Beyond Migrant Penalty: How Marginalization Between Ethnicities in the Labor Market Is Revealed Across 16 Developed Economies

Abstract

This paper analyses the ethnic penalty by focusing on the racialization of labor market outcomes beyond the migrant penalty. An illegitimate statistical or taste-based discrimination can be revealed specifically by distinguishing migrants into ethnic groups. Accordingly, ethnic penalty based on five different ethnic groups was estimated through the difference in employment and job quality with respect to natives. The analysis was conducted at the country and European average levels using 16 European countries under a framework of ethnic penalty processes in the labor market. According to the analysis, Eastern Europeans were the most prominent ethnicity regarding higher employment across the 16 countries, although they were mostly posited in unskilled jobs. Migrants from the Middle East and North Africa were shown to be subject to a double penalty in both measures, and the penalty tendency was much clearer for females. Asians and South Americans showed the least penalty, while sub-Saharan Africans were revealed to hold an in-between position.

Introduction

Racialization of ethnic minorities in the labor market does not only involve biological factors but also cultural-based race discrimination. This could be related to stereotypical prejudices toward a particular ethnicity, unlike the migrant penalty which includes immigrants without ethnicity comparison (Modood and Khattab 2016; Roth et al. 2023; Talaska et al. 2008). Within a meritocratic society in advanced economies, and when controlling individual conditions such as education, age, and marital status, the contingencies that contribute toward migrant penalty regarding labor market integration could be regarded as a “legitimized” penalty (Gracia et al. 2016; Heath and Cheung 2007). This is because there could be issues like security concerns and visa processes to certify their educational attainment, language abilities, skills, and similar in the destination country, which any immigrant can confront regardless of ethnic background (den Heijer 2018; Orgad 2021).

Therefore, beyond the migrant penalty, ethnic penalty studies have expanded specifically to explore which particular ethnicities could be more systematically discriminated against compared to others within a host society in relation to racial hierarchies (Felbo-Kolding et al. 2019; Fox et al. 2015). In this regard, ethnic penalty in the labor market has been investigated through labor outcome analyses and correspondence experiments examining hiring discrimination in order to address the gap regarding binary distinctions between non-white and white by progressively considering different ethnicities. Nevertheless, some limitations still exist, such as a lack of comparative country studies, particularly in labor outcome research, and the omission of post-callback contingencies alongside publication bias explicitly in audit studies. Accordingly, the lack of statistics revealing ethnic penalty status across Western European nations beyond the resume screening stage has been highlighted as the one of reasons hampering monitoring or discrimination reduction in the European labor market compared with the United States (Bartkoski et al. 2018; Thijssen et al. 2022; Quillian and Midtbøen 2021).

Hence, this study addresses three major concerns to contribute to ethnic penalty studies by overcoming the limitations of previous research. First, 16 Western European countries are employed alongside five different ethnicities in order to provide an ethnic penalty level for each country and a Europe average according to gender. Second, how hiring discrimination at the beginning stage of employment processes corresponds to the final labor market outcomes can be discerned by comparing the findings captured in the ethnic penalty patterns. As this study adopted EU-LFS, which conveys individuals’ employment experiences alongside skill levels, four ethnic penalty patterns are investigated in conjunction with employment and job quality. Lastly, this study proposes a framework for the ethnic penalty process in the labor market predicated upon labor demand and supply. This means that an ethnic penalty process is investigated from the perspective not only of natives’ (employers’) prejudice toward varying ethnicities but also the differences in ethnicities’ socioeconomic backgrounds including religious culture and origin countries’ economic development. These issues could simultaneously affect the labor market outcomes of the five ethnicities studied with respect to natives and differentiate integration levels between the ethnicities when controlling individual characteristics (Heath and Cheung 2007).

The five different ethnicities examined are Eastern European (EE), Middle Eastern and North African (MENA), sub-Saharan African (SubAf), Asian, and South American (SA). The main finding of this study showed EE with substantially low job quality but high employment, while MENA mostly revealed a double penalty in both measures or experienced acute positive selection for skilled jobs indicating low employment and high job quality. On the other hand, Asian and SA showed less penalty, whereas SubAf was revealed to hold an in-between penalty compared with other ethnicities.

Previous Research and Hypotheses

One of the critical issues regarding immigrant integration is differential treatment based on race. In this regard, some ethnic penalty studies employed second-generation non-white immigrants and compared their integration outcomes with respect to natives or Westerners in order to control for the legitimate penalty or standardization process of migration. This is because, unlike with first or 1.5 generations (children of the first generation who are not born in the destination countries), the second generation would have similar levels of human capital such as language and education in the destination countries compared with native peers. They also do not have negative experiences that could stem from the migration process, particularly found in the case of refugees (Bakker et al. 2017; Crul and Vermeulen 2003; Gracia et al. 2016; Heath and Cheung 2007). In a related vein, EU/Western immigrants showed higher performance regarding job quality as they were employed more in R&D, IT, real estate, and similar areas, while non-white ethnic groups were investigated to experience a lower level of employment rate and unskilled service or manufacturing positions (Khattab and Johnston 2013, 2015; Kogan 2007).

In terms of low job quality outcomes, ethnic penalties could become more consolidated over time. From the perspective of career trajectories, if employees enter an unskilled position at an earlier career stage, future employers may receive a negative impression regarding the productivity or returns of employees (Lawrence 1984; Verbruggen et al. 2015). Therefore, the result of ethnic penalty regarding prevalent low job quality when controlling education levels could lead to a chronic low job status, so it needs to be re-highlighted as the systematic penalty “trap” for non-white migrants. In this regard, a study in the Netherlands revealed similar results in which first-generation migrants mostly experienced a skill shortage, while negative skill mismatch was prevalently found for the second generation of non-white migrants (Belfi et al. 2021).

Nevertheless, in terms of the migrant penalty research which combines ethnicities as “non-white” immigrants, the ethnic difference in relation to racial or ethnic discrimination could often be ignored under the binary categorization of natives and non-white or third-country migrants (Ballarino and Panichella 2013, 2017; Belfi et al. 2021; Kogan 2007; Lee 2022). Therefore, beyond otherness as non-white, the condition of racial or ethnic difference is required within migrant integration analyses to be able to discern differentiated treatment depending on race (Blank et al. 2004; Siebers and Dennissen 2015, Zschirnt and Ruedin 2016). In line with this, there are several studies that analyzed ethnicity differences in labor market outcomes.

