Migrant integration statistics - over-qualification
Data extracted in July 2021.
Planned article update: December 2022.
In the EU, foreign citizens were more likely than nationals to be over-qualified: the over-qualification rate in 2020 for nationals was 20.8 % compared with 32.3 % for citizens of other EU Member States and 41.4 % for non-EU citizens.
In 2020, women in the EU were more likely to be over-qualified than men, irrespective of their citizenship: there was a gender gap of 1.0 percentage points for nationals, 6.0 points for citizens of other EU Member States and 6.4 points for non-EU citizens.
Migrants often play an important role in the labour markets and economies of the countries in which they settle. This article presents European Union (EU) statistics on migrants — both EU citizens (other than nationals) and citizens of non-member countries (hereafter referred to as non-EU citizens) — considered as over-qualified.
Over-qualified workers are defined here as persons with a tertiary level of educational attainment (as defined by the international standard classification of education (ISCED) levels 5-8) working in low- or medium-skilled occupations (as defined by the international standard classification of occupations (ISCO) groups 4-9) for which a tertiary level of educational attainment is not required.
The over-qualification rate is the share of over-qualified persons in the total number of employed persons with a tertiary level of educational attainment; it is often referred to as a ‘vertical’ skills mismatch. Hereafter, persons with a tertiary level of educational attainment are referred to as ‘tertiary-educated’. This indicator, which is now being used in official statistics as an experimental tool to measure over-qualification, has been proposed for monitoring the integration of migrants in: a set of Council conclusions in 2010 on migrant integration; a subsequent study Indicators of immigrant integration — a pilot study from 2011; and a report titled Using EU indicators of immigrant integration from 2013. The over-qualification rate is included in a list of Zaragoza indicators, agreed by EU Member States in Zaragoza (Spain) in April 2010.
People need skills and qualifications if they are to participate successfully in the labour market; this is particularly true for migrants. Data on qualifications, measured by the highest level of educational attainment, are an important indicator of the skills offered in the labour market as they provide a wide range of information on individuals’ attributes and chances of getting a good job .
In the EU in 2020, the highest proportion of tertiary-educated people aged 20-64 years was observed among nationals (31.8 %). This proportion was 0.9 percentage points higher than for citizens of other EU Member States and 7.7 points higher than for non-EU citizens. While the ranking of these three groups varied among the Member States, in more than two thirds (of those for which data are available for one or both of the migrant populations) at least one of the migrant populations had a higher share than nationals.
As presented in Figure 1, in 7 of the 23 EU Member States for which data are available for all three types of citizenship (Cyprus, Finland, Spain, France, Greece, Germany and Italy), the pattern was similar to that for the EU as a whole: the proportion of tertiary-educated people was highest among nationals. In another set of Member States (Estonia, Poland, Latvia, Sweden, Lithuania, Denmark, the Netherlands, Malta, Belgium, Portugal, Austria, Czechia and Slovenia), citizens of other EU Member States recorded the highest shares. By contrast, in Ireland, Luxembourg and Hungary the proportion of tertiary-educated people was higher among non-EU citizens than among the two other types of citizenship.
Higher levels of education are generally associated with better labour prospects. Figure 2 presents the labour market outcomes of tertiary-educated people, while Figure 3 focuses on unemployment among tertiary-educated people.
In 2020, the employment rate in the EU of tertiary-educated people aged 20-64 years ranged from 64.5 % among non-EU citizens, through 80.3 % among citizens of other EU Member States, to 85.0 % for nationals.
This pattern — higher rates for nationals and lower rates for non-EU citizens — was repeated in the majority of the EU Member States. In all of the 22 Member States for which data are available for all three types of citizenship, the employment rate of tertiary-educated people was higher for nationals than for non-EU citizens. The largest differences (over 20 percentage points) were observed for Belgium, Greece, Sweden, Cyprus, Germany, Austria, the Netherlands, France and Italy. In addition, the comparison between nationals and citizens of other EU Member States shows that in a majority of Member States tertiary-educated nationals had higher employment rates; only in Lithuania, Malta, Portugal, Luxembourg and Hungary was the situation reversed.
In 2020, the unemployment rate in the EU for tertiary-educated migrants aged 20-64 years was consistently higher than the equivalent rate for nationals (see Figure 3). The rate ranged from 4.3 % among nationals, through 7.3 % among citizens of other EU Member States, to 14.4 % for non-EU citizens.
In all 13 EU Member States for which a complete set of data is available, the highest unemployment rate for tertiary-educated people was recorded among non-EU citizens and the lowest among nationals.
