Ethnic Wage Penalty and Human Capital Transferability: A Comparative Study of Recent Migrants in 11 European Countries

Abstract

This article examines the ethnic wage penalty among migrants in 11 Western European countries. It aims to extend the literature on the models of migrant occupational inclusion in European labor markets by studying the wage gap and to disentangle whether the gross wage penalty experienced by foreign-born residents can be explained by human capital-related factors and/or by migrants’ occupational segregation. Estimating probit models with sample selection on European Labour Force Survey data (2009–2016), we find that both male and female migrants experienced a larger gross wage penalty in Southern Europe, where they had lower education levels and faced stronger occupational segregation. In the other countries under study, we find a smaller gross wage penalty among foreign-born women. Results show that migrants from Eastern Europe were not systematically less penalized than migrants from Africa, Asia, and Latin America, except for men in Italy and Greece. Wage penalties were higher among tertiary-educated migrants, compared to their less-educated counterparts, only in Mediterranean countries, where the former were mainly concentrated at the bottom of the occupational structure. Finally, the acquisition of the highest education after migration reduced migrants’ wage penalty, thanks to a better match between educational credentials and job allocation, especially in Southern Europe. Focusing on the ethnic wage penalty and on both human capital- and occupation-related factors of ethnic penalization highlights cross-country differences not yet explored by existing comparative research, allowing a new and more comprehensive picture of migrants’ penalization in Europe.

Keywords

ethnic penalty labor market Europe

1. Introduction

A growing literature on migrant integration in Western European labor markets has highlighted the systematic and substantial disadvantage of non-Western migrants, especially those from Africa and the Near Middle East (Koopmans 2016; Lancee 2016).¹ Empirical research on migrants’ labor market penalty has primarily focused on the average gap in occupational outcomes between foreign-born and native-born workers, controlling for differences in human capital-related characteristics between the two groups (Farkas et al. 1997; Heath and Cheung 2007; Reyneri and Fullin 2011a). Migration studies have, thus, defined the “ethnic penalty” as any remaining residual difference after individual characteristics, such as education, skills, cognitive ability, age, and marital status, have been controlled for (Heath and Cheung 2007). The ethnic penalty is, in this way, an “umbrella concept,” which includes the effects of all social mechanisms that cannot be captured by the confounders added in regression models (Koopmans 2016; Panichella, Avola, and Piccitto 2021).

The most recent literature on migrant occupational integration in Europe has developed in two directions. First, comparative studies show remarkable cross-country differences in the ethnic penalty's characteristics and magnitude, as well as in more general models of migrant inclusion across European labor markets (Kogan 2006; Panichella 2018a; Reyneri and Fullin 2011a). Second, another strand of literature has explored the ethnic penalty's “black box” by analyzing the role of specific mechanisms of penalization, considering, for instance, migrants’ human capital transferability (e.g., the place where the highest educational title has been obtained) (Lancee and Bol 2017; Tibajev and Hellgren 2019).

This article contributes to both strands of literature by studying the migrant–native wage gap—the ethnic wage penalty—among men and women in 11 European countries, with three aims. The first is to extend the literature on the models of migrant inclusion in European labor markets by considering the ethnic wage penalty. Although several studies have analyzed cross-country differences in the ethnic penalty and focused on migrants’ employment opportunities and/or occupational positions (e.g., Heath and Cheung 2007; Panichella 2018a; Reyneri and Fullin 2011a, 2011b), to the best of our knowledge, no comparative studies have systematically analyzed how the wage gap between migrants and natives changes across different Western European countries. The second aim is to disentangle whether the ethnic wage penalty can be explained by human capital-related factors, such as education and its transferability, and/or by migrants’ occupational segregation or whether the penalty holds even when migrants and natives have the same education and are employed in similar job positions. We, thus, disentangle the weight of human capital-related mechanisms from that of mechanisms related to the different inclusion of migrants and natives in receiving countries’ occupational structures. Finally, this article studies how the ethnic penalty changes by education, incorporating the transferability of human capital and educational degrees into our analysis. When migrants and natives share the same education and the same job position, do the former suffer a wage penalty? Furthermore, how does the wage penalty vary among individuals with different education? Do migrants’ wage penalties change according to the place where their educational credentials were obtained?

This article thus analyzes whether the main characteristics of European models of inclusion apply even when the ethnic wage penalty is considered and investigates the role of human capital transferability therein. We argue that focusing on the ethnic wage penalty, which can be controlled not only for human capital-related factors but also for migrants’ overrepresentation in the lowest strata of the occupational structure, allows a new and more comprehensive picture of migrants’ penalization in Europe. The focus on both human capital- and occupation-related factors of penalization can highlight potential cross-country differences not explored yet in comparative studies in Europe, which focus mainly on occupations as a primary indicator of migrants’ socioeconomic integration (e.g., Panichella 2018a; Reyneri and Fullin 2011a, 2011b).

This article is organized as follows. Section 2 discusses the main micro-level mechanisms underlying the ethnic wage penalty, focusing on both human capital- and occupation-related factors. Section 3 highlights the existence of different models of occupational inclusion of recent migrants in Western European labor markets. Section 4 formulates our research hypotheses and empirical strategy, followed by a presentation of the data and methods used for the analyses in Section 5. Section 6 presents the results of our empirical analyses, and the article concludes with a discussion of our findings and their implications for the wider study of international migration.

2. Ethnic Wage Penalty: Human Capital and Occupational Segregation

Research on migrants’ occupational integration has largely shown that foreign-born workers in Western Europe experience labor market penalties, compared to the native-born population (Heath and Cheung 2006, 2007). Besides disparities between migrants and native-born residents in unemployment rates and occupational status (Panichella 2018a; Reyneri and Fullin 2011a), studies focusing on single European countries have shown that migrants also have substantially lower wages than natives (for the United Kingdom, see Longhi and Platt 2008; Longhi et al. 2009; Platt 2006; for Spain, see Rodríguez-Planas 2012; for Italy, see Piazzalunga 2015). The wage penalty experienced by migrants can be explained by different mechanisms related to their individual characteristics (e.g., education, skills, and occupational career), to discrimination practices, and to other meso (i.e., ethnic networks) and macro features of the host countries (Hasmath 2016). Our article focuses on two sets of factors: those associated with migrants’ country-specific human capital (Dustmann 1994; Friedberg 2000; Long 1980) and those related to their inclusion in the occupational structure's lowest strata (Brynin and Güveli 2012; Clark and Drinkwater 2007).