After controlling human capital, immigrants from Europe, North America, India, Pakistan, and Bangladesh in the UK were compared in terms of employment rates. As expected, white immigrants from Western countries were less penalized, indicating 10% higher employment rates with unemployment rates similar to those of white British. However, among non-white ethnicities, Indians were revealed to be a lesser-penalized ethnicity compared with the other ethnic groups (Modood and Khattab 2016). Similarly, Heath and Cheung (2006) found an ethnic penalty with respect to natives and white migrants, particularly for African, Caribbean, Pakistani, and Bangladeshi migrants in terms of unemployment, income, and occupations. Furthermore, they importantly uncovered how ethnic penalty continued for second generations, which turned out to be at a similar level compared with the first generation in the UK. Gracia et al. (2016) found Moroccan and Turkish’ immigrants were significantly penalized with respect to native Dutch in employment and job quality, despite equivalent education and skill possession. Furthermore, cultural influences on labor market integration were uncovered through the low performance of Turkish females when compared with Moroccan females due to the stricter conservative manner of Turkish culture concerning women.

Beyond single-country cases, 11 Western European countries’ ethnic wage penalty was investigated using four different ethnicities. Regardless of gender, a large wage penalty was found in Southern Europe since migrants’ positive selection occurs more in the UK, Ireland, and Continental European countries due to higher income thresholds to obtain a visa, unlike Southern Europe in which migrants usually entered without jobs. Besides, no greater advantage for Eastern Europeans compared with African and Middle Easterners, Asians, and South Americans was uncovered (Cantalini et al. 2023). Although meaningful results were found in the study regarding ethnic wage penalty under the cross-country perspective, the important distinction between MENA and sub-Saharan Africans was not achieved by combining both ethnicities. When discussing racialization in the labor market, the distinction between MENA and SubAf would be imperative since they not only have different racial markers but also predominantly embed different religious cultures largely reflecting Islam and Christianity, respectively. As these racial characteristics including color, religion, and economic concerns are bound to be the fundamental factors for ethnic penalties (Goldberg 2009; Heath and Cheung 2006), the difference between the two ethnic groups should be recognized in large-scale comparative studies.

Meanwhile, correspondence experiment and meta-analysis studies uncovered employers’ hiring discrimination with regard to ethnic migrants’ entrance or access to labor markets by measuring callback rates with detailed ethnicity distinctions. In terms of the cross-country perspective, the GEMM (Growth, Equal Opportunities, Migration and Markets) project was conducted in five European countries (the UK, Spain, Germany, Norway, and the Netherlands) by accounting for socioeconomic backgrounds, including religion, through treatment with eight different phenotypes (see Lancee et al. 2019). According to the findings, Black and Middle Eastern men were penalized more in receiving callbacks in male-typed jobs, which is consistent with previous single-county case research (Arai et al. 2016; Bursell 2014; Dahl and Krog 2018; Derous et al. 2015). Asian men were less discriminated against in female-typed jobs due to their perceived nonthreatening and feminine character, whereas Black women regarded as dominant were also more penalized compared with White and Asian women who are perceived as more competent (Di Stasio and Larsen 2020).

On the other hand, a meta-analysis that synthesizes the correspondence experimental studies over time and investigates the consistency of results between countries uncovered the tendencies of ethnicity-related hiring discrimination. The discrimination levels hardly changed over time across Europe and North America, while the penalty toward MENA increased throughout two decades (Quillian and Lee 2023). Although some European countries’ discrimination decreased compared with the United States, such as France, due to notably higher initial discrimination, age discrimination intersecting with ethnicity is consistently high in European countries (Lippens et al. 2023; Quillian et al. 2017). Despite the significant findings from correspondence studies securing causality and meta-analysis tracing ethnic penalty trends, they have limitations such as the omission of post-callback situations and publication bias. This is because callbacks from the initial stage of the employment process do not necessarily mean the successful employment of ethnicities, so while it is useful to discern racialization in recruiting processes, it could not provide the final reality of ethnic penalty in the labor market. Moreover, publication bias, indicating a higher probability of not publishing non-statistically significant results in experiments, can be expected despite the careful approach of meta-analyses (Quillian and Midtbøen 2021).

Therefore, the current research undertakes a comparative study regarding five ethnicities’ labor market outcomes with respect to natives across 16 Western European countries. This overcomes the confinement of previous research and uncovers ethnic penalty in employment and job quality alongside the distinction between MENA and sub-Saharan Africans. Accordingly, this study developed a framework that helps analyze how ethnic penalty in the labor market can be orientated based on labor demand (native) and supply (ethnicity) sides, respectively (see Figure 1).

Figure 1. A framework regarding ethnic penalty processes in the labor market

Source: Author’s elaboration.

Theories regarding ethnic penalty processes are presented in relation to the hypotheses and organized in two ways (Figure 1). First, how culture-based racial or ethnic discrimination could happen in Western European countries is explored based on the concept of homogeneity and secularism, which stems from natives’ particular perspective on migrants in relation to the perceived illegitimacy of destination countries’ sociocultural backgrounds. These theories are expected to be particularly associated with employment levels among the five ethnicities, which can be compared with the findings on hiring discrimination. Second, the socioeconomic backgrounds of the ethnicities are suggested as another significant factor affecting different labor market outcomes. This is explained by mixed embeddedness (Kloosterman et al. 1999) and locational inequality (Milanovic 2016) in relation to employment and job quality, respectively.

Homogeneity and Secularism in Europe

As significant migrant destinations, when comparing Western Europe with the United States, a unique difference can be found in terms of how they have developed their social system based on racial homogeneity. Other than Southern Europe, which is regarded as representing the new receiving countries from the last few decades, Central and Northern European countries including the UK have demanded unskilled labor from former colonized countries (the UK and France) or Eastern and Southern Europe (Germany and Scandinavian countries) since World War II (Ballarino and Panichella 2013). Hence, although there are some old receiving European countries, they have for a longer time been migrant-sending countries, rather than receiving countries, compared with the United States (Alba and Foner 2015; Carmon 1996). Accordingly, racial homogeneity could be relevant to the different social systems between Europe and the United States. Specifically, European states have developed strong middle-class welfare systems based on sturdy democracy alongside multiparty governments, while in the United States, there has been only limited welfare system development based on plutocracy (Esping-Andersen 1990; Howells 1894; Mahbubani 2022; Pierson 2017; Soskice 2005).

