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24.6.2019 | Von:
Parvati Raghuram
Gunjan Sondhi

Skilled Female Migrants in the EU

The share of women among tertiary level educated migrants in the EU is higher than that of men. However, many of these highly skilled women do not work in jobs that match their qualifications. A look at trends of skilled female migration in Europe.
Eine Wissenschaftlerin betrachtet eine ProbeFemale Scientist examines a sample. The share of women among tertiary level educated migrants in the EU is higher than that of men. (© picture-alliance, imageBROKER)

Between 2012 and 2017, the number of highly skilled persons immigrating to the 28 EU states grew by a third – from 204,403 to 321,597. [1] Tertiary-level educated women – referred to here as highly skilled migrant women – enter the host country through a variety of channels, as workers, family migrants, students and refugees. Since 2009, a number of changes have influenced the direction and nature of female skilled migration. This article explores what we know about skilled migrant women and their flows both into and within Europe in the last decade, a period which has seen the effects of recession and austerity in many countries, as well as new movements, particularly of refugees, which included skilled women.

Data Sources

Skills are variously defined in existing data: as qualification, according to sector of the labour market in which migrants are employed, wages and based on the route of entry, i.e. the migration category. Furthermore, "migrant" is also a variable category with some countries collecting ethnicity data but not on migration status, while others focus on country of qualification rather than that of birth or nationality.

Moreover, data is not always collected and published in a comparable manner. A decade earlier Kofman and Raghuram (2009) pointed to the dearth of gender disaggregated skilled migration data in most data collection systems. In the intervening years, there have been some attempts to collate such data on migrant stocks across different sources. In particular the Global Bilateral Migration Database and the Database on Immigrants in OECD countries have the potential to make significant contributions to our understanding of skilled female migration, but these have not yet been fully exploited. [2] These databases report the stock of migrants by education from each sending country to each country of destination. Highly skilled workers are defined as those with at least one year of tertiary level education. However, these databases are based on individual country statistics which are variable in quality, completeness, gender disaggregation and base year for which data is reported.

A second data source provides insights on migrant flows. Compiled from migrant entry data, this data set is more reliable, country specific, and current. Another source is the labour force surveys that are more or less routinely conducted within different countries. They may have bias because these are surveys of a sample and only of those who are in employment. These too may be collated such as in the European Labour Force Survey to provide some information on migration patterns.

While this data is useful, they provide an insufficient picture of gendered highly skilled migration as data is rarely collected, published or analysed by gender. Second, the data that is available, such as the OECD data, are limited in scope in what it can tell about this population. Because of their breadth and coverage, they do not offer the demographic details which can help us to understand this group. Despite these reservations it is possible to identify some broad patterns in female skilled migration in the EU.

Migrant Stock in the European Union

Within the 28 EU member states, 52 per cent of the foreign-born, first-generation, migrant population who are tertiary level educated, are women. A breakdown by countries shows that across the EU member states, nearly two-thirds of the countries have as many or more foreign-born women who are highly skilled than men. However, these qualifications often do not translate into skilled work, either due to poor participation rate or deskilling.

Figure 1: Gender composition of stock of first generation migrants with tertiary level educationFigure 1: Gender composition of stock of first generation migrants with tertiary level education in the EU-28, 2014 (bpb) Lizenz: cc by-nc-nd/3.0/de/

Even though there are more migrant women than migrant men in the stock population who are highly educated, this female bias is not reflected in the labour force participation rate, i.e. activity and employment rates. [3] The activity rates for migrant women are lower than those for men across all EU member states. This gap between activity rates is particularly high amongst migrants born outside the EU – 83 per cent for men and 63 per cent for women. However, this data is not disaggregated by education or any indicator that would identify the skill level of the migrant. The employment rate data give us a little more insight into highly educated women migrants. These figures show the gender gap in the employment rate by education level, as well as country of birth. While overall the employment rate is higher for tertiary level educated migrant women (66 per cent) as compared to migrant women with lower levels of education (40 per cent for primary educated, and 58 per cent for secondary educated), in comparison to migrant men’s employment rate, there is a large gap. More migrant men (82 per cent) with tertiary level education are employed than women (66 per cent).

