in Canada. In the United States, one native-born with immigrant parents in ten reports discrimination in the workplace. Perceived discrimination is most widespread in the Netherlands, where 38% of children of immigrants say they experience it, France (29%) and Norway (23%). By contrast, less than 10% report discrimination in Ireland, Israel, Switzerland and Austria. In most countries, the native-born children of two immigrant parents are markedly more likely to feel discriminated against than immigrants who arrived as children in the host country. However, in Canada, Sweden, Israel and the United States, the opposite applies and child-arrival immigrants report discrimination more frequently. Comparisons between the periods 2006-10 and 2012-16 in the EU point to an overall slight decline in perceptions of discrimination. The EU-wide share of the native-born children of immigrants who felt discriminated against went down from 24% to 20%. That drop occurred in every subcategory of the population –e.g. among men and women and at all levels of education. It was especially marked, at 10 percentage points, among the native-born children of immigrants who spoke the host country language and were host-country nationals. Only young people of immigrant parentage with foreign nationality experienced a sizeable increase in perceived discrimination, of 11 points.

In the EU, highly educated young people born to immigrants claim discrimination in proportionately greater numbers than the less well educated. Similarly, those whose first language is the host-country language are more than twice as likely to report discrimination as those whose first language is foreign. Immigrant offspring who are host-country nationals are equally more prone to perceptions of discrimination than those having a foreign nationality, as are those with non-EU backgrounds (against their peers with EU origins). Factors like education, language proficiency and citizenship may foster a sense of belonging and identity that prompt people to speak out more readily and harbour greater expectations of the host country. They become more keenly aware of social structures and thus more likely to perceive certain situations as discriminatory. By contrast, neither employment status nor gender significantly affect perceived discrimination in the EU. In Canada and the United States, however, gender is a determinant in reports of discrimination, which are at least 7 percentage points more widespread among men than women.

The second wave of the EU-MIDIS survey which focused on experiences of discrimination of certain key groups found that almost half of respondents with both parents born in a north African country encountered discrimination because of skin colour, ethnic origin or religion in the past 12 months, as did three in ten of those with sub-Saharan parents. As for those with Asian parents, they reported generally low levels of discrimination, except when seeking a job. Native-born young people with sub-Saharan parents felt less commonly discriminated against at work than other ethnic groups. Instances of discrimination were most widespread when respondents sought to use certain public services and private amenities – e.g. when interacting with civil servants or entering bars and restaurants. They also encountered it, albeit to a lesser extent, on the labour market, both when looking for jobs and in the workplace. Instances of discrimination were fewest in health and housing services.

Figure 7.30. Self-reported discrimination, by migrant background

Percentages, 15- to 34-year-olds, 2008-16

StatLink  http://dx.doi.org/10.1787/888933844560

Figure 7.31. Native-born youth with immigrant parents who say they belong to a discriminated group

Percentages, 15- to 34-year-olds, 2012-16

StatLink  http://dx.doi.org/10.1787/888933844579

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

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.

Indicator 7.1, 7.2 and Indicators 7.8 to 7.14: In Germany, the parental origin is based on the country of birth of parents for the native-born still living with their parents, but is based on own citizenship or the citizenship at birth of the parents for those who do not live anymore with their parents. Therefore, the so-called native-born with foreign-born parents may also include native-born with one foreign- and one native-born parent (the latter being an offspring of foreign-born parents), as well as native-born with two native-born parents who are both themselves offspring of foreign-born parents.

Indicator 7.3: Age range covered in the United States is 3 to 5 years

Indicator 7.7: Instances of bullying by other students include the following statements: “they left me out of things on purpose”, “made fun of me”, “took away or destroyed things that belonged to me”, “spread nasty rumours about me”; “I was threatened by them”, or “I got hit or pushed around by them”.

Indicator 7.17: 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. 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. The United States 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 C.1, C.2, D.2 and E.

Table 7.1. Sources by indicator
| 7.1 Youth with a migrant background | 7.2 Regions of parental origin | 7.3 Early Childhood Education and Care | 7.4 Concentration in schools | 7.5, 7.6, 7.7 Reading literacy, lack of basic skills, belonging | 7.8 Young adults' educational attainment levels | 7.9 Early school leaving | 7.10, 7.11, 7.12 NEET, employment, unemployment | 7.13, 7.14 Over-qualification, public sector | 7.15 Relative child poverty | 7.16 Voter participation | 7.17 Perceived discrimination
OECD/EU |  |  |  |  |  |  |  |  |  |  |  |
Australia | Census 2006 & 2016 | Census 2016 | SIH 2015-16 | PISA 2015 | PISA 2015 | Census 2006 & 2016 | Census 2006 & 2016 | Census 2006 & 2016 | Census 2006 & 2016 | SIH 2015-16 | .. | ..
Austria | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2014-16 | ESS 2002-06 & 2014-16
Belgium | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Bulgaria | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | .. | .. | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-12 | ESS 2008-12
Canada | Census 2006 & 2016 | Census 2006 & 2016 | .. | PISA 2015 | PISA 2015 | Census 2006 & 2016 | Census 2006 & 2016 | Census 2006 & 2016 | Census 2006 & 2016 | Census 2006 & 2016 | GSS 2014 | GSS 2004 & 2014
Chile | .. | .. | .. | PISA 2015 | PISA 2015 | .. | .. | .. | .. | .. | .. | ..
Croatia | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-SILC 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-SILC 2016 | ESS 2008-10 | ESS 2008-10
Cyprus1,2 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | .. | .. | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-12 | ESS 2008-12, 2006-08 & 2010-12
Czech Republic | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-04+2008 & 2010-16
Denmark | Population register 2009 & 2017 | Population register 2017 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | Population register 2009 & 2017 | Population register 2009 & 2017 | Population register 2009 & 2016 | .. | EU-SILC 2007 & 2016 | ESS 2008-14 | ESS 2008-14, 2002-08 & 2010-14
Estonia | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2004-08 & 2010-16
Finland | Population register 2016 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2014 | EU-LFS AHM 2014 | Population register 2016 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
France | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Germany | EU-LFS AHM 2008 & Mikrozensus 2017 | Mikrozensus 2017 | .. | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & Mikrozensus 2017 | EU-LFS AHM 2008 & Mikrozensus 2017 | EU-LFS AHM 2008 & Mikrozensus 2017 | EU-LFS AHM 2008 & Mikrozensus 2017 | EU-SILC 2007 & 2017 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Greece | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-10 | ESS 2008-10
Hungary | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-14 | ESS 2008-14, 2002-08 & 2010-14
Iceland | .. | .. | EU-SILC 2015 | PISA 2015 | PISA 2015 | .. | .. | .. | .. | EU-SILC 2015 | ESS 2012+2016 | ESS 2012+2016
Ireland | .. | .. | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | .. | .. | .. | .. | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Israel* | LFS 2008 & 2016 | .. | .. | PISA 2015 | PISA 2015 | LFS 2016 | LFS 2016 | LFS 2016 | LFS 2016 | .. | ESS 2008-16 | ESS 2008-16, 2002+2008 & 2010-16
Italy | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | .. | ..
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | .. | ..
Lithuania | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2010-14 | ESS 2010-14
Luxembourg | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | .. | .. | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | .. | ..
Malta | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | .. | ..
Mexico | .. | .. | .. | PISA 2015 | PISA 2015 | .. | .. | .. | .. | .. | .. | ..
Netherlands | LFS 2008 & 2016 | LFS 2016 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | LFS 2008 & 2016 | LFS 2008 & 2016 | LFS 2008 & 2016 | LFS 2008 & 2016 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
New Zealand | GSS 2016 | .. | .. | PISA 2015 | PISA 2015 | .. | .. | .. | .. | .. | .. | ..
Norway | Population register 2016 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | Population register 2016 | EU-LFS AHM 2014 | Population register 2016 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Poland | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | .. | .. | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Portugal | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-14 | ESS 2008-14, 2002-08 & 2010-14
Romania | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | .. | .. | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | .. | ..
Slovak Republic | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-12 | ESS 2008-12, 2004-08 & 2010-12
Slovenia | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Spain | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-14 | ESS 2008-14, 2002-08 & 2010-14
Sweden | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2014 | .. | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | .. | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Switzerland | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2014 | .. | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & LFS 2017 | EU-LFS AHM 2008 & 2014 | EU-SILC 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
| 7.1 | 7.2 | 7.3 | 7.4 | 7.5-7.7 | 7.8 | 7.9 | 7.10-7.12 | 7.13, 7.14 | 7.15 | 7.16 | 7.17
United Kingdom | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | PISA 2015 | PISA 2015 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-LFS AHM 2008 & 2014 | EU-SILC 2007 & 2016 | ESS 2008-16 | ESS 2008-16, 2002-08 & 2010-16
United States | CPS 2008 & 2017 | CPS 2017 | CPS 2007 & 2017 | PISA 2015 | PISA 2015 | CPS 2008 & 2017 | CPS 2008 & 2017 | CPS 2008 & 2017 | CPS 2008 & 2017 | CPS 2007 & 2017 | USGSS 2012-14 (employed) | USGSS 2006-10 & 2012-14 (employed)
Partner/ G20 countries |  |  |  |  |  |  |  |  |  |  |  |
Argentina | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Brazil | .. | .. | .. | PISA 2015 | PISA 2015 | .. | .. | .. | .. | .. | .. | ..
Colombia | .. | .. | .. | .. | PISA 2015 | .. | .. | .. | .. | .. | .. | ..
Costa Rica | .. | .. | .. | PISA 2015 | PISA 2015 | .. | .. | .. | .. | .. | .. | ..
Indonesia | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Russia | .. | .. | .. | .. | PISA 2015 | .. | .. | .. | .. | .. | ESS 2008-12+2016 | ESS 2008-12+2016, 2006-08 & 2010-12+2016
Saudi Arabia | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
South Africa | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
StatLink  http://dx.doi.org/10.1787/888933844655

Chapter 8.

Third-country nationals’ integration in the European Union

This chapter considers the full set of “Zaragoza indicators” for third-country nationals (TCN) in the European Union, comparing their outcomes with those of host-country nationals and EU nationals. The chapter looks first at the size and composition of third-country national populations (8.1). It then goes on to consider their countries of citizenship and length of residence (8.2), before analysing outcomes in employment and activity (8.3), unemployment (8.4), self-employment (8.5), over-qualification (8.6), levels of education (8.7), income (8.8), poverty (8.9), housing tenure status (8.10), perceived health status (8.11), long-term resident status (8.12), participation in voting (8.13), the acquisition of nationality (8.14), and perceived discrimination (8.15).

Box 8.1. The “Zaragoza” indicators: indicators for monitoring integration policy outcomes in the European Union

“Migrants” in the context of the European Union are understood to be non-EU, or third country, nationals who reside legally in the European Union. Their situations often differ markedly from those of EU citizens moving between or living in EU member states other than their own. Although many enjoy equal rights with host-country nationals, not all third-country nationals have access to the labour market and there are greater restrictions on their mobility within the European Union. Their reasons for migrating are also likely to be different from those that prompt EU nationals to move and are more often related to asylum or family reunification.

The Europe 2020 strategy considers better integration of third-country nationals as a factor that will help it meet its first headline target of a 75% employment rate among 20-64 year-olds, given the share of non-EU nationals in its labour force as well as the gap in employment rate with host-country nationals.

Although integration policies are defined and implemented primarily at national or subnational level, they are closely linked to the EU equality framework and to EU provisions that grant migrants residing in the European Union certain rights (e.g. equal working conditions and equal access to goods and services). The European Union indeed has adopted a number of EU non-discrimination laws that are of relevance for the integration of third-country nationals, in particular the Directive 2000/43/EC on racial equality and the employment equality directive (Directive 2000/78/EC). Moreover, since 2009, the Treaty on the Functioning of the European Union states, in Article 79.4, that the European Union may offer support and incentives to member states who take action to promote the integration of legally resident third-country nationals (though that does not include any legal harmonisation).

The European Union has also developed Common Basic Principles for Immigrant Integration Policy. They were adopted in 2004 and reaffirmed in 2014 as the general framework for EU policy co-operation on integration and for member countries’ assessments of their own efforts. The Common Basic Principles cover the main aspects of integration – employment, education, access to institutions, goods and services, and integration into the society in general. And, most importantly, they define it as a two-way process of mutual accommodation between migrants and host-country nationals.

The so-called “Zaragoza indicators” were introduced at a ministerial conference under the Spanish presidency of the European Union in April 2010. Following the conclusions on integration adopted by the Justice and Home Affairs (JHA) Council in June 2010, the European Commission worked with member states to draw up those indicators for monitoring the results of integration policies in the four areas of employment, education, social inclusion and active citizenship. These indicators are in line with the Europe 2020 strategy and its related monitoring indicators and targets. A pilot study on the common indicators published its findings in a report, “Using EU Indicators of Immigrant integration”, which was unveiled in 2013. Eurostat updates the indicators annually, drawing on already harmonised data sources, such as the EU Labour Force Survey and the EU Survey on Income and Living Conditions. Moreover, since 2018, Eurostat also started publishing some of these indicators at regional level and by level or urbanisation in order to take into account the sub-national dimension of immigrants' integration.

Key findings

In 2017, the EU was home to 21.6 million third-country nationals (TCNs). High numbers of non-EU nationals live in the EU-15 countries and relatively fewer in other member states.

TCNs account for 4.2% of the total population in the European Union. Compared to ten years earlier, rises were steepest in Sweden and Slovenia and declines largest in Latvia and Estonia.

Almost half of third-country nationals in the EU have lived in their host country for 10 years or longer – 49% EU-wide. Further, only 6% of non-EU nationals were born in their country of residence.

EU-wide, 55% of TCNs are in employment compared to nationals’ 68%. Overall, 8.8 million non-EU nationals are employed in the EU, and over 10.5 million economically active.

Labour-related gaps between third-country and host-country nationals are wider among women. Non-EU women are less likely to be economically active than their national peers in almost every EU country (save Portugal). EU-wide, 55% are part of the labour market and 45% employed.

The highly educated always have a greater chance of being in work, while workers with little education have higher unemployment rates, both irrespective of nationality. Gaps between host- and third-country nationals are, however, wider among the highly than the poorly educated.

TCN’s unemployment rate of 16.5% is over double that among nationals (7%), EU-wide. In total, 1.75 million third-country nationals, are unemployed, thus over 9% of all unemployed in the EU are nationals of a non-EU country.

About 1 million third-country nationals are self-employed in the EU and over one in four self-employed TCNs has employees.

In the 10 years following the economic crisis and in those countries most affects by it (Southern Europe and Ireland) the share of self-employed among non-EU nationals in employment rose, while falling among nationals.

EU-wide, 42% of non-EU nationals are over-qualified for the job they do, against 22% of nationals. Over the past decade, the over-qualification gap between third- and host-country nationals has dwindled, as the over-qualification rate fell by 7 percentage points among TCNs and increased by 2 points among nationals.

Having a host-country degree halves the over-qualification rates of non-EU nationals in Sweden, Germany and the Netherlands, compared to their foreign-educated peers. However, even with host-country degree TCNs remain more likely to be overqualified than nationals everywhere except Germany.

Of non-EU nationals aged 15 to 64 years old, 19% – 2.6 million individuals – went no further than primary school education. While that share has declined by 2.5 percentage points over the last decade, it remains almost 4 times as high as among nationals. On the other hand, 24% of third-country nationals are highly educated, a share only 5 percentage points lower than among nationals.

Non-EU nationals have a lower annual disposable household income than nationals in virtually every EU country. In Benelux, Spain and Sweden, it is less than 60% of nationals’ median income.

EU-wide, 5.7 million TCNs live in relative poverty. That number translates into a 39% share, over twice nationals’ 17% and considerably higher than EU mobiles' 24%. In most countries, more than one-third live in poverty, rising to over half in Belgium, the Netherlands, Sweden and Spain.

Poverty hit non-EU nationals harder in the wake of the economic crisis. Their EU-wide poverty rate increased by over 7 percentage points, while remaining the same for nationals.

Less than 25% of non-EU national households own the accommodation that they occupy, compared to over 72% among nationals. At the same time, TCNs are equally or less likely than host-country nationals to be low-rent tenants, with the exception of Finland.

Across the EU, seven out of ten foreigners – 69% of third-country nationals and 70% of EU nationals – report that they are in good health (shares adjusted by age). The share is slightly above the 67% of nationals.

Host-country nationals born in a non-EU country were less likely to vote in national elections than their native-born peers between 2008 and 2016. The respective shares were 73% and 79%, EU-wide. The turnout gap was widest in Ireland, where non-EU-born nationals were 26 percentage points less likely to vote than their native-born peers.

Close to one in five third-country nationals EU-wide feel that they belong to a group that is discriminated against on the grounds of ethnicity, nationality or race. Almost 40% of non-EU nationals in Greece and more than one-third in Belgium consider that they belong to a group that has been subject to discrimination.

8.1. Size and composition by age

Definition

A third-country national is a foreigner who has the nationality of a non-EU country (see Glossary).

Coverage

Total population in EU countries.

The EU is home to over 21.6 million third-country nationals, who make up more than 4% of the population EU-wide. Nearly one-quarter live in Germany, while Italy accounts for 16%, France 14%, and Spain and the United Kingdom for above 11% each. The populations of Estonia and Latvia boast the largest shares of non-EU nationals relative to their population size: about 14%, predominantly Russian citizens. In most other Central and Eastern European countries, by contrast, with the exception of Slovenia and the Czech Republic, third-country nationals make up less than 1% of the population. In Southern Europe, though, more than 5% of the population are non-EU nationals, and their share is even higher in Germany (6%), Luxembourg (7%) and Austria (8%). Just as third-country nationals outnumber the 17 million EU foreigners living in the European Union, so they outnumber them in most countries. Belgium, however, is home to twice as many EU foreigners as non-EU nationals, Ireland and the Slovak Republic to over three times as many, and Luxembourg to six times more.

EU-wide, 77% of third-country nationals are of working age (between 15 and 64 years old), 7% are over 64, and 16% under 15. As the chances of obtaining host-country nationality increase with length of stay, the younger age brackets account for the bulk of the foreign population. One-third of foreigners – third-country and EU-nationals alike – are aged between 25 and 39. Moreover, while one in five host-country nationals is 65 and over, only 1 in 15 non-EU nationals is. Indeed, EU-wide and in most countries non-EU nationals are in their mid- to late-20s and 30s. Only in the Baltic countries are they much older – older, in fact, than the national population – with more than a quarter aged over 64. At the opposite end of the spectrum, many non-EU nationals in Southern Europe and destinations of recent humanitarian migration are children under the age of 15. In Greece, almost a quarter fall into that age group, while in Croatia and Sweden almost 20% non-EU nationals are children.

Over the last decade, the share of third-country nationals in the EU population increased slightly – by about 0.6 percentage points EU-wide. The rise was below 1 point in most countries, including the longstanding immigration destinations that are home to many non-EU nationals, such as France, Germany, Spain and the United Kingdom. In Sweden and Slovenia, however, it was about 2.5 percentage points and in Italy and Austria a little less than 2 points. As for the Baltic countries, whose non-EU population has been shaped by border changes and is older than in other EU countries, they recorded declines in the third-country shares of their populations. In fact, they were the steepest in the EU. In Latvia, for example, the fall was almost 5 percentage points, due mostly to ageing-related deaths of the third-country population. Altogether, the age structure of the non-EU population is fairly similar to a decade ago.

Figure 8.1. Third-country nationals

Percentages of the total population, 2007 and 2017

StatLink  https://doi.org/10.1787/888933845187

Figure 8.2. Age distribution, by citizenship

Percentages of the third-country, EU and host-country national populations, respectively, 2017

StatLink  http://dx.doi.org/10.1787/888933844674

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

8.2. Duration of stay and regions of nationality

Definition

The duration of stay Indicator refers to the length of time that has elapsed since a third-country national’s (TCN’s) year of arrival. Region of nationality denotes five broad regions, namely Asia, Africa, Europe (including Turkey), Latin America and the Caribbean, and Canada-United States-Oceania.

Coverage

TCNs aged between 15 and 64 years old, excluding those whose country of nationality is not reported.

Over one in three third-country nationals of working age EU-wide is citizen of a European non-EU country. Thus, non-EU European countries constitute the top region of nationality among third-country nationals. Next comes Asia – about 30% of non-EU nationals are nationals of an Asian country. One in five has an African nationality, one in seven that of a country from the rest of the world – namely, the Americas and Oceania.

European non-EU citizens make up the largest share of third-country nationals in the populations of most Central and Eastern European countries. In these countries, third-country nationals are chiefly Russian and Ukrainian. In Austria and Germany European non-EU citizens are the largest TCN group, too, with Turks and citizens of former Yugoslavia forming the bulk of non-EU nationals. Asian citizens account for most third-country nationals in Ireland, Hungary and the Nordic countries. Their origins and profiles vary widely, though. Some came to the EU as labour immigrants, like Indians in Ireland and Chinese in Hungary, while others, such as Afghans, Iraqis and Syrians in the Nordic countries, are humanitarian migrants. Historic, cultural and linguistic ties between EU countries and countries outside Europe have shaped immigrant populations. The largest group of third-country nationals in Spain and Portugal, for instance, are nationals of a Latin American country, while the largest group of non-EU citizens in Belgium are nationals of an African country.

Almost half of third-country nationals in the EU have lived in their host country for 10 years or longer – 49% EU-wide. The share is even higher in long-standing immigrant destinations like Austria, France and the Netherlands, as well as in Southern Europe. A number of countries, by contrast, are home to high shares of non-EU nationals who have arrived in the last five years. They include Ireland, Luxembourg, and the United Kingdom, which have seen large inflows of highly educated immigrants and where more than one-third of non-EU nationals – almost a half in Ireland – are recent arrivals. Newcomers account for considerable shares, too, in countries that have taken in significant recent inflows of humanitarian migrants and allow non-EU nationals to naturalise relatively quickly. In Sweden, for instance, 63% of non-EU nationals have arrived over the last five years. Most immigrants from a third country who have settled for 10 years have indeed already acquired the Swedish citizenship.

EU-wide, only 6% of third-country nationals were born in their country of residence. In Estonia and Latvia, by contrast, shares of host-country-born non-EU nationals are much higher. The reason is that, on independence, neither country automatically granted nationality to the offspring of residents who had immigrated during the Soviet era. As for third-country nationals in Germany, 13.5% were born there, as the country did generally not grant German nationality to native-born children of foreigners. This was changed in a reform in 1999 for children born in or after 2000.

Figure 8.3. Region of citizenship

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

StatLink  http://doi.org/10.1787/888933844693

Figure 8.4. Third-country nationals by duration of stay

Total = 100, 15- to 64-year-olds, 2015-16

StatLink  http://dx.doi.org/10.1787/888933844712

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

8.3. 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. The participation rate (or activity rate) 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, including those in education, unless indicated otherwise.

Third-country nationals (TCNs) in the EU are less likely to be employed than their host-country peers. Their EU-wide employment rate is 55%, compared to nationals’ 68%. The gap in labour market participation is narrower, however – two-thirds against 73.5%. Overall, 8.8 million third-country workers are employed in the EU, and 10.5 million economically active. In Finland, Sweden, Greece and longstanding immigration countries with high shares of low-educated immigrants, the employment rate among third-country nationals is particularly low. Less than 55% have a job, while their host-country peers are 1.5 times more likely to have one. Among third-country nationals, no country boasts an employment rate of 75% – the Europe 2020 employment target – and only Denmark, Germany, the Netherlands and Sweden reach it among nationals. As for citizens from other EU countries, they have an employment rate of 73% and labour market participation rate of 79%, thus outperforming nationals. Indeed, eight countries reach the Europe 2020 employment target as far as EU foreigners are concerned.

Employment and labour market participation gaps between third-country and host-country nationals are now higher than ten years earlier. The employment rate fell by 4 percentage points among non-EU nationals and rose by 3 points among nationals. The trend was observable in some two-thirds of countries, and starkest in recent destinations of low-skilled labour migration. In Spain and Greece, where employment rates among host-country nationals declined by 4 and 7 percentage points respectively, they dropped by 15 and 18 points among TCNs. By contrast, in countries where the share of host-country nationals in work increased by over 5 points – such as Germany, Poland and the Czech Republic – third-country nationals are now more likely to have a job than ten years earlier.

Labour-related gaps between third-country and host-country nationals are wider among women and the highly educated. Although male third-country nationals are almost as economically active as their host-country peers, with participation of over 75% in both cases, fewer TCN have jobs – 64% versus 73% of nationals EU-wide. Non-EU women, on the other hand, are less likely to be economically active than female nationals in most EU countries (with the exception of Southern and Central Europe). Across the EU, only 55% are part of the labour market and 45% employed – respectively, 13 and 18 percentage points less than their national peers. And in Germany, Belgium, the Netherlands and Finland, the shortfall exceeds 25 points for both employment and participation rates. The highly educated always have a greater chance of being in work, irrespective of nationality. However, highly educated non-EU nationals fare worse than their national peers in every EU country, with a 16-point lower employment rate in the union as a whole. Overall employment rates among low-educated third-country workers are closer to those of their host-country peers, with a gap of only 4 points. In one-third of countries, particularly those where foreign workers came to meet low-skilled labour market demand (as in Southern Europe), low-educated third-country nationals tend to be more likely to have a job than their national peers.

Figure 8.5. Employment and labour market participation rates, by citizenship

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

StatLink  https://doi.org/10.1787/888933844731

Figure 8.6. Employment rates of third-country nationals, by level of education

Difference in percentage points with nationals, 15- to 64-year-olds not in education, 2015-16

StatLink  https://doi.org/10.1787/888933844750

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

8.4. 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).

The unemployment rate of third-country nationals across the EU is 16.5%, against 8% among EU foreigners and 7% among host-country nationals. In total, 1.75 million third-country nationals and 765 000 EU foreigners are unemployed. In other words, over 9% of all unemployed in the EU are nationals of a non-EU country. In most EU countries, their jobless rates exceed those of their host-country peers. In Spain and Greece, the two countries with the highest unemployment rates among nationals in the EU, more than one-quarter of non-EU nationals are unemployed. This is also the case in Sweden, where many third-country nationals are recent refugees. At 15 percentage points or more, unemployment rate differences compared with nationals are particularly pronounced in Belgium, France and again Sweden. Indeed, in all Nordic countries and long-standing immigration destinations with large shares of poorly educated immigrants, third-country national unemployment rates are over twice those of nationals. The unemployment gap between nationals from other EU countries and their host-country peers is narrower, however, at less than 3 percentage points in most countries. In most Central European and Baltic countries, EU foreigners are even less affected by unemployment than nationals.

The EU-wide unemployment rates of host-country nationals and EU foreigners are back to their levels prior to the economic crisis. By contrast, the rate among third-country workers is 1.6 percentage points higher than before. In most countries, third- and host-country national unemployment rates evolved in the same direction over the 10 years that followed the crisis. In around one-third of countries, particularly in Southern Europe and Sweden, joblessness among non-EU nationals increased by at least 5 percentage points. Overall unemployment dropped in Germany, however – once again more significantly among third-country nationals. Poland was the only country where it fell markedly among nationals, but remain similar than before the crisis for third-country nationals.