The United States has experienced strong white and Black segregation, and, in turn, the unbalanced economic power and larger cultural disparity between them could have resulted in less solidarity. Meanwhile, the strong white middle class in Western Europe has consolidated a reciprocity that shares benefits and contributions so that solidarity has been manifested within European states’ social systems (Lindert 2014; Milanovic 2016). Hence, the enormous influx of refugees during the 2015 Migrant Crisis in Europe could be acceptable for natives on humanitarian grounds, while it would have been difficult for them as general migrants to be integrated into society as a formal workforce, which could consequently share benefits (Baekgaard et al. 2023; Lahey 2010).

In this regard, Lindert (2014) pointed to how the middle class could have more prevalent incompatible views against migrants due to racially and economically homogeneous backgrounds. Specifically, populist parties were supported more in elections given gradual increases in the number of migrants in Western Europe, comparing populist parties’ vote shares between 2000 and 2015 in eight Western European countries. As a result, apart from Belgium, the seven other countries including Austria, Denmark, Finland, France, Greece, Sweden, and the UK showed at least a two or three times higher vote share for populists in 2015 than was the case in 2000 (Milanovic 2016). In the same vein, expanding nationalism has been observed alongside a concurrent increase in the number of migrants in Europe, particularly from non-Western countries (Poynting and Mason 2007).

On top of this homogeneity characteristic in Europe, secularism could also be considered as one of the critical reasons behind MENA’s penalty in the labor market. Secularism in Europe has distinguished sociopolitical practices from religions so that institutions and Europeans themselves have not remained committed to religious practice. In this sense, group threat theory can be applied as the increased number of Muslims among migrants could make natives conceive of themselves as being challenged internally through a perceived decline in societal principles or cultural values predicated upon secularism (Abdelgadir and Fouka 2020; Alba and Foner 2015; Carmon 1996; Jackson 2018; Quillian and Midtbøen 2021). Thus, based on Islamophobia, which may perceive Muslim migrants as outlaws or “others,” various studies revealed that Muslims have experienced hostility in Western European countries (Adida et al. 2016; Helbling and Traunmüller 2020; Sniderman and Hagedoorn 2007).

Accordingly, the first hypothesis relates to homogeneity and secularism in Europe. European countries have traditionally been emigration countries and racially homogeneous. Furthermore, secularization has been institutionally as well as culturally established. Subsequently, these aspects would make natives or employers favor white immigrants to a greater extent than non-white ethnicities, and they might be reluctant to employ these others, particularly MENA migrants. This racialization is interlinked with taste-based and statistical discrimination. This is because racialization is a system of categorization based on physical characteristics with race associated with sociopolitical, economic, and cultural concerns including religious practices (Jackson 2018; Roth et al. 2023; Younis and Jadhav 2020).

Hence, taste-based discrimination that reflects racial prejudice (Becker 1957) could be more interlinked with racialization than statistical discrimination stemming from a lack of information regarding certain ethnicities in terms of productivity (Arrow 1973). Accordingly, if information about ethnicities is fulfilled to employers, the ethnic penalty in principle should disappear. Nevertheless, statistical discrimination still requires more information regarding ethnic candidates than that of the majority beyond qualifications, so it cannot be regarded as fundamentally less discriminatory behavior than taste-based discrimination (Quillian and Midtbøen 2021). Therefore, (hypothesis 1) employer preferences in terms of racialization would significantly affect employment in the labor market, so EE might be the least penalized while MENA would be the most penalized among the five ethnicities in Western Europe.

Mixed Embeddedness and Locational Inequality

While previous discussions are relevant to the factors that may affect ethnic discrimination in the destination countryside, the concept of mixed embeddedness and locational inequality explain how the ethnic side of socioeconomic differences could influence migrant integration levels in a positive or detrimental manner. First of all, mixed embeddedness demonstrates how immigrants who were rooted in the origin country would successfully be embedded in the destination country by incorporating the new institutions. Therefore, empirical results have shown that immigrants who incorporated the new regulations and social capital of host countries have successful integration outcomes compared to those who stuck to their origin culture or informal embeddedness (Kloosterman et al. 1999). In this sense, previously explained research regarding ethnic penalty in the Netherlands can be interpreted as reflecting how the less strict informal embeddedness of Moroccan female migrants was highlighted through relatively better labor market integration than Turkish female migrants by better securing a trajectory for achieving mixed embeddedness (Gracia et al. 2016).

Accordingly, mixed embeddedness could be regarded as the “integration will or behavior” of migrants regarding how they can make a decision to be embedded under the receiving countries’ social networks and market opportunities (Bisignano and El-Anis 2018; Kloosterman 2010), and, in turn, employment could be particularly affected by this rather than job quality. Thus, if communities of certain ethnicities have very strong religious or cultural norms from their origin society, they would not be willing to try to assimilate or follow the receiving countries’ new institutions, which are embedded in the labor market (Bakewell 2014; Fadahunsi et al. 2000; Somerville 2016). Hence, migrants from MENA might be the most difficult to be integrated not only given Islamophobia in the destination culture but also from a tendency that leans on informal embeddedness on their own side. Furthermore, recent research uncovered that migrant women’s labor market participation was much more affected by their culture of origin than was the case for migrant men (Lee et al. 2022). By extension, (hypothesis 2) female MENA migrants who have greater restrictions than males in their conservative culture would be penalized to a further extent compared to male counterparts, as well as to any other ethnicities, particularly in employment.

On the other hand, migration processes could endure throughout the global South and North due to livelihood (or life standard) differences of equivalent classes between countries, attributed to different levels of economic development. This is because locational inequality, which indicates different economic development between countries (Milanovic 2016), would stimulate push and pull factors, and, in turn, migration toward advanced economies would be much more prominent under globalization given the easier accessibility of mobility and information (Milanovic 2015, 2016; Misra 2007). It was projected that the citizenship premium of advanced economies necessarily keeps continuing as a global phenomenon; hence, the more migration flows increase to developed countries, the more conditions to accept migrants are to be drawn up by authorities. Accordingly, human capital citizenship that opens borders preferentially to migrants who have high education or capital has been introduced in many destination countries, reflecting the high demand for the “premium” related to their citizenship (Ellermann 2020; Paul 2013, 2018).