This could be due to labour market problems or due to family pressures. First, across Europe, migrant women continue to dominate the socially reproductive sectors of the labour market: teaching, nursing, social care and domestic work. [4] Migrant women with skills in the former two often find jobs in the latter two, which are less regulated and where their skills are used but not valued or remunerated. Some of this work may be done informally. Thus, even if in work, they may not appear in formal employment statistics or if they do, they appear as less skilled. Moreover, their skills may end up being discounted because, for instance, in Germany, nursing and midwifery are not considered as skilled professions due to their local apprenticeship structures. This leads to devaluing of skills of migrant women who are tertiary level educated trained and experienced nurses. Secondly, migrant women are often removed from family support, especially with childcare, which enables them to enter the labour market so that the unequal division of socially reproductive labour within the family affects their labour market participation. The nature of deskilling however varies with ethnicity, language ability, level and type of qualification and the masculinity of the sector itself. Thus, even in vibrant economies such as Germany, women may find it difficult to be accepted into some sectors with labour market shortages because of a combination of these factors. [5]

Migrant Flows

Highly skilled migrants from outside the EU are usually required to obtain a visa/permit to enter an EU member state. These first permits – granted for a minimum of three months – show the annual flow of migrants entering the EU countries for work, education, family and other reasons. [6]

There was a steady increase from 2010 to 2015 followed by a sharp rise in 2016 as entries increased across all the above four categories of migration.

Figure 2: First permits issued to immigrants in EU-28, 2010-2016Figure 2: First permits issued to immigrants in EU-28, 2010-2016 Lizenz: cc by-nc-nd/3.0/de/ (bpb)

Women constitute approximately 46 per cent of migrant flows. Figure 3 provides a breakdown of the reasons for migration by gender.

Figure 3: First permits issued by EU-28 member states by reason for migrationFigure 3: First permits issued by EU-28 member states by reason for migration Lizenz: cc by-nc-nd/3.0/de/ (bpb)

Labour Migration

Between 2010 and 2016, there has been a decline in the number of first permits granted to women migrants for work. Figure 3 suggests that this might be due to the masculinisation of labour migration flows into the EU. However, this data is not skills sensitive. One approach to try and understand this is to draw on gender differentiated skilled labour market sector data from professional bodies such as nursing and engineering associations to identify the issues facing migrant women in EU member countries. Another is to look at more general schemes such as the EU Blue Card scheme and we do both below.

Sectoral: Female Dominated Labour Market Sectors

The Nursing and Midwifery Council (NMC) data on the UK highlights the changing nature of the relations between the UK, the EU and other overseas countries. In 2016, 85 per cent of all registered (male and female) nurses and midwives working in the UK were first registered as nurses or midwives (post-qualification) in the UK, ten per cent overseas, and five per cent in other EU member states. [7] Importantly, in some sectors such as health, data is collected and analysed according to country of qualification which is often used as proxy for country of birth or migration status.

Figure 4: Total initial registrations of nurses in the UK by sex, 2005-2016Figure 4: Total initial registrations of nurses in the UK by sex, 2005-2016 Lizenz: cc by-nc-nd/3.0/de/ (bpb)

Women from the Philippines, India and Australia – all English-speaking countries due to their US and UK colonial histories – make up the three largest sources of overseas trained nurses in the UK, as figure 5 shows.

Figure 5: Female nurses registered in UK, with initial registrations overseas, 2005-2016Figure 5: Female nurses registered in UK, with initial registrations overseas, 2005-2016 Lizenz: cc by-nc-nd/3.0/de/ (bpb)

The five per cent of the nurses who were initially registered in other EU states, make up the smallest group. The breakdown by the top five EU countries is shown in figure 6. Italian trained nurses are a relatively new addition to the register. However, Spain and Romania have been consistent sources of trained nurses currently registered in the UK.