Development of over-qualification rates in the EU
There were widespread disparities in terms of over-qualification rates when analysed by citizenship and country of birth
The previous section showed that, in the majority of EU Member States, the share of tertiary-educated migrants is not lower than that of nationals, but having a tertiary education does not necessarily provide migrants with the same opportunities or returns in the labour market as it does for nationals. In most cases, tertiary-educated migrants appear to have a less positive employment situation: their formal qualifications are not — or not fully — used in the labour market. Even if employed, tertiary-educated migrants are more often to be found in low- or medium-skilled jobs than tertiary-educated nationals.
In 2020, the over-qualification rate in the EU for employed nationals aged 20-64 years was 20.8 % (see Figure 4). This was much lower than the corresponding rates recorded for employed foreign citizens in the EU, as nearly one third (32.3 %) of employed citizens of other EU Member States were over-qualified, while the over-qualification rate among employed non-EU citizens was 41.4 %.
During the period from 2010 to 2020, the over-qualification rate in the EU for nationals aged 20-64 years rose by 1.4 percentage points while among citizens of other EU Member States it rose by 1.8 points. By contrast, the over-qualification rate of non-EU citizens fell, declining overall by 6.3 points between 2010 and 2020.
Figure 4 also presents a similar set of information by country of birth (rather than by citizenship as presented so far in this article). Across the EU, native-born people aged 20-64 years had the lowest over-qualification rate (20.4 %) in 2020. The rate for people born in other EU Member States was 29.2 %, while the rate for people born outside the EU was 35.7 %. Note that the remainder of this article concentrates on over-qualification rates by citizenship.
Over-qualification rates in the EU Member States
Foreign citizens were more likely than nationals to be over-qualified
In 2020, the over-qualification rate in the EU for nationals (aged 20-64 years) stood at 20.8 %; the range across the EU Member States was from a low of 3.2 % in Luxembourg up to a high of 34.5 % in Spain. By contrast, in 2020 around one in three (32.3 %) citizens of other EU Member States in the EU were over-qualified. The rate for citizens of other EU Member States ranged from 3.5 % in Luxembourg to 47.8 % in Italy. Finally, the over-qualification rate for non-EU citizens in the EU was 41.4 %, ranging from 10.8 % once again in Luxembourg to 71.6 % in Greece; over-qualification rates for non-EU citizens were also higher than 50.0 % in three other southern EU Member States (Italy, Spain and Cyprus).
Figure 5 summarises over-qualification rates for people aged 20-64 years, analysed by citizenship. In 2020, the highest over-qualification rates were recorded among non-EU citizens and the lowest among nationals. Of the 16 EU Member States for which a complete set of data is available, this pattern was observed in all but three:
- in Germany and Ireland, the over-qualification rate for citizens of other EU Member States was higher than the over-qualification rate for non-EU citizens;
- in Czechia, the over-qualification rate for nationals was higher than the over-qualification rate for citizens of other EU Member States.
As noted above, the over-qualification rate in the EU for citizens of other EU Member States rose between 2010 and 2020. This development was observed in 5 of the 12 Member States for which a complete set of data is available, with over-qualification rates for citizens of other EU Member States rising at a particularly fast pace in Austria, Italy and Germany.
By contrast, over-qualification rates for non-EU citizens decreased across the EU as a whole during the period from 2010 to 2020 and this development was observed in 12 of the 17 EU Member States for which a complete set of data is available. The largest decreases were registered in Spain, Sweden, Italy, Greece, Finland and most notably in Portugal (see Figure 6).
In 2020, among the EU Member States the largest differences between over-qualification rates for non-EU citizens and nationals were recorded in Italy (48.5 percentage points) and Greece (41.2 percentage points) — see Figure 7. By contrast, the gap between these two rates was less than 10 percentage points in Luxembourg, Ireland and Lithuania (which was also the only Member State where the over-qualification rate for non-EU citizens was lower than the rate for nationals).
There was generally a smaller difference between over-qualification rates for citizens of other EU Member States and those for nationals. In 2020, the largest gap among the 16 Member States for which data are available was recorded in Italy (29.8 percentage points), followed by Cyprus (17.2 points). There was only one Member State where the over-qualification rate for citizens of other Member States was lower than the rate for nationals, namely Czechia.
Over-qualification rates by sex
Women were more likely to be over-qualified than men, irrespective of their citizenship
A comparison of over-qualification rates between the sexes is shown in Figure 8. It reveals that there was a gender gap in the EU for all three types of citizenship. In 2020, the over-qualification rate among nationals was 1.0 percentage points higher for women than for men; the gap between the sexes was wider for citizens of other EU Member States and for non-EU citizens (6.0 points and 6.4 points respectively).