Subsequently, after the Second World War and globalization, economic development levels across different regions where ethnic groups belong to could be an important factor concerning how ethnic penalties can be intensified in destination countries’ labor markets. In this regard, Asia was revealed as the only continent that drastically reduced the economic inequality gap compared to advanced economies among developing nations (Milanovic 2016). Despite the slowing momentum nowadays, Asia, including the East and Pacific, is still reported to be the fastest-growing regions that show a higher average growth rate than that among developing economies (World Bank 2023). Therefore, under this strengthening human capital-based immigration system, Asians who have relatively more socioeconomic resources compared with other ethnicities alongside longer educational history have reached better positions to acquire citizenship (Alba and Foner 2015) and experience greater employment assimilation alongside lower hire discrimination (Zwysen et al. 2021).

Thus, predicated upon locational inequality, the different socioeconomic growth of origin countries between ethnicities could affect ethnic penalty in the labor market, especially for job quality (Milanovic 2016). Moreover, the expanded notion of human capital citizenship in Western society would complementarily contribute toward ethnic penalty in job quality by preventing migrants who have less capital or low-skilled backgrounds from acquiring citizenship and, in turn, pushing them into black or secondary labor markets (Ballarino and Panichella 2013; Lee 2022; Orgad 2021; Paul 2018). In this sense, based on locational inequality, (hypothesis 3) Asians who have better material resources based on regional economic growth would experience higher job quality compared with the other ethnicities. On the other hand, (hypothesis 4) EE and MENA could be much more penalized in job quality as they are locationally nearest to Western Europe while originating from lower economic development contexts that can also accompany conflicts in some of these regions.

This means that low-skilled migrants could fall proportionally more into these ethnicities’ demography judging by the feasibility of migration with low resources, which is relevant to the findings regarding the low MENA penalty in the United States compared with European countries (Bartkoski et al. 2018). Beyond this, the large flow of certain ethnic groups is likely to consolidate statistical or taste-based discrimination in destination countries (Lippens et al. 2022). Accordingly, the job quality of both ethnicities would be additionally penalized compared with other ethnicities.

Methodology

The 16 Western European case countries included Austria, Belgium, Switzerland, Germany, Denmark, Spain, Finland, France, Greece, Ireland, Italy, Portugal, the Netherlands, Norway, Sweden, and the United Kingdom. In order to test the four hypotheses, the analysis proceeded in two ways. First, ethnic penalty regarding employment and job quality at the European level was analyzed alongside the total ethnicity figures of the 16 Western European countries according to gender and labor market outcomes in employment and job quality. Second, ethnic penalty at the country level proceeded according to each country, gender, and labor market outcomes.

A linear probability model (LPM) was employed to compare the labor market outcomes between natives and the five ethnicities. Dichotomous dependent variables need to be used in LPM so that employment and job quality are distinguished through binary conditions such as employed or unemployed and skilled or unskilled, respectively. Probit or logit models are also binary variable methods by presenting odd ratios rather than probabilities like LPM, which is much more straightforward to interpret the results and regarded to be realistic (Greene 2011; Holm et al. 2015; Long 2009). This investigation drew upon realistic measurements, such as the EU Labor Force Survey (LFS) (registered number: RPP 373/2020-LFS), which is survey data reflecting the current work status rather than projections. Hence, LPM is employed to convey ethnic penalty with respect to natives.

The binary data was organized into two dependent variables (employment and job quality) by setting “1” as employed/skilled and “0” as unemployed (including inactivity)/unskilled, respectively. In terms of job quality, the International Standard Classification of Occupations (ISCO) from the International Labour Organization (ILO), which distinguishes four occupational levels based on nine occupational codes, was used to distinguish skilled and unskilled jobs in a binary variable. Specifically, unskilled positions consist of the bottom two levels, the first (elementary occupations (9)) and second (plant, craft, skilled agriculture, service workers (5–8)), while skilled jobs are comprised of the two higher levels including technicians (3) for the third level and professionals (1 and 2) for the fourth level alongside the top-skilled jobs such as doctors and managers, excluding the military (0). To note, clerical support staff (4) belongs to the second lowest level alongside codes 5–8. However, it pertains to public administration and bankers, which cannot be regarded as unskilled jobs, so it was classified as among skilled jobs.

With regard to the independent variable, migrants were divided into five ethnicities coded 1–5 according to country of birth based on regions such as Eastern Europe (EE), the Middle East and North Africa (MENA), sub-Saharan Africa (SubAf), Asia, and South America (SA). ¹ As ethnic penalty is measured with respect to natives, natives are coded as 0 for the reference category. Immigrants from the EU-15 and European Free Trade Association (EFTA), as well as North America including Oceania, were treated as a residual category that has a contribution but without the results being shown in the statistical analysis since they have the same status as natives within the European labor market (see Ballarino and Panichella 2013, 2017; Khattab and Johnston 2013, 2015; Kogan 2007; Modood and Khattab 2016; Zwysen et al. 2021).

There were four control variables in this study, including education, age, marriage status, and year terms. Education was divided into three levels based on ISCED-97: 1 (up to lower secondary), 2 (upper secondary), and 3 (tertiary). Three age blocks were presented from 25–34 to 35–44 and 45–66. Marriage status was categorized as divorced/widowed, single, and married. The year term was employed to control for specific time effects because the employed LFS data comprised an 11-year timespan from 2005 to 2015 in 16 countries. Accordingly, Models 1 and 2, which show ethnic penalty at the European level, included the $COUNTRY\# YR$ term to control for the 16 countries’ different contingencies interlinked with years. Meanwhile, Models 3 and 4, which identify ethnic penalty at the country level, included only the $YR$ term as it was separately analyzed according to each country case (full regression tables can be found in appendix Tables 10–13 according to gender and labor market outcomes).