Figure 6: Female nurses registered in UK, with initial registration within EU countries, 2005-2016Figure 6: Female nurses registered in UK, with initial registration within EU countries, 2005-2016 Lizenz: cc by-nc-nd/3.0/de/ (bpb)

Significantly, the introduction of the Code of Ethics, i.e. cap on recruitment of skilled migrants (brain drain migration) from overseas countries with small nursing workforce, the opening of the UK labour market to EEA applicants (A8 countries in 2004 and A2 countries in 2012), and the effects of recession, particularly in Southern European countries like Italy and Spain, have led to sharp increases in EU qualified migrants with a concomitant significant decline in the numbers of overseas qualified nurses (Figure 5). Thus from 2011 onwards there was a significant jump in nurses from Spain, Romania and Italy; only to then take a sudden drop since 2015. However, in July 2016 the NMC introduced language checks for EEA nurses which led to a rapid increase followed by a sharp drop in numbers. The reasons for this sharp increase are not clear but may be due to the registration of those who met the qualification criteria. The subsequent drop in EU qualified nurses has been further exacerbated by the Brexit vote which has led to a 96 per cent drop in EU applicants in less than a year after the Brexit vote. [8] In light of Brexit, it will be important to monitor how these patterns of EU origin countries shift, and how that impacts the mobility of highly skilled women from EU countries.

The overseas nurse market has acted as a reserve army of labour, declining and growing inversely to the EEA intakes. Thus, after low levels of registration for overseas trained nurses between 2008-2013, there appears to be growth, particularly sharply since 2015.

General Programs for Skilled Migration: EU Blue Card

Beyond the UK, EU policies have aimed to encourage intra-European mobility and attract highly skilled migration from non-EU countries. One such policy is the EU Blue Card, which, although adopted by 25 of the 28 countries, has primarily been deployed by Germany (see Table below). In 2016, nearly 50 per cent of the EU Blue Card holders in Germany came from just five countries: India (22.1 per cent), China (8.7 per cent), the Russian Federation (7.9 per cent), Ukraine (5.3 per cent) and Syria (4.7 per cent). [9]

EU Blue Cards granted

Total EU3.66412.96413.86917.10420.979

Source: Eurostat: EU Blue Cards by type of decision, occupation and citizenship (migr_resbc1), (accessed: 12-12-2018).

Concerns have been raised about the potential gender selectivity of the policy with salary thresholds, years of professional experience and an inflection towards male dominated sectors of the labour market influencing how many women receive these cards. [10] Systematic gender pay gaps between women and men in the same sector but also the preponderance of women in lower paying sectors, caring responsibilities and hence, career breaks and the gender selectivity of the sectors all shape the outcomes of such directives. [11] The scheme is currently being renegotiated to enable recruitment of highly skilled migrants in greater numbers.

Other Migration Streams

Women make up over two-thirds of the family migration stream with children and then men making up the rest of the flows. Skilled women often enter through the family route as "trailing spouses". They may move into and out of the labour market but deskilling amongst this group is noticeable globally. Women may be disadvantaged because of ethnicity, gender, language acquisition and due to their socially reproductive responsibilities. [12]

One of the streams with a visibly higher proportion of skilled women (55 per cent) than men is student migration. As figure 3 above shows, it is a rapidly growing flow within and across Europe. Many of these students will work while they study or may move in to work after study as part of a two-step migration system, potentially forming a part of current or future skilled migration stocks.

Finally, across Europe, the last decade has seen significant flows of asylum seekers and refugees. Germany, during 2015 and 2016 admitted more than one million people seeking protection. [13] Women made up a third of this population. [14] Although many of the women were not skilled and have struggled to integrate into the labour market more than men because of language barriers, unequal responsibility for childcare reducing their access to reskilling programmes and due to previous skill levels in ‘desirable’ parts of the labour market, others have upskilled and tried to find jobs. The impact of these flows on overall skilled migrant stocks is yet to be analysed.