Among the individual EU Member States it was also common to find that the proportion of nationals considered as over-qualified was higher for women than for men: this pattern was most apparent in Malta, Finland, Portugal and Italy (with the largest gender gap, 6.9 percentage points). In Slovenia and Spain, the rates were almost identical. By contrast, there were 10 Member States where over-qualification rates for nationals were higher among men than women. This situation was observed in Croatia, Estonia, Austria, Sweden, Romania, Greece, Poland, Lithuania and Bulgaria, as well as in Latvia, which had the largest gender gap (6.5 percentage points).
Among the 15 EU Member States for which a complete set of data are available for citizens of other EU Member States, five recorded a higher male (rather than female) over-qualification rate; in Greece the gap reached 21.1 percentage points. In three Member States the rate for women was at least 10.0 percentage points higher than that for men: with a gap of 10.8 percentage points in France, 20.6 points in Cyprus and 21.9 points in Italy.
For non-EU citizens, there were three cases (among the 20 EU Member States for which data are available) where over-qualification rates for non-EU citizens were higher among men than women: France, Finland and Austria. However, it was again more common to find that the share of non-EU citizens considered as over-qualified was higher among women than men; the gap exceeded 10.0 percentage points in Poland, Ireland, the Netherlands, Malta, Czechia and Slovenia, as well as in Cyprus, which had the biggest gap (20.5 percentage points).
The alternative presentation provided in Figure 9 confirms that the highest over-qualification rates in the EU in 2020 for males and for females were recorded for non-EU citizens. Equally, the lowest rates were recorded for nationals. Among the 15 individual EU Member States for which a complete set of data is available, there were however some exceptions:
- in Ireland, male over-qualification rates were higher for citizens of other EU Member States and for nationals than they were for non-EU citizens, while female over-qualification rates were higher for citizens of other EU Member States than they were for non-EU citizens;
- in Germany, over-qualification rates were higher for citizens (both for men and for women) of other EU Member States than they were for non-EU citizens;
- in Belgium, male over-qualification rates were higher for nationals than they were for citizens of other Member States;
- in Czechia, female over-qualification rates were higher for nationals than they were for citizens of other Member States.
Over-qualification rates by age
Over-qualification rates for nationals were generally higher among younger rather than older people
The over-qualification rate in the EU for younger (aged 20-34 years) citizens of other EU Member States was 30.8 % in 2020, which was 2.2 percentage points lower than the corresponding rate for older (aged 35-64 years) citizens of other EU Member States. This situation (a higher rate for older people) was only observed in Ireland, Spain, Germany, Sweden, the Netherlands and Austria. Greece, Malta and Cyprus recorded notably higher rates for younger citizens of other EU Member States than for older citizens.
Equally, the over-qualification rate in the EU for non-EU citizens in 2020 was higher among older people (45.3 %) than it was for younger people (35.9 %), a difference of 9.4 percentage points. This pattern was repeated in 11 out of 20 EU Member States for which data are available, with the biggest differences observed in Estonia and Italy. By contrast, in nine Member States the proportion of younger over-qualified non-EU citizens was higher; this was most apparent in Slovenia and Malta.
In 2020, over-qualification rates in the EU for nationals were higher for people aged 20-34 years (24.0 %) than they were for people aged 35-64 years (19.4 %). Among the EU Member States, it was common to find that younger people were more likely to be over-qualified than older people. This pattern was observed in the vast majority of the Member States, with the only exceptions being Estonia (where the reverse situation was observed) and Finland, Germany and Belgium (where the shares were almost the same).
Source data for tables and graphs
The data presented in this article are derived from the EU’s labour force survey (LFS). The LFS is a large quarterly sample survey that covers the resident population aged 15 years and above in private households. It is carried out in the EU Member States, EFTA (except Liechtenstein) and candidate countries. The survey is designed to provide population estimates for a set of main labour market characteristics, covering areas such as employment, unemployment, economic inactivity and hours of work, as well as providing analyses for a range of socio-demographic characteristics, such as sex, age, educational attainment, occupation, household characteristics and region of residence.
The data presented in this article generally refer to people aged 20-64 years.
This article focuses on comparisons between national and migrant populations. Migrant indicators can be calculated for two broad groups: the foreign population determined by country of citizenship and the foreign population determined by country of birth. Although providing some main indicators for the latter, this article focuses on providing information on migrant integration analysed by country of citizenship. The results for the migrant population are usually disaggregated into migrants who are citizens of other EU Member States and migrants who are citizens of non-member countries, in other words countries outside the EU.
Over-qualified workers are defined as employed persons who have a tertiary level of educational attainment and who are working in occupations for which a tertiary level of educational attainment is not required.