Workers with little education have higher unemployment rates irrespective of their nationality. Among third-country nationals, the unemployment rate of those who are educated only to low levels reaches 23%, a full 10 percentage points higher than the rate of the highly educated. Unemployment hits poorly educated third-country nationals in the labour force particularly hard in Sweden, Spain and Belgium. Gaps between host- and third-country nationals are, however, wider among the highly than the poorly educated. In Belgium, Sweden and in all German-speaking countries, highly-educated third-country nationals are at least four times more likely to be unemployed than their national peers.

Figure 8.7. Unemployment rates, by citizenship and education

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

StatLink  https://doi.org/10.1787/888933844769

Figure 8.8. How unemployment rates have evolved, by citizenship

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

StatLink  https://doi.org/10.1787/888933844788

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

8.5. Self-employment

Definition

The self-employed are people who work in their own firms or create their own businesses, sometimes hiring employees. Self-employment includes entrepreneurs, the liberal professions, artisans, traders, and many other freelance activities.

Coverage

Population aged between 15 and 64 who are in employment, excluding the agricultural sector.

About 1 million third-country nationals (TCN) and roughly 1.1 million EU foreigners are self-employed in the EU. At 12%, the self-employment rates of third- and host-country nationals are similar, whereas they are 1 percentage point higher among EU foreigners. High proportions of TCN are self-employed in countries where numbers of immigrants are low. In some Central European countries, for example, more than 25% of third-country nationals are self-employed, double the share of nationals. They are, however, less likely to be self-employed than nationals in countries with larger numbers of immigrants, especially in those where many TCN arrived prior to the economic crisis to fill labour market needs. In Italy and Greece, for instance, more than one in five employed nationals are self-employed, but only between one in seven to one in nine third-country nationals.

In the 10 years following the economic crisis, the share of the self-employed among non-EU nationals increased by 3 percentage points, fell by 2 points among their host-country peers, and remained the same among EU foreigners. Variations relative to host-country nationals in the proportions of third-country nationals in self-employment were sharpest in the countries worst hit by the economic crisis. In Southern Europe and Ireland, for example, the share of self-employed workers among non-EU nationals rose, while falling among nationals. Self-employment among immigrants is often a strategy to avoid marginalisation in the labour market, and indeed the observed growth in these countries was partly driven by self-employed with no employees. When self-employment followed the same trend in both groups – increasing in the United Kingdom and the Netherlands, for instance, and declining in Sweden, Hungary and Poland – that trend was always more pronounced among non-EU nationals.

EU-wide, over one in four self-employed third-country nationals has employees – slightly less (5 percentage points) than among host-country nationals. Most businesses are small, however, and less than 3% employ more than 10 people, while 6% of those run by nationals do. EU foreigners are the most likely to run one-person businesses, with 78% of self-employed EU foreigners operating as sole traders and only 2% employing more than 10 people. In two countries in five, third-country nationals are more likely to have employees than nationals. In Finland, for instance, almost every second business owned by third-country nationals employs at least another person, while among business owned by nationals the share is roughly one in three. In Austria, most non-EU entrepreneurs provide jobs for at least one person and 10% for more than 10. In Southern Europe, by contrast, self-employed third-country nationals are more likely to have no employees.

Figure 8.9. Self-employed workers, by citizenship

Percentages, excluding the agricultural sector, 15- to 64-year-olds in employment, 2015-16

StatLink  https://doi.org/10.1787/888933844807

Figure 8.10. The self-employed, by firm size and citizenship

Total =100, excluding agricultural sector, 15- to 64-year-olds, 2015-16

StatLink  https://doi.org/10.1787/888933844826

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

8.6. Over-qualification

Definition

The over-qualification rate is the share of the highly educated, i.e. educated to ISCED Levels 5-8 (see Indicator 8.7), who work in a job that is ISCO-classified as low- or medium-skilled, i.e. ISCO Levels 4-9 (see Indicator 3.9).

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).

In all EU countries, highly educated non-EU nationals are more likely than their host-country peers to work in jobs for which they are over-qualified. EU-wide, 42% are over-qualified, against 22% of nationals. In the new destinations of Southern Europe, where many immigrants arrived prior to the economic crisis to do low-skilled work, over-qualification is particularly widespread, affecting at least two-thirds of highly educated third-country nationals. Against this backdrop, the gap between host-country and non-EU nationals is widest in Italy, where over-qualification is 4 times more prevalent among the latter, and in Portugal, where it is 5 times greater. In fact, Luxembourg and the United Kingdom are the only countries where non-EU nationals’ over-qualification rates are less than 10 percentage points above those of nationals.

In most countries, women are more likely than men to work in jobs for which they are over-qualified, irrespective of their nationality. Yet, the over-qualification ratio between third-country female nationals and their host-country peers is similar than that for men, around two to one.

Over the past decade, the over-qualification gap between third- and host-country nationals has dwindled, with third-country over-qualification rates falling by 7 percentage points and those of nationals increasing by 2 points. In Poland, however, as in Portugal, Ireland and the United Kingdom, third-country nationals are now more likely to be over-qualified than a decade ago. In Southern Europe and Luxembourg, by contrast, over-qualification among third-country nationals dropped drastically, while rising among nationals. It also declined among EU foreigners in almost all countries, and by 15 percentage points EU-wide.

Across the EU, third-country nationals who obtained their degree in the host country slot into jobs more in keeping with their level of education than those with foreign credentials. Nevertheless, they remain more likely to be over-qualified than nationals everywhere except Germany. A host-country degree halves the over-qualification rates of non-EU nationals in Sweden, Germany and the Netherlands, compared to their foreign-educated peers. In Estonia and Latvia, by contrast, where many third-country nationals are native-born Russians, as well as in the United Kingdom and Ireland, where non-EU nationals are particularly highly educated, those trained in the country fare worse than those trained abroad.

Figure 8.11. Over-qualification rates, by citizenship and gender

Percentages of highly educated, 15- to 64-year-olds, 2015-16

StatLink  https://doi.org/10.1787/888933844845

Figure 8.12. How over-qualification rates have evolved, by citizenship

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

StatLink  http://dx.doi.org/10.1787/888933844864

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

8.7. 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 EU, third-country nationals have lower levels of educational attainment than nationals. About 6.4 million non-EU nationals are educated to a low level. That number translates into a share of 45%, almost twice that of nationals, of whom 23% went no further than lower secondary school. On the other hand, 24% of third-country nationals are highly educated – about 3.3 million individuals – a share only 5 percentage points lower than among nationals. In countries of longstanding immigration, in recent Southern European destinations, and in Latvia and Estonia, third-country nationals’ levels of educational attainment are low. Fewer than half in Italy, Spain, Greece and France have gone any further than lower-secondary school. Over half, by contrast, in Ireland and the United Kingdom have at least completed short-cycle higher education programmes, about 1.5 times as many as nationals. The poorly educated account for a four times higher share of third- than host-country nationals in Germany. In Poland and the Slovak Republic, by contrast, non-EU nationals are almost twice as likely to be highly educated than host-country nationals. As for EU foreigners, they are overrepresented at both ends of the educational attainment scale: 26% of them lie towards the bottom and 32% at the top. They are thus more often both poorly and highly educated than nationals.

As for nationals, educational attainment among third-country nationals has improved across the EU. The share of those who are highly educated has risen by 6.4 percentage points over pre-crisis levels, while that of the poorly educated has dropped 2.7 points. However, the growth in the proportion of individuals with higher education credentials was greater among third- than host-country nationals in only a third of countries. In the United Kingdom, the share of highly educated non-EU nationals almost doubled. At the other end of the scale, the poorly educated shares of non-EU populations fell most steeply, by 13 and 16 percentage points, in Slovenia and Portugal. Third-country nationals further caught up in Denmark, Germany and Slovenia, as their poorly educated shares declined more significantly than among nationals. This was also the case in Austria, albeit at the same time there was less of an increase among the highly educated non-EU nationals. In one-third of countries, by contrast, chiefly in Southern, Central and Eastern Europe, the educational attainment of non-EU nationals fell further behind those of host-country nationals.

Of non-EU nationals aged 15 to 64 years old, 19% – 2.6 million individuals – are considered to have very low levels of education. In other words, they went no further than primary school. While that share has declined by 2.5 percentage points over the last decade, it remains almost 4 times as high as among nationals. In Belgium, France, the Netherlands, and Spain, over 25% of non-EU nationals are educated to very low levels. In Central and Eastern Europe, by contrast, less than 2% of third-country nationals have very low levels of education. Over the last decade, the largest falls in shares of very-low-educated third-country nationals have come in Portugal, France and Belgium, with drops as steep as 8 percentage points or more.

Figure 8.13. Low- and highly educated, by citizenship

Percentages, 15- to 64-year-olds, 2015-16

StatLink  https://doi.org/10.1787/888933844883

Figure 8.14. How shares of highly educated have evolved, by citizenship

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

StatLink  http://dx.doi.org/10.1787/888933844902

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

8.8. Household income

Definition

A household’s annual equivalised disposable income is the income per capita adjusted by the square root of household size. Income is expressed in euros (EUR) at constant prices (2010=100) based on purchasing power parity (PPP) for 2014. It includes earnings from labour and capital. The median income divides households into two halves: one-half receives less and the other more than the median income. One-tenth of the population has an income lower than the first decile (D1) and one-tenth higher than the ninth decile (D9).

Coverage

People aged 16 years old and over who live in ordinary housing. The household's annual equivalised income is attributed to each individual member.

Non-EU nationals have a lower annual disposable household income than nationals in virtually every EU country. EU-wide, their median income is EUR 10 500, compared to host-country nationals’ EUR 13 700 and EU foreigners’ EUR 13 800. In Benelux, Spain and Sweden, it is less than 60% of nationals’ median income. In absolute terms, it is highest in Malta and the United Kingdom at around EUR 14 500, very similar to the income of nationals in these countries.

With regard to income distribution, third-country nationals (TCN) are strongly overrepresented in the lowest household income decile. A full 24% live in such households EU-wide, while less than 4% live in a household in the highest income decile. Only in four countries are third-country nationals overrepresented in the highest income decile: the United Kingdom, Lithuania, the Czech Republic and Bulgaria. Further, only in the latter two are third-country nationals more often represented in the highest income decile than in the lowest.

In the vast majority of countries, the household top income decile is about 3 to 6 times the amount of the lowest decile, and this ratio is broadly similar among nationals and non-EU nationals. However, in Sweden, the Netherlands and Lithuania third-country nationals in the top income decile boast a household income that is at least ten times as high as the lowest income decile of their peers. This is not the case among nationals, where the respective ratios for these countries are between three and six.

Over the last decade, the median household income of third-country nationals has declined, while increasing among nationals. Across the EU, it is equivalent to about 77% of nationals’ income, compared to close to 88% prior to the economic crisis. While EU-wide the income in the top and the lowest income decile declined among TCNs, it increased among nationals, thus poor and rich TCNs tended to get poorer while poor and rich nationals got richer. However, there are considerable differences between countries. Non-EU median income levels have fallen most sharply in Southern Europe and Luxembourg but increased in some Eastern European countries. Third-country nationals are almost equally under-represented in the top income decile compared to before the economic downturn. By contrast, their over-representation in the poorest income bracket is a further 5 percentage points higher than at the onset of the crisis.

Figure 8.15. Income deciles of third-country nationals

Percentages, aged 16 and above, 2016

StatLink  http://dx.doi.org/10.1787/888933844921

Figure 8.16. Median income, by citizenship

EUR in constant prices (based on 2014 PPP), aged 16 and above, 2016

StatLink  https://doi.org/10.1787/888933844940

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

8.9. Relative poverty

Definition

The poverty rate is the proportion of individuals living below the poverty threshold. The Eurostat definition of the poverty threshold used here is 60% of the median equivalised disposable income in each country.

Coverage

All people aged 16 years old and over living in ordinary housing. The annual equivalised household income is attributed to each individual.

A large number of third-country nationals – 5.7 million – live in relative poverty. That number translates into a 39% share, over twice nationals’ 17% and considerably higher than EU foreigners’ 24%. In most countries, more than one-third live in poverty, rising to over half in Belgium, the Netherlands, Sweden and Spain. Fewer than a quarter are affected in four countries only, namely the United Kingdom and Malta, both destination countries for the highly educated, as well as Bulgaria and the Czech Republic.

While non-EU nationals are 2.3 times more likely to live in poverty than their host-country peers across the EU, the widest in-country differences come in Luxembourg where non-EU nationals are over 4 times more likely than nationals to be poor. Gaps are also high in longstanding destinations like the Netherlands, Belgium and France as well as in Sweden. In Central Europe and the United Kingdom, discrepancies are narrower between host- and third-country nationals, with the latter less likely than the former to be poor only in Bulgaria.

Poverty hit non-EU nationals harder in the wake of the economic crisis. Their EU-wide poverty rate increased by over 7 percentage points, while remaining the same among nationals. In Portugal, they were over twice as likely to live in poverty 10 years after the crisis as before it. In about one-third of countries, by contrast, non-EU national poverty rates declined, most steeply in the Czech Republic, where it halved.

As for EU foreigners, their situation worsened in the 10 years after the onset of the crisis, with their EU-wide poverty rate rising 5 percentage points. Increases were steep in the Southern European countries of Spain, Greece, and Italy at over 10 percentage points. The steepest of all, however, came in Austria, where the 18-point rise doubled the share of those in poverty among EU nationals.

Figure 8.17. Relative poverty rates, by citizenship

Percentages, aged 16 and above, 2016

StatLink  https://doi.org/10.1787/888933844959

Figure 8.18. How relative poverty rates have evolved, by citizenship

Changes in percentage points, aged 16 and above, between 2007 and 2016

StatLink  http://doi.org/10.1787/888933844978

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

8.10. Housing tenure

Definition

There are three main types of housing tenure: owner occupancy, tenancy, and free occupancy. In most EU member states, tenants pay rents at market rates or occupy low-rent accommodation, i.e. public social housing, employer-funded social housing, or housing where rents are capped by law.

Coverage

Households with individuals living in an ordinary residence where at least one principal occupant is aged over 15 years old.

Across the EU, less than 25% of non-EU national households own the accommodation that they occupy, compared to over 72% among nationals. As for EU nationals’, owner occupancy is at 37% and thus more widespread than among third-country nationals, and just over half the rate of nationals. In fact, the majority of nationals in every country own the property in which they live, while the opposite is true of third-country nationals in most countries. While large shares own their homes in some Central and Eastern European countries, less than a quarter do so in longstanding immigration destinations and Southern Europe. The lowest shares come in Austria, Belgium, Ireland, Italy and Sweden with less than one-fifth.

In some two-thirds of countries, non-EU national households are less likely to own their homes than ten years ago. How home ownership by citizenship has evolved is attributable partly to a change in the make-up of the foreign population. In countries with large recent intakes of migrants, many may not yet have been able to save enough to buy their own homes. Accordingly, 4 percentage points fewer non-EU nationals own their homes than before the crisis EU-wide, while nationals’ home ownership rates have shown a slight increase of 2 points. The fall is very steep among immigrant households in Sweden, – 19 percentage points. Among EU foreigners, owner-occupancy rates are down 7 points on pre-crisis levels, with the largest drop of 17.5 points coming in the United Kingdom.

As the incomes of third-country nationals are generally lower than nationals’, it may be expected that they are more likely to occupy low-rent housing. In fact, the opposite is true. While 24% of host-country nationals are low-rent tenants EU-wide, only 13% of third-country nationals are. They are more likely to be so in Finland alone, while in about a quarter of countries they are just as likely as nationals. They are markedly less likely to live in low-rent accommodation in countries like Malta, Ireland and the United Kingdom, with recent high intakes of highly educated third-country nationals.

Several factors affect the lower access to housing of third-country nationals, such as their lower income and their lack of knowledge of the housing market. They may also be prone to discrimination from property owners. According to the second wave of the European Union Minorities and Discrimination Survey (EU-MIDIS II), 7% of the third-country nationals in the largest ethnic minorities stated that they had experienced discrimination in the last 12 months because of skin colour/ethnic origin or religion when trying to rent or buy housing. Perceptions of discrimination were most widespread among nationals from Africa, especially in Belgium, Austria, Luxembourg and Italy.

Figure 8.19. Rates of home ownership, by citizenship

Percentages of all households, 2016

StatLink  https://doi.org/10.1787/888933844997

Figure 8.20. How home ownership rates have evolved, by citizenship

Changes in percentage points, between 2007 and 2016

StatLink  http://doi.org/10.1787/888933845016

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

8.11. Self-reported health status

Definition

Self-reported health status denotes how people perceive their physiological and psychological health. The share of those in good health is the share of individuals who rate their health as “good” or better. As health status is strongly age-dependent, and immigrants tend to be younger in most countries, that share in immigrant populations is adjusted to estimate what outcomes would be if immigrants had the same age structure as the native-born.

Coverage

People aged 16 years and over.

Across the EU, seven out of ten foreigners – 69% of third-country nationals and 70% of EU nationals – report that they are in good health. The share is slightly above the 67% of nationals. Differences between countries are wider than between nationals and foreigners within countries. Third-country nationals feel particularly healthy in Sweden, destinations with intakes of recent highly educated immigrants (like Ireland and the United Kingdom), and in the Southern European countries. At the other end of the scale lies Germany, where less than one in two third-country nationals (and host-country nationals) claim good health.

Ten years after the crisis, fewer third-country nationals reported good health than before, albeit only by a single percentage point EU-wide. The trend was much the same among nationals. EU nationals, by contrast, felt slightly healthier – they were 2 percentage points more likely than before the crisis to rate their own health as good or better. Third-country nationals reported better health in nearly half of countries, with a particular improvement in Austria. As for the biggest drops in self-reported good health, they came in Germany and Sweden. In two-third of countries the same trend was observed among third- and host-country nationals. Not in Sweden and Luxembourg, however, where nationals reported much better health status and non-EU nationals much worse.

Differences in the self-reported health status of third- and host-country nationals may also be attributable to a number of factors not included in the analysis – e.g. gender, lifestyle or other social and economic circumstances. They may also indicate different degrees of satisfaction with health and social security systems.

Figure 8.21. Good self-reported health status, by citizenship

Percentages, aged 16 and above, 2016

StatLink  https://doi.org/10.1787/888933845035

Figure 8.22. How the shares of individuals in good health have evolved, by citizenship

Changes in percentage points, aged 16 and above, between 2007 and 2016

StatLink  https://doi.org/10.1787/888933845054

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

8.12. Long-term residents

Definition

A long-term resident is a third-country national who has been granted long-term residence status in accordance with Directive 2003/109/EC of 25 November 2003. The status may be granted to all non-EU citizens if they have resided legally and continuously for five years in an EU member state, have health insurance coverage, and enjoy sufficient financial resources not to have to rely on social assistance. Some countries may also have additional requirements, such as proficiency in the host-country language. Long-term residents enjoy the same rights of residence as EU nationals, particularly that of residing in an EU country other than the one where they were awarded long-term residence.

This indicator relates to the share of long-term residents among third-country nationals who live legally in the European Union. EU member countries may deliver permanent residence permits that confer more advantageous conditions than foreseen in the Directive. The collection of data on long-term residence permits includes countries’ permanent residence permits, even if they do not entitle residents to live in other EU countries.

Coverage

All third-country nationals with a valid residence permit.

An average of four TCNs in ten benefit from long-term residence status in the EU (both EU long-term resident status and national permanent status included). From country to country, however, that share varies greatly. More than 85% of third-country nationals are long-term residents in Latvia and Estonia, countries where the non-EU population was largely shaped by border changes and includes national minorities. In Sweden and France, about 75% of non-EU nationals enjoy long-term residence status. Fewer than one in two do, however, in two-thirds of countries, and only about 1 in 50 in Portugal and Finland.

The permanent residence permits granted by some countries before Directive 2003/109/EC came into effect may be more advantageous than the long-term status conferred by the directive. In countries that grant permanent residence entitlements, it is not in third-country nationals’ interest to apply for a long-term EU residence permit unless they wish to settle later on in another member state. The long-term residence data generally collected include the permanent residence permits granted by certain countries. For instance in France, Germany, Belgium and Spain, where more than half of third-country nationals enjoy long-term residence status, it is mainly under national permanent residence permits, while the share of those having EU long-term residence status is lower than 3%.

The proportion of third-country nationals with long-term residence status has increased in most countries over the past decade. However, because countries have taken different lengths of time to implement Directive 2003/109/EC and because data collection does not always include the permanent permits granted by certain countries, cross-country comparisons over time may not be meaningful.

Figure 8.23. Proportions of third-country nationals with long-term residence status

Percentages, EU long-term resident status and national permanent status included, 2016

StatLink  https://doi.org/10.1787/888933845073

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

8.13. Voter participation

Definition

Voter participation is the share of voters who report that they cast a ballot in the most recent national parliamentary election in the country of residence.

Coverage

All nationals of the country of residence aged 18 and above who are eligible to vote in national elections.

EU-wide, host-country nationals born in a third country were less likely to vote in national elections than their native-born peers between 2008 and 2016. The respective shares were 73% and 79%. Host-country nationals born in other EU countries came in between, with 76% voting in the most recent national election.

Non-EU-born voter turnout was lower not only EU-wide, but in most countries, too. The turnout gap was widest in Ireland, where non-EU-born with host-country citizenship were 26 points less likely to cast a vote than their native-born peers. In the Nordic countries, Southern Europe, Germany and the Netherlands, gaps were wide, too, at over 10 percentage points. In about half of countries, however, including most of Eastern Europe and some longstanding destinations like Austria, France and the United Kingdom, differences in non-EU-born and native-born voter participation were not significant.

As for host-country nationals born in other EU countries, though less likely to vote than their native-born peers EU-wide, there is no significant difference in turnout in half of countries. In virtually all countries, however, they are more likely to vote than non-EU-born nationals. In fact, only in Belgium and the United Kingdom do nationals born in other EU countries vote in lower proportions than non-EU-born nationals. Commonwealth citizenship may have something to do with the relatively high turnout of non-EU-born nationals in the United Kingdom. As immigrants with a nationality from the Commonwealth can take part in national elections when residing in the United Kingdom, they may be familiar with the voting system and more likely to cast their vote, including after naturalisation.

Figure 8.24. Self-reported participation in most recent election by place of birth

Percentages of population with the country’s nationality, aged 18 and above, 2008-16

StatLink  https://doi.org/10.1787/888933845092

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

8.14. Acquisition of nationality

Definition

The acquisition of nationality is the process through which immigrants become citizens of the host country in which they reside. Immigrants must have lived for a certain time in the host country before they can apply for nationality. Required durations vary according to the host country and the immigrant group. On average, most immigrants are eligible for citizenship after 10 years of residence. This section uses the term “acquisition rate” to denote the share of immigrants who have resided in the host country for at least 10 years and hold its nationality. This rate is based on EU-LFS and not administrative headcount data.

Coverage

Immigrants aged 15 years and above who have resided in the host country for at least 10 years (settled immigrants). Immigrants who acquire the nationality of the host country at birth (e.g. expatriates) are also included since they cannot be separately identified.

A majority of non-EU born – 60% EU-wide – who resided in the EU in 2015-16 have taken their host-country’s nationality after 10 years of residence. Among EU migrants, the share is lower, at 46%. Over 90% of settled non-EU migrants in Croatia, Lithuania, Sweden and the Slovak Republic became host-country citizens, while less than half did so in about one-third of countries. Rates are particularly low in Latvia, Estonia, Italy and Greece, where no more than 36% of settled non-EU born take up nationality.

In most countries, EU migrants are less likely to take up host-country nationality than non-EU migrants – partly because they already enjoy the benefits of EU citizenship. Immigrants born in another EU country are much more likely to have naturalised in member states that joined the EU and its free movement area more recently – particularly in the Czech Republic and, to a lesser extent, in Latvia, Hungary and Slovenia. This is partly linked to border changes. By contrast, no more than one in three settled EU-migrants has acquired nationality in the United Kingdom, Denmark, Spain or Luxembourg.

The EU-wide nationality acquisition rate has fallen substantially over the last 10 years. In three countries in five actually, immigrants from a third country were less likely to be host-country nationals 10 years after the economic crisis than at its onset. The decline was close to 9 percentage points among non-EU migrants and 13 points among those from other EU countries. By contrast, there were significant rises among both non-EU and EU migrants in Luxembourg and Portugal. In almost all countries, the rates of non-EU and EU migrants acquiring nationality followed the same trend. The only exceptions were Greece, Spain and Finland, where immigrants from other EU-28 countries became less likely to take up host-country nationality, while those from outside the EU became more likely to do so.

Figure 8.25. Acquisition of nationality, by place of birth

Percentages of settled immigrants who became host-country nationals, aged 15 and above, 2015-16

StatLink  https://doi.org/10.1787/888933845111

Figure 8.26. How the acquisition of nationality rate has evolved, by place of birth

Changes in percentage points, aged 15 and above, between 2006-07 and 2015-16

StatLink  https://doi.org/10.1787/888933845130

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

8.15. Perceived discrimination

Definition

This section considers shares of immigrants who report having experienced 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 (2016 data).

Coverage

Foreigners aged 15 to 64 years old.

Close to one in five third-country nationals EU-wide feel that they belong to a group that is discriminated against on the grounds of ethnicity, nationality or race. The sentiment varies widely from country to country, however. Almost 40% of non-EU nationals in Greece and more than one-third in Belgium consider that they belong to a group that has been subject to discrimination. Shares are lower in the Nordic countries, Ireland and the United Kingdom.

The sentiment is much less prevalent among EU foreigners, only 10% of whom feel part of a group that is singled out for discrimination. The incidence is particularly low in Belgium, where less than 7% of EU foreigners share that perception. By contrast, EU foreigners in Sweden and the United Kingdom feel similarly or even more discriminated against than non-EU nationals.

Fewer third-country nationals EU-wide feel discriminated against today than a decade ago. Between 2010 and 2016, a 5-point lower share than between 2002 and 2008 reported discrimination on the grounds of belonging to a particular group. Although the sense of discrimination lessened among third-country nationals in Austria and, to a lesser extent, the Netherlands and Ireland, it rose to double pre-crisis levels in Belgium. As for EU foreigners, the reported incidence of discrimination remained much the same EU-wide. It rose however by about 5 percentage points in France, Belgium and the United Kingdom, while declining particularly steeply in Austria and to a lesser extent in Germany.

While shares of male and female EU nationals report to be part of a discriminated group in similar proportions, non-EU national men were much more likely to do so than non-EU national women. Foreigners from North and Sub-Saharan Africa and Asia perceive greater discrimination than those born in other parts of the world – i.e. Europe, Oceania and the Americas. Discrimination seems to single out foreigners of working age (between the ages of 25 and 54), particularly those who are unemployed and regardless of whether they are EU or third-country nationals. Language, however, appears to be less of a factor in perceptions of discrimination among third-country nationals, who report broadly the same shares irrespective of whether or not their first language is the host-country’s. By contrast, EU nationals whose first language is not the one spoken in the host-country are twice as likely to feel that they belong to a group that is discriminated against as those who share a first language with host-country nationals.