Model Specification

Model 1. Male ethnic penalty (Europe average)

$$\small {E(X|Y) = \alpha + {\beta _1}METH + {\beta _2}EDU{\rm{\;}} + {\beta _3}MRRG{\rm{\;}} + {\beta _4}AGE{\rm{\;}} + {\beta _5}COUNTRY\# YR + {\rm{\;}}\varepsilon} $$

Model 2. Female ethnic penalty (Europe average)

$$\small{E (X|Y) = \alpha + {\beta _1}FETH + {\beta _2}EDU{\rm{\;}} + {\beta _3}MRRG{\rm{\;}} + {\beta _4}AGE{\rm{\;}} + {\beta _5}COUNTRY\# YR + {\rm{\;}}\varepsilon} $$

Model 3. Male ethnic penalty by country

$$E(X|Y) = \alpha + {\beta _1}METH + {\beta _2}EDU{\rm{\;}} + {\beta _3}MRRG{\rm{\;}} + {\beta _4}AGE{\rm{\;}} + {\beta _5}YR + {\rm{\;}}\varepsilon $$

Model 4. Female ethnic penalty by country

$$E(X|Y) = \alpha + {\beta _1}FETH + {\beta _2}EDU{\rm{\;}} + {\beta _3}MRRG{\rm{\;}} + {\beta _4}AGE{\rm{\;}} + {\beta _5}YR + {\rm{\;}}\varepsilon $$

Note: METH (male ethnicity), FETH (female ethnicity), EDU (education), MRRG (marriage), AGE (age), COUNTRY#YR (interaction between country and year), YR (year).

The total sample of the 16 European countries from the LFS was 16,413,798. In terms of ethnicity, among the five ethnicities migrants from EE is the highest population, standing at 628,797 subjects, and the second largest population turned out to be MENA (290,987). They were followed by Asians (180,444), SubAf (162,949), and SA (147,280), respectively. This demographic profile shows how geographical adjacency concentrates particular ethnicities in receiving countries, and this would also indicate the negative selection of migrants in the main analysis. On the other hand, the residual category including EU-15 and EFTA (Norway, Switzerland, Iceland, and Lichtenstein) alongside North America and Oceania accounted for 3.13% (see Table 1 and a detailed descriptive analysis regarding variables by country found in appendix Tables 1–8).

Table 1. Demographic breakdown according to ethnicity

Results

The ethnic penalty was measured in employment and job quality between each ethnicity and natives according to gender. Therefore, the four tables within Figures 2–5 summarize the male and female ethnic penalty with respect to natives regarding employment and job quality. The ethnic penalty level is presented alongside a bar chart in the order of EE, MENA, SubAf, Asian, and SA according to each country. Moreover, the “total” label indicates the average ethnic penalty at the European level including all 16 countries, which was conducted in Models 1 and 2. Furthermore, in order to navigate the result more efficiently, the outcomes are organized in ascending order of MENA in employment and EE in job quality according to country, showing the largest to smallest penalty. This order follows the hypotheses of this study as employment and job quality would be expected to be the most penalized outcomes for MENA and EE, respectively.

Male Ethnic Penalty

In terms of the specific ethnic penalty in male employment (Figure 2), MENA migrants experienced the highest discrimination, which was followed by SubAf among the five ethnicity categories, except for Greece, which showed the highest penalty for SA. The other three ethnicities showed different figures depending on the countries. On the other hand, the least penalized ethnicity was EE and Asians, with this shown in six countries for both ethnicities. The least penalty for EE can be found in the UK, DE, IE, FI, NO, and AT, while Asians experienced this in DK, BE, FR, CH, GR, and IT.

Figure 2. Male ethnic penalty in employment

Note: Full regression table found in the appendix Table 8. EE is Eastern European, MENA is Middle Eastern and North African, SubAf is sub-Saharan African, Asia is combining South and East Asians, and SA is South American including the Caribbean. The non-statistical significance (p >.05, CI overlapping 0) includes DE (SubAf, Asia, SA), PT (Asia, SA), and IT (EE, SA).

However, at the European level, Asians turned out to be the least penalized ethnicity in terms of employment; standing at –4 percentage points (pp) difference with respect to natives, followed by SA (–5pp) and EE (–6pp). As expected in the hypotheses, the most substantial ethnic penalty regarding employment at the European average level could be found for MENA with figures uncovered to be 16 pp lower than natives. Less penalty variations could also be found in Asian figures, which means the range of penalties was relatively steady compared with the other ethnicities by securing statistical significance in every country apart from Portugal and Germany.

When it comes to job quality, male ethnic penalty showed a different trend unlike employment (Figure 3). The most penalized ethnicity among the five ethnicities turned out to be EE, which is notably penalized with a range from –48 pp (PT) to –7 pp (FI) with respect to natives across the 16 countries. On the other hand, in some countries, MENA was equally (NL standing at –19 pp) or more penalized (SE, GR) than EE. Subsequently, MENA was still the second most penalized ethnicity regarding the job quality penalty. Nevertheless, in the total figure, the difference between EE (–24pp) and MENA (–15 pp) was large enough, and the difference between MENA and the others was around 2–5 pp, so the most significant job quality penalty was surely found for EE among the five ethnicities. On the other hand, SubAf was the least penalized standing at a –10 pp difference with respect to natives, and it was followed by SA (–12 pp) and Asians (–13 pp).

Figure 3. Male ethnic penalty in job quality

Note: Full regression table found in the appendix Table 9. EE is Eastern European, MENA is Middle Eastern and North African, SubAf is sub-Saharan African, Asia is combining South and East Asians, and SA is South American including the Caribbean. The non-statistical significance (p > .05, CI overlapping 0) includes AT (SA), CH (SA), DE (MENA, SubAf, Asia, SA), DK (SA), FI (MENA, Asia), GR (SA), and PT (SubAf, Asia).

When considering these two labor market outcomes simultaneously, Asians and South Americans mostly showed the least penalty despite some countries returning non-statistically significant results (see specific indications in Figures 2–3). They were positively selected, showing a higher probability of being in skilled jobs as well as gaining high employment when compared to the other ethnicities. Therefore, the higher performance of these two ethnicities could be explained in relation to locational inequality since Asia and South America have more geographical distance from Europe and relatively higher economic development than the other ethnicities’ regions. Subsequently, not only these geographical and economic factors but also the lower hiring discrimination attributed to the lower migration flow could contribute to these better labor market outcomes compared with the other ethnicities by vindicating the argument that the larger migration flows of certain ethnicities, the higher negative statistical discrimination toward them (see Abel and Sander 2014; Lippens et al. 2022).