Concerning the first set of factors, foreign-born male and female workers, particularly those from developing nations, have less education than natives in most European countries (Kahn 2004; Schoeni 1998), although they are, on average, more educated than non-migrants in their origin country (Ichou 2014). Migrants often lack country-specific human capital—most importantly, language proficiency (Dustmann and Fabbri 2003)—that is rewarded in the receiving country's labor market, both in Europe and elsewhere (Borjas 1994; Chiswick 1978). That is, the skills acquired in the origin-country educational system may not fit the labor demand in the destination country, making migrants less productive and efficient at work and, consequently, less well paid. Even the educational certificates obtained by migrants in their origin countries are difficult to transfer to Western European societies, especially for those coming from non-Western nations outside the European Union (EU) (Kanas and van Tubergen 2009; Lancee and Bol 2017). Conversely, migrants from Eastern EU countries (i.e., Romanians, Poles, etc.) should have benefited from the convergence of European educational systems driven by the EU integration process (Fellini, Guetto, and Reyneri 2018). Educational titles obtained abroad are also less valued by employers in Western Europe, since risk-averse employers often avoid hiring potential employees with educational credentials that they do not fully understand (Chiswick and Miller 2009). For these reasons, returns to education are systematically lower among migrants than among natives in European societies (Cebolla-Boado, Miyar-Busto, and Muñoz-Comet 2019; Panichella, Avola, and Piccitto 2021). Such lower returns to foreign education can be mitigated by formal recognition of educational credentials achieved abroad, a practice that can reduce employment and wage penalties experienced by migrants (Tibajev and Hellgren 2019). Such recognition, however, also often entails several long and complex bureaucratic procedures. Similarly, migrants’ returns to education can be improved, and the ethnic penalty reduced, when migrants acquire educational degrees in the host country (Fellini, Guetto, and Reyneri 2018), as such degrees are better known and, thus, rewarded by European employers.

Occupational segregation is the second source of the ethnic wage penalty for migrants (Brynin and Güveli 2012; Clark and Drinkwater 2007). Migrants and native-born residents are unequally distributed in European societies’ occupational structure, with foreign-born non-Western workers concentrated in the so-called “3D” jobs (dirty, dangerous, and demanding) in the secondary labor market (Reyneri and Fullin 2011a). For instance, in most European countries, migrant men from Asia, Africa, and Latin America are mainly employed in unstable jobs in labor-intensive sectors such as agriculture, distribution, manufacturing, and construction (Heath and Cheung 2006; Piore 1979). Occupational segregation is even stronger among non-Western women (Ballarino and Panichella 2018), who are often concentrated in unskilled employment positions in the domestic and personal care sector, especially in Southern Europe (Ambrosini 2001; Sciortino 2004). Migration studies have, in fact, shown that female migrants frequently suffer a “double occupational penalty,” which reflects the combined negative impact of birthplace and sex (Boyd 1984; Donato, Piya, and Jacobs 2014).

More recent research on migrants’ occupational integration has shown that foreign-born workers in Europe tend to remain entrapped in low-skilled jobs even long after migration, with low chances of accessing the primary segment of higher-skill, better-rewarded jobs (Fellini and Guetto 2019; Panichella, Avola, and Piccitto 2021). This entrapment in the occupational hierarchy's lowest positions might also depend, at least in part, on the greater difficulties that migrants encounter in finding jobs that match their skills, since migrants often lack information on existing job and training opportunities in the destination country (Chiswick 1978). However, migrants’ embeddedness in social networks primarily involving strong ties with members of their own origin country may also affect their occupational integration (Koopmans 2016), helping them reduce the job search time after migrating but risking stronger occupational segregation (Portes 1995; Waldinger 2005).

Since the wage penalty experienced by migrants depends on both of the above-mentioned sets of factors (i.e., those related to their human capital and those related to their segregation in the occupational structure's lowest strata), our work aims to disentangle the weight of these two main sources of penalization, using different definitions of the ethnic wage penalty. We start from the gross ethnic wage penalty, which is the wage difference between migrants and natives not controlled for human-capital characteristics or occupation and job-related characteristics. This wage penalty is, thus, affected by both the different education levels between migrants and natives and their different distribution in the occupational structure. The second definition, the ethnic wage penalty net of education, is the average wage difference between migrants and natives once education is controlled for. This definition, which is commonly labeled “ethnic penalty” in migration studies (Heath and Cheung 2007), measures the ethnic wage penalty between migrants and natives with the same educational level. Finally, the third definition, the ethnic wage penalty net of occupation, is the migrant–native wage gap controlled for both individual characteristics, including education and position in the occupational structure. This third definition measures the ethnic wage penalty within occupations and jobs (i.e., the average difference between migrants and natives with the same education level and the same occupation and type of job) and is, thus, an indicator of those mechanisms of penalization that are not captured by education or by occupational segregation, such as discrimination on the part of employers and/or institutions (Heath and Di Stasio 2019; Mandel and Semyonov 2016).

3. The Ethnic Penalty in Western European Labor Markets

Migrants’ socioeconomic integration also depends on the receiving country's institutional context, particularly the host labor market's structure and regulation (Kogan 2006; Lewin-Epstein et al. 2003; Portes and Rumbaut 1996). Comparative research in Europe shows that ethnic minorities have systematically worse labor market outcomes than natives, although the penalties occur with different magnitudes and characteristics across host countries and across different groups of ethnic minorities (Guetto 2018; Panichella 2018a; Reyneri and Fullin 2011a, 2011b).