Figure 8.27. Self-reported discrimination, by citizenship

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

StatLink  https://doi.org/10.1787/888933845149

Figure 8.28. Self-reported discrimination, by several characteristics and citizenship

Percentages, 15- to 64-year-olds, 2012-16

StatLink  https://doi.org/10.1787/888933845168

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

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

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

EU totals in figures on evolutions from Indicator 8.3 to 8.11 and 8.14 exclude Latvia.

In the EU-LFS data for Malta it is not possible to distinguish EU nationals from third-country nationals. As a result, all figure EU totals for third-country nationals based on EU-LFS data exclude Malta.

Figure 8.1: EU total (25) excludes data for Croatia, Romania and Malta for 2007 and 2017. The figure displays EU-25 4.4% (2017) and 3.8% (2007). The EU-28 share for 2017 is 4.2%.

Figure 8.3: For Spain, the region of citizenship United States, Canada and Oceania only includes data for the United States.

Figure 8.15 and Figure 8.16: Purchasing power parities (PPP) in national currencies per euro (EU=1.00), 2014.

Figure 8.21 and Figure 8.22: Adjusted rates refer to the hypothetical situation if third-country nationals had the same age distribution as nationals.

For further detailed data, see Annex A.

Table 8.1. Sources by indicator
| 8.1 Size and composition by age and gender | 8.2 Duration of stay and regions of nationality | 8.3, 8.4 Employment and labour market participation, unemployment | 8.5 Self-employment | 8.6, 8.7 Over-qualification, Educational attainment | 8.8 – 8.11 Household income, relative poverty, housing tenure, reported health status | 8.12 Long-term residents | 8.13 Voter participation | 8.14 Acquisition of nationality | 8.15 Perceived discrimination
EU countries |  |  |  |  |  |  |  |  |  |
Austria | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Belgium | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Bulgaria | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Croatia | Eurostat Database 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2017 | EU-LFS 2015-16 | EU-LFS 2015-16 | EU-SILC 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2015-16 | ESS 2008-16
Cyprus1,2 | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Czech Republic | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Denmark | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Estonia | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Finland | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 AHM 2014 | EU-LFS 2006-07 & 2015-16 | EU-LFS AHM 2014 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
France | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Germany | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & Mikrozensus 2016 | EU-LFS 2006-07 & Mikrozensus 2016 | G-SOEP 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | .. | ESS 2008-16
Greece | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Hungary | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Ireland | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Italy | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Latvia | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Lithuania | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007& 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Luxembourg | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Malta | Eurostat Database 2017 | Eurostat Database 2017 | .. | .. | .. | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | .. | ESS 2008-16
Netherlands | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Poland | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | .. | ESS 2008-16
Portugal | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Romania | Eurostat Database 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Slovak Republic | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Slovenia | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Spain | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Sweden | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
United Kingdom | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2017 | EU-LFS 2006-07 & 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007 & 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16
Non-EU countries |  |  |  |  |  |  |  |  |  |
Iceland | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007& 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16 | .. | ..
Norway | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007& 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16 | .. | ..
Switzerland | Eurostat Database 2007 & 2017 | Eurostat Database 2017 & EU-LFS 2015-16 | EU-LFS 2006-07 & 2015-16 | EU-SILC 2007& 2016 | Eurostat Database 2016 | ESS 2008-16 | EU-LFS 2006-07 & 2015-16 | ESS 2008-16 | .. | ..
Turkey | Eurostat Database 2007 & 2017 | .. | .. | .. | .. | .. | .. | .. | .. | ..
StatLink  https://doi.org/10.1787/888933845206

Annex A.

Characteristics of immigrant populations and households

Access the data for tables in Annex A: http://dx.doi.org/10.1787/888933869165

Table A A.1. Size and composition, 2006 and 2017
Total population
. | Foreign-born population | Native-born population | Foreign-born - Change since 2006 (% points)
| Total population (thousands) | % of the total population | 0-14 | 65+ | Women | Household size (Nb of persons) | 0-14 | 65+ | Women | Household size (Nb of persons) | % of the total population | Women
% of the foreign-born population | % of the native-born population | % of the foreign-born population
Australia | 6 873 | 28 | 6 | 20 | 51 | 3 | 24 | 14 | 50 | .. | 4 | 1
Austria | 1 656 | 19 | 6 | 12 | 51 | 2 | 16 | 19 | 50 | 2 | 4 | -1
Belgium | 1 893 | 17 | 7 | 17 | 51 | - | 19 | 19 | 51 | 2 | 4 | -1
Bulgaria | 145 | 2 | 16 | 15 | 51 | 2 | 14 | 21 | 51 | 2 | .. | -4
Canada | 7 433 | 20 | 6 | 21 | 52 | 3 | 20 | 14 | 50 | .. | 1 | 0
Chile | 465 | 3 | 13 | 4 | 52 | 3 | 21 | 13 | 53 | 3 | 1 | -7
Croatia | 540 | 13 | 1 | 28 | 54 | 3 | 16 | 19 | 52 | 3 | .. | ..
Cyprus1,2 | 174 | 20 | 6 | 9 | 56 | 2 | 19 | 17 | 50 | 3 | 1 | 0
Czech Republic | 798 | 7 | 4 | 21 | 40 | 2 | 16 | 19 | 51 | 2 | 2 | -3
Denmark | 641 | 11 | 8 | 8 | 50 | 2 | 18 | 20 | 50 | 2 | 5 | -1
Estonia | 136 | 10 | 2 | 42 | 58 | 2 | 18 | 16 | 52 | 2 | -6 | -2
Finland | 358 | 6 | 8 | 6 | 49 | 2 | 17 | 21 | 51 | 2 | 3 | -2
France | 8 210 | 12 | 5 | 22 | 52 | 2 | 21 | 18 | 51 | 2 | 1 | 0
Germany | 12 738 | 16 | 5 | 21 | 49 | 2 | 15 | 21 | 51 | .. | 3 | -1
Greece | 648 | 6 | 3 | 7 | 54 | 3 | 15 | 22 | 51 | 3 | .. | 2
Hungary | 514 | 5 | 4 | 20 | 50 | 2 | 15 | 18 | 52 | 2 | 2 | -5
Iceland | 47 | 14 | .. | .. | 49 | 2 | .. | .. | 49 | 2 | 4 | 4
Ireland | 810 | 17 | 11 | 6 | 51 | 3 | 24 | 15 | 50 | 3 | 3 | 3
Israel | 1 818 | 22 | .. | .. | 55 | 2 | .. | .. | 50 | 4 | -6 | 0
Italy | 6 054 | 10 | 5 | 5 | 54 | 2 | 15 | 24 | 51 | 2 | 0 | 0
Japan | 2 383 | 2 | 9 | 8 | 52 | .. | 13 | 27 | 51 | .. | 0 | -1
Korea | 1 143 | 2 | 4 | 4 | 43 | .. | 14 | 14 | 50 | .. | 1 | 0
Latvia | 251 | 13 | 2 | 46 | 61 | 2 | 17 | 16 | 53 | 2 | -3 | 1
Lithuania | 127 | 4 | 6 | 35 | 58 | 2 | 15 | 18 | 54 | 2 | -1 | 3
Luxembourg | 270 | 46 | .. | .. | 49 | 3 | .. | .. | 52 | 2 | 9 | -1
Malta | 70 | 15 | 6 | 13 | 46 | 2 | 15 | 19 | 50 | 3 | 8 | -2
Mexico | 1 007 | 1 | 51 | 4 | 49 | 2 | 26 | 8 | 52 | 4 | 0 | 0
Netherlands | 2 137 | 13 | 5 | 11 | 52 | 2 | 18 | 19 | 50 | 2 | 2 | 0
New Zealand | 1 169 | 24 | .. | .. | 52 | 3 | .. | .. | 51 | 3 | 3 | 0
Norway | 800 | 15 | .. | .. | 48 | 2 | .. | .. | 49 | 2 | 6 | -3
Poland | 1 649 | 4 | 8 | 48 | 56 | 2 | 15 | 17 | 52 | 3 | 3 | -1
Portugal | 893 | 9 | 4 | 8 | 54 | 3 | 15 | 22 | 52 | 3 | 1 | 3
Romania | 422 | 2 | 43 | 1 | 46 | - | 15 | 17 | 51 | 3 | .. | ..
Slovak Republic | 186 | 3 | 7 | 32 | 49 | - | 15 | 14 | 51 | 3 | .. | ..
Slovenia | 350 | 16 | 6 | 17 | 44 | 2 | 16 | 19 | 51 | 2 | 4 | 0
Spain | 6 025 | 13 | 4 | 7 | 52 | 3 | 17 | 20 | 51 | 2 | 1 | 4
Sweden | 1 784 | 18 | .. | .. | 50 | 2 | .. | .. | 49 | 2 | 5 | -2
Switzerland | 2 480 | 29 | .. | .. | 51 | 2 | .. | .. | 51 | 2 | 5 | -1
Turkey | 1 777 | 2 | .. | .. | 53 | .. | .. | .. | 51 | .. | .. | -3
United Kingdom | 9 369 | 14 | 8 | 11 | 52 | 3 | 20 | 19 | 50 | 2 | 5 | 1
United States | 43 739 | 13 | 5 | 15 | 51 | 3 | 21 | 15 | 51 | 2 | 1 | 2
OECD total | 128 507 | 10 | 6 | 15 | 51 | 3 | 19 | 17 | 51 | 3 | 1 | 1
EU total | 58 851 | 12 | 6 | 15 | 51 | 2 | 17 | 20 | 51 | 2 | 2 | 0
Source: Totals: Indicator 2.1, Age: Indicator 2.3; Women: Indicator 6.1; Recent migrants: Indicator 2.8; Household size: Indicator 2.5.

Table A A.2. Defining characteristics of immigrant populations, 2015-16
15-64, total = 100
| Region of birth | Duration of stay | Advanced host-country language proficiency (%)
| Europe | Of which: EU | Africa | Asia | Latin America | United States, Canada, Oceania | <5 years | 5 to 9 years | ≥10 years
Australia | 34 | .. | 6 | 45 | 2 | 13 | 21 | 19 | 60 | 70
Austria | 82 | 42 | 3 | 12 | 2 | 1 | 23 | 15 | 62 | 63
Belgium | 55 | 41 | 31 | 10 | 3 | 1 | 21 | 21 | 58 | 63
Bulgaria | 100 | 24 | 0 | 0 | 0 | 0 | 36 | 14 | 51 | 62
Canada | 22 | .. | 9 | 51 | 13 | 4 | 14 | 16 | 70 | ..
Chile | 5 | .. | 0 | 2 | 90 | 3 | 46 | 20 | 34 | ..
Croatia | 100 | 12 | 0 | 0 | 0 | 0 | 1 | 4 | 95 | 97
Cyprus1,2 | 62 | 51 | 4 | 32 | 0 | 2 | 27 | 27 | 46 | 42
Czech Republic | 87 | 58 | 1 | 10 | 1 | 1 | 15 | 17 | 67 | 76
Denmark | 54 | 38 | 7 | 33 | 3 | 3 | 30 | 21 | 49 | ..
Estonia | 92 | 8 | 0 | 7 | 0 | 0 | 3 | 4 | 93 | 21
Finland | 65 | 38 | 7 | 23 | 2 | 2 | 16 | 27 | 56 | 58
France | 32 | 23 | 53 | 9 | 5 | 1 | 12 | 13 | 75 | 65
Germany | 74 | 42 | 3 | 20 | 2 | 1 | 22 | 9 | 69 | 58
Greece | 79 | 19 | 2 | 15 | 1 | 2 | 7 | 15 | 79 | 63
Hungary | 90 | 70 | 1 | 7 | 0 | 1 | 14 | 14 | 73 | 92
Iceland | 72 | 68 | 2 | 15 | 4 | 7 | 13 | 26 | 61 | ..
Ireland | 65 | 62 | 8 | 16 | 6 | 5 | 24 | 28 | 47 | ..
Israel | .. | .. | .. | .. | .. | .. | 6 | 7 | 87 | ..
Italy | 56 | 35 | 17 | 15 | 11 | 2 | 9 | 25 | 66 | 66
Japan | 5 | .. | 1 | 80 | 10 | 4 | .. | .. | .. | ..
Korea | 3 | .. | 1 | 92 | 0 | 4 | 59 | 26 | 14 | ..
Latvia | 92 | 11 | 0 | 8 | 0 | 0 | 4 | 2 | 94 | 37
Lithuania | 89 | 10 | 0 | 10 | 0 | 0 | 3 | 3 | 94 | 56
Luxembourg | 86 | 80 | 7 | 4 | 2 | 1 | 28 | 18 | 53 | 90
Malta | .. | .. | .. | .. | .. | .. | 11 | 17 | 72 | 24
Mexico | 10 | .. | 0 | 4 | 35 | 50 | .. | .. | .. | ..
Netherlands | 39 | 24 | 19 | 19 | 19 | 2 | 8 | 11 | 81 | ..
New Zealand | 25 | .. | 9 | 42 | 2 | 23 | 16 | 16 | 68 | ..
Norway | 52 | 41 | 12 | 28 | 5 | 3 | 33 | 20 | 47 | 46
Poland | 100 | 29 | 0 | 0 | 0 | 0 | .. | .. | .. | 70
Portugal | 36 | 29 | 41 | 2 | 18 | 2 | 7 | 11 | 81 | 90
Romania | .. | .. | .. | .. | .. | .. | - | - | - | 56
Slovak Republic | 93 | 70 | 1 | 4 | 0 | 2 | 16 | 9 | 75 | 87
Slovenia | 100 | 22 | 0 | 0 | 0 | 0 | 10 | 18 | 72 | 51
Spain | 34 | 30 | 21 | 7 | 37 | 1 | 9 | 25 | 66 | 76
Sweden | 44 | 27 | 10 | 39 | 5 | 2 | 23 | 21 | 56 | 65
Switzerland | 77 | 58 | 6 | 8 | 6 | 2 | 26 | 17 | 57 | 63
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 41 | 37 | 16 | 33 | 4 | 5 | 26 | 22 | 52 | 68
United States | 11 | .. | 5 | 30 | 52 | 2 | 13 | 11 | 76 | ..
OECD total | 32 | .. | 11 | 28 | 26 | 3 | 16 | 15 | 69 | 65
EU total | 53 | 35 | 18 | 18 | 9 | 2 | 17 | 17 | 66 | 66
Source: Indicator 2.8; Language proficiency: Indicator 3.2.

Annex B.

Skills and the labour market

Access the data for tables in Annex B: http://dx.doi.org/10.1787/888933869184

Table A B.1. Distribution by level of education, 2017
Percentages, 15-64 population not in education
| Foreign-born | EU-born | Non-EU-born | Native-born
| Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+) | Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+) | Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+) | Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+)
Australia | .. | 16 | 52 | .. | .. | .. | .. | .. | .. | .. | 23 | 36
Austria | 3 | 28 | 29 | 0 | 11 | 39 | 5 | 41 | 20 | 0 | 13 | 31
Belgium | 20 | 38 | 30 | 12 | 29 | 37 | 25 | 44 | 25 | 7 | 23 | 37
Bulgaria | 2 | 8 | 47 | - | .. | .. | 1 | 3 | 57 | 4 | 20 | 26
Canada | .. | 10 | 60 | .. | .. | .. | .. | .. | .. | .. | 15 | 45
Chile | 11 | 21 | 13 | .. | .. | .. | .. | .. | .. | 23 | 36 | 9
Croatia | 4 | 24 | 18 | 1 | 10 | 25 | 4 | 26 | 16 | 2 | 17 | 21
Cyprus1,2 | 9 | 25 | 34 | 5 | 19 | 34 | 13 | 30 | 35 | 12 | 20 | 40
Czech Republic | 0 | 13 | 31 | 1 | 14 | 29 | 0 | 14 | 27 | 0 | 7 | 22
Denmark | 6 | 26 | 40 | 1 | 11 | 52 | 10 | 28 | 36 | 3 | 27 | 31
Estonia | 0 | 10 | 40 | 1 | 7 | 51 | 0 | 7 | 42 | 1 | 18 | 34
Finland | 4 | 24 | 32 | 1 | 21 | 31 | 6 | 25 | 32 | 4 | 14 | 39
France | 21 | 39 | 29 | 20 | 36 | 32 | 22 | 42 | 27 | 6 | 19 | 35
Germany | 13 | 35 | 23 | 9 | 29 | 24 | 17 | 41 | 21 | 2 | 10 | 28
Greece | 19 | 39 | 17 | 14 | 32 | 24 | 21 | 46 | 14 | 15 | 26 | 31
Hungary | 2 | 15 | 29 | 3 | 15 | 26 | 1 | 15 | 34 | 1 | 17 | 23
Iceland | 1 | 26 | 31 | 0 | 21 | 32 | 1 | 37 | 30 | 0 | 28 | 36
Ireland | 4 | 12 | 50 | 4 | 13 | 44 | 4 | 8 | 62 | 9 | 24 | 38
Israel | 6 | 10 | 57 | .. | .. | .. | .. | .. | .. | 6 | 15 | 41
Italy | 9 | 49 | 13 | 4 | 34 | 12 | 12 | 54 | 12 | 6 | 38 | 18
Japan | .. | 22 | 32 | .. | .. | .. | .. | .. | .. | .. | 13 | 37
Korea | .. | 30 | 30 | .. | .. | .. | .. | .. | .. | .. | 8 | 50
Latvia | 0 | 6 | 33 | 1 | 8 | 38 | 0 | 6 | 32 | 1 | 11 | 33
Lithuania | 1 | 3 | 37 | 1 | 6 | 36 | 1 | 5 | 35 | 2 | 6 | 39
Luxembourg | 12 | 29 | 45 | 13 | 29 | 46 | 9 | 28 | 42 | 4 | 24 | 28
Malta | 1 | 46 | 27 | 1 | 41 | 30 | 0 | 0 | 0 | 8 | 56 | 18
Mexico | 16 | 37 | 36 | .. | .. | .. | .. | .. | .. | 30 | 64 | 15
Netherlands | 15 | 30 | 28 | 5 | 19 | 35 | 18 | 35 | 24 | 6 | 22 | 36
New Zealand | .. | 13 | 43 | .. | .. | .. | .. | .. | .. | .. | 20 | 25
Norway | 5 | 26 | 39 | 2 | 14 | 47 | 7 | 35 | 34 | 0 | 19 | 40
Poland | 1 | 4 | 53 | 0 | 9 | 55 | 1 | 6 | 47 | 1 | 8 | 28
Portugal | 15 | 32 | 31 | 9 | 30 | 34 | 17 | 38 | 28 | 34 | 54 | 22
Romania | - | - | - | - | - | - | - | - | - | 5 | 26 | 16
Slovak Republic | 0 | 10 | 28 | 0 | 10 | 22 | 0 | 6 | 38 | 1 | 10 | 22
Slovenia | 2 | 23 | 18 | 2 | 16 | 24 | 2 | 32 | 9 | 1 | 12 | 32
Spain | 19 | 44 | 24 | 8 | 31 | 33 | 24 | 47 | 22 | 9 | 43 | 34
Sweden | 10 | 30 | 41 | 3 | 19 | 46 | 13 | 35 | 36 | 1 | 12 | 37
Switzerland | 6 | 24 | 41 | 6 | 21 | 44 | 7 | 32 | 30 | 1 | 13 | 37
Turkey | .. | 54 | 19 | .. | .. | .. | .. | .. | .. | .. | 65 | 17
United Kingdom | 3 | 17 | 49 | 1 | 16 | 44 | 4 | 21 | 50 | 1 | 21 | 39
United States | 9 | 23 | 40 | .. | .. | .. | .. | .. | .. | 0 | 7 | 45
OECD total | 11 | 27 | 37 | .. | .. | .. | .. | .. | .. | 7 | 26 | 33
EU total | 12 | 34 | 29 | 7 | 26 | 31 | 15 | 39 | 27 | 5 | 22 | 29
Source: Indicator 3.1.

Table A B.2. Distribution by level of education and gender, 2017
Percentages, 15-64 population not in education
| Foreign-born men | Foreign-born women | Native-born men | Native-born women | Recent migrants (<10 years)
| Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+)
Australia | 15 | 34 | 51 | 16 | 30 | 54 | 22 | 48 | 30 | 23 | 35 | 42 | 10 | 28 | 62
Austria | 25 | 47 | 27 | 30 | 39 | 31 | 11 | 57 | 32 | 16 | 53 | 31 | 21 | 41 | 38
Belgium | 37 | 33 | 29 | 39 | 30 | 31 | 25 | 43 | 32 | 22 | 38 | 41 | 36 | 31 | 33
Bulgaria | 9 | 52 | 39 | 8 | 38 | 54 | 20 | 60 | 20 | 19 | 49 | 32 | 9 | 40 | 51
Canada | 10 | 32 | 58 | 11 | 27 | 62 | 17 | 45 | 39 | 13 | 35 | 52 | 11 | 27 | 61
Chile | 17 | 68 | 15 | 24 | 65 | 11 | 36 | 55 | 9 | 35 | 55 | 10 | 19 | 67 | 13
Croatia | 17 | 66 | 17 | 31 | 51 | 18 | 14 | 68 | 18 | 19 | 56 | 25 | 11 | 65 | 23
Cyprus1,2 | 26 | 42 | 32 | 24 | 40 | 36 | 21 | 45 | 34 | 20 | 33 | 47 | 30 | 40 | 29
Czech Republic | 11 | 60 | 29 | 16 | 51 | 33 | 5 | 74 | 20 | 8 | 68 | 24 | 13 | 52 | 34
Denmark | 28 | 35 | 37 | 23 | 34 | 43 | 29 | 44 | 27 | 24 | 40 | 36 | 14 | 29 | 57
Estonia | 11 | 56 | 33 | 8 | 46 | 46 | 22 | 54 | 25 | 14 | 43 | 43 | 4 | 20 | 76
Finland | 30 | 42 | 29 | 18 | 47 | 35 | 17 | 52 | 32 | 11 | 43 | 46 | 26 | 48 | 26
France | 37 | 35 | 29 | 41 | 30 | 30 | 19 | 49 | 32 | 19 | 43 | 38 | 37 | 28 | 36
Germany | 33 | 44 | 23 | 36 | 42 | 22 | 9 | 60 | 31 | 11 | 64 | 24 | 35 | 35 | 30
Greece | 45 | 42 | 12 | 33 | 45 | 22 | 27 | 44 | 30 | 25 | 42 | 33 | 53 | 32 | 15
Hungary | 14 | 58 | 28 | 17 | 54 | 29 | 15 | 65 | 19 | 19 | 56 | 26 | 22 | 51 | 27
Iceland | 30 | 47 | 23 | 23 | 38 | 39 | 28 | 43 | 29 | 27 | 29 | 43 | 25 | 50 | 25
Ireland | 12 | 41 | 48 | 11 | 36 | 53 | 28 | 39 | 33 | 20 | 38 | 42 | 8 | 39 | 52
Israel | 11 | 35 | 54 | 10 | 31 | 59 | 17 | 47 | 37 | 13 | 41 | 45 | 20 | 32 | 49
Italy | 55 | 36 | 9 | 45 | 39 | 16 | 40 | 45 | 15 | 36 | 43 | 21 | 51 | 37 | 13
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | 29 | 41 | 30 | 32 | 38 | 30 | 7 | 41 | 52 | 10 | 42 | 48 | 30 | 39 | 31
Latvia | 6 | 61 | 33 | 5 | 61 | 33 | 14 | 63 | 23 | 8 | 50 | 42 | 3 | 32 | 65
Lithuania | 4 | 61 | 35 | 3 | 60 | 38 | 8 | 61 | 31 | 4 | 49 | 47 | - | - | -
Luxembourg | 29 | 26 | 45 | 28 | 27 | 45 | 22 | 51 | 27 | 26 | 45 | 29 | 19 | 22 | 59
Malta | 48 | 28 | 24 | 44 | 26 | 30 | 56 | 27 | 17 | 55 | 27 | 19 | 37 | 22 | 40
Mexico | 33 | 28 | 39 | 41 | 26 | 33 | 64 | 20 | 16 | 64 | 21 | 15 | .. | .. | ..
Netherlands | 30 | 42 | 27 | 29 | 41 | 30 | 22 | 43 | 36 | 22 | 42 | 37 | 25 | 42 | 32
New Zealand | 12 | 47 | 40 | 14 | 42 | 45 | 21 | 58 | 21 | 19 | 53 | 29 | 10 | 45 | 45
Norway | 24 | 39 | 36 | 28 | 29 | 43 | 20 | 45 | 35 | 18 | 37 | 45 | 25 | 36 | 39
Poland | 3 | 49 | 47 | 5 | 37 | 58 | 8 | 69 | 23 | 8 | 58 | 34 | .. | .. | ..
Portugal | 37 | 38 | 25 | 28 | 36 | 36 | 58 | 25 | 17 | 49 | 24 | 27 | 39 | 41 | 20
Romania | - | - | - | - | - | - | 24 | 61 | 15 | 28 | 55 | 17 | - | - | -
Slovak Republic | 10 | 62 | 28 | 10 | 62 | 28 | 8 | 74 | 18 | 11 | 64 | 25 | 8 | 48 | 44
Slovenia | 18 | 67 | 15 | 29 | 49 | 22 | 11 | 63 | 26 | 12 | 48 | 40 | 19 | 68 | 13
Spain | 47 | 31 | 22 | 41 | 33 | 26 | 45 | 23 | 32 | 40 | 23 | 37 | 41 | 31 | 28
Sweden | 31 | 31 | 38 | 29 | 27 | 44 | 13 | 57 | 30 | 11 | 44 | 45 | 38 | 19 | 44
Switzerland | 24 | 36 | 41 | 24 | 35 | 41 | 13 | 45 | 42 | 14 | 54 | 32 | 18 | 31 | 51
Turkey | 53 | 27 | 19 | 50 | 27 | 22 | 62 | 21 | 17 | 73 | 14 | 13 | .. | .. | ..
United Kingdom | 17 | 36 | 47 | 18 | 33 | 50 | 22 | 42 | 37 | 21 | 38 | 41 | 16 | 35 | 49
United States | 25 | 37 | 38 | 22 | 36 | 42 | 8 | 51 | 41 | 7 | 45 | 48 | 20 | 34 | 46
OECD total | 28 | 37 | 35 | 27 | 35 | 38 | 26 | 44 | 31 | 26 | 40 | 34 | 24 | 34 | 42
EU total | 34 | 39 | 27 | 34 | 36 | 30 | 23 | 50 | 27 | 22 | 46 | 32 | 32 | 34 | 34
Source: Indicator 3.1.