Accordingly, although belonging to the same ethnicity, where they can migrate to could be an important condition behind how their integration might be differentiated when considering the case of South Americans and MENA. This is because SA was much more penalized in occupations (unskilled jobs), but MENA was relatively less penalized than SA, in the United States (Alba and Foner 2015). Meanwhile, male migrants from MENA and EE showed the most penalty in employment and job quality, respectively. These results thereby proved how ethnic penalty in the labor market is also affected by locational inequality, including geographical conditions and the level of economic development of origin countries, which was discussed by Milanovic (2016).

Female Ethnic Penalty

In line with male migrants’ results, female migrants from MENA and EE showed the most penalty with respect to natives regarding employment and job quality, respectively. There were a few exceptions, such as in France and Greece, where EE and SA were most penalized in employment while, in terms of job quality, Denmark, Sweden, Finland, Germany, and France revealed SubAf as the most penalized ethnicity. Additionally, Norway showed Asians and Greece presented EE to be the most penalized ethnicities in job quality (Figures 4 and 5). Interestingly, SubAf and SA in Italy and Portugal, Asians and SA in Spain, and Asians in Greece revealed higher employment than natives alongside statistical significance. This visible result clearly reflects institutional effects regarding the familiarism of Mediterranean countries in which female natives are induced to remain as housewives or in an inactivity status, rather than being active in the labor market (Esping-Andersen 2002; Lee 2022). Therefore, these facts imply that the familiarism of Mediterranean countries could be much stronger than in Continental European states so that lower migrant penalty or even native penalty in employment was found in these countries.

Figure 4. Female ethnic penalty in employment

Note: Full regression table found in the appendix Table 10. EE is Eastern European, MENA is Middle Eastern and North African, SubAf is sub-Saharan African, Asia is combining South and East Asians, and SA is South American including the Caribbean. The non-statistical significance (p >.05, CI overlapping 0) includes DE (SubAf, Asia), GR (EE, SubAf), and IT (EE, Asia). There is no data regarding SA reported for DE.

Figure 5. Female ethnic penalty in job quality

Note: Full regression table found in the appendix Table 11. EE is Eastern European, MENA is Middle Eastern and North African, SubAf is sub-Saharan African, Asia is combining South and East Asians, and SA is South American including the Caribbean. The non-statistical significance (p >.05, CI overlapping 0) includes DE (MENA, Asia), PT (SubAf, Asia), and FI (MENA, SA). There is no data regarding SA reported for DE.

At the European average level, MENA and EE showed the highest penalty in employment and job quality by standing at –25 pp and –30 pp, respectively, a pattern similar to the male case. However, female migrants were much more penalized compared with male counterparts in every ethnicity apart from SubAf, where employment was uncovered to be at the same figure for each gender. MENA female employment penalty was 9 pp higher than for male counterparts, while female migrants from EE were 6 pp more penalized than male counterparts in job quality. Consequently, female MENA was the most penalized in employment when considering ethnicities and gender simultaneously. This result is significant to discuss since previous audit research uncovered that MENA males are much more penalized in receiving callbacks than female MENA applicants (Arai et al. 2016; Bursell 2014; Derous et al. 2015; Di Stasio and Larsen 2020). Nevertheless, since the actual employment penalty in the labor market is much higher for female MENA than male MENA beyond employer’s preferences at the application screening stage, the cultural background that deters female MENA’s labor market participation can be vindicated as a key consideration through these empirical results.

The Four Ethnic Penalty Patterns with Migrant Men

To analyze the pattern of how employment and job quality were compatibly related to each other as a labor market outcome, quadrant matrixes were employed according to gender (Figures 6 and 7). Specifically, employment and job quality were simultaneously investigated so that, even if there were the same results within one outcome (i.e., employment) for the different ethnicities, alternative patterns could emerge when collating the other outcomes (i.e., job quality). Accordingly, four patterns can be recognized through each quadrant section within the matrices: trade-off pattern 1 (low employment and high job quality), trade-off pattern 2 (high employment and low job quality), a double penalty pattern (low employment and job quality), and a less penalty pattern (high employment and job quality). Additionally, Tables 2 and 3 organize the four penalty patterns to help understand the frequencies for each pattern according to the five ethnicities and by country.

Figure 6. Male ethnic penalty including five ethnicities in the quadrant matrix

Figure 7. Female ethnic penalty including five ethnicities in quadrant matrix

Table 2. Summary of male ethnic penalty pattern by ethnicity and country

Table 3. Summary of female ethnic penalty pattern by ethnicity and country

In terms of male ethnic penalty (Figure 6 and Table 2), trade-off pattern 2 can be found in the bottom-right quadrant and immigrants from Eastern Europe mostly occupied this space, representing nine out of 19 plots including all Mediterranean nations, Ireland, the UK, Norway, Switzerland, and Austria. Regarding the opposite trade-off pattern 1 (low employment and high job quality), although there was a minor difference between MENA/SA (six plots each) and SubAf (5), MENA showed a clearer pattern given its positioning toward the edge of the matrix (indicating much lower employment) in the first quadrant. It can therefore be confirmed that MENA was the representative ethnicity in this pattern. Remarkably, EE was absent from this pattern, so it uncovered how EE and MENA are differently penalized into opposite patterns. The most prominent ethnicity within the double penalty pattern was indisputably MENA since seven plots at the edge of the matrix were occupied by them and this was followed next by SubAf (6), EE (5), and Asian (2). When it came to the less penalty pattern, Asians were distributed here the most, represented by eight out of 21 plots, while there were no plotted points at all for MENA in this pattern like EE with trade-off pattern 1. However, SA and SubAf accounted for seven and four plots, respectively.

In line with these four patterns, there are important points to be emphasized. First, male migrants who were in Mediterranean countries, and especially those from EE, mostly experienced a trade-off pattern regarding low job quality but high employment. Italy showed the inclusion of five ethnicities in the same trade-off pattern 2, which are posited nearest to the outer edge in the first matrix. In addition to this, Spain (EE, SA), Greece (EE, MENA), and Portugal (EE, MENA) also added two ethnicities to this pattern. Second, it can be confirmed that MENA is less preferred by employers as hypothesized, so they could not help but turn out to be doubly penalized (DK, NL, NO, SE, BE, AT, ES) or experience a trade-off between high job quality and low employment (IE, FI, DE, FR, UK, CH). Hence, these findings support the identification of white preference in Western Europe based on the homogeneity and locational inequality hypotheses.