In Mediterranean countries such as Italy, Spain, Greece, and Portugal, migrants have similar employment and unemployment rates as natives but are strongly concentrated in unskilled, non-standard, and poorly rewarded job positions in the secondary labor market (Kogan 2007; Reyneri and Fullin 2011a). This trade-off between employment and job quality mainly depends on the large underground economy and extensive and growing demand for “3D” jobs in such countries—a demand which is barely satisfied by the native-born workforce (Ambrosini 2018; Reyneri 1998). Hence, more than elsewhere, in Southern Europe, foreign-born workers tend to fill jobs in the occupational hierarchy's lowest strata (Bernardi, Garrido, and Miyar 2011; Panichella, Avola, and Piccitto 2021), where chances of social mobility are scarce (Fellini and Guetto 2019). Entrapment in worse jobs and occupations in Southern Europe is even stronger for migrant women, who, independent of their education and qualification, are mostly concentrated in the domestic and personal care sector (Ambrosini 2016).

Human capital-related factors, such as education, should be relevant, too, in these countries, especially for Asian and African migrants, who often have lower education levels compared to native-born residents (Fullin and Reyneri 2011). However, migrants in Southern Europe are also penalized when they have a similar (or even higher) education level than natives (Guetto 2018; Reyneri and Fullin 2011a). The southern European model of migrants’ occupational inclusion generates a leveling-down process, which reduces migrants’ returns to education and more generally limits the mechanisms that enable individuals with good skills and qualification levels to achieve good occupational returns (Panichella, Avola, and Piccitto 2021).

In the “Continental” model of migrants’ inclusion, conversely, migrants experience a sort of double penalty (Panichella 2018a). That is, in Continental European countries, such as Germany, Austria, the Netherlands, and France, not only are foreign-born workers allocated to the secondary labor market, but also, and differently from the pattern seen in the Mediterranean countries, both female and male migrants face higher unemployment risks than comparable natives (Reyneri and Fullin 2011a). However, due to the higher demand for skilled non-manual jobs in Continental European labor markets, once migrants become employed (i.e., they pass the selection process), they face a lower penalization in terms of job quality than do migrants in Southern Europe (Panichella 2018a). Thus, the ethnic wage penalty should be comparatively lower in Continental Europe, where migrants are, on average, more educated than in Southern Europe (Cebolla-Boado, Miyar-Busto, and Muñoz-Comet 2019) and where the credential-based labor market (Esping-Andersen, Rohwer, and Sørensen 1994) might ease the integration of highly qualified migrants, especially when their educational credentials are recognized in the host countries. It is worth mentioning that the Continental model also includes Scandinavian countries, where patterns of migrant labor market incorporation are quite similar to those of other Continental European countries (Ballarino and Panichella 2015; Guetto 2018).

The United Kingdom and Ireland identify a third model of migrants’ occupational inclusion in Europe. First, the flexible regulation of the labor market and weak industrial relations in these two countries facilitate migrants’ employment opportunities (Kogan 2006). Moreover, both countries’ selective immigration policy favoring the entry of highly educated workers and lower demand for low-skilled jobs should boost stronger returns to education among migrants, compared to what is seen in Mediterranean countries (Guetto 2018; Panichella 2018a). In general, the “free” nature of the labor market and the targeted immigration policy that characterize the United Kingdom and Ireland should reduce migrants’ risks of being allocated to low-paid occupations, compared to Continental and, especially, Southern European countries.

The research identifying these cross-national differences across Western European countries has primarily studied migrants’ economic integration using measures of their occupational position, such as class attainment (Panichella, Avola, and Piccitto 2021), job quality/stability (Panichella 2018a; Reyneri and Fullin 2011a), and the International Socio-Economic Index of Occupational Status (ISEI; Avola and Piccitto 2020; Fellini and Guetto 2019). To the best of our knowledge, no wide comparative studies have focused on wages, which can be considered an alternative indicator of migrant economic integration and are strongly correlated with occupational position and type of job. The focus on wages, thus, allows us to extend this comparative literature by asking whether, and to what extent, migrants face penalties in their wages across European countries and whether such wage penalties still hold between migrants and natives with the same education level (ethnic wage penalty net of education) and in the same jobs and occupations (ethnic wage penalty net of occupation).

4. Empirical Strategy and Hypotheses

This article analyzes the wage penalty of male and female migrants, compared to natives, in 11 Western European countries chosen according to their place in the three models of migrant labor market inclusion outlined in the previous section: Greece, Italy, Portugal, and Spain; Austria, Belgium, France, Germany, and the Netherlands; and the United Kingdom and Ireland.² Our empirical strategy consists of two steps. The first measures the ethnic wage penalty in each selected country, distinguishing between those factors of penalization related to human-capital differences between migrants and natives and those related to the uneven distribution of the two groups in the occupational hierarchy (see section 2). In other words, we ask to what extent the ethnic wage penalty depends on the fact that migrants have different human capital than natives and/or on their overrepresentation in the “3D” jobs and if an ethnic wage penalty still holds when migrants and natives have similar jobs and individual characteristics.

We expect migrants to be penalized with respect to natives in all countries, but with notable cross-country differences. More precisely, we expect the gross ethnic wage penalty to be lower in the United Kingdom and Ireland and in Continental Europe than in Mediterranean countries (H1). Moreover, the migrant–native gap should decrease when education is controlled for (ethnic wage penalty net of education), as well as when job characteristics such as occupation and job type are included in the models (ethnic wage penalty net of occupation).³ However, we expect these compositional factors to be more important in accounting for the ethnic wage penalty in Southern Europe, where migrants are less educated than natives and more likely to be overrepresented in bad jobs (H2). We also expect that the gross ethnic wage penalty is higher among women than among men, especially in Southern Europe (H3), due to women's entrapment in service-sector jobs which offer lower chances of occupational and income mobility. Finally, we hypothesize that in all countries, migrants from Africa, the Near Middle East, Asia, and Latin America are more penalized than migrants coming from the new EU member states in Eastern Europe (H4).