Table A B.3. Distribution by level of education, evolution between 2006-07 and 2017
Change in percentage points, 15-64 population not in education
| Foreign-born | EU-born | Non-EU-born | Native-born
| Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+) | Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+) | Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+) | Very low (ISCED 0-1) | Low (ISCED 0-2) | High (ISCED 5+)
Australia | .. | -11 | 14 | .. | .. | .. | .. | .. | .. | .. | -14 | 10
Austria | 1 | -8 | .. | 0 | -4 | .. | 2 | -8 | .. | 0 | -4 | ..
Belgium | -8 | -7 | 4 | -10 | -10 | 8 | -8 | -5 | 2 | -5 | -8 | 6
Bulgaria | 2 | 4 | -3 | - | - | - | 1 | -1 | 11 | -1 | -6 | 6
Canada | .. | -3 | 8 | .. | .. | .. | .. | .. | .. | .. | -1 | 4
Chile | .. | -2 | -19 | .. | .. | .. | .. | .. | .. | .. | -12 | -5
Croatia | -6 | -9 | 5 | -2 | -5 | 3 | -6 | -9 | 5 | -3 | -7 | 6
Cyprus1,2 | 3 | -4 | 0 | 2 | -2 | -4 | 5 | -3 | 3 | -8 | -9 | 11
Czech Republic | 0 | -10 | 13 | -1 | -13 | 14 | 0 | -2 | 2 | 0 | -5 | 10
Denmark | -1 | -4 | 8 | 0 | -4 | 6 | -1 | -9 | 11 | 3 | 3 | 1
Estonia | 0 | .. | 5 | 0 | .. | 8 | 0 | .. | 7 | -1 | .. | 4
Finland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
France | -7 | -9 | 6 | -13 | -14 | 12 | -4 | -6 | 3 | -5 | -11 | 9
Germany | 0 | -4 | 5 | -1 | -10 | 2 | 4 | 3 | 5 | 0 | -3 | 3
Greece | -1 | -7 | 3 | 5 | 3 | 3 | -3 | -4 | 1 | -11 | -14 | 10
Hungary | 1 | -1 | -1 | 1 | -3 | 0 | 0 | 5 | -9 | 0 | -6 | 6
Iceland | -2 | -7 | -5 | -1 | -7 | -7 | -2 | -4 | -2 | -2 | -14 | 10
Ireland | -3 | -9 | 8 | -3 | -10 | 6 | -1 | -5 | 4 | -7 | -11 | 10
Israel | -4 | -7 | 9 | .. | .. | .. | .. | .. | .. | -6 | -6 | 8
Italy | -4 | 2 | 0 | -2 | -1 | -2 | -4 | -1 | 1 | -9 | -11 | 6
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | -1 | -7 | 10 | -1 | -17 | 15 | -1 | -5 | 8 | -1 | -10 | 14
Lithuania | 0 | -4 | 13 | - | - | - | 0 | -3 | 12 | -1 | -9 | 14
Luxembourg | -15 | -8 | 15 | -15 | -9 | 16 | -9 | -3 | 10 | -6 | -10 | 9
Malta | .. | -8 | 7 | .. | .. | .. | .. | .. | .. | .. | -12 | 7
Mexico | .. | 7 | -8 | .. | .. | .. | .. | .. | .. | .. | -4 | 3
Netherlands | 1 | -3 | 6 | -1 | 4 | 3 | 2 | -3 | 5 | -1 | -7 | 7
New Zealand | .. | -6 | 0 | .. | .. | .. | .. | .. | .. | .. | -5 | -5
Norway | -1 | -7 | 5 | -1 | -5 | 0 | -2 | -7 | 8 | -2 | -6 | 10
Poland | -1 | -16 | 27 | 0 | -16 | 36 | -2 | -15 | 27 | 0 | -6 | 11
Portugal | -14 | -21 | 10 | -13 | -14 | 8 | -14 | -18 | 9 | -23 | -22 | 10
Romania | - | - | - | - | - | - | - | - | - | -2 | -2 | 5
Slovak Republic | -1 | -4 | 6 | -1 | -5 | 2 | 0 | -1 | 4 | 0 | -3 | 9
Slovenia | -3 | -9 | 6 | -2 | -7 | 6 | -3 | -6 | 1 | -1 | -7 | 12
Spain | -1 | 3 | 0 | -2 | 0 | 2 | -1 | 3 | 1 | -11 | -9 | 6
Sweden | -1 | -1 | 12 | -6 | -8 | 16 | 0 | 1 | 8 | -4 | -9 | 10
Switzerland | -4 | -8 | 13 | -5 | -8 | 12 | -3 | -4 | 8 | -1 | 4 | 9
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 1 | -7 | 19 | 1 | -5 | 17 | 2 | -5 | 20 | 0 | -8 | 10
United States | -4 | -7 | 7 | .. | .. | .. | .. | .. | .. | 0 | -2 | 6
OECD total | -3 | -5 | 7 | .. | .. | .. | .. | .. | .. | -3 | -5 | 6
EU total | -3 | -4 | 7 | -4 | -7 | 5 | -1 | -2 | 7 | -4 | -8 | 8
Source: Indicator 3.1.

Table A B.4. Distribution by level of education and gender, evolution between 2006-07 and 2017
Change in percentage points, 15-64 population not in education
| Foreign-born men | Foreign-born women | Native-born men | Native-born women | Recent migrants (<10 years)
| Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+) | Low (ISCED 0-2) | Medium (ISCED 3-4) | High (ISCED 5+)
Australia | -9 | -5 | 13 | -14 | -1 | 15 | -12 | 4 | 7 | -16 | 3 | 13 | -7 | -7 | 14
Austria | -6 | .. | .. | -10 | .. | .. | -1 | .. | .. | -8 | .. | .. | -12 | .. | ..
Belgium | -6 | 4 | 3 | -8 | 2 | 6 | -7 | 3 | 4 | -9 | 1 | 8 | -3 | 2 | 1
Bulgaria | - | - | - | 3 | -12 | 9 | -6 | 1 | 5 | -6 | -1 | 8 | .. | .. | ..
Canada | -2 | -4 | 6 | -4 | -5 | 9 | -1 | -1 | 2 | -2 | -4 | 6 | -4 | -2 | 6
Chile | -2 | 22 | -20 | -1 | 19 | -19 | -11 | 16 | -5 | -12 | 17 | -4 | .. | .. | ..
Croatia | -6 | 3 | 4 | -11 | 5 | 6 | -5 | 1 | 4 | -9 | 1 | 8 | .. | .. | ..
Cyprus1,2 | -4 | 3 | 1 | -4 | 3 | 0 | -7 | 1 | 7 | -11 | -5 | 16 | -3 | 4 | 0
Czech Republic | -8 | -3 | 11 | -11 | -5 | 16 | -3 | -4 | 7 | -6 | -6 | 13 | 0 | -9 | 9
Denmark | -2 | -2 | 5 | -7 | -5 | 12 | 7 | -6 | -1 | 0 | -2 | 3 | -16 | -5 | 21
Estonia | .. | .. | 1 | .. | .. | 10 | .. | .. | 3 | .. | .. | 5 | .. | .. | 27
Finland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
France | -9 | 3 | 6 | -10 | 3 | 7 | -9 | 2 | 8 | -12 | 2 | 10 | -6 | 0 | 7
Germany | -2 | -2 | 4 | -6 | 0 | 6 | -2 | -2 | 3 | -4 | 0 | 4 | -5 | -2 | 7
Greece | -8 | 7 | 1 | -5 | 1 | 4 | -14 | 6 | 8 | -14 | 2 | 12 | 2 | -4 | 3
Hungary | 1 | 2 | -3 | -2 | 0 | 1 | -5 | 0 | 5 | -8 | 0 | 8 | 4 | -4 | 0
Iceland | -2 | 14 | -12 | -11 | 9 | 2 | -11 | 5 | 6 | -17 | 3 | 14 | .. | .. | ..
Ireland | -10 | 1 | 9 | -8 | 2 | 6 | -11 | 3 | 9 | -11 | -1 | 12 | -9 | 1 | 8
Israel | -7 | -1 | 8 | -7 | -3 | 9 | -6 | -1 | 7 | -5 | -4 | 8 | 6 | -2 | -4
Italy | 3 | -3 | -1 | 1 | -2 | 1 | -10 | 6 | 4 | -13 | 5 | 8 | 3 | -3 | 1
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | -9 | -1 | 11 | -6 | -3 | 9 | -11 | 2 | 9 | -9 | -10 | 18 | -5 | -24 | 29
Lithuania | -5 | -9 | 14 | -3 | -9 | 11 | -9 | -1 | 10 | -9 | -9 | 18 | .. | .. | ..
Luxembourg | -8 | -6 | 14 | -9 | -7 | 15 | -7 | 0 | 6 | -13 | 1 | 12 | -5 | -7 | 12
Malta | -4 | -3 | 7 | -11 | 3 | 8 | -7 | 2 | 5 | -17 | 9 | 8 | .. | .. | ..
Mexico | 3 | 0 | -4 | 10 | 3 | -13 | -3 | 1 | 2 | -4 | 0 | 4 | .. | .. | ..
Netherlands | -3 | -2 | 4 | -4 | -4 | 8 | -5 | 0 | 5 | -9 | 0 | 10 | -3 | -3 | 6
New Zealand | -6 | 6 | 0 | -6 | 6 | 1 | -4 | 9 | -5 | -6 | 12 | -6 | -5 | 5 | -1
Norway | -8 | 3 | 5 | -6 | 0 | 6 | -5 | -3 | 8 | -7 | -5 | 11 | -12 | 2 | 10
Poland | -14 | -5 | 20 | -18 | -16 | 34 | -5 | -3 | 8 | -7 | -8 | 14 | .. | .. | ..
Portugal | -19 | 12 | 8 | -22 | 10 | 12 | -20 | 12 | 8 | -23 | 10 | 13 | -11 | 8 | 3
Romania | - | - | - | - | - | - | 0 | -5 | 4 | -5 | -2 | 7 | .. | .. | ..
Slovak Republic | 4 | -6 | 2 | -12 | 2 | 10 | -2 | -3 | 5 | -4 | -8 | 12 | -10 | -17 | 27
Slovenia | -5 | 3 | 1 | -14 | 3 | 11 | -6 | -3 | 8 | -8 | -7 | 15 | -3 | 16 | -13
Spain | 5 | -4 | -1 | 2 | -3 | 0 | -7 | 4 | 4 | -12 | 4 | 8 | 1 | -7 | 6
Sweden | 0 | -11 | 11 | -3 | -11 | 14 | -8 | 0 | 8 | -9 | -4 | 13 | 2 | -6 | 3
Switzerland | -5 | -5 | 10 | -12 | -5 | 16 | 7 | -11 | 4 | 1 | -15 | 14 | -7 | -2 | 9
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | -5 | -12 | 17 | -9 | -11 | 20 | -4 | -4 | 8 | -11 | -1 | 12 | -4 | -21 | 24
United States | -8 | 1 | 7 | -5 | -1 | 7 | -2 | -2 | 4 | -2 | -6 | 8 | -15 | 0 | 15
OECD total | -5 | -1 | 6 | -5 | -1 | 6 | -4 | 0 | 4 | -6 | -2 | 8 | -9 | -3 | 12
EU total | -3 | -3 | 6 | -6 | -2 | 8 | -6 | 0 | 6 | -9 | 0 | 9 | -5 | -6 | 11
Source: Indicator 3.1.

Table A B.5. Share of migrants with foreign education, 2015-16
Percentages, 15-64 population not in education
| Total | Highly educated | Highly educated born in an EU country | Highly educated born in a non-EU country
| Total | Men | Women | Total | Men | Women | Total | Men | Women | Total | Men | Women
Australia | 47 | 48 | 45 | 42 | 39 | 44 | .. | .. | .. | .. | .. | ..
Austria | 71 | 69 | 72 | 66 | 67 | 66 | 67 | 69 | 66 | 65 | 64 | 66
Belgium | 68 | 68 | 68 | 62 | 63 | 62 | 68 | 71 | 65 | 57 | 56 | 57
Bulgaria | 75 | - | - | - | - | - | - | - | - | - | - | -
Canada | .. | .. | .. | 50 | .. | .. | .. | .. | .. | .. | .. | ..
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 46 | 41 | 50 | 18 | 16 | 20 | 9 | - | - | 19 | 18 | 21
Cyprus1,2 | 79 | 77 | 79 | 69 | 68 | 69 | 67 | 69 | 66 | 70 | 66 | 72
Czech Republic | 70 | 71 | 69 | 57 | 62 | 53 | 48 | 57 | 39 | 69 | 68 | 69
Denmark | 59 | 61 | 57 | 57 | 60 | 55 | 58 | 61 | 56 | 57 | 59 | 55
Estonia | 29 | 30 | 28 | 27 | 33 | 22 | 61 | - | - | 24 | 27 | 21
Finland | 36 | 38 | 35 | 18 | 7 | 27 | 12 | 8 | 15 | 20 | 6 | 31
France | 53 | 50 | 55 | 41 | 38 | 43 | 52 | 55 | 51 | 37 | 33 | 40
Germany | 56 | 56 | 56 | 61 | 60 | 63 | 64 | 64 | 64 | 64 | 61 | 67
Greece | 73 | 73 | 74 | 60 | 59 | 61 | 52 | 53 | 51 | 64 | 61 | 66
Hungary | 68 | 70 | 66 | 47 | 51 | 44 | 51 | 61 | 40 | 40 | 30 | 50
Iceland | 56 | 57 | 54 | 45 | 58 | 36 | 42 | 58 | 32 | 53 | 59 | 47
Ireland | 67 | 68 | 65 | 68 | 68 | 68 | 63 | 65 | 62 | 74 | 72 | 75
Israel | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Italy | 73 | 71 | 74 | 61 | 55 | 64 | 51 | 47 | 53 | 67 | 58 | 71
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | 95 | 96 | 94 | 89 | 93 | 85 | .. | .. | .. | .. | .. | ..
Latvia | 33 | 34 | 33 | 25 | 24 | 25 | 11 | 6 | 14 | 27 | 27 | 27
Lithuania | 73 | 75 | 71 | 54 | 54 | 54 | - | - | - | 57 | 57 | 57
Luxembourg | 75 | 76 | 74 | 81 | 83 | 79 | 81 | 82 | 80 | 79 | 85 | 75
Malta | 49 | 51 | 46 | 54 | 52 | 56 | 55 | 56 | 55 | .. | .. | ..
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 40 | 37 | 43 | 34 | 30 | 37 | 42 | 40 | 43 | 30 | 25 | 34
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 64 | 66 | 60 | 64 | 66 | 62 | 69 | 72 | 66 | 58 | 59 | 56
Poland | .. | .. | .. | .. | .. | .. |  |  |  | .. | .. | ..
Portugal | 35 | 33 | 36 | 22 | 22 | 23 | 20 | 20 | 19 | 24 | 23 | 24
Romania | 56 | - | - | - | - | - | - | - | - | - | - | -
Slovak Republic | 57 | 58 | 56 | 46 | 46 | 45 | 33 | - | - | 63 | - | -
Slovenia | 64 | 63 | 64 | 35 | 36 | 35 | 27 | 24 | 29 | 39 | 41 | 38
Spain | 72 | 72 | 72 | 64 | 67 | 62 | 64 | 71 | 58 | 64 | 64 | 64
Sweden | 60 | 59 | 60 | 57 | 60 | 55 | 56 | 63 | 51 | 58 | 59 | 57
Switzerland | 67 | 67 | 66 | 65 | 66 | 64 | 67 | 70 | 64 | 60 | 55 | 64
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom5 | 51 | 52 | 49 | 42 | 44 | 39 | 45 | 52 | 41 | 40 | 41 | 38
United States | 61 | 60 | 62 | 55 | 54 | 55 | .. | .. | .. | .. | .. | ..
OECD total | 61 | 60 | 61 | 53 | 54 | 54 | .. | .. | .. | .. | .. | ..
EU total | 60 | 59 | 60 | 51 | 52 | 51 | 56 | 60 | 53 | 49 | 48 | 51
Source: Indicator 3.1.

Table A B.6. Employment rates, 2017
Percentages of the 15-64 population
| Foreign-born | Native-born
| Total | Men | Women | Low- educated | Highly educated | EU-born | Non- EU-born | Recent (<5 years) | Settled (≥10 years) | Total | Men | Women | Low- educated | Highly educated
Australia | 72 | 80 | 64 | 54 | 82 | .. | .. | 59 | 73 | 75 | 79 | 72 | 60 | 87
Austria | 66 | 72 | 59 | 55 | 83 | 73 | 58 | 60 | 66 | 74 | 77 | 71 | 62 | 91
Belgium | 54 | 61 | 47 | 41 | 75 | 64 | 46 | 50 | 54 | 64 | 67 | 61 | 46 | 87
Bulgaria | 60 | 70 | 52 | - | 76 | - | 58 | 52 | 71 | 63 | 66 | 60 | 37 | 85
Canada | 72 | 78 | 66 | 55 | 80 | .. | .. | 61 | 74 | 74 | 76 | 72 | 54 | 84
Chile | 74 | 84 | 65 | 77 | 87 | .. | .. | 77 | 74 | 59 | 71 | 49 | 60 | 81
Croatia | 56 | 64 | 49 | 35 | 79 | 63 | 55 | - | 57 | 56 | 61 | 52 | 37 | 82
Cyprus1,2 | 66 | 70 | 64 | 69 | 72 | 66 | 66 | 69 | 63 | 62 | 67 | 57 | 47 | 81
Czech Republic | 77 | 87 | 68 | 59 | 84 | 72 | 74 | 66 | 73 | 73 | 81 | 66 | 50 | 88
Denmark | 65 | 71 | 59 | 58 | 79 | 75 | 59 | 63 | 64 | 76 | 78 | 74 | 70 | 89
Estonia | 72 | 78 | 66 | 65 | 76 | 69 | 70 | 73 | 70 | 74 | 77 | 71 | 66 | 88
Finland | 60 | 67 | 53 | 61 | 76 | 70 | 53 | 45 | 67 | 70 | 71 | 70 | 53 | 86
France | 57 | 65 | 49 | 48 | 75 | 66 | 52 | 39 | 58 | 66 | 69 | 63 | 53 | 87
Germany | 67 | 74 | 60 | 58 | 80 | 77 | 60 | 49 | 73 | 76 | 79 | 73 | 66 | 91
Greece | 53 | 65 | 42 | 56 | 57 | 55 | 53 | 38 | 54 | 54 | 63 | 45 | 48 | 73
Hungary | 74 | 79 | 68 | 70 | 82 | 73 | 70 | 63 | 76 | 68 | 75 | 61 | 53 | 86
Iceland | 83 | 87 | 80 | 86 | 90 | 85 | 80 | 80 | 80 | 85 | 88 | 83 | 82 | 95
Ireland | 64 | 72 | 57 | 45 | 79 | 68 | 57 | 59 | 65 | 64 | 69 | 59 | 46 | 86
Israel | 79 | 81 | 77 | 66 | 86 | .. | .. | 68 | 81 | 66 | 70 | 62 | 48 | 89
Italy | 60 | 72 | 49 | 55 | 69 | 61 | 58 | 40 | 63 | 58 | 66 | 49 | 41 | 79
Japan | 70 | 80 | 62 | .. | .. | .. | .. | .. | .. | 73 | 81 | 65 | .. | ..
Korea | 71 | 83 | 56 | 76 | 78 | .. | .. | 66 | 80 | 68 | 77 | 58 | 70 | 81
Latvia | 67 | 72 | 62 | 47 | 77 | 69 | 64 | 41 | 65 | 70 | 72 | 69 | 59 | 89
Lithuania | 70 | 72 | 69 | - | 81 | 62 | 69 | - | 69 | 70 | 71 | 70 | 47 | 91
Luxembourg | 69 | 75 | 64 | 63 | 85 | 72 | 59 | 71 | 67 | 63 | 66 | 59 | 54 | 88
Malta | 68 | 83 | 54 | 62 | 81 | 71 | .. | 57 | 70 | 65 | 77 | 52 | 55 | 93
Mexico | 52 | 66 | 39 | 60 | 79 | .. | .. | .. | .. | 61 | 79 | 45 | 65 | 81
Netherlands | 64 | 71 | 57 | 51 | 82 | 75 | 57 | 45 | 63 | 78 | 82 | 74 | 70 | 91
New Zealand | 74 | 80 | 67 | 63 | 85 | .. | .. | 68 | 76 | 75 | 79 | 70 | 64 | 90
Norway | 68 | 73 | 63 | 56 | 82 | 81 | 60 | 63 | 70 | 76 | 76 | 75 | 63 | 92
Poland | 70 | 75 | 67 | - | 82 | 65 | 58 | .. | .. | 66 | 73 | 59 | 43 | 89
Portugal | 74 | 78 | 71 | 73 | 85 | 72 | 68 | 47 | 73 | 67 | 70 | 64 | 68 | 88
Romania | 60 | - | - | - | - | - | - | - | - | 62 | 70 | 53 | 51 | 87
Slovak Republic | 68 | 78 | 61 | 37 | 82 | 58 | 67 | 61 | 60 | 66 | 72 | 60 | 37 | 83
Slovenia | 67 | 73 | 61 | 54 | 82 | 60 | 62 | 56 | 60 | 70 | 72 | 67 | 48 | 89
Spain | 60 | 66 | 54 | 56 | 72 | 61 | 54 | 48 | 59 | 61 | 67 | 56 | 54 | 83
Sweden | 67 | 71 | 63 | 57 | 83 | 76 | 60 | 48 | 73 | 80 | 81 | 80 | 67 | 94
Switzerland | 76 | 84 | 68 | 69 | 84 | 81 | 68 | 74 | 76 | 82 | 85 | 79 | 78 | 92
Turkey | 46 | 64 | 26 | 40 | 66 | .. | .. | .. | .. | 52 | 70 | 31 | 51 | 74
United Kingdom | 73 | 83 | 64 | 62 | 86 | 78 | 67 | 67 | 73 | 76 | 79 | 72 | 65 | 88
United States | 70 | 82 | 59 | 64 | 78 | .. | .. | 58 | 73 | 68 | 72 | 65 | 35 | 83
OECD total | 68 | 77 | 59 | 58 | 79 | .. | .. | 57 | 71 | 67 | 74 | 60 | 55 | 84
EU total | 64 | 73 | 57 | 55 | 80 | 71 | 59 | 53 | 66 | 68 | 73 | 63 | 53 | 87
Source: Indicator 3.4.

Table A B.7. Employment rates, evolution between 2006-07 and 2017
Change in percentage points, 15-64 population
| Foreign-born | Native-born
| Total | Men | Women | Low educated | Highly educated | EU-born | Non- EU-born | Recent (<5 years) | Settled (≥10 years) | Total | Men | Women | Low- educated | Highly educated
Australia | 4 | 3 | 5 | -4 | -2 | .. | .. | .. | .. | 1 | -2 | 3 | -8 | -2
Austria | 3 | 1 | 5 | -1 | .. | 7 | -2 | 7 | 0 | 3 | 1 | 6 | 2 | ..
Belgium | 3 | 0 | 6 | 3 | 0 | 7 | 2 | 2 | 3 | 1 | -2 | 4 | -4 | 1
Bulgaria | -1 | 7 | -7 | - | -6 | - | -1 | - | -2 | 3 | 2 | 4 | -1 | 2
Canada | 2 | 1 | 3 | -3 | 0 | .. | .. | .. | .. | 0 | -1 | 1 | -3 | -1
Chile | 9 | 5 | 11 | 7 | 7 | .. | .. | .. | .. | 3 | -1 | 7 | 2 | -3
Croatia | 3 | -1 | 6 | 1 | -1 | 5 | 2 | - | 2 | -1 | -4 | 1 | -7 | 0
Cyprus1,2 | -5 | -6 | -5 | -7 | -5 | 0 | -9 | -3 | -8 | -8 | -13 | -2 | -15 | -8
Czech Republic | 14 | 13 | 14 | 20 | 3 | 10 | 5 | -11 | 16 | 8 | 6 | 9 | 13 | 2
Denmark | 1 | 0 | 1 | -5 | -3 | 3 | -1 | 1 | -2 | -3 | -4 | -1 | -3 | -1
Estonia | -1 | 1 | -4 | .. | -6 | -1 | -3 | - | -4 | 6 | 6 | 7 | .. | -1
Finland | -2 | -1 | -3 | .. | .. | 0 | -2 | -1 | 0 | 0 | -1 | 1 | .. | ..
France | -1 | -2 | 0 | -5 | 2 | 1 | -3 | 1 | -4 | 1 | 0 | 3 | -6 | 2
Germany | 8 | 6 | 9 | 5 | 9 | 11 | 2 | 5 | 10 | 6 | 4 | 8 | 3 | 3
Greece | -13 | -19 | -7 | -17 | -16 | -8 | -15 | -22 | -14 | -7 | -11 | -3 | -9 | -11
Hungary | 11 | 6 | 15 | 22 | 1 | 11 | 8 | 2 | 13 | 11 | 11 | 10 | 18 | 4
Iceland | -2 | -3 | 0 | -5 | -2 | 1 | -6 | .. | .. | 1 | 0 | 2 | -4 | 0
Ireland | -8 | -10 | -5 | -15 | -5 | -7 | -6 | -16 | -3 | -4 | -8 | 0 | -12 | -4
Israel | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Italy | -6 | -10 | -1 | -11 | -7 | -4 | -8 | -18 | -5 | 0 | -3 | 3 | -10 | -4
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | -5 | -7 | -5 | -7 | 0 | 4 | -9 | -40 | -7 | 4 | 1 | 7 | 6 | -1
Lithuania | 0 | -4 | 4 | - | -2 | - | 0 | - | -1 | 6 | 4 | 9 | 5 | 1
Luxembourg | -1 | -4 | 3 | -5 | 0 | 0 | 1 | -5 | 0 | 3 | -2 | 8 | 0 | 2
Malta | 12 | 7 | 15 | 9 | 10 | .. | .. | .. | .. | 11 | 4 | 17 | 8 | 6
Mexico | -2 | -9 | 5 | -2 | 10 | .. | .. | .. | .. | 0 | -2 | 1 | 4 | 0
Netherlands | 3 | 1 | 4 | -3 | 2 | 4 | -1 | -4 | -1 | 2 | -1 | 4 | 2 | 2
New Zealand | 3 | 2 | 5 | 1 | 3 | .. | .. | .. | .. | -2 | -4 | 0 | -4 | 3
Norway | 0 | 0 | -1 | -2 | -7 | 0 | -1 | 0 | -2 | -1 | -3 | 1 | -2 | 0
Poland | 34 | 30 | 39 | - | 17 | 30 | 29 | .. | .. | 10 | 10 | 10 | 2 | 4
Portugal | 2 | 0 | 4 | -1 | -2 | 5 | -6 | -25 | -1 | 0 | -3 | 3 | -2 | 1
Romania | 0 | - | - | - | - | - | - | - | - | 3 | 5 | 0 | 0 | 1
Slovak Republic | 9 | 7 | 12 | 10 | -2 | -1 | 4 | - | 0 | 6 | 4 | 8 | 13 | -2
Slovenia | -1 | 0 | -1 | -5 | 1 | -4 | -7 | - | -8 | 2 | 1 | 4 | -7 | 0
Spain | -11 | -15 | -5 | -12 | -6 | -9 | -16 | -20 | -11 | -3 | -9 | 2 | -5 | -3
Sweden | 4 | 4 | 4 | 0 | 2 | 4 | 3 | 1 | 6 | 4 | 3 | 6 | -1 | 3
Switzerland | 3 | 1 | 4 | 0 | 0 | 3 | 1 | 3 | 2 | 2 | -1 | 5 | 2 | -1
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 7 | 6 | 8 | 10 | 0 | 3 | 5 | 0 | 6 | 4 | 2 | 6 | -1 | -2
United States | -1 | -2 | 0 | -3 | -2 | .. | .. | -8 | -1 | -2 | -3 | -1 | -6 | -1
OECD total | 0 | -1 | 2 | -2 | -1 | .. | .. | -6 | 0 | 1 | -1 | 2 | 1 | 0
EU total | 1 | 0 | 3 | 1 | 3 | 3 | -3 | -6 | 2 | 3 | 1 | 5 | -2 | 0
Source: Indicator 3.4.