Changing Migration Patterns

As suggested above, one of the key factors influencing skilled migration flows is the changing boundaries of Europe itself. The expansion of the EU has led to an increase in intra-European flows of skilled migrants, [15]particularly from Eastern Europe to Western Europe leading to fears of brain drain. However, these flows are gender-selective. For instance, although men outnumber women among overall Polish migration to Germany, this is reversed amongst highly skilled migrants. Moreover, highly skilled women are also less likely to want to return to Poland. [16] Although Poland represents a particularly extreme case, similar flows of highly skilled women can be seen across the EU.

Another key factor influencing migration in the last decade has been the global financial crisis and its differential impact across Europe. Countries have readjusted migration polices, shifting from an open-door policy welcoming skilled migrants, to closing off parts of the labour market to certain skilled professions by imposing work permit quotas, higher income threshold to acquire work permits, and stricter return programmes. [17] Mandatory return programmes have resulted in highly skilled women migrants being the first to be fired and therefore the first to forcefully return. [18] Gender segregation across sectors within the labour market puts women in more precarious positions during times of recession which means they bear a disproportionate share of job loss compared to men. [19] Moreover, the austerity programmes have led to cuts in the socially reproductive sectors which are often government funded, leading to a greater impact on skilled women.


The last decade has seen a number of economic and political changes. There have been continuities as well as new patterns to skilled female migration in Europe as a result. Linguistic alignment due to colonial histories continues to shape migration and female migration is still predominantly in the socially reproductive sectors of the labour market. However, political changes affected female skilled migration. First, the boundaries of the EU have altered as it has expanded and now threatens to shrink, following Brexit. The flows of some overseas migrants have also varied according to whether intra-European flows were able to fill the labour market needs or not. Secondly, political turmoil outside European boundaries has influenced flows to the EU. There has been a sharp growth in the number of female and male refugees some of whom are upskilling, thus leading to a potential increase in skilled migrant stock. Thirdly, economic challenges such as the recession have shaped female labour markets. Migrant women are more likely to lose their jobs than migrant men because women are more likely to be employed in the socially reproductive sectors, which are often government-funded and have seen job losses due to the effects of austerity. Through these political and economic changes, gender continues to be an important divider in how skilled migration is experienced. In the coming decade, as anti-immigration sentiment rises across some parts of Europe, and the contours of the EU are being redrawn due to Brexit, the shape of skilled female migration too is likely to change.


Arslan, Cansin, Jean-Christophe Dumont, Zovanga Kone, Yasser Moullan, Caglar Ozden, Christopher Parsons, and Theodora Xenogiani. 2015. ‘A New Profile of Migrants in the Aftermath of the Recent Economic Crisis’, January. https://doi.org/10.1787/5jxt2t3nnjr5-en (accessed: 12-12-2018).

BAMF. 2016. ‘Figures on the EU Blue Card’. BAMF - Bundesamt Für Migration Und Flüchtlinge - Figures on the EU Blue Card. 2016. http://www.bamf.de/ (accessed: 12-12-2018).

Beaumont, Karolina, Matthias Kullas, Matthias Dauner, Izabela Styczyyska, and Paul Lirette. 2017. ‘Female Brain Drain in Poland and Germany: New Perspectives for Research’. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.2996506 (accessed: 12-12-2018).

Boucher, A. 2018. Female High-Skilled Migration: The Role of Policies. In Mathias Czaika (Ed.) High-skilled migration: Drivers and Policies. Oxford: Oxford University Press, pp. 65-86.

Cerna, Lucie, and Czaika, Mathias. 2016. ‘European Policies to Attract Talent: The Crisis and Highly Skilled Migration Policy Changes’. In Anna Triandafyllidou, and Irina Isaakyan (eds). High-Skill Migration and Recession: Gendered Perspectives (part of the Migration, Diasporas and Citizenship book series), London: Palgrave Macmillan, pp. 44–68.