The second part of the empirical strategy studies how the ethnic wage penalty varies among individuals with different education levels and provides evidence of differences in returns to education between migrants and natives. Moreover, we further investigate country-specific human capital and the transferability of educational degrees across borders, distinguishing between educational titles acquired in the origin and destination countries. We hypothesize that the ethnic penalty will be higher among highly educated migrants, especially in Mediterranean countries, where these migrants are pushed more than elsewhere into the worst positions in the occupational hierarchy (H5). Finally, we hypothesize that in all countries, the ethnic penalty will be stronger among migrants who did not acquire their highest educational title, especially university degrees, in the destination country (H6).⁴

5. Data, Variables, and Methods

5.1. Data and Analytical Sample

We used data from the European Labour Force Survey (EU-LFS) for the years 2009 to 2016.⁵ The EU-LFS is the EU's primary data source on the labor market at the household level and provides standardized cross-sectional information on employment status, hours worked, previous work experience, and income, as well as on education and other sociodemographic characteristics for all household members. We focus on men and women aged 25 to 54 years living in 11 Western European countries, excluding the self-employed (N = 5,406,755).⁶ We restrict our analyses to natives and “recent” migrants, thus eliminating those who moved to the destination country more than 10 years before (N = 5,088,839). Focusing on recent migrants allowed us to consider a more homogenous group of migrants by excluding those who migrated within the recruitment programs organized by most of the Central-Northern European countries until the 1970s (Panichella 2018b; Reyneri and Fullin 2011a). Finally, we considered only those who migrated after age 15. After list wise deletion of missing cases, the analytical sample included 5,068,593 individuals (2,255,895 men and 2,812,698 women), 3,340,541 (65.9%) of whom were employed (as employees).

5.2. Ethnic Wage Penalty

We focus on the wage gap between migrants and natives. Among employees, the EU-LFS collects information on the monthly net income decile of the main dependent job, after deduction of income tax and national social security contributions. We present results focusing on the probability of being above the fifth decile (the median) of the wage distribution. Compared to alternative thresholds (e.g., 30^th, 60^th, 70^th, and 80^th percentile, whose results are available on request), this choice allowed us to have both migrants and natives well distributed in both categories of the dependent dummy variable (i.e., below and above the median). For instance, among men, 32.8% of natives had a wage below the median and 67.2% above the median, whereas, among migrants from Africa and the Near Middle East, Asia, Latin America, and new EU member states in Eastern Europe, the same figures were 68.8% and 31.2%, respectively (see also Tables A1 and A2 in the Online Appendix).

5.3. Ethnic Origin

The main independent variable in our analysis is the geographical origin, distinguishing migrants from the native population based on birth country, except for Germany, where we used nationality because information on the birth country was not available.⁷ Migrants were further divided into seven categories: (1) Africa and the Near Middle East; (2) Asia; (3) Latin America; (4) Eastern Europe (i.e., member states who joined the EU in 2004, 2007, and 2013); (5) EU15 (EU member states prior to the accession of 10 candidate countries on 1 May 2004)⁸; (6) North America and Oceania; and (7) other European countries. For the sake of parsimony, the results for the fifth and sixth groups (EU15 and North America and Oceania) are not reported, since these migrants are highly selected and their employment conditions and wages are generally similar to, or more favorable than, those of the native population (results available on request). In addition, in some destination countries, the sample sizes of migrants from North America and Oceania are so small that model estimates would be highly unreliable. We do not report results for the seventh group (other European countries) either, given its heterogeneity in terms of both origin-country characteristics, as it includes migrants from all European countries that have never been a part of the EU, such as Switzerland, Norway, Balkan countries, and Ukraine, and occupational integration in the destination country. We realize that a single category for immigrants from Asia may also hide substantial heterogeneity. However, data constraints and sample size issues forced us to rely on broad geographical origins.

5.4. Controls

All models control for region of residence dummies,⁹ age group (six 5-year dummies), and year of the survey dummies. We additionally control for the highest educational attainment, coded in three categories: lower secondary or less (ISCED 0–2); upper secondary or post-secondary non-tertiary (ISCED 3–4); and tertiary (ISCED 5–6). Additionally, models control for respondents’ occupation, entered in dummy variables measuring each of the 1-digit of the International Standard Classification of Occupation (ISCO) codes provided by the International Labor Organization (ILO), as well as for other job characteristics (i.e., the number of hours usually worked in the main job, the time since the person started work with the current employer (in months), and duration of the work contract (fixed-term or permanent)).¹⁰ Finally, we controlled for marital status (single, married, widowed, separated, or divorced) and the number of dependent children in the household (no children, one child, two children, three or more). Descriptive statistics of the analytical sample are provided in Tables A1 and A2 in the Online Appendix.

5.5. Methods and Models’ Specification

We apply probit models with sample selection (see Van de Ven and Van Praag 1981), which allows us to measure the ethnic wage penalty controlling for the different selectivity—“sample selection bias” (Heckman 1979)—of migrants and natives into dependent employment (i.e., paid employment jobs; see Bozzon and Murgia 2022 for details). Since wages are estimated on a selected sample (in our case, employees), probit models with sample selection provide consistent, asymptotically efficient estimates for all parameters. Therefore, there are two dependent variables in this model: the binary outcome $y_{j}$ (i.e., wage above the median) and a binary variable indicating if the individual is observed in the sample (i.e., being an employee). Both are modeled jointly through the simultaneous estimation of a probit model for the former (the probit—or wage—equation) and a probit model for the latter (the selection equation).

Mathematically, the probit model with sample selection assumes the existence of the following underlying relationship, the so-called latent equation:

y_{j}^{*} = x_{j} β + u_{1 j}

such that we observe only the binary outcome, identified with the following probit equation:

y_{j}^{probit} = (y_{j}^{*} > 0)

where

x_{j} β

is a vector of coefficients for the covariates included in the probit equation and

u_{1 j}

is a random-error term. The dependent variable

y_{j}

is not always observed, but only if

y_{j}^{select} = (z_{j} γ + u_{2 j} > 0)

where

z_{j} γ

is a vector of coefficients for the covariates included in the selection equation and

u_{2 j}

is a random-error term. The two error terms (

u_{1 j}

u_{2 j}

) have a bivariate normal distribution with correlation ρ. If

ρ \neq 0

, then standard probit models are biased.