Table A B.8. Unemployment rates, 2017
Percentages of the labour force, 15-64 population
| Foreign-born | Native-born
| Total | Men | Women | Low- educated | Highly educated | EU-born | Non- EU-born | Recent (<5 years) | Settled (≥10 years) | Total | Men | Women | Low- educated | Highly educated
Australia | 6 | 5 | 7 | 9 | 4 | .. | .. | 9 | 5 | 6 | 6 | 5 | 8 | 3
Austria | 11 | 11 | 10 | 19 | 6 | 8 | 14 | 11 | 11 | 4 | 5 | 4 | 10 | 2
Belgium | 16 | 17 | 16 | 24 | 10 | 10 | 22 | 19 | 15 | 7 | 7 | 6 | 14 | 3
Bulgaria | 4 | 7 | - | - | 2 | - | 0 | - | 2 | 8 | 9 | 8 | 23 | 4
Canada | 7 | 6 | 8 | 10 | 7 | .. | .. | 11 | 6 | 6 | 7 | 5 | 14 | 4
Chile | 6 | 5 | 7 | 5 | 4 | .. | .. | 6 | 5 | 8 | 7 | 9 | 8 | 6
Croatia | 15 | 12 | 18 | 23 | 8 | 15 | 15 | - | 14 | 15 | 14 | 15 | 20 | 9
Cyprus1,2 | 14 | 15 | 12 | 14 | 12 | 14 | 13 | 9 | 17 | 14 | 14 | 15 | 20 | 11
Czech Republic | 3 | 2 | 4 | 9 | 3 | 6 | 5 | 7 | 6 | 3 | 2 | 4 | 14 | 1
Denmark | 10 | 9 | 10 | 14 | 8 | 9 | 14 | 16 | 10 | 5 | 5 | 5 | 9 | 4
Estonia | 6 | 7 | 6 | 6 | 6 | 4 | 9 | 8 | 9 | 6 | 6 | 5 | 12 | 3
Finland | 16 | 15 | 17 | 19 | 12 | 13 | 21 | 25 | 15 | 8 | 9 | 8 | 18 | 5
France | 15 | 15 | 16 | 20 | 10 | 10 | 19 | 27 | 15 | 9 | 9 | 9 | 16 | 5
Germany | 7 | 7 | 6 | 10 | 5 | 5 | 9 | 11 | 6 | 4 | 4 | 3 | 10 | 2
Greece | 30 | 26 | 34 | 28 | 27 | 25 | 33 | 36 | 32 | 21 | 17 | 25 | 24 | 16
Hungary | 3 | 3 | 4 | 7 | 1 | 5 | 8 | 5 | 7 | 4 | 4 | 5 | 11 | 2
Iceland | 5 | 6 | 5 | 6 | 5 | 7 | 3 | 10 | 6 | 3 | 3 | 4 | 6 | 2
Ireland | 10 | 11 | 9 | 19 | 8 | 10 | 11 | 11 | 10 | 8 | 10 | 7 | 16 | 4
Israel | 4 | 4 | 4 | 4 | 3 | .. | .. | 8 | 4 | 4 | 4 | 5 | 8 | 3
Italy | 14 | 13 | 16 | 16 | 11 | 14 | 16 | 24 | 14 | 11 | 10 | 12 | 16 | 6
Japan | 5 | 6 | 5 | .. | .. | .. | .. | .. | .. | 4 | 5 | 4 | .. | ..
Korea | 4 | 3 | 6 | 3 | 5 | .. | .. | 4 | 3 | 4 | 4 | 4 | 3 | 4
Latvia | 8 | 9 | 7 | 19 | 3 | 10 | 11 | 30 | 10 | 9 | 10 | 8 | 19 | 4
Lithuania | 8 | 8 | 7 | - | 4 | 14 | 9 | - | 9 | 7 | 9 | 6 | 22 | 3
Luxembourg | 9 | 8 | 9 | 12 | 5 | 7 | 16 | 11 | 8 | 5 | 5 | 4 | 10 | 2
Malta | 6 | 4 | 8 | 7 | 3 | 3 | .. | 10 | 6 | 5 | 5 | 5 | 8 | 2
Mexico | 4 | 4 | 4 | 6 | 2 | .. | .. | .. | .. | 4 | 3 | 4 | 3 | 4
Netherlands | 9 | 8 | 9 | 13 | 5 | 7 | 14 | 15 | 12 | 4 | 4 | 5 | 8 | 3
New Zealand | 5 | 5 | 6 | 6 | 4 | .. | .. | 7 | 5 | 6 | 5 | 6 | 8 | 2
Norway | 10 | 10 | 11 | 17 | 7 | 6 | 13 | 16 | 6 | 4 | 4 | 3 | 9 | 2
Poland | 9 | 7 | 8 | - | 7 | 9 | 14 | .. | .. | 5 | 5 | 5 | 13 | 2
Portugal | 10 | 10 | 11 | 11 | 8 | 10 | 16 | 27 | 13 | 9 | 9 | 9 | 10 | 6
Romania | - | - | - | - | - | - | - | - | - | 7 | 7 | 6 | 9 | 4
Slovak Republic | 8 | 8 | 8 | - | 3 | 12 | 9 | 10 | 12 | 8 | 8 | 8 | 30 | 4
Slovenia | 8 | 5 | 10 | 10 | 4 | 10 | 12 | 16 | 11 | 6 | 6 | 7 | 12 | 5
Spain | 23 | 22 | 25 | 29 | 17 | 23 | 31 | 29 | 28 | 16 | 15 | 18 | 24 | 9
Sweden | 15 | 16 | 15 | 28 | 10 | 7 | 20 | 29 | 10 | 4 | 4 | 4 | 13 | 2
Switzerland | 8 | 7 | 9 | 10 | 7 | 6 | 12 | 10 | 7 | 3 | 4 | 3 | 7 | 2
Turkey | 12 | 12 | 14 | 12 | 11 | .. | .. | .. | .. | 11 | 9 | 13 | 10 | 13
United Kingdom | 5 | 4 | 7 | 8 | 4 | 5 | 6 | 8 | 5 | 4 | 5 | 4 | 8 | 3
United States | 4 | 4 | 5 | 5 | 3 | .. | .. | 6 | 4 | 5 | 5 | 5 | 14 | 3
OECD total | 8 | 7 | 9 | 12 | 5 | .. | .. | 10 | 8 | 6 | 6 | 6 | 10 | 4
EU total | 12 | 11 | 12 | 17 | 8 | 9 | 16 | 15 | 12 | 7 | 7 | 8 | 15 | 4
Source: Indicator 3.5.

Table A B.9. Unemployment rates, evolution between 2006-07 and 2017
Change in percentage points, 15-64 population
| Foreign-born | Native-born
| Total | Men | Women | Low- educated | Highly educated | EU-born | Non- EU-born | Recent (<5 years) | Settled (≥10 years) | Total | Men | Women | Low- educated | Highly educated
Australia | 1 | 0 | 1 | 2 | 0 | .. | .. | .. | .. | 1 | 2 | 1 | 1 | 1
Austria | 1 | 1 | 0 | 5 | .. | 1 | 1 | -3 | 2 | 0 | 1 | -1 | 2 | ..
Belgium | 0 | 1 | -3 | 1 | 0 | 0 | -1 | -1 | 0 | 0 | 1 | -2 | 2 | 0
Bulgaria | -3 | - | - | - | - | - | - | - | -4 | 1 | 1 | -1 | 4 | 1
Canada | 0 | 0 | 0 | 0 | 1 | .. | .. | .. | .. | 0 | 1 | 0 | 2 | 1
Chile | 0 | 2 | -2 | -1 | 1 | .. | .. | .. | .. | 0 | 1 | -1 | 0 | 1
Croatia | 2 | 1 | 3 | 5 | 2 | - | 2 | - | 2 | 4 | 5 | 3 | 7 | 2
Cyprus1,2 | 8 | 9 | 7 | 9 | 8 | 8 | 8 | 3 | 12 | 10 | 10 | 10 | 15 | 8
Czech Republic | -7 | -6 | -9 | -21 | -2 | -5 | -4 | 3 | -8 | -3 | -3 | -4 | -9 | -1
Denmark | 2 | 1 | 3 | 3 | 2 | 4 | 5 | 7 | 3 | 1 | 2 | 1 | 3 | 1
Estonia | 0 | -1 | 0 | .. | 0 | 3 | 2 | - | 2 | 1 | 1 | 1 | .. | 1
Finland | 0 | 1 | -2 | .. | .. | 2 | 0 | -4 | 1 | 1 | 1 | 0 | .. | ..
France | 2 | 3 | 2 | 5 | 0 | 2 | 4 | 3 | 4 | 1 | 2 | 0 | 5 | 0
Germany | -9 | -9 | -9 | -10 | -6 | -7 | -7 | -16 | -7 | -5 | -5 | -5 | -7 | -1
Greece | 21 | 21 | 19 | 20 | 17 | 16 | 24 | 26 | 22 | 12 | 12 | 12 | 16 | 9
Hungary | -2 | 0 | -5 | -4 | -1 | 0 | 3 | 0 | 1 | -3 | -3 | -3 | -6 | -1
Iceland | 3 | 4 | 2 | 1 | 4 | 4 | 1 | .. | .. | 1 | 1 | 1 | 2 | 1
Ireland | 4 | 5 | 3 | 10 | 3 | 4 | 4 | 4 | 5 | 4 | 5 | 3 | 9 | 2
Israel | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Italy | 6 | 7 | 4 | 7 | 4 | 6 | 8 | 13 | 7 | 5 | 5 | 4 | 8 | 1
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 1 | 4 | -1 | 10 | -2 | 3 | 4 | 27 | 4 | 2 | 3 | 2 | 5 | 0
Lithuania | 0 | 1 | -1 | - | 1 | - | 2 | - | 2 | 2 | 4 | 1 | 13 | 1
Luxembourg | 3 | 4 | 2 | 5 | 2 | 3 | 3 | 4 | 4 | 1 | 2 | 0 | 4 | 0
Malta | -2 | -3 | -1 | -3 | -2 | .. | .. | 10 | 6 | -2 | -1 | -3 | -1 | -1
Mexico | -2 | 0 | -7 | 1 | -5 | .. | .. | .. | .. | 0 | 0 | 0 | 0 | 0
Netherlands | 0 | -1 | 0 | 2 | 0 | 1 | 3 | 3 | 3 | 1 | 1 | 1 | 2 | 1
New Zealand | 1 | 1 | 1 | 1 | 0 | .. | .. | .. | .. | 2 | 2 | 2 | 2 | -1
Norway | 3 | 2 | 5 | 5 | 4 | 4 | 4 | 5 | 2 | 1 | 2 | 0 | 3 | 0
Poland | 1 | -2 | 1 | - | -1 | 4 | 3 | .. | .. | -7 | -6 | -8 | -8 | -3
Portugal | 0 | 2 | -1 | 1 | -1 | 1 | 6 | 18 | 3 | 1 | 2 | 0 | 2 | 0
Romania | - | - | - | - | - | - | - | - | - | -1 | -1 | 0 | 0 | 0
Slovak Republic | -3 | -1 | -5 | - | -2 | 2 | -4 | .. | 2 | -4 | -3 | -5 | -17 | 1
Slovenia | 2 | 0 | 2 | 2 | 0 | 4 | 5 | - | 5 | 1 | 1 | 1 | 4 | 2
Spain | 13 | 14 | 11 | 17 | 8 | 15 | 19 | 17 | 17 | 8 | 8 | 7 | 14 | 4
Sweden | 2 | 3 | 2 | 9 | 0 | 1 | 3 | 9 | -1 | -1 | -1 | -2 | 2 | -1
Switzerland | 0 | 1 | 0 | 0 | 2 | 1 | 0 | 0 | 1 | 1 | 1 | 0 | 2 | 1
Turkey | .. | .. | .. | .. | .. | .. | .. | - | .. | .. | .. | .. | .. | ..
United Kingdom | -2 | -3 | -2 | -4 | -1 | -1 | -2 | -1 | -1 | -1 | -1 | -1 | -1 | 0
United States | 0 | 0 | 0 | 0 | 1 | .. | .. | 0 | 0 | 0 | 0 | 0 | 0 | 0
OECD total | 0 | 0 | 0 | 2 | 0 | .. | .. | 1 | 1 | 0 | 0 | 0 | 0 | 0
EU total | 0 | 1 | 0 | 3 | 0 | 1 | 3 | 1 | 2 | 0 | 0 | -1 | 4 | 0
Source: Indicator 3.5.

Table A B.10. Over-qualification rates, 2017
Percentage of the highly educated employed population aged 15-64 not in education
| Foreign-born | Native-born
| Total | Men | Women | Recent (<10 years) | Settled (≥10 years) | Foreign- educated | Host-country educated | EU-born | Non-EU-born | Total | Men | Women
Total | Foreign- educated | Host-country educated
Australia | 32 | 33 | 32 | .. | 28 | 35 | 29 | .. | .. | .. | .. | 23 | 23 | 23
Austria | 38 | 35 | 41 | 40 | 36 | 45 | 31 | 34 | 49 | 56 | 40 | 27 | 26 | 28
Belgium | 29 | 27 | 31 | 30 | 28 | 32 | 23 | 21 | 39 | 50 | 26 | 18 | 17 | 19
Bulgaria | - | - | - | - | - | - | - | - | - | - | - | 23 | 25 | 21
Canada | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Chile | 38 | 37 | 40 | 38 | 41 | .. | .. | .. | .. | .. | .. | 41 | 42 | 39
Croatia | 16 | 18 | 15 | - | 16 | - | 16 | 15 | 16 | - | 16 | 14 | 14 | 14
Cyprus1,2 | 45 | 33 | 53 | 50 | 41 | 51 | 35 | 38 | 53 | 60 | 40 | 33 | 28 | 36
Czech Republic | 18 | 15 | 21 | 20 | 18 | 22 | 16 | 14 | 27 | 34 | 12 | 14 | 11 | 17
Denmark | 29 | 30 | 28 | 37 | 24 | 40 | 18 | 24 | 33 | 47 | 22 | 11 | 11 | 11
Estonia | 38 | 35 | 40 | 22 | 42 | 36 | 41 | 15 | 41 | 43 | 42 | 20 | 20 | 21
Finland | 30 | 30 | 30 | 42 | 24 | 28 | 14 | 13 | 42 | - | 19 | 18 | 14 | 21
France | 30 | 28 | 32 | 38 | 28 | 43 | 22 | 22 | 33 | 52 | 24 | 21 | 18 | 23
Germany | 31 | 28 | 35 | 33 | 31 | 41 | 18 | 31 | 33 | 44 | 18 | 16 | 16 | 17
Greece | 61 | 59 | 62 | 56 | 54 | 69 | 36 | 40 | 62 | 78 | 39 | 32 | 34 | 30
Hungary | 19 | 19 | 19 | 19 | 17 | 16 | 20 | 15 | 24 | - | 26 | 13 | 13 | 12
Iceland | 33 | 33 | 32 | 55 | 20 | 49 | 15 | 31 | 38 | 57 | 14 | 11 | 9 | 12
Ireland | 41 | 37 | 45 | 43 | 39 | 43 | 38 | 42 | 39 | 38 | 40 | 29 | 29 | 30
Israel | 35 | 35 | 36 | 56 | 33 | .. | .. | .. | .. | .. | .. | 18 | 20 | 17
Italy | 52 | 49 | 53 | 66 | 46 | 67 | 32 | 37 | 62 | 77 | 35 | 17 | 13 | 20
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | 74 | 79 | 65 | 77 | 59 | 77 | 47 | .. | .. | .. | .. | 60 | 64 | 53
Latvia | 26 | 26 | 25 | 13 | 23 | 23 | 21 | 20 | 22 | 24 | 21 | 19 | 21 | 17
Lithuania | 22 | 21 | 22 | - | 23 | 23 | 22 | - | 22 | 23 | 21 | 23 | 27 | 21
Luxembourg | 5 | 4 | 7 | 5 | 6 | 6 | 5 | 5 | 8 | 10 | 4 | 3 | 5 | 2
Malta | 23 | 16 | 30 | 30 | 18 | .. | .. | 15 | .. | .. | .. | 12 | 9 | 14
Mexico | 32 | 31 | 34 | .. | .. | .. | .. | .. | .. | .. | .. | 33 | 34 | 32
Netherlands | 22 | 19 | 25 | 30 | 21 | 36 | 17 | 18 | 25 | 42 | 20 | 16 | 14 | 17
New Zealand | 31 | 26 | 35 | 36 | 27 | .. | .. | .. | .. | .. | .. | 20 | 18 | 22
Norway | 35 | 37 | 33 | 48 | 22 | 44 | 20 | 34 | 35 | 45 | 24 | 10 | 12 | 9
Poland | 30 | 29 | 31 | 22 | .. | .. | .. | - | 31 | .. | .. | 20 | 20 | 19
Portugal | 25 | 24 | 26 | 46 | 17 | 47 | 13 | 18 | 21 | 58 | 11 | 12 | 10 | 13
Romania | - | - | - | - | - | - | - | - | - | - | - | 18 | 19 | 18
Slovak Republic | 18 | 17 | 20 | - | 22 | 15 | 23 | 27 | - | - | - | 21 | 19 | 22
Slovenia | 20 | 20 | 19 | 39 | 13 | 26 | 17 | 11 | 26 | - | 23 | 15 | 15 | 14
Spain | 54 | 50 | 57 | 50 | 52 | 57 | 43 | 45 | 56 | 64 | 43 | 37 | 37 | 37
Sweden | 30 | 32 | 28 | 40 | 23 | 42 | 14 | 21 | 35 | 50 | 17 | 11 | 12 | 11
Switzerland | 17 | 16 | 18 | 16 | 20 | 18 | 17 | 14 | 26 | 30 | 21 | 19 | 20 | 17
Turkey | 30 | 27 | 34 | .. | .. | .. | .. | .. | .. | .. | .. | 32 | 34 | 29
United Kingdom | 32 | 29 | 34 | 38 | 27 | 31 | 31 | 36 | 29 | 27 | 30 | 23 | 22 | 25
United States | 37 | 36 | 37 | 37 | 36 | 40 | 32 | .. | .. | .. | .. | 36 | 39 | 33
OECD total | 35 | 34 | 36 | 38 | 34 | 40 | 29 | .. | .. | .. | .. | 31 | 33 | 29
EU total | 34 | 31 | 37 | 38 | 31 | 42 | 28 | 31 | 35 | 46 | 27 | 21 | 20 | 22
Source: Indicator 3.10.

Table A B.11. Over-qualification rates, evolution between 2006-07 and 2017
Change in percentage points aged 15-64 not in education
| Foreign-born | Native-born
| Total | Men | Women | Recent (<10 years) | Settled (≥10 years) | Total | Men | Women
Australia | 3 | 4 | 3 | .. | 2 | 3 | 4 | 2
Austria | .. | .. | .. | .. | .. | .. | .. | ..
Belgium | 1 | 3 | -1 | -1 | 3 | -3 | -2 | -5
Bulgaria | - | - | - | - | - | 2 | 2 | 2
Canada | .. | .. | .. | .. | .. | .. | .. | ..
Chile | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 2 | - | - | .. | .. | 2 | 2 | 2
Cyprus1,2 | -6 | -4 | -8 | -13 | 4 | 4 | 4 | 4
Czech Republic | 4 | 2 | 5 | 2 | 10 | 8 | 6 | 11
Denmark | 3 | 3 | 4 | 5 | 8 | -1 | -2 | 0
Estonia | -2 | -7 | 2 | - | 1 | -4 | -4 | -4
Finland | .. | .. | .. | .. | .. | .. | .. | ..
France | 5 | 8 | 1 | -6 | 6 | 1 | 3 | -1
Germany | 1 | -2 | 3 | -3 | 2 | -4 | -6 | 0
Greece | 0 | -2 | 2 | -17 | 2 | 15 | 17 | 13
Hungary | 5 | 10 | -1 | 2 | 5 | 2 | 3 | 2
Iceland | 5 | 7 | 2 | .. | .. | 1 | 1 | 1
Ireland | 0 | -3 | 3 | -5 | 15 | 1 | 1 | 1
Israel | .. | .. | .. | .. | .. | .. | .. | ..
Italy | 10 | 5 | 12 | 8 | 19 | 5 | 4 | 6
Japan | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | -4 | -2 | -6 | .. | -7 | 4 | 4 | 4
Lithuania | -8 | - | - | .. | -8 | 1 | -1 | 2
Luxembourg | 1 | 1 | 1 | 1 | 1 | 1 | 3 | -1
Malta | 6 | 0 | 12 | .. | .. | 5 | 3 | 6
Mexico | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 3 | 1 | 5 | 6 | 4 | 3 | 3 | 3
New Zealand | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 8 | 11 | 6 | 10 | 3 | -1 | 0 | -2
Poland | 16 | 14 | - | - | .. | 5 | 6 | 4
Portugal | 1 | -1 | 2 | -6 | 5 | 0 | 0 | 0
Romania | - | - | - | - | - | 9 | 9 | 9
Slovak Republic | 11 | 8 | - | - | 16 | 12 | 10 | 14
Slovenia | 10 | 9 | 12 | .. | 5 | 8 | 8 | 8
Spain | -6 | -8 | -5 | -19 | 23 | 5 | 3 | 6
Sweden | 1 | -2 | 3 | 3 | -3 | 0 | 0 | 1
Switzerland | -1 | 0 | -2 | 2 | -1 | -2 | -3 | 0
Turkey | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 9 | 9 | 8 | 12 | 5 | 1 | 3 | -1
United States | -3 | -3 | -2 | -3 | -3 | 0 | 0 | 0
OECD total | -1 | -2 | -1 | -4 | 0 | 0 | 0 | 0
EU total | 1 | 0 | 1 | -7 | 7 | 1 | 1 | 2
Source: Indicator 3.10.

Annex C.

Living conditions

Access the data for tables in Annex C: http://dx.doi.org/10.1787/888933869203

Table A C.1. Relative poverty rates, 2016
Percentages, population aged 16 and above
| Foreign-born | Native-born | Children in an immigrant household | Children in a native household
| Total | EU-born | Non- EU-born | Employed | Unemployed | Out of the labour force | Total | Employed | Unemployed | Out of the labour force
Australia | 22 | .. | .. | 9 | 44 | 36 | 20 | 7 | 42 | 36 | .. | ..
Austria | 31 | 31 | 31 | 19 | 51 | 38 | 11 | 6 | 31 | 15 | 39 | 13
Belgium | 33 | 21 | 44 | 12 | 60 | 46 | 13 | 3 | 37 | 20 | 41 | 12
Bulgaria | 15 | - | 16 | - | - | - | 23 | 11 | 50 | 30 | - | 32
Canada | 27 | .. | .. | 16 | 41 | 42 | 18 | 9 | 27 | 31 | 32 | 21
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 28 | 17 | 29 | 8 | 53 | 36 | 20 | 5 | 40 | 27 | 30 | 20
Cyprus1,2 | 30 | 25 | 36 | 25 | 49 | 24 | 15 | 4 | 30 | 22 | 45 | 14
Czech Republic | 16 | 19 | 11 | 8 | - | 19 | 11 | 4 | 48 | 15 | 32 | 15
Denmark | 22 | 18 | 25 | 13 | - | 28 | 13 | 4 | 37 | 19 | 30 | 9
Estonia | 32 | .. | .. | 10 | - | 48 | 22 | 9 | 43 | 39 | 27 | 17
Finland | 24 | 19 | 27 | 6 | 37 | 42 | 14 | 3 | 38 | 20 | 34 | 8
France | 23 | 18 | 25 | 15 | 44 | 24 | 12 | 7 | 35 | 12 | 48 | 16
Germany | 22 | .. | .. | 16 | .. | 38 | 16 | 9 | 74 | 31 | 19 | 16
Greece | 42 | 26 | 45 | 27 | 65 | 42 | 19 | 13 | 41 | 17 | 51 | 24
Hungary | 16 | 15 | - | 12 | - | 20 | 15 | 10 | 44 | 15 | - | 21
Iceland | 18 | 17 | 19 | 15 | 25 | 24 | 12 | 6 | 20 | 21 | 36 | 13
Ireland | 21 | 19 | 26 | 7 | 42 | 36 | 18 | 5 | 43 | 29 | 31 | 18
Israel | 21 | .. | .. | .. | .. | .. | 23 | .. | .. | .. | 19 | 34
Italy | 38 | 35 | 40 | 30 | 55 | 41 | 19 | 9 | 41 | 21 | 49 | 24
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 31 | .. | .. | 9 | 51 | 44 | 22 | 9 | 42 | 38 | 13 | 19
Lithuania | 27 | - | 28 | 10 | - | 37 | 23 | 9 | 57 | 36 | - | 27
Luxembourg | 24 | 19 | 42 | 20 | 50 | 26 | 11 | 8 | 38 | 11 | 30 | 15
Malta | 22 | .. | 22 | 11 | 41 | 33 | 17 | 6 | 50 | 29 | 38 | 21
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 30 | 21 | 33 | 14 | 56 | 40 | 12 | 4 | 28 | 18 | 48 | 11
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 25 | 16 | 32 | 13 | 49 | 39 | 13 | 5 | 35 | 22 | 38 | 12
Poland | 15 | 14 | 16 | - | - | 17 | 19 | 12 | 41 | 23 | - | 22
Portugal | 20 | 13 | 21 | 14 | 45 | 23 | 19 | 10 | 39 | 24 | 32 | 21
Romania | - | - | - | - | .. | - | 23 | 18 | 48 | 27 | - | 35
Slovak Republic | 13 | 11 | - | - | .. | 22 | 12 | 7 | 46 | 13 | - | 22
Slovenia | 27 | .. | .. | 18 | - | 26 | 14 | 5 | 38 | 19 | 37 | 11
Spain | 43 | 41 | 44 | 31 | 63 | 49 | 20 | 10 | 46 | 20 | 64 | 24
Sweden | 31 | 21 | 36 | 14 | 55 | 46 | 15 | 5 | 37 | 25 | .. | ..
Switzerland | 20 | 19 | 23 | 13 | 31 | 33 | 14 | 6 | 30 | 25 | 28 | 14
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 22 | 16 | 25 | 12 | 69 | 37 | 17 | 8 | 51 | 27 | 31 | 21
United States | 32 | .. | .. | 23 | 45 | 47 | 24 | 14 | 40 | 39 | 56 | 37
OECD total | 29 | .. | .. | 20 | 50 | 42 | 19 | 10 | 41 | 28 | 48 | 26
EU total | 30 | 24 | 33 | 19 | 56 | 37 | 17 | 9 | 42 | 21 | 40 | 20
Source: Indicator 4.2; children: Indicator 7.15.