This congruence certainly assures that unskilled positions may require the preference of employers toward particular candidates, which could be grounded on appearance or race rather than on objective skills. Thus, racialization could happen more in unskilled positions, and by extension, the results of this analysis evidently prove that Eastern Europeans are much more preferred by employers. Furthermore, this can be highlighted in line with the locational inequality assumption because both ethnicities (EE and MENA) are locationally adjacent to Western Europe.

The competition within both ethnicities could be more severe than with the other ethnicities, which could lead to overqualification issues that have been found in previous research through EE migrants being employed in low-skilled positions despite high education backgrounds (see Leschke and Weiss 2020). Hence, the first and second highest penalties in job quality can be explained with the marginsplot conditioned by education level (see appendix Figures 7 and 8). This is since EE and MENA even with tertiary education revealed a 10–15 pp lower probability of being in skilled jobs than those of the other ethnicities regardless of gender, which corresponds to the highest (EE) and second highest (MENA) populations among the five ethnicities (Table 1). However, due to homogeneity and secularism, a double penalty pattern could be notably found for MENA but not for EE who are preferred by unskilled job employers.

The Four Ethnic Penalty Patterns with Migrant Women

The pattern of the female ethnic penalty provided a more dispersed distribution compared with male ethnic penalty patterns (Figures 6 and 7), which means there were more variations in female results across the 16 countries. Accordingly, male ethnic penalty patterns were almost evenly distributed according to each pattern, while female patterns showed an uneven distribution. Therefore, the female matrix’s slope revealed a much steeper line across the first and fourth quadrants, which means that female migrants in the 16 Western European countries experienced drastic trade-off penalty patterns in employment and job quality than those of male migrants.

Thus, correlation analysis between employment and job quality was additionally conducted according to ethnicities and gender to see the trend in greater detail (see Table 9 in the appendix). As found through Figures 6 and 7, it revealed that overall female ethnic workforces experienced more than three times higher negative association between employment and job quality than male counterparts (–.14) by standing at –.52. In terms of ethnicities, other than SubAf (both genders), every ethnicity showed a negative relationship indicating low job quality and high employment relationship, while this pattern is clearly pronounced for EE females with a –.69 coefficient. Accordingly, what can be implied in this result is that the increased female workforce demand since the post-industrial era, particularly in unskilled service positions, has necessarily been fulfilled by female migrants due to the lack of supply of native female workforces (Lee 2022; Soskice 2005).

Hence, “the more employed, the more taking unskilled positions” can be clearly found in female ethnic penalty patterns alongside notably higher correlation coefficients between job quality and employment (–.52) than for male counterparts (–.14). This can be particularly highlighted with the example of Mediterranean countries. Since native female labor participation as well as public care services are well known to be very low compared with other European countries, female migrants are arguably bound to be employed in private (or low-end) service sectors. Accordingly, within these countries the female ethnic penalty regardless of ethnicity was substantially closer to the edge of trade-off penalty 2’s quadrant, indicating an extremely strong pattern of high employment and low job quality.

Beyond this overall trend, the female MENA ethnic penalty was characterized by the double penalty pattern as well as trade-off pattern 1 regarding low employment and high job quality, which means that 50% of MENA females (15 out of 31 cases) were included in these two patterns in the same way as with the male migrants (see Table 3). SubAf female migrants were also the second highest in these patterns, but the proportion was much lower than with MENA, standing at 22% (seven out of 31 cases). This result evidently demonstrates cultural backgrounds, which could be much more disadvantageous for MENA females based on religious norms, influencing their lower rates of employment. Accordingly, the strong positive selection of MENA females in the labor market was uncovered, which means highly skilled female migrants from MENA could technically participate in the labor market more than low-skilled ones who could remain mostly in inactivity. This is quite a different trend compared with the other female ethnicities, especially EE. In terms of the less penalty pattern, the representative ethnicity across the 16 countries was revealed to be SA despite the small difference to the other ethnicities, which was followed by both Asian and SubAf. However, there were only four plots for EE and no MENA cases.

Discussion and Conclusion

The four hypotheses were examined through the level and pattern of ethnic penalty in the labor market with the five ethnicities across 16 Western European countries. The trade-off pattern regarding high employment and low job quality was occupied most prominently by EE for both genders. On the one hand, MENA was found as the most penalized ethnicity in both patterns regarding double penalty and trade-off in low employment and high job quality. Here, female MENA migrants showed a greater dominance than male counterparts by occupying half of the plots allocated in these patterns, in contrast to the male cases of about 30% across 16 countries. Therefore, the first hypothesis which expected an EE preference in employment and the highest discrimination for MENA based on homogeneity and secularism was supported. Furthermore, the second hypothesis regarding gender difference based on the mixed embeddedness assumption was also confirmed since the level of employment penalty regarding MENA was much more prominent among female cases.

Meanwhile, the locational inequality hypothesis based on the origin countries’ socioeconomic backgrounds was diverged into two hypotheses. First, the locational inequality assumption regarding the lowest penalty for Asians thanks to the economic development of the origin countries (hypothesis 3) is half supported through the male case. This is because, in the female case, the less penalty pattern was comprised mostly of SA and the second-best ethnicity in this regard was revealed to be not only Asians but SubAf as well. Hypothesis 4 regarding how the nearest sending countries’ larger immigrant population might lead to a significant penalty in job quality was supported. As can be seen in the descriptive analysis regarding this point, EE and MENA were the first and second largest ethnicities in the 16 subject countries, so EE revealed the most penalty in terms of job quality, followed by MENA (except MENA female cases).