The vector $z_{j} γ$ of the selection equation should include at least one regressor not included in the probit equation, the so-called exclusion restriction, which would affect the probability of being employed, but not the wage. Typical exclusion restrictions used in existing research on women's labor supply and wages are marital status and number of dependent children in the household (Heckman 1976). We, thus, opted for an interaction between these two variables, which are also interacted with sex to account for the different effects of family and parental responsibilities on the probability of working (as employee) across genders. Besides the exclusion restriction, the vector $z_{j} γ$ also includes age, region of residence, education, and year of survey.

Empirical analyses were performed separately by destination country and divided into two parts, following the structure of the empirical strategy described in section 4. In the first part, which focuses on the ethnic wage penalty, the wage equation was estimated with three nested models:

M 1 : Y_{j} = β_{0} + β_{1} (Origin \times Gender) + β_{2} (Age) + β_{3} (Reg \times Year) + u_{1 j}

M 2 : Y_{j} = β_{0} + β_{1} (Origin \times Gender) + β_{2} (age) + β_{3} (Reg \times Year) + β_{3} (Edu) + u_{1 j}

\begin{aligned} M 3 : Y_{j} = & β_{0} + β_{1} (Origin \times Gender) + β_{2} (Age) \\ + β_{3} (Reg \times Year) + β_{3} (Edu) + β_{4} (Occ) + β_{5} (Job) + u_{1 j} \end{aligned}

Model 1 estimated the gross ethnic wage penalty, controlling only for age and an interaction between region and year of survey.¹¹ We estimated variation of the ethnic wage penalty between men and women, including an interaction between geographical origin and gender. Model 2 estimated the ethnic wage penalty net of education, including education in the wage equation. Model 3 added occupation and the other variables related to job and work history (i.e., working hours, tenure, and duration of work contract) to the wage equation, thus estimating the ethnic wage penalty net of occupation and job characteristics. A comparison of these three models allows us to disentangle the mechanisms underlying migrant disadvantage described in section 2 and to distinguish between those related to human-capital differences between migrants and natives and those related to the two groups’ uneven distribution in the occupational hierarchy.

The second part explored heterogeneity in the ethnic penalty by education and investigated the issue of human capital transferability. We, first, estimated models 1 and 3 by educational attainment. Second, these separate models by education were further estimated, using, as the independent variable, a combination between geographical origin and a dummy distinguishing among those migrants who attained their highest educational title in the origin country and those who attained it in the destination country, to study the role of place of education and the transferability of educational degrees across borders.

6. Empirical Results

6.1. Ethnic Wage Penalties in European Labor Markets: Differences by Gender and Area of Origin

Figures 1 and 2 show, for males and females, the cross-country variation in the associations between ethnic origin and the probability of being above the median of the wage distribution. The results from three nested models are reported, estimating the gross ethnic penalty (model 1), as well as the ethnic penalty net of education (model 2) and occupation (model 3). The point estimates report the difference in the predicted probabilities of being above the median of the wage distribution for migrants from Africa and the Near Middle East, Asia, Latin America, and Eastern Europe, with respect to natives.¹²

Figure 1.

Ethnic wage penalty, by geographical origin and country of destination: males. Probit with sample selection. Average marginal effects (w.r.t. natives) on the probability to be in the top 50% of the wage distribution. Controls: age, region, year of survey (mod 1), education (mod 2), occupation, temporary job, work hours, and work experience (mod 3). Notes: Afr-NME: Africa and Near Middle East; LAme: Latin America; EastEU: Eastern Europe (EU). Average marginal effects for Latin American immigrants in Greece are not reported because of the small sample size.

Figure 2.

Ethnic wage penalty, by geographical origin and country of destination: females. Probit with sample selection. Average marginal effects (w.r.t. natives) on the probability to be in the top 50% of the wage distribution. Controls: age, region, year of survey (mod 1), education (mod 2), occupation, temporary job, work hours, and work experience (mod 3). Notes: Afr-NME: Africa and Near Middle East; LAme: Latin America; EastEU: Eastern Europe (EU). Average marginal effects for Latin American immigrants in Greece and Asian immigrants in Portugal are not reported because of the small sample size.

Cross-country variation partially resembles previous findings on job quality (Panichella 2018a), although the consideration of different compositional factors provides a more nuanced picture of the ethnic wage penalty. Consistent with H1, among males, the gross ethnic wage penalty (model 1) was generally higher in Mediterranean countries for all ethnic groups considered (Figure 1). However, except in Portugal, in Southern Europe, male migrants from Asian countries suffered the strongest penalization, ranging from 42 percentage points (p.p.) in Spain to 55 p.p. in Greece. In Continental European countries, such as Belgium, France, and the Netherlands, differences among ethnic groups were more blurred or even null. Moreover, contrary to our expectations (H4), in the United Kingdom, Ireland, and Germany, the highest penalty was observed among male migrants from Eastern EU countries, who had 24–27 p.p. lower probabilities of being above the median of the wage distribution, compared to natives.

In line with H2, when education is included (model 2), the ethnic penalty decreased more in Southern Europe than elsewhere because male migrants in Italy, Spain, Greece, and Portugal are generally less educated than those who migrate to other countries. The reduction of the ethnic penalty was, indeed, weaker in Continental European countries, the UK, and Ireland, where migrants had a similar—or even higher, in the case of Latin Americans in Germany or Asians in Ireland—education than natives.¹³

The ethnic wage penalty further decreased if occupation and job characteristics were controlled for (model 3), especially in Southern Europe, again confirming H2. In fact, in Mediterranean countries, there was a stronger reduction in migrants’ ethnic wage penalty, which depends on both their more frequent inclusion in temporary jobs and their overrepresentation in the occupational hierarchy's lowest strata (see Table A1). In the other countries considered, the mediation effect of occupations and jobs was much weaker, and, for most ethnic groups, results did not substantially change from model 2 to model 3.