Table A C.2. Relative poverty rates, evolution between 2006 and 2015
Change in percentage points, population aged 16 and above
| Foreign-born | Native-born | Children in an immigrant household | Children in a native household
| Total | EU-born | Non-EU-born | Employed | Unemployed | Out of the labour force | Total | Employed | Unemployed | Out of the labour force
Australia | -4 | .. | .. | .. | .. | .. | -1 | .. | .. | .. | .. | ..
Austria | 8 | 15 | 5 | 6 | 4 | 9 | -1 | 0 | -4 | -1 | 4 | -2
Belgium | 2 | -2 | 3 | 2 | 4 | 2 | -1 | 0 | -7 | -2 | 2 | -2
Bulgaria | -6 | - | -4 | - | - | - | 0 | 4 | -2 | 2 | - | 2
Canada | 2 | .. | .. | .. | .. | .. | -1 | .. | .. | .. | -1 | -2
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Cyprus1,2 | 6 | 8 | 8 | 6 | - | -4 | -2 | -1 | 6 | -10 | 16 | 2
Czech Republic | -1 | 4 | -12 | 0 | - | 2 | 1 | 1 | 7 | 3 | -21 | -1
Denmark | -3 | -6 | 0 | 0 | - | -7 | -1 | 0 | 11 | -4 | -11 | 1
Estonia | 5 | .. | .. | 0 | - | 5 | 2 | 1 | -9 | 4 | 11 | -1
Finland | -1 | 4 | -5 | -5 | -11 | 6 | -2 | -1 | 0 | -6 | -1 | 0
France | 0 | 4 | -2 | 1 | 9 | -3 | 0 | 1 | 3 | -3 | 10 | 3
Germany | 0 | .. | .. | 5 | .. | 14 | 1 | 2 | 24 | 11 | -12 | 1
Greece | 8 | 3 | 9 | 4 | 3 | -1 | 0 | 0 | 15 | -6 | 8 | 2
Hungary | 3 | 7 | - | 4 | - | 5 | 2 | 4 | 6 | 1 | - | 1
Iceland | 3 | 1 | 7 | 3 | - | 7 | 0 | -1 | - | 0 | 17 | -2
Ireland | -4 | -3 | -5 | -4 | -15 | 1 | -2 | -2 | 6 | -6 | 0 | -3
Israel | -1 | .. | .. | .. | .. | .. | 1 | .. | .. | .. | .. | ..
Italy | 6 | 5 | 7 | 6 | 13 | 0 | -1 | 0 | 1 | -4 | 3 | -2
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 2 | .. | .. | -1 | -1 | -2 | 1 | -1 | -4 | 3 | -10 | -1
Lithuania | 7 | - | 8 | 1 | - | 5 | 2 | 0 | 12 | 1 | - | 3
Luxembourg | 3 | 1 | 7 | 3 | 2 | 2 | 3 | 3 | 4 | 2 | -5 | 4
Malta | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 1 | 1 | 3 | 2 | - | -2 | -1 | 0 | 1 | -2 | 1 | 2
Poland | 2 | 5 | 0 | - | - | 4 | 1 | 0 | -1 | 5 | - | -4
Portugal | 6 | -5 | 11 | 8 | - | 1 | -1 | -1 | 8 | -6 | - | -3
Romania | - | - | - | - | .. | - | -2 | 0 | 6 | -3 | .. | 1
Slovak Republic | 0 | -2 | - | - | - | 7 | 0 | 2 | 5 | -3 | - | 5
Slovenia | 8 | .. | .. | 7 | 13 | 3 | 1 | 0 | 2 | 1 | 13 | -2
Spain | 17 | 13 | 18 | 14 | 20 | 13 | -1 | -1 | 16 | -9 | 17 | 1
Sweden | 8 | 4 | 9 | 0 | 16 | 14 | 4 | -1 | 16 | 8 | .. | ..
Switzerland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | -5 | -15 | -1 | 0 | - | -9 | -2 | 1 | -5 | -9 | -16 | -2
United States | 0 | .. | .. | -1 | 4 | -1 | 0 | 0 | 1 | -5 | 5 | 6
OECD total | 1 | .. | .. | 0 | 8 | 4 | 0 | 0 | 3 | -2 | 1 | 3
EU total | 5 | 4 | 5 | 5 | 9 | 6 | 0 | 0 | 2 | -3 | 1 | 0
Source: Indicator 4.2; Children: Indicator 7.15.

Table A C.3. Overcrowded housing rates, 2007 and 2016
Percentages, population aged 16 and above
| 2016 | Change between 2007 and 2016 (in % points)
| Foreign-born | Native-born | Foreign-born | Native-born
| Total | Owners | Tenants | EU-born | Non- EU-born | Total | Owners | Tenants | Total | Owners | Tenants | EU-born | Non- EU-born | Total | Owners | Tenants
Australia | 8 | .. | .. | .. | .. | 4 | .. | .. | 2 | .. | .. | .. | .. | 0 | .. | ..
Austria | 29 | 5 | 38 | 17 | 37 | 6 | 2 | 14 | 0 | -4 | 0 | 5 | .. | 0 | -1 | 0
Belgium | 6 | 2 | 10 | 3 | 9 | 1 | 0 | 3 | -4 | 0 | -9 | -2 | -7 | 0 | 0 | -1
Bulgaria | 47 | 42 | - | - | 55 | 32 | 29 | 74 | 5 | 1 | - | - | 14 | -8 | -8 | -6
Canada | 2 | .. | 6 | .. | .. | 1 | .. | 2 | .. | .. | .. | .. | .. | .. | .. | ..
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 29 | 27 | 49 | 26 | 30 | 29 | 27 | 61 | .. | .. | .. | .. | .. | .. | .. | ..
Cyprus1,2 | 2 | 0 | 4 | 2 | 3 | 1 | 0 | 1 | -1 | 0 | -4 | 0 | -2 | 0 | 0 | -4
Czech Republic | 23 | 10 | 44 | 23 | 25 | 12 | 8 | 33 | -3 | -7 | 4 | -3 | -4 | -8 | -6 | -10
Denmark | 11 | 3 | 17 | 12 | 10 | 6 | 2 | 13 | -7 | -6 | -11 | -3 | -10 | 2 | 0 | 3
Estonia | 8 | 7 | - | .. | .. | 8 | 7 | 22 | -32 | -31 | - | .. | .. | -24 | -24 | -35
Finland | 11 | 3 | 17 | 9 | 12 | 6 | 2 | 16 | 4 | 2 | 8 | 3 | 5 | 0 | 0 | 1
France | 11 | 4 | 20 | 5 | 14 | 4 | 1 | 10 | -3 | 1 | -5 | -3 | -3 | -1 | 0 | -2
Germany | 13 | .. | .. | .. | .. | 6 | .. | .. | 5 | .. | .. | .. | .. | 1 | .. | ..
Greece | 37 | 31 | 41 | 21 | 40 | 16 | 15 | 17 | -1 | 8 | -9 | -7 | -1 | 1 | 2 | -3
Hungary | 29 | 32 | - | 31 | - | 30 | 28 | 57 | -6 | 6 | - | -1 | - | -7 | -7 | -16
Iceland | 16 | 7 | 30 | 16 | 16 | 4 | 3 | 12 | 6 | 4 | 0 | 7 | 5 | 1 | 1 | -4
Ireland | 4 | - | 5 | 3 | 6 | 1 | 0 | 6 | 0 | 0 | -3 | -1 | 2 | 0 | 0 | 1
Israel | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Italy | 38 | 30 | 45 | 32 | 41 | 16 | 14 | 27 | 5 | 8 | 3 | 6 | 5 | 2 | 2 | 1
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | … | .. | .. | . | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 31 | 29 | 53 | .. | .. | 37 | 33 | 59 | -15 | -16 | -9 | .. | .. | -13 | -14 | -10
Lithuania | 15 | 13 | - | - | 15 | 18 | 17 | 34 | -28 | -29 | - | - | -29 | -21 | -20 | -48
Luxembourg | 9 | 4 | 17 | 7 | 15 | 2 | 1 | 12 | -2 | 0 | -5 | -2 | -6 | 0 | 0 | 1
Malta | 2 | 1 | 4 | .. | .. | 1 | 1 | 3 | 1 | 1 | 2 | .. | .. | 0 | 0 | -2
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 8 | 2 | 13 | 4 | 9 | 2 | 1 | 5 | 4 | 0 | 7 | 3 | 4 | 1 | 0 | 3
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 11 | 3 | 22 | 9 | 14 | 3 | 1 | 20 | 1 | -2 | -3 | 4 | -2 | 0 | 0 | 1
Poland | 26 | 21 | - | 20 | 29 | 31 | 26 | 63 | 1 | 4 | - | -10 | 9 | -10 | -8 | -1
Portugal | 11 | 6 | 24 | 3 | 13 | 5 | 4 | 9 | -6 | -3 | 0 | 0 | -7 | -3 | -1 | -6
Romania | - | - | .. | - | - | 35 | 34 | 69 | - | - | .. | - | - | -8 | -8 | -13
Slovak Republic | 36 | 25 | - | 31 | - | 24 | 22 | 47 | 5 | 0 | - | 1 | - | -3 | -3 | -4
Slovenia | 18 | 9 | 36 | .. | .. | 7 | 5 | 28 | -23 | -27 | -37 | .. | .. | -20 | -18 | -45
Spain | 8 | 6 | 9 | 2 | 11 | 2 | 1 | 5 | -2 | 0 | -4 | -7 | 1 | 0 | 0 | -1
Sweden | 23 | 6 | 34 | 13 | 28 | 10 | 4 | 24 | 8 | 0 | 11 | 4 | 9 | 2 | 1 | 5
Switzerland | 9 | 0 | 12 | 7 | 12 | 2 | 0 | 4 | 0 | -3 | 1 | 1 | -1 | -1 | -1 | -1
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 14 | 3 | 23 | 17 | 12 | 3 | 1 | 6 | 7 | 2 | 9 | 11 | 5 | 1 | 0 | 2
United States | 21 | 10 | 35 | .. | .. | 8 | 4 | 17 | -1 | -1 | -4 | .. | .. | 1 | 1 | 1
OECD total | 17 | 9 | 28 | .. | .. | 8 | 6 | 15 | -1 | 0 | 0 | .. | .. | -1 | 0 | 1
EU total | 17 | 9 | 25 | 14 | 20 | 11 | 11 | 15 | 2 | 1 | 4 | 2 | 2 | -2 | -1 | 1
Source: Indicator 4.3.

Table A C.4. Self-reported health, 2007 and 2016
Percentages, adjusted by age, population aged 16 and above
| 2016 | Change between 2007 and 2016 (in % points)
| Foreign-born | EU-born | Non-EU-born | Native-born | Foreign-born | Native-born
| Good health status | Unmet medical needs | Unmet dental needs | Good health status | Unmet medical needs | Good health status | Unmet medical needs | Good health status | Unmet medical needs | Unmet dental needs | Good health status | Unmet medical needs | Unmet dental needs | Good health status | Unmet medical needs | Unmet dental needs
Australia | 83 | 5 | .. | .. | .. | .. | .. | 83 | 6 | .. | .. | .. | .. | .. | .. | ..
Austria | 65 | 1 | 2 | 83 | 1 | 59 | 0 | 71 | 1 | 1 | -3 | -2 | -2 | -2 | -1 | -2
Belgium | 68 | 5 | 8 | 72 | 4 | 65 | 7 | 74 | 2 | 5 | 3 | 4 | 5 | -1 | 2 | 2
Bulgaria | 61 | - | - | - | - | 63 | - | 66 | 9 | - | -4 | - | - | 3 | -16 | -12
Canada | 89 | 10 | .. | .. | .. | .. | .. | 89 | 11 | .. | 0 | .. | .. | -1 | 11 | ..
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 62 | 7 | 8 | 64 | 9 | 63 | 7 | 60 | 8 | 7 | .. | .. | .. | .. | .. | ..
Cyprus1,2 | 86 | 2 | 10 | 87 | 1 | 83 | 2 | 77 | 1 | 7 | 1 | -5 | -1 | 1 | -6 | -6
Czech Republic | 58 | 6 | 5 | 56 | 8 | 65 | 2 | 60 | 4 | 4 | 0 | 2 | 0 | -1 | 0 | 0
Denmark | 60 | 13 | 14 | 66 | 14 | 56 | 13 | 69 | 8 | 6 | -1 | 10 | 7 | -5 | 7 | 0
Estonia | 48 | 25 | 17 | .. | .. | .. | .. | 58 | 15 | 9 | -3 | 7 | -4 | -1 | 4 | -2
Finland | 62 | 10 | 24 | 68 | 13 | 59 | 9 | 67 | 8 | 12 | -2 | 9 | 20 | 1 | 7 | 9
France | 63 | 6 | 14 | 63 | 6 | 64 | 6 | 67 | 5 | 10 | -3 | 1 | 6 | -5 | 1 | 3
Germany | 63 | 2 | 4 | .. | .. | .. | .. | 67 | 1 | 2 | 3 | -8 | -10 | 6 | -8 | -7
Greece | 73 | 25 | 21 | 79 | 12 | 72 | 27 | 73 | 14 | 14 | 0 | 15 | 11 | -3 | 7 | 7
Hungary | 67 | 9 | - | 65 | 9 | - | - | 60 | 13 | - | 13 | -1 | - | 13 | 1 | 6
Iceland | 70 | 10 | 13 | 77 | 14 | 59 | 3 | 74 | 9 | 15 | -14 | 6 | 4 | -2 | 1 | 3
Ireland | 82 | 4 | 6 | 81 | 5 | 86 | 2 | 82 | 3 | 4 | -3 | -4 | -4 | -1 | 1 | 1
Israel | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Italy | 74 | 13 | 25 | 77 | 10 | 73 | 15 | 69 | 8 | 13 | 7 | 5 | 13 | 7 | 1 | 4
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 49 | 12 | 18 | .. | .. | .. | .. | 51 | 11 | 16 | 13 | -15 | -8 | 5 | -11 | -5
Lithuania | 44 | 6 | 16 | - | - | 43 | 7 | 45 | 5 | 8 | -4 | -4 | 3 | -5 | -5 | -1
Luxembourg | 69 | 2 | 4 | 69 | 3 | 68 | 2 | 69 | 1 | 2 | -2 | -1 | 1 | -5 | -2 | -1
Malta | 86 | 2 | 6 | .. | .. | .. | .. | 72 | 4 | 6 | 3 | 1 | 5 | -1 | 2 | 4
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 62 | 6 | 8 | 66 | 5 | 61 | 5 | 75 | 2 | 3 | -5 | 3 | -3 | 2 | 0 | -5
New Zealand | 62 | 6 | 8 | 66 | 5 | 61 | 5 | 75 | 2 | 3 | -5 | 3 | -3 | 2 | 0 | -5
Norway | 73 | 5 | 13 | 82 | 5 | 62 | 6 | 75 | 2 | 6 | 3 | 1 | 0 | 0 | 0 | -3
Poland | 65 | 9 | 2 | 66 | 9 | 65 | 8 | 59 | 12 | 7 | 6 | -4 | -9 | 1 | 0 | -5
Portugal | 52 | 5 | 17 | 61 | 6 | 51 | 4 | 47 | 4 | 18 | -8 | -8 | 13 | 2 | -8 | 13
Romania | - | - | - | - | - | - | - | 71 | 10 | - | - | - | - | 2 | -6 | -9
Slovak Republic | 57 | 4 | 0 | 54 | 5 | - | - | 67 | 6 | 5 | 4 | -3 | -7 | 13 | 2 | 0
Slovenia | 57 | 3 | 6 | .. | .. | .. | .. | 62 | 1 | 3 | 8 | 2 | 6 | 6 | 1 | 2
Spain | 71 | 1 | 21 | 71 | 2 | 71 | 1 | 72 | 2 | 11 | 2 | -1 | 15 | 5 | 0 | 5
Sweden | 67 | 13 | 13 | 69 | 9 | 67 | 15 | 73 | 10 | 6 | 2 | -4 | -4 | -3 | -4 | -3
Switzerland | 71 | 4 | 14 | 72 | 3 | 68 | 6 | 80 | 5 | 9 | -5 | 1 | 2 | -3 | 3 | 4
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 72 | 4 | 7 | 72 | 4 | 71 | 4 | 67 | 3 | 6 | -3 | 0 | 3 | -10 | 0 | 2
United States | 88 | 5 | .. | .. | .. | .. | .. | 88 | 5 | .. | -1 | -1 | .. | 0 | -2 | ..
OECD total | 79 | 6 | 12 | .. | .. | .. | .. | 76 | 5 | 8 | 1 | -1 | 2 | 2 | -1 | 1
EU total | 68 | 5 | 13 | 70 | 5 | 68 | 7 | 67 | 6 | 9 | 2 | -2 | 2 | 1 | -1 | 0
Source: Good health: Indicator 4.5; Medical needs: Indicator 4.6.

Annex D.

Civic engagement and social indicators

Access the data for tables in Annex D: http://dx.doi.org/10.1787/888933869222

Table A D.1. Acquisition of nationality, 2006-07 and 2017
Percentages of host-country nationals among settled immigrants aged 15 and above
| 2017 | 2006-07
| Total (thousands) | Total | Men | Women | EU-born | Non- EU-born | Total (thousands) | Total | Men | Women | EU-born | Non- EU-born
Australia | 3 024 | 81 | .. | .. | .. | .. | 2 473 | 82 | .. | .. | .. | ..
Austria | 454 | 48 | 47 | 52 | 51 | 49 | 440 | 59 | 54 | 63 | 68 | 52
Belgium | 615 | 61 | 59 | 63 | 40 | 79 | 401 | 57 | 54 | 60 | 40 | 77
Bulgaria | 8 | 77 | - | - | - | 71 | 10 | 72 | 77 | 69 | - | 59
Canada | 4 904 | 90 | .. | .. | .. | .. | 3 917 | 90 | .. | .. | .. | ..
Chile | 38 | 34 | 35 | 32 | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 392 | 99 | 99 | 99 | 98 | 99 | 385 | 99 | 99 | 99 | 97 | 99
Cyprus1,2 | 30 | 43 | 38 | 46 | 44 | 42 | 19 | 57 | 51 | 62 | 55 | 61
Czech Republic | 150 | 64 | 65 | 73 | 79 | 46 | 113 | 88 | 87 | 90 | 92 | 69
Denmark | 141 | 46 | 51 | 48 | 31 | 55 | 98 | 64 | 66 | 63 | 52 | 70
Estonia | 55 | 37 | 24 | 41 | 43 | 34 | 76 | 52 | 43 | 57 | 67 | 51
Finland | 89 | 62 | 64 | 72 | 67 | 68 | 53 | 71 | 71 | 72 | 79 | 63
France | 3 593 | 60 | 60 | 63 | 48 | 67 | 3 045 | 65 | 63 | 67 | 52 | 71
Germany | 5 959 | 61 | .. | .. | 53 | 53 | 5 921 | 70 | 68 | 72 | .. | ..
Greece | 222 | 41 | 36 | 40 | 44 | 36 | 114 | 36 | 31 | 41 | 47 | 32
Hungary | 90 | 83 | 82 | 87 | 89 | 75 | 85 | 82 | 81 | 82 | 83 | 75
Iceland | 6 | 75 | 76 | 74 | 68 | 85 | .. | .. | .. | .. | . | ..
Ireland | 173 | 51 | 50 | 51 | 46 | 61 | 102 | 68 | 67 | 68 | 67 | 72
Israel | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Italy | 1 520 | 35 | 34 | 40 | 40 | 36 | 943 | 62 | 53 | 69 | 80 | 52
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 57 | 36 | 28 | 30 | 47 | 28 | .. | .. | .. | .. | .. | ..
Lithuania | 123 | 92 | 92 | 94 | 96 | 93 | 122 | 95 | 93 | 96 | - | 95
Luxembourg | 31 | 22 | 22 | 24 | 20 | 38 | 14 | 15 | 14 | 16 | 13 | 27
Malta | 10 | 72 | 72 | 72 | 46 | .. | .. | .. | .. | .. | .. | ..
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 859 | 75 | 75 | 77 | 50 | 83 | 983 | 82 | 81 | 83 | 60 | 87
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 198 | 73 | 72 | 73 | 46 | 85 | 119 | 73 | 76 | 71 | 51 | 88
Poland | 129 | 84 | 80 | 87 | .. | .. | 310 | 96 | 97 | 96 | 95 | 97
Portugal | 486 | 84 | 85 | 84 | 84 | 85 | 278 | 76 | 75 | 76 | 77 | 75
Romania | - | - | - | - | - | - | 3 | 35 | 28 | 46 | - | -
Slovak Republic | 17 | 79 | 90 | 93 | 92 | 91 | 27 | 90 | 90 | 90 | 90 | 87
Slovenia | 117 | 88 | 84 | 92 | 94 | 84 | 122 | 96 | 95 | 96 | 97 | 95
Spain | 2 479 | 63 | 33 | 39 | 21 | 43 | 340 | 35 | 32 | 38 | 28 | 40
Sweden | 783 | 87 | 85 | 88 | 74 | 92 | 605 | 82 | 81 | 82 | 70 | 89
Switzerland | 603 | 44 | 40 | 51 | 43 | 50 | 434 | 40 | 31 | 48 | 41 | 38
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 3 144 | 58 | 64 | 62 | 33 | 74 | 2 154 | 68 | 69 | 67 | 46 | 78
United States | 21 701 | 62 | 60 | 65 | .. | .. | 15 874 | 62 | 59 | 64 | .. | ..
OECD total | 51 635 | 63 | .. | .. | .. | .. | 39 042 | 67 | 62 | 67 | .. | ..
EU total | 21 725 | 59 | 55 | 58 | 46 | 60 | 16 763 | 68 | 66 | 70 | 57 | 71
Source: Indicator 5.1.

Table A D.2. Voter participation rates, 2008-16
Percentages of the population aged 18 and above with the nationality of the country of residence
| Foreign-born | Native-born | Native-born aged 18-34
| Total | Men | Women | EU-born | Non- EU-born | Recent (<10 years) | Settled (≥10 years) | Total | Men | Women | with native-born parents | with foreign-born parents
Australia | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Austria | 75 | 77 | 74 | 75 | 75 | - | 75 | 83 | 85 | 81 | 71 | 53
Belgium | 88 | 89 | 88 | 83 | 91 | 76 | 90 | 94 | 94 | 94 | 95 | 94
Bulgaria | - | - | - | - | - | - | - | 76 | 75 | 76 | 61 | ..
Canada | 79 | 80 | 77 | .. | .. | 59 | 81 | 81 | 81 | 81 | 68 | 62
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 75 | 79 | 72 | - | 76 | - | 75 | 77 | 79 | 76 | 62 | -
Cyprus1,2 | 69 | - | - | 62 | - | - | 72 | 90 | 90 | 89 | 81 | -
Czech Republic | 56 | 55 | 57 | 58 | - | - | 58 | 61 | 64 | 59 | 47 | -
Denmark | 91 | 90 | 92 | 96 | 88 | - | 92 | 95 | 94 | 95 | 89 | -
Estonia | 75 | 71 | 77 | - | 76 | - | 75 | 73 | 69 | 76 | 62 | 61
Finland | 68 | 71 | 65 | 73 | 63 | - | 72 | 84 | 82 | 85 | 74 | -
France | 73 | 76 | 70 | 73 | 73 | - | 73 | 74 | 76 | 73 | 54 | 41
Germany | 73 | 73 | 73 | 81 | 67 | - | 75 | 86 | 87 | 84 | 78 | 61
Greece | 72 | - | 73 | - | 73 | - | 75 | 86 | 86 | 87 | 80 | -
Hungary | 77 | - | 76 | 78 | - | - | 77 | 75 | 76 | 74 | 67 | -
Iceland | 72 | - | - | - | - | - | 74 | 92 | 91 | 93 | 86 | -
Ireland | 60 | 54 | 65 | 65 | 54 | 28 | 68 | 80 | 80 | 80 | 59 | -
Israel | 83 | 85 | 82 | 88 | 82 | 64 | 85 | 82 | 81 | 83 | 70 | 71
Italy | - | - | - | - | - | - | - | 84 | 86 | 82 | 81 | ..
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 61 | - | 66 | - | 64 | - | 61 | 70 | 64 | 73 | 47 | -
Lithuania | 63 | 61 | 65 | - | 62 | - | 63 | 59 | 59 | 59 | 36 | -
Luxembourg | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Malta | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 71 | 69 | 73 | 77 | 70 | - | 72 | 85 | 85 | 85 | 78 | 61
New Zealand | 84 | 84 | 84 | .. | .. | 69 | 87 | 85 | 82 | 88 | 72 | 87
Norway | 77 | 78 | 75 | 80 | 74 | 67 | 81 | 90 | 90 | 89 | 79 | -
Poland | 78 | - | 77 | - | - | - | 79 | 72 | 72 | 71 | 65 | -
Portugal | 62 | 57 | 65 | 61 | 62 | - | 63 | 74 | 76 | 72 | 62 | -
Romania | - | - | - | - | - | - | - | 68 | 71 | 66 | 56 | ..
Slovak Republic | 74 | - | 74 | 72 | - | - | 74 | 76 | 75 | 77 | 64 | -
Slovenia | 70 | 73 | 67 | 77 | 65 | - | 70 | 73 | 72 | 74 | 62 | 44
Spain | 69 | 71 | 67 | 72 | 68 | 48 | 75 | 82 | 83 | 82 | 75 | 53
Sweden | 85 | 86 | 84 | 89 | 82 | 54 | 87 | 94 | 94 | 94 | 89 | 73
Switzerland | 58 | 55 | 61 | 68 | 46 | - | 59 | 71 | 74 | 68 | 56 | 32
Turkey | - | - | - | - | - | - | - | 80 | 80 | 79 | 65 | -
United Kingdom | 72 | 72 | 72 | 59 | 76 | 46 | 78 | 74 | 74 | 73 | 52 | 59
United States | 65 | 64 | 65 | .. | .. | 57 | 65 | 73 | 75 | 70 | 60 | 53
OECD average | 74 | 75 | 74 | .. | .. | 53 | 77 | 79 | 80 | 78 | 67 | 58
EU total | 74 | 74 | 73 | 76 | 73 | 51 | 76 | 79 | 80 | 78 | 67 | 57
Source: Indicator 5.2; Native-born by parents' country of birth: Indicator 7.16.