Furthermore, the two hypotheses (1 and 4) are complementarily linked since these two ethnicities dominantly occupied the ethnic demographic composition in Europe, so the trade-off pattern regarding low job quality and high employment could have happened for both ethnicities. However, only EE turned out to be the prominent ethnicity in this pattern, which means that employers’ preferences were clearly placed on EE rather than MENA. This is because the unskilled sectors do not require higher formal qualifications, so employers’ impressions of ethnicity are more important than those of skilled job positions. Therefore, in the end, racialization regarding the double penalty indicates not only how low job quality but also how low employment is strongly occupied by MENA, representing a majority Muslim demographic with strong religious norms maintained from their origin countries in contrast to host society practices, alongside host’s Islamophobia.

Overall, three implications emerge from this study. First, the comprehensive statistics of Western European countries’ ethnic penalties were provided according to gender so that the different strategies not only for ethnicities but also for gender can be applied to policymakers to ameliorate ethnic penalties at country and European levels. Second, the consistency between employers’ hiring discrimination and the labor market outcomes was discovered as well. Specifically, MENA females were less discriminated against compared with males in the correspondence experiments (Arai et al. 2016; Bursell 2014; Derous et al. 2015; Di Stasio and Larsen 2020), while the employment penalty was much higher for females than that for males. The discrepancy can be explained through hypothesis 2 regarding the cultural effect of the labor supply, rather than demand side when considering findings between previous research and this study.

Moreover, Asian women in the experiments showed callback rates similar to white minorities (Di Stasio and Larsen 2020), whereas this study revealed their second highest penalty in employment at the European average level after MENA females. This can arguably be like the MENA case associated with mixed embeddedness since Asians may not be discriminated against by employers as severely as women of other ethnicities; however, conservative male breadwinners or Confucian (in the case of East Asian) cultures could influence on the low performance in employment.

In line with this, SubAf revealed notably interesting results regardless of gender by holding an in-between position regarding ethnic penalty patterns throughout the 16 case countries: a double penalty and trade-off regarding low employment and high job quality (MENA), the trade-off penalty regarding low job quality and high employment (EE), and less penalty (Asian or SA). Besides this, the least penalty in job quality was found for both genders of SubAf at the European average level. Hence, this result clearly supports once more how the use of a categorization distinguishing between North and sub-Saharan Africa is required as the penalty patterns are clearly different between them. Moreover, this fact also can be linked to the result of hiring discrimination, which showed less callback rates for Black women than white minorities and Asians (Di Stasio and Larsen 2020), but in the labor market outcome, they are less penalized compared to Asian women in employment and Asian and EE women in job quality.

Lastly, the ethnic penalty pattern showed another interesting tendency that classifies subject countries according to similar regional or institutional blocks. Apart from the less penalty pattern, most of the other three penalty patterns’ cases are predominantly clustered in relation to Mediterranean countries (trade-off penalty 2 with EE), Continental European countries (trade-off penalty 1 with MENA and SubAf), and Northern Europe (double penalty with MENA). This result well-matches previous analysis’ findings which revealed how labor market characteristics and relevant institutions’ differences affected migrant penalty patterns across Western European countries, although specific ethnic distinctions were not treated within these studies (see Ballarino and Panichella 2013, 2017; Kogan 2007; Lee 2022).

Hence, why the ethnic penalty phenomenon represents a great complexity regarding migrant labor market outcomes, which are compatible with not only racialization but also prospects for comparative institutional connectivity in the European context. This should be revealed further in detail alongside time effects including cohort differences because past penalties connected with institutions and racialization can affect contemporary ethnic penalties (Lang and Spitzer 2020; Lippens et al. 2023; Small and Pager 2020). Therefore, these interlinked factors should be complementarily investigated further to provide better solutions for migrant integration into European society.

Although this study revealed the association between racialization processes and ethnic penalty in the 16 European labor markets based on the sociocultural and economic backgrounds of both native and ethnicity sides, there remain areas that can be considered for future research. First, more detailed differences within the ethnicities could be investigated where there is available data. For example, although it is expected that Asian migrants can be differently clustered according to East Asian and South (East) Asian based on culture and economic development, these specific categorizations for migrant status have not been available in EU-LFS data.

To be specific, there are categories in EU-LFS covering East Asian and South Asian or South American and Caribbean countries as origin countries, but the categories of the source questionnaire are necessarily merged as Asia and South America in the end. This is because there are multiple options that migrants could choose for their country of origin so that South Asian or East Asian could answer to Asia rather than choosing a specific regional category. Therefore, in order to secure statistical power and prevent substantial missing values, the higher regional-level category was considered to be the better option by merging the subcategories. Accordingly, this study suggests that survey questionnaires would be better should they distinguish more clearly the country of origin, benefiting future migrant research in measuring ethnicity penalties in greater detail.

Second, individual characteristics such as education, age, and marriage status were controlled in this study in order to highlight ethnic differences associated with labor market penalty with respect to natives. Hence, in future studies, the detailed interaction between ethnicities and individual socioeconomic conditions should be analyzed according to gender. As can be seen in the appendix, Figures 1–12 visualized how the employment and job quality of each ethnicity can be differentiated according to the condition of education, age, and marriage status at the European average level. Interestingly, although education is similarly effective for both genders by revealing that the higher the education, the better employment and job quality outcomes, in terms of age and marriage conditions, differences between genders were able to be found.

Specifically, the lowest employment was uncovered in older (45–65) and younger (25–34) ages for male and female migrants, respectively, while married migrant men showed the highest performances in both measures, but married migrant women revealed the lowest employment rates. Moreover, the probability of being in the highest employment and lowest job quality categories turned out to be divorced/widowed female migrants. This critical information can arguably be discussed regarding how ethnic women could experience more conservative cultures that hinder their employment, particularly when married so that, in case of divorced or widowed situations, they could experience substantial livelihood issues as the results clearly stated (see appendix Figures 6 and 12).

Last but not least, an unobservable area that may also highlight ethnic penalty could be considered. When it comes to ethnic penalty, the umbrella concept is referred to as there are many aspects that could be difficult to disentangle (Panichella et al. 2021). In this sense, this study also considered multiple factors at the macro-level, including labor supply and demand sides alongside socioeconomic and cultural backgrounds, while different levels of analyses alongside other factors could be developed as well. In line with this, micro- and meso-levels could be assessed which are relevant to individual’s motivations or experiences regarding migration processes and community-level ethnic networks or similar. Furthermore, how colonial histories and the relevant linguistic proximity may influence labor market ethnic penalty could be meaningfully analyzed through country-specific research.