Moreover, the ethnic wage penalty net of occupation, which measures those mechanisms of penalization over and above both education and occupational position, remained higher in Southern Europe than in other countries (e.g., Belgium, France, and Germany) for most migrant groups, but with relevant internal differences. For instance, controlling for occupation and job characteristics, the wage penalty for migrants from Africa and the Near Middle East remained substantial in Greece (17 p.p.) and Italy (14 p.p.), while it became negligible in Spain (5 p.p.) and Portugal (1 p.p.). Hence, focusing on wages helps shed further light on differences within the Mediterranean model of migrant integration (Avola 2015). Although migrants were strongly penalized in terms of job quality (occupation and type of job) in all Southern European countries, the unequal distribution of migrants and natives in the occupational structure accounted for the whole wage penalty in Portugal and, to a lesser extent, Spain, whereas a large wage penalty within occupations and jobs still remained in Italy and Greece for all ethnic groups. In other words, the ethnic wage penalty appeared to be primarily occupation-based in Iberian countries, with migrants experiencing a disadvantage in wages only because they were allocated worse jobs and occupations. Conversely, the “residual” wage gap found in Italy and Greece suggests the presence of additional mechanisms such as possible discriminatory practices against migrants.

Other interesting results emerge among females (Figure 2). Here as well, the gross ethnic wage penalty was generally higher in Southern Europe, where migrant women are strongly concentrated at the bottom of the occupational hierarchy, especially in the personal care sector (Ambrosini 2016). Indeed, in these countries, the penalization's magnitude strongly decreased in model 3, where the occupational position was controlled for. For instance, the gross ethnic wage penalty amounted to 30 p.p. for female migrants from Africa and the Near Middle East in Italy, whereas the ethnic wage penalty net of education and net of occupation decreased to 20 and 4 p.p., respectively. As in the case of males, the ethnic wage penalty for women was lower in Continental European countries, as well as in the United Kingdom and Ireland, where the compositional effects related to education (model 2) and occupation (model 3) were also weaker. Interestingly, in all countries analyzed, the differences among women from different ethnic origins were lower than those observed among men (Mandel and Semyonov 2016).

While previous studies have shown the ethnic penalty to be systematically higher among women in Western Europe (e.g., Panichella 2018a), our focus on wages allows us to highlight some gender differences in migrants’ integration paths. In fact, women's gross wage penalty was stronger than that observed among men only in Southern Europe, while in other countries, the penalty's magnitude was similar or lower, not confirming H3. Moreover, in most Continental European countries and in the United Kingdom, Ireland, and Portugal, the ethnic penalty turned into a wage premium, especially among female migrants from Africa and the Near Middle East, Asia, and Latin America. In these countries, therefore, the ethnic penalty faced by migrant women was primarily related to their lower labor market participation, and their risks of being concentrated in the occupational structure's lowest strata were higher, compared to that of native women, but lower compared to that of their counterparts who migrated to Mediterranean countries (see also Table A2). Migrant women's lower penalization in Europe is in line with findings from Mandel and Semyonov (2016), who show that the ethnic wage gap in the United States was higher among males than among females, also because the weight of discrimination practices, measured with the “residual” gap (see above), was much larger among men.

6.2. Ethnic Wage Penalty, Education, and Human Capital Transferability

Turning to the second step of the empirical strategy, Figures 3 and 4, which collapse migrants from Africa and the Near Middle East, Asia, and Latin America into a single category, present results from two models estimating the gross ethnic penalty (circles) and the ethnic penalty net of occupation (crosses) separately by educational attainment for men and women, respectively.¹⁴ Values can be interpreted as in Figures 1 and 2.

Figure 3.

Ethnic wage penalty, by geographical origin, country of destination, and education: males. Probit with sample selection. Average marginal effects (w.r.t. natives) on the probability to be in the top 50% of the wage distribution. Controls: age, region, year of survey (circles), occupation, temporary job, work hours, and work experience (crosses). Notes: Afr-As-Lam: Africa, Near Middle East, Asia, Latin America; EastEU: Eastern Europe (EU); L: lower secondary; M: upper secondary; H: tertiary.

Figure 4.

Ethnic wage penalty, by geographical origin, country of destination, and education: females. Probit with sample selection. Average marginal effects (w.r.t. natives) on the probability to be in the top 50% of the wage distribution. Controls: age, region, year of survey (circles), occupation, temporary job, work hours, and work experience (crosses). Notes: Afr-As-Lam: Africa, Near Middle East, Asia, Latin America; EastEU: Eastern Europe (EU); L: lower secondary; M: upper secondary; H: tertiary. Average marginal effects for those who attained upper secondary education are not reported for the Netherlands because of the small sample size and large uncertainty of estimation.

Figure 3 shows small differences in education among men in Belgium, France, and Germany. In other countries, such as Austria, and for male migrants from Africa and the Near Middle East, Asia, and Latin America in Ireland and the United Kingdom, there were even smaller ethnic disadvantages for the tertiary educated than for those with primary or lower secondary education. However, there is, again, a different pattern in Mediterranean countries (except Greece), where the gross wage penalty was higher for those with upper secondary and tertiary education, as expected by H5. This penalization was primarily driven by the uneven allocation of highly educated migrants and natives into occupations and jobs, as shown by the comparison between gross and net penalties: when models control for occupation and job characteristics, the disadvantage of male migrants with high education levels strongly decreased in Southern Europe. This evidence further confirms the leveling-down process that characterizes migrants’ inclusion in the Mediterranean occupational hierarchy, which pushes foreign-born workers into the lowest strata, independently of their skills, education, and experience (Panichella, Avola, and Piccitto 2021). In Italy, for example, the gross ethnic penalty for Africans, Asians, and Latin Americans amounted to 32 p.p. for those with lower secondary education or less and to 37 p.p. for those with tertiary education, whereas the respective penalty net of occupation and job characteristics was 20 and 12 p.p. In other words, if highly educated male migrants had good chances to access better jobs in Continental European countries, the United Kingdom, and Ireland, their counterparts in Mediterranean countries were segregated into “3d” jobs and, for this reason, strongly penalized also in terms of wages.