Table A D.3. Sense of belonging to the country of residence, around 2014
Percentages of the population aged 15 and above who feel close to the country of residence
| Foreign-born | Native-born
| Total | Men | Women | Total | Men | Women
| Very close | Close | Very close | Close | Very close | Close | Very close | Close | Very close | Close | Very close | Close
Australia | 53 | 34 | .. | .. | .. | .. | 74 | 20 | .. | .. | .. | ..
Austria | 23 | 60 | 18 | 65 | 26 | 56 | 56 | 37 | 55 | 36 | 56 | 37
Belgium | 30 | 54 | 28 | 54 | 31 | 54 | 32 | 55 | 31 | 53 | 32 | 57
Bulgaria | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Canada | 65 | 27 | .. | .. | .. | .. | 63 | 28 | .. | .. | .. | ..
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Cyprus1,2 | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Czech Republic | 35 | 56 | - | - | - | - | 62 | 33 | 63 | 32 | 61 | 35
Denmark | 42 | 52 | 51 | 43 | 35 | 58 | 70 | 26 | 69 | 26 | 71 | 26
Estonia | 23 | 58 | 18 | 57 | 25 | 58 | 47 | 42 | 43 | 44 | 50 | 41
Finland | 44 | 49 | 46 | 44 | 42 | 54 | 67 | 29 | 65 | 30 | 68 | 29
France | 49 | 46 | 54 | 41 | 45 | 49 | 49 | 43 | 53 | 39 | 45 | 47
Germany | 35 | 48 | 36 | 48 | 34 | 49 | 35 | 51 | 38 | 49 | 31 | 54
Greece | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Hungary | - | - | - | - | - | - | 54 | 39 | 49 | 43 | 58 | 36
Iceland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Ireland | 29 | 57 | 25 | 55 | 33 | 58 | 55 | 39 | 57 | 37 | 54 | 40
Israel | 75 | 18 | 75 | 16 | 75 | 19 | 65 | 26 | 64 | 26 | 66 | 26
Italy | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Lithuania | 20 | 61 | 15 | 55 | 25 | 66 | 36 | 52 | 34 | 52 | 37 | 52
Luxembourg | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Malta | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 26 | 61 | 26 | 62 | 26 | 60 | 40 | 52 | 41 | 50 | 40 | 55
New Zealand | 52 | 34 | .. | .. | .. | .. | 65 | 25 | .. | .. | .. | ..
Norway | 45 | 45 | 46 | 43 | 43 | 47 | 74 | 24 | 72 | 25 | 77 | 22
Poland | - | - | - | - | - | - | 57 | 39 | 58 | 37 | 55 | 40
Portugal | 49 | 43 | 51 | 40 | 46 | 46 | 47 | 43 | 51 | 40 | 44 | 46
Romania | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Slovak Republic | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Slovenia | 40 | 49 | 42 | 52 | 38 | 46 | 32 | 46 | 33 | 47 | 31 | 46
Spain | 43 | 49 | 46 | 48 | 40 | 50 | 56 | 32 | 55 | 29 | 56 | 34
Sweden | 45 | 46 | 53 | 39 | 37 | 55 | 63 | 33 | 64 | 32 | 61 | 34
Switzerland | 43 | 51 | 42 | 54 | 44 | 49 | 61 | 36 | 61 | 36 | 60 | 36
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 40 | 48 | 45 | 48 | 35 | 48 | 36 | 42 | 39 | 38 | 34 | 44
United States | 32 | 56 | .. | .. | .. | .. | 49 | 49 | .. | .. | .. | ..
EU total | 39 | 49 | 41 | 49 | 37 | 50 | 44 | 44 | 46 | 41 | 42 | 46
Source: Indicator 5.6.

Table A D.4. Immigrants who report discrimination based on ethnicity, nationality or race, 2008-16
Percentages, 15-64 population
| 2008-16 | Change between 2002-08 and 2010-16
| Total | Men | Women | EU-born | Non-EU-born | Foreigner | Host-country national | Recent (<10 years) | Settled (≥10 years) | Total | Men | Women
Australia | 16 | 16 | 16 | .. | .. | 21 | 14 | .. | .. | .. | .. | ..
Austria | 11 | 11 | 11 | 6 | 16 | 9 | 14 | 8 | 12 | -12 | -20 | -4
Belgium | 16 | 18 | 13 | 7 | 24 | 18 | 13 | 18 | 14 | 8 | 10 | 7
Bulgaria | - | - | - | - | - | - | - | - | - | - | - | -
Canada | 13 | 13 | 12 | .. | .. | 10 | 13 | .. | .. | -3 | -3 | -2
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 3 | 4 | 2 | - | 4 | - | 3 | - | 4 | 0 | - | 5
Cyprus1,2 | 13 | 14 | 12 | 10 | 20 | 18 | 5 | 19 | 5 | 2 | 8 | -3
Czech Republic | 12 | 6 | 17 | 13 | - | - | 13 | - | 14 | -2 | -2 | -3
Denmark | 15 | 17 | 13 | 7 | 21 | 12 | 17 | 17 | 14 | 0 | -1 | 1
Estonia | 16 | 17 | 14 | 13 | 16 | 20 | 9 | 8 | 16 | -2 | 0 | -3
Finland | 11 | 10 | 12 | 5 | 16 | 11 | 11 | 12 | 10 | 2 | 2 | 2
France | 17 | 19 | 15 | 6 | 21 | 19 | 15 | 20 | 16 | 1 | 3 | -1
Germany | 11 | 13 | 8 | 4 | 15 | 12 | 10 | 14 | 10 | -4 | 0 | -9
Greece | 28 | 24 | 31 | 21 | 31 | 37 | 11 | 33 | 26 | 3 | -10 | 14
Hungary | 10 | - | 9 | 11 | - | - | 8 | - | 6 | 5 | - | -
Iceland | 8 | - | - | 7 | 11 | - | 3 | - | 2 | - | - | -
Ireland | 9 | 11 | 7 | 8 | 12 | 13 | 4 | 13 | 4 | -1 | -1 | -1
Israel | 6 | 7 | 6 | .. | .. | - | 6 | 11 | 6 | 2 | 1 | 2
Italy | 14 | - | - | - | - | - | - | - | - | - | - | -
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 25 | 21 | 28 | - | 27 | 31 | 15 | - | 25 | .. | -21 | ..
Lithuania | 9 | 10 | 8 | - | 8 | - | 8 | - | 10 | 9 | .. | 8
Luxembourg | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Malta | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 19 | 19 | 19 | 7 | 23 | 15 | 20 | 17 | 20 | 0 | 1 | -1
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 9 | 8 | 9 | 4 | 12 | 7 | 11 | 8 | 10 | -1 | -3 | 1
Poland | - | - | - | - | - | - | - | - | - | - | - | -
Portugal | 16 | 14 | 17 | 3 | 19 | 24 | 10 | 23 | 10 | -3 | -7 | 1
Romania | - | - | - | - | - | - | - | - | - | .. | .. | ..
Slovak Republic | 5 | 5 | - | 4 | - | - | 6 | - | 5 | 3 | - | -
Slovenia | 4 | - | 5 | 4 | 4 | - | 4 | 4 | 4 | -1 | 0 | -2
Spain | 15 | 16 | 14 | 10 | 17 | 17 | 10 | 19 | 9 | -8 | -9 | -7
Sweden | 12 | 11 | 13 | 7 | 15 | 5 | 14 | 10 | 12 | 0 | -4 | 3
Switzerland | 9 | 10 | 8 | 6 | 13 | 11 | 6 | 10 | 8 | 1 | 2 | 1
Turkey | - | - | - | .. | .. | - | - | - | - | .. | .. | ..
United Kingdom | 14 | 15 | 13 | 11 | 15 | 14 | 14 | 13 | 15 | -1 | -4 | 1
United States | 7 | 6 | 8 | .. | .. | 10 | 4 | .. | .. | .. | .. | ..
EU total | 14 | 15 | 13 | 8 | 17 | 16 | 12 | 16 | 13 | -2 | -2 | -3
Source: Indicator 5.7.

Annex E.

Young people with a migrant background

Access the data for tables in Annex E: http://dx.doi.org/10.1787/888933869241

Table A E.1. Composition of the young population with a migrant background, 2017
Percentages of the 15-34 population
| 2017 (%) | Change since 2008 (% points) | Father's region of birth of native-born with foreign-born parents (Total =100)
| Native-born with foreign-born parents | Native-born with mixed background | Foreign-born arrived before 15 | Foreign-born arrived as adults | Native-born with foreign-born parents | Native-born with mixed background | Foreign-born arrived before 15 | Foreign-born arrived as adults | Europe | Africa | Asia | Latin America | United States, Canada, Oceania
Australia | 10 | 13 | 10 | 20 | -2 | -1 | 0 | 9 | 38 | 5 | 45 | 3 | 10
Austria | 8 | 5 | 7 | 15 | 3 | 1 | 0 | 3 | 88 | 3 | 7 | 1 | 1
Belgium | 7 | 8 | 5 | 11 | 0 | 3 | 1 | 3 | 49 | 42 | 8 | 1 | 1
Bulgaria | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | .. | .. | .. | .. | ..
Canada | 11 | 9 | 9 | 10 | 1 | 0 | 1 | 1 | 23 | 8 | 47 | 20 | 2
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 5 | 10 | 4 | 2 | .. | .. | .. | .. | - | - | - | - | -
Cyprus1,2 | 0 | 5 | 6 | 15 | 0 | 2 | 1 | -3 | - | - | - | - | -
Czech Republic | 0 | 3 | 1 | 2 | 0 | 0 | 0 | 1 | 80 | 0 | 20 | 0 | 0
Denmark | 5 | 6 | 5 | 12 | 2 | 1 | 0 | 4 | 77 | 4 | 16 | 1 | 2
Estonia | 7 | 11 | 2 | 2 | -4 | -1 | -1 | 2 | 95 | 0 | 5 | 0 | 0
Finland | 1 | 3 | 3 | 7 | .. | .. | .. | .. | 24 | 17 | 52 | 8 | 0
France | 8 | 10 | 4 | 6 | -1 | 1 | 1 | 1 | 17 | 67 | 14 | 2 | 0
Germany | 8 | 3 | 6 | 12 | 2 | 0 | -1 | 4 | 79 | 4 | 17 | 1 | -
Greece | 2 | 2 | 4 | 6 | 1 | 0 | 1 | -3 | 84 | 5 | 11 | 0 | 1
Hungary | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 95 | 0 | 5 | 0 | 0
Iceland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Ireland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Israel | 12 | 13 | 9 | 5 | -6 | -3 | 0 | -4 | .. | .. | .. | .. | ..
Italy | 1 | 4 | 4 | 10 | 1 | 2 | 1 | 3 | 27 | 41 | 23 | 6 | 2
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 5 | 12 | 2 | 0 | 3 | 5 | -1 | 0 | - | - | - | - | -
Lithuania | 1 | 4 | 1 | 0 | 0 | 0 | 0 | 0 | 95 | 0 | 5 | 0 | 0
Luxembourg | 16 | 9 | 12 | 29 | 0 | 1 | 1 | 2 | 92 | 5 | 1 | 1 | 0
Malta | 1 | 2 | 4 | 4 | .. | .. | .. | .. | - | - | - | - | -
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 8 | 8 | 5 | 5 | 2 | 1 | -1 | -1 | 76 | 10 | 5 | 10 | 0
New Zealand | 8 | 11 | 14 | 17 | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 3 | 6 | 6 | 15 | 3 | 6 | 0 | 7 | 35 | 4 | 54 | 3 | 5
Poland | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | - | - | - | -
Portugal | 1 | 7 | 5 | 4 | 0 | 5 | 0 | -2 | 2 | 83 | 7 | 7 | 1
Romania | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | - | - | - | - | -
Slovak Republic | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | - | - | - | - | -
Slovenia | 7 | 7 | 3 | 5 | 1 | 1 | 0 | 3 | - | - | - | - | -
Spain | 1 | 3 | 6 | 10 | 1 | 1 | 2 | -7 | 32 | 29 | 8 | 28 | 3
Sweden | 6 | 8 | 8 | 12 | 2 | 1 | 3 | 6 | 49 | 4 | 38 | 10 | 0
Switzerland | 12 | 13 | 9 | 18 | 3 | 2 | 0 | 2 | 83 | 4 | 12 | 1 | 0
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 5 | 5 | 4 | 14 | 0 | 1 | 1 | 1 | 9 | 24 | 61 | 5 | 1
United States | 10 | 5 | 6 | 9 | 3 | 1 | 0 | -1 | 5 | 3 | 26 | 66 | 1
OECD total | 7 | 5 | 5 | 9 | 2 | 1 | 0 | 0 | 21 | 11 | 27 | 40 | 1
EU total | 4 | 5 | 4 | 8 | 1 | 1 | 1 | 1 | 45 | 27 | 24 | 3 | 1
Source: Indicator 7.1; Region of origin: Indicator 7.2.

Table A E.2. Reading literacy, 2015
PISA score points, pupils aged 15
| Native-born with foreign-born parents | Foreign-born | Native-born with native-born parents
| Total | Men | Women | Lowest ESCS | Highest ESCS | Host-country language at home | Foreign language at home | Total | Lowest ESCS | Highest ESCS | Total | Men | Women | Lowest ESCS | Highest ESCS
Australia | 523 | 508 | 538 | 496 | 564 | 502 | 394 | 502 | 441 | 548 | 500 | 486 | 515 | 460 | 547
Austria | 448 | 440 | 456 | 434 | 494 | 501 | 454 | 426 | 392 | 478 | 500 | 487 | 513 | 454 | 546
Belgium | 456 | 453 | 459 | 433 | 495 | 520 | 469 | 453 | 408 | 517 | 517 | 509 | 526 | 469 | 565
Bulgaria | - | - | - | - | - | 446 | 353 | 357 | - | - | 438 | 417 | 461 | 378 | 499
Canada | 539 | 525 | 551 | 514 | 569 | 528 | 498 | 529 | 482 | 557 | 526 | 513 | 539 | 487 | 560
Chile | 451 | - | - | - | - | 461 | 440 | 449 | 401 | 504 | 460 | 455 | 466 | 419 | 506
Croatia | 468 | 451 | 484 | 448 | 513 | 491 | 450 | 477 | 441 | 519 | 490 | 477 | 502 | 458 | 536
Cyprus1,2 | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Czech Republic | 468 | 449 | 487 | 428 | - | 492 | 411 | 449 | 416 | 483 | 491 | 478 | 504 | 438 | 547
Denmark | 448 | 441 | 456 | 440 | 485 | 508 | 450 | 476 | 436 | 511 | 507 | 495 | 519 | 474 | 545
Estonia | 492 | 487 | 501 | 484 | 513 | 528 | 463 | 512 | - | 550 | 526 | 513 | 539 | 496 | 561
Finland | 482 | 468 | 500 | 453 | - | 531 | 493 | 454 | 393 | 502 | 531 | 509 | 554 | 497 | 570
France | 469 | 452 | 483 | 456 | 550 | 513 | 411 | 436 | 397 | 504 | 511 | 496 | 526 | 448 | 569
Germany | 477 | 462 | 493 | 461 | 496 | 530 | 439 | 441 | 415 | 490 | 529 | 521 | 537 | 483 | 573
Greece | 437 | 424 | 451 | 431 | 504 | 475 | 368 | 421 | 413 | 477 | 474 | 456 | 493 | 425 | 523
Hungary | 507 | 490 | 522 | - | 543 | 469 | 431 | 475 | - | 511 | 468 | 456 | 481 | 412 | 529
Iceland | 417 | - | - | - | - | 489 | - | 451 | 394 | 484 | 488 | 467 | 507 | 464 | 511
Ireland | 519 | 506 | 531 | 477 | 574 | 524 | - | 519 | 482 | 549 | 524 | 518 | 530 | 487 | 565
Israel | 486 | 465 | 504 | 446 | 528 | 485 | 416 | 428 | 398 | 478 | 482 | 471 | 492 | 425 | 520
Italy | 463 | 446 | 477 | 446 | 445 | 497 | 458 | 433 | 413 | 485 | 492 | 484 | 500 | 446 | 528
Japan | - | - | - | - | - | 517 | - | 467 | - | - | 517 | 511 | 524 | 478 | 555
Korea | .. | .. | .. | .. | .. | 518 | - | - | - | - | 518 | 499 | 538 | 480 | 557
Latvia | 485 | 459 | 508 | 446 | 540 | 493 | 454 | 442 | - | - | 490 | 469 | 511 | 459 | 524
Lithuania | 479 | 477 | 484 | - | 498 | 478 | 420 | 446 | - | 484 | 475 | 456 | 495 | 435 | 521
Luxembourg | 467 | 455 | 478 | 437 | 551 | - | 511 | 462 | 403 | 557 | 510 | 503 | 518 | 433 | 543
Malta | 463 | - | - | - | - | 522 | 442 | 459 | - | 496 | 448 | 429 | 468 | 401 | 503
Mexico | - | - | - | - | - | 429 | 352 | 384 | 348 | 408 | 426 | 419 | 434 | 391 | 461
Netherlands | 470 | 469 | 470 | 460 | 512 | 512 | 433 | 461 | 430 | 505 | 511 | 500 | 522 | 469 | 556
New Zealand | 508 | 491 | 524 | 470 | 570 | 511 | 417 | 515 | 443 | 558 | 509 | 496 | 521 | 466 | 555
Norway | 501 | 488 | 515 | 490 | 510 | 521 | 449 | 465 | 436 | 488 | 521 | 501 | 540 | 489 | 550
Poland | - | - | - | - | - | 507 | 445 | 493 | - | - | 507 | 493 | 521 | 469 | 552
Portugal | 512 | 508 | 516 | 489 | 572 | 497 | - | 475 | 456 | 511 | 497 | 489 | 505 | 457 | 551
Romania | - | - | - | - | - | 435 | 420 | 421 | - | - | 434 | 426 | 442 | 390 | 485
Slovak Republic | 385 | - | - | - | - | 468 | 347 | 403 | - | - | 458 | 443 | 474 | 406 | 511
Slovenia | 477 | 461 | 494 | 462 | - | 512 | 415 | 444 | 449 | - | 511 | 490 | 535 | 474 | 550
Spain | 480 | 468 | 492 | 447 | - | 502 | 491 | 460 | 428 | 509 | 500 | 490 | 510 | 463 | 540
Sweden | 478 | 461 | 494 | 463 | 512 | 514 | 497 | 448 | 417 | 507 | 513 | 493 | 533 | 472 | 552
Switzerland | 458 | 447 | 470 | 442 | 517 | 516 | 472 | 465 | 420 | 529 | 514 | 501 | 527 | 472 | 553
Turkey | 449 | - | - | - | - | 433 | 380 | 425 | - | - | 429 | 416 | 443 | 405 | 465
United Kingdom | 501 | 493 | 509 | 487 | 534 | 503 | 458 | 478 | 445 | 517 | 502 | 492 | 513 | 468 | 547
United States | 489 | 483 | 495 | 473 | 543 | 507 | 429 | 463 | 444 | 504 | 505 | 494 | 517 | 464 | 541
OECD total | 487 | 478 | 496 | 469 | 537 | 492 | 426 | 463 | 432 | 510 | 490 | 479 | 501 | 449 | 531
EU total | 476 | 463 | 488 | 458 | 518 | 503 | 456 | 455 | 420 | 506 | 501 | 490 | 513 | 456 | 548
Source: Indicator 7.5.

Table A E.3. Reading literacy, evolution between 2006 and 2015
Changes in PISA score points, pupils aged 15
| Native-born with foreign-born parents | Foreign-born | Native-born with native-born parents
| Total | Men | Women | Lowest ESCS | Highest ESCS | Host-country language at home | Foreign language at home | Total | Lowest ESCS | Highest ESCS | Total | Men | Women | Lowest ESCS | Highest ESCS
Australia | 2 | 3 | -1 | 6 | 5 | -5 | 16 | -12 | -21 | -11 | -12 | -7 | -16 | -10 | -7
Austria | 29 | 29 | 30 | 44 | - | 12 | 30 | -29 | -27 | -34 | 1 | 11 | -10 | -3 | 5
Belgium | 23 | 44 | 3 | 20 | 7 | -3 | 30 | 27 | 25 | 24 | -3 | 6 | -14 | -3 | 0
Bulgaria | - | - | - | - | - | - | - | -72 | - | - | 34 | 41 | 27 | 42 | 22
Canada | 7 | 9 | 3 | 13 | 0 | -1 | 15 | 15 | 23 | -4 | -3 | -1 | -6 | -6 | -6
Chile | - | - | - | - | - | - | .. | -15 | - | - | 17 | 19 | 14 | 26 | -4
Croatia | 4 | 9 | 3 | -4 | 29 | 4 | - | 7 | -5 | 6 | 10 | 22 | -3 | 19 | 21
Cyprus1,2 | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Czech Republic | 86 | - | - | - | - | 22 | - | 32 | 47 | -13 | 3 | 11 | -11 | -4 | 9
Denmark | 12 | 19 | 5 | 16 | - | -8 | 8 | 28 | 16 | 15 | 6 | 10 | 3 | 5 | 8
Estonia | 29 | 45 | 13 | 41 | 18 | 37 | -28 | 64 | - | - | 13 | 21 | 5 | 12 | 16
Finland | - | - | - | - | - | - | - | -50 | -83 | -55 | -19 | -15 | -20 | -25 | -9
France | 10 | 15 | 5 | 16 | 39 | 4 | 6 | -22 | -12 | -29 | 17 | 19 | 16 | 12 | 22
Germany | 50 | 58 | 41 | 58 | 6 | 5 | 55 | -2 | -11 | -29 | 17 | 30 | 3 | 33 | 18
Greece | -17 | - | -14 | - | - | -23 | - | -14 | -8 | 5 | 10 | 19 | 2 | 7 | 16
Hungary | - | - | - | - | - | - | .. | -3 | - | - | -15 | -8 | -23 | -15 | -8
Iceland | - | - | - | - | - | - | - | -22 | 3 | -34 | -1 | 2 | -5 | 0 | -5
Ireland | 17 | - | - | - | - | 15 | - | -4 | 17 | -14 | 5 | 17 | -5 | 9 | 3
Israel | 46 | 54 | 36 | 45 | 38 | 36 | 38 | -17 | 1 | -34 | 41 | 50 | 34 | 35 | 47
Italy | -2 | 35 | -43 | -2 | -51 | -20 | 1 | 12 | 29 | 12 | 19 | 30 | 7 | 13 | 22
Japan | - | - | - | - | .. | - | - | -25 | - | - | 19 | 27 | 10 | 17 | 25
Korea | .. | .. | .. | .. | .. | .. | .. | - | .. | - | -39 | -41 | -36 | -53 | -31
Latvia | 21 | 17 | 16 | 0 | 47 | 25 | 7 | -17 | - | - | 7 | 9 | 6 | 8 | 3
Lithuania | 39 | 51 | 31 | - | - | 39 | - | -50 | - | - | 2 | 8 | -3 | 9 | -1
Luxembourg | 24 | 33 | 16 | 27 | 25 | 41 | 8 | 17 | 15 | 26 | 1 | 9 | -8 | -31 | 1
Malta | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Mexico | - | - | - | - | - | - | - | 46 | 44 | 0 | 9 | 20 | 0 | 18 | -3
Netherlands | 15 | 28 | 1 | 23 | -29 | 15 | 13 | -4 | 14 | -31 | -5 | -5 | -5 | -11 | -2
New Zealand | -11 | -22 | -1 | -13 | -29 | -20 | 2 | 1 | -2 | -12 | -15 | -7 | -21 | -9 | -25
Norway | 52 | 67 | 36 | 38 | - | 40 | 51 | 22 | 33 | -12 | 30 | 31 | 27 | 36 | 26
Poland | - | - | - | .. | - | - | - | -4 | .. | - | -2 | 4 | -7 | 3 | -6
Portugal | 70 | 87 | 53 | - | 65 | 58 | - | 35 | 59 | 13 | 21 | 31 | 12 | 33 | 18
Romania | .. | .. | .. | .. | .. | .. | .. | - | - | - | 39 | 52 | 25 | 32 | 48
Slovak Republic | - | - | - | - | - | - | - | -78 | - | - | -9 | -4 | -15 | -10 | -6
Slovenia | 10 | 21 | -2 | 9 | - | 6 | 13 | -33 | 5 | - | 12 | 17 | 10 | 22 | 6
Spain | 28 | 46 | -5 | - | - | 42 | - | 42 | 46 | 46 | 35 | 43 | 28 | 35 | 39
Sweden | -8 | -11 | -6 | 3 | -13 | -6 | -17 | -11 | -4 | -37 | -2 | -2 | -3 | -7 | 9
Switzerland | -9 | -6 | -12 | -1 | -10 | -15 | -11 | 24 | 19 | 5 | -3 | -1 | -6 | -4 | -1
Turkey | 9 | - | - | - | - | - | - | -16 | - | - | -19 | -12 | -29 | -5 | -31
United Kingdom | 9 | 18 | 2 | 23 | -25 | -3 | 25 | 7 | 30 | -19 | 4 | 8 | 1 | 13 | 1
United States | 8 | 13 | 2 | 32 | -11 | -4 | 18 | 2 | 33 | -32 | 3 | 6 | 1 | 16 | -11
OECD total | 15 | 22 | 7 | 31 | -1 | 2 | 23 | 8 | 25 | -15 | 0 | 6 | -6 | 4 | -3
EU total | 23 | 31 | 14 | 31 | 3 | 5 | 27 | 6 | 8 | -10 | 12 | 19 | 5 | 14 | 14
Source: Indicator 7.5.