Data on educational attainment, in other words the highest level of education successfully completed, are classified according to the UNESCO international standard classification of education (ISCED) — 2011 version. Tertiary education covers ISCED levels 5-8:
- level 5: short-cycle tertiary education;
- level 6: bachelor’s or equivalent level;
- level 7: master’s or equivalent level;
- level 8: doctoral or equivalent level.
Data on occupations are classified according to the International Labour Organization’s international standard classification of occupations (ISCO) — 2008 version. ISCO major groups 4-9 cover the following occupations:
- major group 4: clerical support workers;
- major group 5: service and sales workers;
- major group 6: skilled agricultural, forestry and fishery workers;
- major group 7: craft and related trades workers;
- major group 8: plant and machine operators, and assemblers;
- major group 9: elementary occupations.
A set of Council, European Parliament and European Commission Regulations define how the LFS is carried out, while some EU Member States have their own national legislation for the implementation of the survey. The key advantage of using LFS data is that they come from a survey which is highly harmonised and optimised for comparability. However, there are some limitations when considering the coverage of the LFS for migrant populations, as the survey is designed to target the whole resident population and not specific populations, such as migrants.
The following issues should be noted when analysing migrant integration statistics.
- Recently arrived migrants — this group of migrants is missing from the sampling frame in every host EU Member State, which results in under-coverage of the actual migrant population for LFS statistics.
- Non-response — one disadvantage of the LFS is the high percentage of non-response that is recorded among migrant populations. This may reflect: language difficulties; misunderstanding concerning the purpose of the survey; difficulties in communicating with the survey interviewer; or fear concerning the negative impact that participation in the survey could have (for example, damaging a migrants chances of receiving the necessary authorisation to remain in the host EU Member State);
- Sample size — given the LFS is a sample survey, it is possible that some of the results presented for labour market characteristics of migrants are unrepresentative, especially in those EU Member States with small migrant populations (note that for cases where data are considered to be of particularly low reliability, the data are not published).
In Germany, since the first quarter of 2020 the LFS has been integrated as a subsample into the newly designed micro census. Technical issues and the COVID-19 crisis have had a large impact on the data collection processes for the German LFS, resulting in low response rates and a biased sample; changes in the survey methodology also led to a break in the data series. The published German data are preliminary and may be revised in the future. For more information, see here.
In the last two to three decades, socioeconomic changes such as increasing global competition, the introduction and widespread use of information and communication technology, and the ageing of the EU’s population have led to a situation whereby it may be difficult to find the right people for particular jobs within some labour markets. Migratory flows may provide one potential solution to some of these issues: for example, migrants may resolve labour market shortages in specific areas.
The continued development of EU migration policy remains a key priority in order to meet the challenges and harness the opportunities that migration represents globally. As emphasised in the New Pact on Migration and Asylum, a successful integration and inclusion policy is an essential part of a well-managed and effective migration and asylum policy. The Action Plan on Integration and Inclusion 2021-2027, being a part of this new pact, tackles migrant integration challenges.
EU legislation provides a common legal framework regarding the conditions of entry and stay and a common set of rights for certain categories of migrants. More information on the policies and legislation in force in this area can be found in an introductory article on migrant integration statistics.
Well-functioning labour markets depend largely on matching the skills and qualifications of the labour force to those sought by employers. Although some mismatches are inevitable (especially in rapidly developing sectors of the economy), high and persistent mismatches may be costly for employers, workers and society at large. As such, the modernisation of curricula and teaching methods within educational systems can provide students with skills closer to the needs of the labour market, for example by developing specific knowledge and skills.
Education and skills mismatches are considered especially relevant for vulnerable groups, such as older workers, young people moving from education into work, or migrants. One particular issue that impacts on non-EU citizens is (lower) recognition for their professional qualifications: EU citizens may benefit from initiatives such as the recognition of professional qualifications. Easier and more accessible recognition procedures, as well as opportunities for adult migrants to upgrade or equalise their qualifications, including access to lifelong learning, may help enhance the employability of migrants.
Some additional policy background may be found under the heading External links.
Direct access to
- Migrant integration statistics — online publication
- Migrant integration statistics introduced
- Migrant integration statistics — education
- Migrant integration statistics — employment conditions
- Migrant integration statistics — labour market indicators
- Migrant integration statistics — regional labour market indicators
- Migration and migrant population statistics
Employment and unemployment
- LFS series - detailed annual survey results (lfsa)
- Employment rates - LFS series (lfsa_emprt)
- Employment rates by sex, age, educational attainment level and citizenship (lfsa_ergaedn)
- Employment rates - LFS series (lfsa_emprt)
- Employment (mii_emp)
- Activity rates (mii_act)
- Unemployment (mii_une)
- Employment and self-employment (mii_em)
- LFS series — detailed annual survey results (ESMS metadata file — lfsa_esms)