Chaloff, Jonathan, Jean-Christophe Dumont, and Thomas Liebig. 2012. ‘The Impact of the Economic Crisis on Migration and Labour Market Outcomes’. CESifo. https://www.cesifo-group.de/ (accessed: 12-12-2018).

Cuban, Sondra. 2016. ‘The Problem of Skill Waste among Highly Skilled Migrant Women in the UK Care Sector’. In Anna Triandafyllidou, and Irina Isaakyan (eds). High-Skill Migration and Recession: Gendered Perspectives (part of the Migration, Diasporas and Citizenship book series), London: Palgrave Macmillan, pp. 241-64.

Department of Health. 2017. ‘Evidence on EEA Nationals Working within the Health and Care Systems, Focusing on England’. https://assets.publishing.service.gov.uk/ (accessed: 12-12-2018).

Eurostat. 2018. ‘Migrant Integration Statistics – Labour Market Indicators’. Eurostat. https://ec.europa.eu/ (accessed: 12-12-2018).

Fihel, Agnieszka, and Agata Górny. 2013. ‘To Settle or to Leave Again? Patterns of Return Migration to Poland During the Transition Period’. Central and Eastern European Migration Review 2 (1), pp. 55–76.

González Enrique, Carmen, and Anna Triandafyllidou. 2016. ‘Female High-Skilled Migration from Southern Europe and Ireland after the Crisis’. InAnna Triandafyllidou, and Irina Isaakyan (eds). High-Skill Migration and Recession: Gendered Perspectives (part of the Migration, Diasporas and Citizenship book series), London: Palgrave Macmillan, pp. 44–68.

Grigoleit-Richter, Grit. 2017. ‘Highly Skilled and Highly Mobile? Examining Gendered and Ethnicised Labour Market Conditions for Migrant Women in STEM-Professions in Germany’. Journal of Ethnic and Migration Studies 43 (16), pp. 2738–55. https://doi.org/10.1080/1369183X.2017.1314597 (accessed: 12-12-2018).

Gropas, Ruby, and Laura Bartolini. 2016. ‘Southern European Highly Skilled Female Migrants in Male-Dominated Sectors in Times of Crisis: A Look into the IT and Engineering Sectors’. In Anna Triandafyllidou, and Irina Isaakyan (eds). High-Skill Migration and Recession: Gendered Perspectives (part of the Migration, Diasporas and Citizenship book series), London: Palgrave Macmillan, pp. 160-92.

Isaakyan, Irina, and Anna Triandafyllidou. 2016. ‘Female High-Skill Migration in the 21st Century: The Challenge of the Recession’. In Anna Triandafyllidou, and Irina Isaakyan (eds). High-Skill Migration and Recession: Gendered Perspectives (part of the Migration, Diasporas and Citizenship book series), London: Palgrave Macmillan, pp. 3-22.

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Triandafyllidou, Anna, and Irina Isaakyan (eds). 2016. High Skill Migration and Recession: Gendered Perspectives (part of the Migration, Diasporas and Citizenship book series), London: Palgrave Macmillan.

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Calculations based on first permits issued for education, and EU Blue card permits issued.
Ghosh and Chanda (2015), Kerr et al. (2016).
Eurostat (2018).
Kofman and Raghuram (2015).
Grigoleit-Richter (2017).
Other reasons include: international protection, residence without the right to work (for example, pensioners), or people in the intermediate stages of a regularisation process.
NMC (2017).
Siddique (2017).
BAMF (2016).
BAMF (2016).
Enriquez and Triandafyllidou (2016), Gropas and Bartolini (2016), Boucher (2018).
Grigoleit-Richter (2017).
UNHCR (2018).
Weise (2017).
Beaumont et al (2017).
Fihel and Górny (2013).
Fihel and Górny (2013).
Issakayan and Triandafyllidou (2015), Arslan et al (2015).
Rubery and Rafferty (2013).
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