Table A E.4. Distribution by level of education of young adults, 2017
Percentages, 25-34 population not in education
| Native-born with foreign-born parents | Native-born with foreign-born parents, EU background | Native-born with foreign-born parents, non-EU background | Native-born with mixed background | Foreign-born arrived before 15 | Native-born with native-born parents
| Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+)
Australia | 2 | 51 | .. | .. | .. | .. | 2 | 44 | 1 | 57 | 3 | 38
Austria | 20 | 23 | - | - | 29 | 13 | 11 | 38 | 22 | 20 | 8 | 39
Belgium | 30 | 26 | 39 | 25 | 27 | 26 | 17 | 40 | 20 | 44 | 12 | 49
Bulgaria | .. | .. | .. | .. | .. | .. | - | - | - | - | 19 | 31
Canada | 6 | 67 | .. | .. | .. | .. | 7 | 61 | 7 | 67 | 11 | 50
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | - | - | .. | .. | - | - | - | - | - | - | 5 | 31
Cyprus1,2 | - | - | .. | .. | - | - | 8 | 53 | 14 | 62 | 9 | 58
Czech Republic | - | - | - | - | - | - | 14 | 24 | - | - | 5 | 30
Denmark | 30 | 37 | .. | .. | .. | .. | 20 | 45 | 32 | 35 | 16 | 44
Estonia | 7 | 34 | - | - | 7 | 33 | 12 | 35 | - | - | 13 | 40
Finland | - | - | - | - | - | - | 3 | 38 | 20 | 38 | 7 | 43
France | 20 | 36 | 23 | 47 | 20 | 37 | 11 | 47 | 26 | 29 | 11 | 45
Germany | 25 | 17 | 23 | 19 | 25 | 17 | .. | .. | 20 | 21 | 8 | 32
Greece | 38 | 17 | - | - | 46 | 19 | 2 | 51 | 33 | 14 | 14 | 43
Hungary | - | - | - | - | - | - | 2 | 53 | 11 | 28 | 13 | 31
Iceland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Ireland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Israel | 5 | 56 | .. | .. | .. | .. | 4 | 61 | 6 | 54 | 12 | 44
Italy | - | - | - | - | - | - | 16 | 28 | 39 | 11 | 24 | 24
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 8 | 48 | - | - | 8 | 50 | 15 | 41 | 10 | 25 | 15 | 38
Lithuania | 5 | 62 | - | - | - | - | 18 | 56 | - | - | 9 | 53
Luxembourg | 13 | 27 | 13 | 26 | - | - | 16 | 36 | 15 | 45 | 7 | 49
Malta | - | - | - | - | .. | .. | - | - | - | - | 46 | 25
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 22 | 32 | 20 | 40 | 22 | 31 | 15 | 48 | 31 | 30 | 13 | 47
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 29 | 46 | - | - | 32 | 32 | 14 | 58 | 30 | 35 | 20 | 45
Poland | - | - | - | - | - | - | - | - | - | - | 6 | 41
Portugal | 21 | 33 | .. | .. | 21 | 33 | 24 | 46 | 22 | 41 | 39 | 30
Romania | - | - | - | - | - | - | .. | .. | .. | .. | 25 | 25
Slovak Republic | - | - | - | - | - | - | 2 | 32 | - | - | 8 | 28
Slovenia | 7 | 26 | - | - | 8 | 20 | 8 | 38 | - | - | 5 | 43
Spain | 45 | 29 | 8 | 64 | 68 | 7 | 44 | 37 | 46 | 29 | 35 | 45
Sweden | 13 | 42 | 29 | 26 | - | - | 13 | 44 | 21 | 38 | 6 | 47
Switzerland | 9 | 40 | 15 | 47 | 27 | 41 | 5 | 54 | 10 | 30 | 3 | 52
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 16 | 59 | 15 | 49 | 16 | 60 | 15 | 50 | 19 | 49 | 18 | 41
United States | 7 | 49 | .. | .. | .. | .. | 5 | 53 | 12 | 43 | 6 | 48
OECD total | 12 | 46 | .. | .. | .. | .. | 9 | 49 | 16 | 41 | 11 | 42
EU total | 20 | 35 | 21 | 36 | 21 | 35 | 15 | 44 | 27 | 30 | 16 | 37
Source: Indicator 7.8.

Table A E.5. Distribution by level of education and gender of young adults, 2017
Percentages, 25-34 population not in education
| Men | Women
| Native-born with foreign-born parents | Foreign-born arrived before 15 | Native-born with native-born parents | Native-born with foreign-born parents | Foreign-born arrived before 15 | Native-born with native-born parents
| Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+) | Low (ISCED 0-2) | High (ISCED 5+)
Australia | 2 | 43 | 2 | 50 | 4 | 28 | 1 | 61 | 1 | 65 | 2 | 48
Austria | 19 | 21 | 20 | 19 | 8 | 35 | 21 | 24 | 26 | 21 | 7 | 44
Belgium | 31 | 18 | 25 | 42 | 15 | 42 | 29 | 34 | 15 | 45 | 8 | 57
Bulgaria | .. | .. | - | - | 18 | 23 | .. | .. | .. | .. | 19 | 39
Canada | 7 | 59 | 9 | 60 | 14 | 39 | 4 | 77 | 5 | 75 | 9 | 62
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | - | - | - | - | 5 | 26 | - | - | - | - | 4 | 37
Cyprus1,2 | .. | .. | 21 | 44 | 15 | 46 | - | - | 9 | 76 | 4 | 70
Czech Republic | - | - | - | - | 6 | 23 | - | - | - | - | 5 | 37
Denmark | 39 | 28 | 39 | 27 | 19 | 34 | 21 | 48 | 24 | 44 | 13 | 54
Estonia | 9 | 26 | - | - | 16 | 31 | 5 | 44 | - | - | 10 | 48
Finland | - | - | 29 | 32 | 9 | 34 | - | - | - | - | 6 | 54
France | 25 | 31 | 25 | 26 | 12 | 40 | 15 | 41 | 27 | 33 | 10 | 50
Germany | 27 | 16 | 21 | 22 | 9 | 32 | 22 | 19 | 17 | 21 | 8 | 33
Greece | 35 | 15 | 47 | 6 | 18 | 37 | 43 | 22 | 17 | 23 | 10 | 50
Hungary | - | - | - | - | 13 | 25 | - | - | - | - | 14 | 38
Iceland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Ireland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Israel | 7 | 48 | 8 | 48 | 15 | 36 | 3 | 64 | 4 | 61 | 9 | 52
Italy | - | - | 49 | 7 | 27 | 19 | - | - | 28 | 14 | 20 | 30
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 16 | 36 | 9 | 18 | 20 | 24 | 1 | 58 | - | - | 10 | 53
Lithuania | - | - | - | - | 11 | 45 | - | - | - | - | 7 | 62
Luxembourg | - | - | - | - | 10 | 39 | - | - | - | - | 5 | 59
Malta | - | - | - | - | 49 | 25 | - | - | - | - | 43 | 26
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 30 | 23 | 38 | 29 | 16 | 41 | 17 | 40 | 23 | 33 | 11 | 53
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 35 | 39 | 42 | 25 | 23 | 33 | 22 | 54 | 20 | 43 | 17 | 57
Poland | - | - | - | - | 8 | 32 | - | - | - | - | 5 | 50
Portugal | - | - | 26 | 31 | 48 | 21 | - | - | 18 | 50 | 30 | 38
Romania | - | - | .. | .. | 26 | 23 | .. | .. | .. | .. | 24 | 28
Slovak Republic | - | - | - | - | 8 | 22 | - | - | - | - | 7 | 35
Slovenia | - | - | - | - | 6 | 30 | 3 | 30 | - | - | 3 | 57
Spain | 17 | 51 | 55 | 22 | 42 | 37 | 72 | 8 | 37 | 35 | 28 | 53
Sweden | 15 | 35 | 28 | 28 | 8 | 39 | 9 | 53 | 14 | 49 | 5 | 57
Switzerland | 10 | 36 | 12 | 31 | 2 | 54 | 7 | 44 | 7 | 29 | 3 | 50
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 23 | 51 | 28 | 42 | 21 | 36 | 9 | 67 | 10 | 56 | 16 | 45
United States | 8 | 44 | 13 | 39 | 6 | 44 | 6 | 55 | 11 | 48 | 5 | 53
OECD total | 13 | 40 | 19 | 35 | 13 | 37 | 9 | 51 | 14 | 43 | 10 | 48
EU total | 24 | 30 | 29 | 24 | 18 | 32 | 17 | 40 | 20 | 31 | 14 | 43
Source: Indicator 7.8.

Table A E.6. Shares of early school leavers, 2014
Percentages, 15-24 population
| Native-born with foreign-born parents | Native-born with mixed background | Foreign-born arrived before 15 | Native-born with native-born parents
| Total | EU background | Non-EU background | Total | Total | EU background | Non-EU background | Total
Australia | 1 | .. | .. | 1 | 1 | .. | .. | 4
Austria | 14 | 1 | 17 | 9 | 14 | 10 | 16 | 4
Belgium | 9 | 5 | 11 | 10 | 13 | 13 | 13 | 6
Bulgaria | .. | .. | .. | - | - | .. | - | 11
Canada | 4 | .. | .. | 6 | 4 | .. | .. | 9
Chile | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | - | - | - | 1 | 2 | - | - | 2
Cyprus1,2 | - | - | - | 2 | 6 | 0 | 13 | 4
Czech Republic | - | - | - | 13 | 6 | 10 | - | 4
Denmark | 16 | .. | .. | .. | 16 | .. | .. | 12
Estonia | 5 | - | 5 | 9 | - | - | - | 11
Finland | 11 | - | 10 | 10 | 13 | 10 | 15 | 7
France | 10 | - | 10 | 4 | 5 | - | 6 | 6
Germany | 9 | .. | .. | .. | 19 | .. | .. | 6
Greece | 2 | 0 | 3 | 5 | 15 | 27 | 14 | 5
Hungary | 7 | 8 | - | 8 | 4 | 1 | - | 9
Iceland | .. | .. | .. | .. | .. | .. | .. | ..
Ireland | .. | .. | .. | .. | .. | .. | .. | ..
Israel | 3 | .. | .. | 3 | 4 | .. | .. | 6
Italy | 10 | 0 | 11 | 11 | 17 | 14 | 19 | 10
Japan | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 2 | - | - | 9 | - | - | - | 7
Lithuania | - | .. | - | 12 | - | - | - | 4
Luxembourg | 4 | 3 | - | 5 | 3 | 3 | - | 4
Malta | - | - | .. | - | - | .. | .. | 18
Mexico | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 9 | .. | .. | 6 | 9 | .. | .. | 6
New Zealand | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 6 | - | 5 | 8 | 12 | 5 | 15 | 9
Poland | .. | .. | .. | - | - | - | - | 4
Portugal | 5 | - | 6 | 12 | 12 | 7 | 16 | 13
Romania | .. | .. | .. | - | - | - | - | 15
Slovak Republic | .. | .. | .. | 9 | - | - | - | 5
Slovenia | 11 | - | 12 | 2 | - | - | - | 3
Spain | 16 | 15 | 17 | 15 | 21 | 25 | 21 | 14
Sweden | 14 | 17 | 14 | 12 | 14 | 15 | 17 | 11
Switzerland | 5 | 2 | 8 | 4 | 12 | 9 | 14 | 4
Turkey | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 8 | 2 | 9 | 7 | 4 | 12 | 1 | 11
United States | 7 | .. | .. | 7 | 10 | .. | .. | 8
OECD total | 7 | .. | .. | 7 | 11 | .. | .. | 8
EU total | 9 | 7 | 10 | 8 | 14 | 14 | 13 | 8
Source: Indicator 7.9.

Table A E.7. NEET (Not in Employment, Education or Training) rates, 2017
Percentages, 15-34 population
| Native-born with foreign-born parents | Native-born with native-born parents
| Total | Men | Women | Low educated | Highly educated | EU background | Non-EU background | 15-24 years old | Total | Men | Women | Low- educated | Highly educated | 15-24 years old
Australia | 11 | 10 | 12 | 11 | 8 | .. | .. | 8 | 14 | 12 | 16 | 19 | 7 | 11
Austria | 15 | 17 | 12 | 17 | 10 | 13 | 20 | 18 | 7 | 7 | 7 | 12 | 3 | 10
Belgium | 22 | 20 | 23 | 27 | 18 | .. | .. | 16 | 12 | 12 | 12 | 17 | 5 | 12
Bulgaria | .. | .. | .. | .. | .. | .. | .. | .. | 30 | 26 | 33 | 45 | 17 | 30
Canada | 9 | 10 | 9 | 11 | 8 | .. | .. | .. | 13 | 12 | 13 | 20 | 7 | ..
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | 17 | - | - | - | - | - | 17 | - | 22 | 21 | 23 | 17 | 18 | 19
Cyprus1,2 | - | - | - | - | - | - | - | - | 19 | 20 | 18 | 13 | 20 | 16
Czech Republic | 29 | - | - | - | - | 36 | - | - | 20 | 12 | 29 | 24 | 18 | 19
Denmark | 19 | 20 | 18 | 21 | 14 | .. | .. | .. | 13 | 12 | 14 | 18 | 8 | ..
Estonia | 19 | 10 | 31 | - | 37 | - | 18 | 18 | 12 | 8 | 17 | 11 | 12 | 12
Finland | 18 | 20 | 15 | 15 | - | - | 17 | 21 | 16 | 17 | 15 | 16 | 9 | 10
France | 25 | 23 | 27 | 30 | 21 | 20 | 26 | 21 | 14 | 12 | 16 | 19 | 10 | 14
Germany | 10 | 8 | 12 | 10 | 6 | 8 | 10 | .. | 7 | 6 | 10 | 11 | 4 | ..
Greece | 27 | 26 | 28 | 19 | - | 29 | 27 | 22 | 30 | 28 | 32 | 23 | 34 | 21
Hungary | 13 | - | - | - | - | 15 | - | 10 | 19 | 13 | 26 | 21 | 16 | 15
Iceland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Ireland | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Israel | 10 | 10 | 11 | 7 | 7 | .. | .. | 11 | 17 | 11 | 22 | 13 | 12 | 15
Italy | 20 | 22 | 19 | 12 | - | 21 | 20 | 18 | 27 | 24 | 29 | 27 | 24 | 21
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 25 | 18 | 30 | - | 36 | - | 26 | 22 | 19 | 17 | 21 | 24 | 10 | 18
Lithuania | 10 | - | - | - | - | - | - | - | 18 | 17 | 20 | 24 | 9 | 18
Luxembourg | 25 | 23 | 28 | 32 | - | 25 | - | 34 | 17 | 16 | 18 | 26 | 10 | 26
Malta | - | - | - | - | - | - | .. | - | 14 | 9 | 19 | 27 | 2 | 11
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 15 | 12 | 17 | 15 | 10 | 14 | 14 | .. | 7 | 5 | 8 | 9 | 4 | ..
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | 14 | 15 | 14 | 15 | 10 | 35 | 15 | 18 | 11 | 11 | 11 | 16 | 6 | 15
Poland | - | - | - | - | - | - | - | .. | 18 | 13 | 22 | 14 | 12 | 13
Portugal | 13 | 12 | 14 | 15 | - | - | 14 | 12 | 17 | 17 | 18 | 19 | 15 | 15
Romania | - | - | .. | .. | .. | - | - | .. | 19 | 15 | 24 | 24 | 11 | 16
Slovak Republic | - | - | - | .. | - | - | - | .. | 22 | 16 | 29 | 21 | 18 | 14
Slovenia | 23 | 25 | 21 | 35 | 19 | - | 25 | 21 | 11 | 10 | 13 | 7 | 11 | 8
Spain | 32 | 25 | 40 | 32 | 17 | 22 | 37 | 29 | 27 | 27 | 27 | 37 | 19 | 28
Sweden | 12 | 12 | 11 | 12 | 0 | 23 | 20 | 22 | 8 | 9 | 8 | 14 | 4 | 12
Switzerland | 9 | 10 | 8 | 9 | 8 | 6 | 9 | 6 | 6 | 6 | 7 | 6 | 4 | 7
Turkey | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
United Kingdom | 16 | 14 | 19 | 27 | 13 | 22 | 15 | 14 | 15 | 12 | 19 | 31 | 8 | 15
United States | 14 | 12 | 16 | 14 | 10 | .. | .. | 13 | 16 | 13 | 18 | 18 | 9 | 14
OECD total | 14 | 12 | 16 | 15 | 11 | .. | .. | 14 | 16 | 14 | 18 | 20 | 10 | 15
EU total | 17 | 15 | 19 | 18 | 15 | 16 | 17 | 19 | 17 | 15 | 20 | 22 | 12 | 17
Source: Indicator 7.10.

Table A E.8. Youth with a migrant background who report discrimination based on ethnicity, nationality or race, 2008-16
Percentages, 15-34 population
| Native-born with foreign-born parents | Native-born with mixed background | Foreign-born arrived before 15 | Foreign-born arrived after 15
| Total | EU background | Non-EU background | Total | EU background | Non-EU background | Total | EU background | Non-EU background | Total | EU background | Non-EU background
Australia | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Austria | 10 | - | 12 | 2 | - | - | - | - | - | 7 | - | -
Belgium | 18 | - | 24 | 4 | 0 | 12 | 14 | 3 | 23 | 22 | 9 | 31
Bulgaria | .. | .. | .. | - | - | - | - | .. | - | .. | .. | ..
Canada | 14 | .. | .. | 7 | .. | .. | 17 | .. | .. | .. | .. | ..
Chile | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Croatia | - | - | - | 3 | - | 3 | - | - | - | .. | .. | ..
Cyprus1,2 | - | .. | - | - | - | - | - | - | - | 22 | - | -
Czech Republic | - | - | .. | 10 | 8 | - | - | - | - | - | - | -
Denmark | 16 | - | - | 6 | - | - | 13 | - | - | 15 | - | -
Estonia | 16 | - | 17 | 11 | - | 12 | 16 | - | - | - | - | -
Finland | - | - | - | 6 | - | - | 21 | - | 28 | 13 | - | -
France | 29 | - | 30 | 10 | 4 | 13 | 25 | - | 30 | 22 | - | 25
Germany | 14 | 12 | 15 | 9 | 2 | 19 | 12 | 6 | 14 | 15 | 0 | 21
Greece | - | - | - | - | - | - | 24 | - | - | 31 | - | 37
Hungary | - | - | - | - | - | - | - | - | - | - | - | -
Iceland | - | - | - | - | - | - | - | - | - | - | - | -
Ireland | 4 | - | - | 1 | 1 | - | 5 | 4 | - | 15 | 15 | 16
Israel | 8 | .. | .. | 3 | .. | .. | 12 | .. | .. | 7 | .. | ..
Italy | - | - | - | - | - | - | - | - | - | - | - | -
Japan | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Korea | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Latvia | 21 | - | - | 8 | - | 9 | - | - | - | - | .. | -
Lithuania | - | - | - | 3 | - | 1 | - | .. | - | - | .. | -
Luxembourg | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Malta | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Mexico | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Netherlands | 38 | - | 38 | 10 | 3 | 14 | 37 | - | 40 | 18 | - | -
New Zealand | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | .. | ..
Norway | - | - | - | 5 | 4 | - | 13 | - | - | 10 | 10 | -
Poland | - | - | .. | - | - | - | - | - | - | - | - | -
Portugal | - | - | - | 3 | - | 2 | 15 | - | 17 | 27 | - | 29
Romania | .. | .. | .. | - | - | - | - | - | .. | - | - | -
Slovak Republic | - | .. | .. | - | - | - | - | - | - | - | - | -
Slovenia | 5 | - | 6 | 1 | 1 | 2 | - | - | - | - | - | -
Spain | - | - | - | 2 | - | - | 15 | - | 16 | 18 | 14 | 20
Sweden | 15 | - | 23 | 5 | 1 | 15 | 17 | - | 19 | 13 | - | 13
Switzerland | 9 | 2 | 15 | 2 | 0 | 7 | 10 | 3 | 12 | 11 | 13 | 8
Turkey | - | .. | .. | - | .. | .. | - | .. | .. | - | .. | ..
United Kingdom | 17 | - | 18 | 11 | 5 | 15 | 16 | - | 24 | 15 | 16 | 15
United States | 10 | .. | .. | 1 | .. | .. | 12 | .. | .. | 12 | .. | ..
OECD total | 19 | .. | .. | 8 | .. | .. | 16 | .. | .. | 17 | .. | ..
EU total | 20 | 13 | 22 | 8 | 3 | 13 | 16 | 7 | 20 | 17 | 12 | 20
Source: Indicator 7.17.

Glossary

Active: Active, or economically active, refers to people who are in employment or unemployed (see definitions below).

Adjusted rate: Adjusted rates show what outcomes would be for immigrants if their socio-demographic attributes were the same as those of the reference population. Adjustments are made using the Oaxaca-Blinder decomposition method and selected attributes are chosen depending on the topic covered.

Economic, Social and Cultural Status (ESCS) index: The social and economic environment of a pupil is a vague concept that is difficult to measure. The OECD Programme for International Student Assessment (PISA) assesses it through the ESCS index. The variables that it factors in are the education level and occupation of the parents, an estimate of the family’s monetary wealth, and the number and nature of the cultural assets available in the household. Students are considered socially privileged if they belong to the 25% of students with the highest ESCS index. They are considered socially underprivileged if they are among the 25% of students with the lowest ESCS index.

Employed person: The definition of an employed person is that used by the International Labour Organization (ILO). Persons in employment are those who worked at least one hour in the course of the reference week and those who had a job but were absent from work.

EU average: When it is not possible to calculate the EU total, the unweighted EU average is used. It considers each EU country as a single entity with equal weight. The “EU average” is thus the arithmetical average derived from the statistics of the countries whose data are available. When some data are missing, the number of EU countries included in calculations is shown in brackets.

EU migrant: All foreign-born born in an EU or an EFTA country.

EU national: An EU national (or EU foreigner), a notion to be understood in the context of the European Union, is a national from an EU country, not including host-country nationals.

EU national household: An EU-national household is one in which all heads have the nationality of an EU country (other than the host-country nationality), or one in which one head is of an EU nationality and the other is a third-country national.

EU total: The EU total is the summary statistic generally used. It takes differences in population size into account. It is thus the weighted average for EU countries. When some data are missing, the number of EU countries included in calculations is shown in brackets.

European Free Trade Agreement (EFTA): In 2018, the EFTA comprises Iceland, Liechtenstein, Norway and Switzerland.

European Union: In 2018, the EU comprises Austria, Belgium, Bulgaria, Croatia, Cyprus1,2, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, the Netherlands, Poland, Portugal, the Slovak Republic, Spain, Romania, Slovenia, Sweden and the United Kingdom.

Foreign-born: see Immigrant.

Head of household: Defined differently depending on the data source. The EU Statistics of Income and Living Conditions (EU-SILC) identifies one or two persons “responsible for the household”. It considers that they are the person(s) owning or renting the accommodation or the person(s) to whom the accommodation is provided if it is provided free. If more than two persons share the responsibility, only the oldest two are registered.

Israeli Labour Force Survey: The head of the household is the one who fills in the household questionnaire. His/her partner (if any) is the second head.

US Current Population Survey: The term “householder” refers to the person (or one of the persons) in whose name the housing unit is owned or rented (maintained) or, if there is no such person, any adult member, excluding roomers, boarders, or paid employees. If the house is owned or rented jointly by a married couple, the householder may be either the husband or the wife.

The concept of head of household is not used in Australia, New Zealand or Canada. Instead, the person with the highest wage and his/her partner (if any) are identified as the head of the household in this publication.

Highly educated person: Person having completed at least the first stage of tertiary education (falling into ISCED groups 5-8).

Host-country language: A language that is one of the official language(s) of the country of residence.

Household: A person who resides alone or two or more people who usually reside together and share facilities (e.g. eating and cooking spaces, bathroom, toilet, and living area).

Immigrant: Person born abroad.

Immigrant household: A household in which all heads (one or two persons) were born abroad. Unlike the 2015 Settling In edition, that concept is not used anymore in living conditions indicators (Chapter 4), which rather use the immigrant concept but only in Chapter 2.

Immigrant who arrived as adults: Immigrant who arrived at the age of 15 or older.

Immigrant who arrived as children: Immigrant who arrived before the age of 15.

Inactive person: A person without work who is not actively seeking or not available for work.

International Standard Classification of Education (ISCED): A classification developed by UNESCO to facilitate comparisons of education statistics and indicators across countries on the basis of uniform and internationally agreed definitions. See http://uis.unesco.org/en/topic/international-standard-classification-education-isced.

International Standard Classification of Occupations (ISCO): ISCO is a tool developed by the International Labour Organization for organising jobs into a clearly defined set of groups according to the tasks and duties undertaken in the job. It is intended for use in statistical applications and lends itself to international comparisons. http://www.ilo.org/public/english/bureau/stat/isco/isco88/.

Labour force: People available for work and who are either employed or unemployed.

Low-educated person: Person having no formal education or no more than a lower-secondary level of education (falling into ISCED groups 0-2).

Migrant background: A person with a migrant background is either born abroad or having at least one parent born abroad.

National household: Household in which at least one head is a host-country national. Unlike the 2015 Settling In edition, that concept is not used anymore in indicators 8.8 and 8.9, which rather use the nationality of the person.

Native-born: Person born in the country of residence.

Native-born household: A household in which at least one head is born in the current country of residence. Native-born households include mixed households, ones in which one of the head was born abroad. Unlike the 2015 Settling In edition, that concept is not used anymore in living conditions indicators (Chapter 4), which rather use the native-born concept.

Native-born with a non-EU background: Native-born with foreign-born parents whose all known parents were born in a non-EU/EFTA country.

Native-born with an EU background: Native-born with foreign-born parents whose at least one parent was born in an EU/EFTA country.

Native-born with foreign-born parents: Person born in the current country of residence to two foreign-born parents, or to one foreign-born parent and one parent which country of birth is unknown.

Native-born with mixed background: Person born in the current country of residence to one native-born and one foreign-born parent.

Native-born with native-born parents: Person born in the current country of residence to two native-born parents, or to one native-born parent and one parent whose country of birth is unknown.

Nomenclature of Territorial Units for Statistics: A classification developed and regulated by the EU to define subdivisions of countries for statistical purposes. There are three NUTS level for each EU country.

See https://ec.europa.eu/eurostat/web/nuts/background.

Non-EU migrant: All immigrants born in a non-EU or non-EFTA country.

OECD average: When it is not possible to calculate the OECD total, the unweighted OECD average is calculated instead. It takes each OECD country as a single entity with equal weight. The “OECD average” is thus the arithmetical mean derived from the statistics of the countries whose data are available. When some data are missing, the number of OECD countries included in calculations is shown in brackets.

OECD total: The OECD total is the summary statistic generally used for OECD countries. It takes differences in population size into account. It is thus the weighted average for OECD countries. When some data are missing, the number of OECD countries included in calculations is shown in brackets.

Ordinary residence: An ordinary residence or dwelling is a place of residence that is not a hostel, group home, retirement home, military barrack, encampment, hospital, or prison.

Recent immigrant: Immigrant who entered the host country within the last five years unless otherwise specified. For some indicators, however, a period of ten years is considered.

Resilient student: A student that the PISA ESCS index considers being from a socially underprivileged family but who performs in the top quartile of all students in the country where they are schooled.

Settled immigrant: Immigrant who has lived in the host country for at least 10 years.

Third country: All countries that are not members of the European Union in 2015. It includes EFTA countries.

Third-country national: A third-country national, a notion to be understood in the context of the European Union, is a national with a nationality from a third country who resides in the European Union.

Unemployed person: A person without work who has been actively seeking work for the last four weeks and would be available for work within two weeks.

Very low-educated person: Person having no formal education or who have completed at best primary education (ISCED Levels 0-1).

This work is published on the OECD iLibrary, which gathers all OECD books, periodicals and statistical databases.

Visit www.oecd-ilibrary.org for more information.

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