The strongest penalty for the highly educated in Southern Europe is confirmed also for women, who entered the occupational structure's lower strata and, thus, also had wage disadvantages (Figure 4). For instance, the gross wage penalty for women from Africa and the Near Middle East, Asia, and Latin America moving to Italy was 15 p.p. for those with lower secondary education or less and 29 p.p. for those with tertiary education, whereas their net ethnic penalty amounted to 11 and 3 p.p., respectively. Furthermore, the analyses confirm that migrant women's wage penalty was lower than that of men and that the wage premium described in the previous section mainly concerned women with a low education level, as in the case of Belgium, Austria, and the Netherlands.

We now turn to the last empirical evidence, which investigates how the place of education affected the ethnic wage gap. Figures 5 and 6 report the wage penalty (gross and net of occupation) for men and women, respectively. In them, we split the migrant groups—which collapse Africans, Asians, Latin Americans, and Eastern Europeans into a single category because of sample size issues—into two groups, depending on where the highest educational qualification was obtained (i.e., in the host country (dt.) or in the origin country (or.)). We also focus only on those with upper-secondary and tertiary education, since among migrants with lower secondary education or less, few individuals (who migrated after 15 years of age) obtained their highest educational title after migrating.¹⁵

Figure 5.

Ethnic wage penalty, by geographical origin, country of destination, and (place of) education: males. Probit with sample selection. Average marginal effects (w.r.t. natives) on the probability to be in the top 50% of the wage distribution. Controls: age, region, year of survey (circles), occupation, temporary job, work hours, and work experience (crosses). Notes: M(or.): upper secondary (attained in origin country); M(dt.): upper secondary (attained in destination country); H(or.): tertiary (attained in origin country); H(dt.): tertiary (attained in destination country). Average marginal effects for those who attained upper secondary and tertiary education at the destination are not reported for Greece because of the small sample size and large uncertainty of estimation and for the United Kingdom (see note 15).

Figure 6.

Ethnic wage penalty, by geographical origin, country of destination, and (place of) education: females. Probit with sample selection. Average marginal effects (w.r.t. natives) on the probability to be in the top 50% of the wage distribution. Controls: age, region, year of survey (circles), occupation, temporary job, work hours, and work experience (crosses). Notes: M(or.): upper secondary (attained in origin country); M(dt.): upper secondary (attained in destination country); H(or.): tertiary (attained in origin country); H(dt.): tertiary (attained in destination country). Average marginal effects for those who attained upper secondary education are not reported for the Netherlands because of the small sample size and large uncertainty of estimation. Average marginal effects for those who attained upper secondary and tertiary education at the destination are not reported for Greece because of the small sample size and large uncertainty of estimation and for the United Kingdom (see note 15).

In line with H6, among both men and women, the gross ethnic penalty (circles) confirms that the acquisition of educational credentials in the destination country was beneficial for migrants in almost all countries, especially in Southern Europe. For instance, among the tertiary educated in Spain, migrant men and women experienced a gross penalty of 18–20 p.p. if the educational credential was obtained before migration and a gross penalty of 6–13 p.p. if the educational title was obtained in the destination country. However, when the ethnic penalty net of occupation (crosses) is considered, the picture changes, and the differences between migrants who obtained their educational title in the origin country and those who obtained it in the destination country strongly decreased. This finding, which is again particularly evident in Mediterranean countries, confirms that those migrants educated in the origin country were more likely to enter the worst jobs and occupations, whereas those who achieved the educational title in the destination country were less penalized in terms of inclusion in the occupational structure. In other words, when comparing migrants in the same type of job and occupation, differences between those migrants who obtained their qualifications in the origin country and those who obtained them in the destination country were very small in all countries considered. This result suggests that the negative effect of imperfect human capital transferability on migrants’ wages can be explained through its negative consequences for migrants’ inclusion into the occupational structure.

7. Conclusion

This article provided a comprehensive picture of migrants’ labor market integration across 11 Western European countries, extending the existing comparative literature (e.g., Kogan 2006, 2007; Panichella 2018a; Reyneri and Fullin 2011a, 2011b) by analyzing an unexplored measure of occupational integration—namely, the wage gap between migrants and natives. Empirical results show that male migrants from all origin areas experienced a wage penalty with respect to natives, especially in Mediterranean European countries (H1 confirmed), where higher employment opportunities come at the price of lower job quality for migrants (Panichella 2018a; Reyneri and Fullin 2011a), including lower wages. In Southern Europe, moreover, a large part of the gross ethnic wage penalty for male and female foreign-born workers was explained by migrants’ lower education level and occupational segregation (H2 confirmed). Therefore, besides the importance of the stronger segmentation in Southern European labor markets, which pushes migrants into the occupational structure's lowest positions, the fact that countries with lower average education, such as those in southern Europe, attracted the least educated migrants (Brunori, Luijkx, and Triventi 2020) crucially affects foreign-born workers’ wage penalty.

Contrary to our expectations, we did not confirm the ethnic wage penalty to be stronger among women than among men (H3 not confirmed). In fact, we found that female migrants experienced a lower penalization, if not even a small premium, in Continental European countries, the United Kingdom, and Ireland. This finding contradicts the expectations that migrant women suffer a “double disadvantage” because of the intersection of gender and ethnicity (Parent, DeBlaere, and Moradi 2013) and confirms for the European case the existing evidence for the United States, where discrimination has been found to be the prime source of racial wage gaps among men, but not among women (Sidanius and Pratto 1999). Women's lower ethnic wage penalty could be related to the universal tension between work and family responsibilities that women face, regardless of ethnic origin (Mandel and Semyonov 2016). Migrant women's “advantage” should, therefore, be understood within the context of “gender inequality” in Western European labor markets: since wage inequality is strongly gendered, both migrant and native women share considerable wage disadvantages relative to men, making the ethnic wage gap lower among females.

Partly as a consequence of migrant women's lower overall disadvantage, our expectation concerning higher wage penalties for immigrants from Africa and the Near Middle East, Asia, and Latin America was not confirmed for females. However, H4 is not entirely supported even among males. In particular, we did not find that migrants from new EU member states in Eastern Europe were systematically less penalized than non-Western migrants from outside the EU (H4 partially not confirmed). This finding can be related to differences in the prevailing reasons for migration and types of visas upon arrival in the host country across ethnic groups. Indeed, while migrants from eastern EU countries enjoy free movement in the Schengen (Fellini and Guetto 2020), non-Western migrants from outside the EU need a job offer and a certain wage threshold to obtain a regular visa, especially in countries with more selective immigration policies, such as the United Kingdom and Ireland (Reyneri 2016). These differences in the means of entry in the host country may account for the lower ethnic penalties found in many Continental European countries, and especially in Ireland and the United Kingdom, for male and female migrants from Africa and the Near Middle East. Migrants from these origin countries were likely to be more positively selected, as they needed a good job to obtain a visa, compared to Eastern Europeans, and such positive selection could have mitigated their wage penalty. On the contrary, in Southern Europe, where migrants usually arrive in the destination countries without a job (Reyneri 1998, 2016), Eastern European migrants were equally or slightly less disadvantaged than other ethnic groups, as we predicted, especially among male migrants in Italy and Greece.

Concerning differences in education and transferability of educational certificates, we found mixed results and, to some extent, results contrary to our expectations. For instance, gross ethnic wage penalties were found to be higher among highly educated men and women only in Southern Europe, whereas in Continental European countries, the United Kingdom, and Ireland, no differences emerged according to education, or even lower penalties for the tertiary educated (H5 partially confirmed). Moreover, the stronger penalization of highly educated migrants, from all ethnic origins considered, in Southern Europe was found to decrease when occupation and job characteristics were controlled for, showing that foreign-born workers’ concentration in “3D” jobs (Fellini, Guetto, and Reyneri 2018; Panichella, Avola, and Piccitto 2021) is the main source of their wage penalty. Finally, concerning the transferability of educational credentials, we found that those who achieved their highest educational title in the host countries were less penalized, thanks to a better match between educational credentials and job allocation (H6 confirmed). Also, in this case, findings are more evident in Southern Europe, where migrants had more to gain from investing in country-specific human capital as a way to escape entrapment in the secondary labor market (Fellini and Guetto 2019).

To sum up, then, our results confirmed the specificity of the Southern European model of inclusion of recent migrants, even when the ethnic wage penalty is considered: strong labor market segmentation, combined with the fact that Mediterranean countries mainly attract immigrants with low education levels, has created a peculiar model of inclusion which combines high participation in the labor market with a consistent ethnic occupational penalization. However, different from the existing comparative literature on European models of inclusion (e.g., Fullin and Reyneri 2011), focusing on the ethnic wage gap allowed us to identify important variations within the Southern model. For instance, the unequal occupational sorting of migrants and natives accounted for the whole wage penalty in Portugal and, to a lesser extent, Spain, whereas a strong wage penalty remained even after controlling for occupation and job characteristics in Italy and Greece. This result suggests that the ethnic wage penalty was primarily occupation-based in the Iberian countries, while in Italy and Greece, discriminatory practices against immigrants, but also unmeasured sociocultural factors such as interethnic social ties and language proficiency (Koopmans 2016), may also penalize migrants’ wages.¹⁶

Future research is needed to grasp the specific causal mechanisms underlying the ethnic penalty documented here. Much of the literature on migrants’ penalization in European labor markets considers the ethnic penalty as an “umbrella concept” (i.e., the sum of the effects of all social mechanisms that are not captured by the confounders included in the regression analysis), using data that do not allow a causal analysis of these mechanisms (e.g., Koopmans 2016; Panichella, Avola, and Piccitto 2021). An exception here is the growing number of experimental analyses of hiring discrimination (Zschirnt and Ruedin 2016; see Lancee 2019 for a cross-national field experiment), but much work remains. Moreover, the investigation of the ethnic wage penalty can also be extended by analyzing panel data, which not only are better suited for causal analysis but also allow the study of whether the migrant–native wage gap changes over the individuals’ careers and how it is affected by other events (family formation, parenthood, etc.).

In line with theoretical debate and empirical analyses on the different models of migrants’ labor market incorporation in Europe, our work focused on the national level, following the methodological nationalism paradigm, which (implicitly or explicitly) defines the unit of analysis by the boundaries of nation-states (Panichella 2018b; Wimmer and Schiller 2002, 2003). This focus on nations may be particularly problematic for Southern European countries that are internally heterogeneous in terms of labor market structure and economic performance (Avola 2015; Cantalini et al. 2022). Finally, future research on the ethnic wage penalty should distinguish between first- and second-generation migrants (Li and Heath 2016). Our data were not suitable for this aim because of the lack of information on parents’ birth country for respondents not living with their parents, but this distinction is crucial for the study of migrant integration in Europe in the long run. All in all, an effort to implement comparative longitudinal surveys focusing on first- and second-generation migrants can deepen understanding of migrants’ economic integration across European societies, which, in the last few decades, have experienced a dramatic increase in immigration.

Supplemental Material

sj-docx-1-mrx-10.1177_01979183221099481 - Supplemental material for Ethnic Wage Penalty and Human Capital Transferability: A Comparative Study of Recent Migrants in 11 European Countries

Supplemental material, sj-docx-1-mrx-10.1177_01979183221099481 for Ethnic Wage Penalty and Human Capital Transferability: A Comparative Study of Recent Migrants in 11 European Countries by Stefano Cantalini, Raffaele Guetto, and Nazareno Panichella in International Migration Review

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Nazareno Panichella

Supplemental Material

Supplemental material for this article is available online.

Notes

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