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Success on European Labor Markets:

A Cross-national Comparison ofAttainment between Immigrant andMajority Populations1

Florian PichlerUniversity of Vienna

Typical labor market outcomes vary considerably between majorityand migrant populations. Drawing on scholarship from across thesocial sciences, we assess competing micro- and macro-level explana-tions of differential occupational attainment among immigrant groupsacross 28 countries. The analyses of occupational attainment are runseparately for first- and second-generation migrants as well as childrenof mixed marriage and take into account their wider social and cul-tural background. Results from four rounds of the European SocialSurvey show that people with a migration background do not neces-

sarily achieve a lower labor market success than the majority. How-ever, human capital, social mobility, and cultural background explainthese outcomes to different degrees, suggesting tailored pathways tolabor market success for each group of migrants. We also find thatoccupational attainment varies considerably across countries, althoughthis is hardly attributable to immigration policies. These and otherfindings are discussed in the light of previous studies on immigrantincorporation.

1I am indebted to Martin Bulmer and Laura Hyman, both from the University of Surrey,Great Britain, Stefanie Smoliner from the Centre for Social Innovation, Vienna, Austria,and three anonymous reviewers of IMR for their careful reading and commenting onearlier versions of this article. Florian Pichler is Assistant Professor at the University ofVienna, Austria. He has obtained his PhD in 2006 from the University of Aberdeen, Scot-land, and has held a lectureship in the Sociology department at the University of Surrey

until 2010. His current research includes comparative studies on migration, anti-foreignsentiment, cosmopolitanism, social capital, and identities. He has recently published inInternational Journal of Comparative Sociology, International Sociology, and EuropeanSociological Review.

2011 by the Center for Migration Studies of New York. All rights reserved.DOI: 10.1111/j.1747-7379.2011.00873.x

938 IMR Volume 45 Number 4 (Winter 2011):938978

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INTRODUCTION

Participation in the labor market is one of the most important factors ofsuccessful integration of immigrants into society (e.g., Alba and Nee,2003; Joppke and Morawska, 2003). However, an abundant literature onintegration, here broadly defined as committing groups in one way oranother to the dominant mainstream in society, gives evidence thatmigrants face persisting inequality on labor markets (Portes and Borocz,1989; Van Tubergen, Maas, and Flap, 2004; Kogan, 2006; Fleischmann

and Dronkers, 2007). Migrants have been shown to experience lowerwages, participation rates, and occupational status which in turn hindertheir opportunities for a more comprehensive integration and often denythem a proportional share of the economic resources needed to equallysucceed in modern societies (Alba and Nee, 1997; Zhou, 1997a; Rum-baut, 1997; Mouw and Kalleberg, 2010).

How can we explain the labor market outcomes of immigrants?Lower levels of education, foreign education, deficiencies in language skills,belonging to classes of lower social status as well as ethnic and culturalattributes are frequently reported as important obstacles to labor marketsuccess among migrants (e.g., Farley and Alba, 2002; Crul and Doomernik,2003; Cheung and Heath, 2007; Heath, Rothon, and Kilpi, 2008; Portesand Fernandez-Kelly, 2008). Beyond these explanations, a growing numberof studies investigate characteristics of national labor markets and how theyaffect outcomes of immigrants (Buchel and Frick, 2005; Kogan, 2006,2007; Fleischmann and Dronkers, 2007; for a theoretic overview, see Reitz,2002). Such studies demonstrate that the welfare provision, employment

regulation and immigration policies of different countries impact upon theways in which migrants fare in their host labor markets.

To better understand lower labor market outcomes of migrants, thisarticle empirically assesses a series of potential individual and structuraldeterminants of outcomes across Europe. We elaborate on the existing liter-ature in three important ways. First, we distinguish between four differentgroups defined by origin to study similarities and differences in correlates oflabor market success. We provide separate accounts of labor market success

for first- and second-generation migrants as well as children of mixed mar-riages and compare them to the majority. Such a disassociation might yieldsome new insights because various migrant groups may differ from eachother on a series of dimensions. For instance, first-generation immigrants

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may experience disadvantages on the labor markets from factors such as adisruptive migration process, insufficient destination-country language skills

or foreign education; however, the second generation have lived in thecountry for their whole life and have obtained domestic education. There-fore, they should not be disadvantaged on the same terms (Heath, Rothon,and Kilpi, 2008). Fleischmann and Dronkers (2007) also give empiricalevidence of such different inroads into labor market outcomes; however,their study largely assumes that children of mixed marriages face the samesituation as second generation (see alsoBuchel and Frick, 2005). Yet, havingone native parent may result in their social and cultural integration being

substantially different from their second-generation counterparts, and theirsituation may instead more closely resemble that of the majority popu-lation. Hence, separate analyses could therefore lead to more accurateportrayals of labor market outcomes and minimize the danger of deceptiveconclusions concerning different groups of immigrants.

Second, we extend the list of correlates of labor market success toinclude a wider set of parental and cultural background characteristics.

We investigate consequences of parental education and differentiatebetween various countries of origin of respondents and their parents. Suchmultiple-origin studies are not necessarily new (Van Tubergen, Maas, andFlap, 2004), but they have rarely been combined with separate analysesof migrant groups (cf. Fleischmann and Dronkers, 2007). This shouldshed some more light on whether particular groups are locked in to dis-advantageous situations to different degrees depending on their socio-cultural background (e.g., Platt, 2005; Portes and Fernandez-Kelly, 2008).

Although the caveat exists that migrant groups are generally difficultto compare across countries because of variegated historical contexts of

population movements, previous empirical research that has done so pro-vides valuable insights into shared patterns of labor market integrationacross countries (e.g., Van Tubergen, Maas, and Flap, 2004; Kogan,2006; Fleischmann and Dronkers, 2007; Heath and Cheung, 2007). Yet,these studies have mainly been limited to Western Europe. Notwithstand-ing that immigration has been the larger issue in more affluent WesternEurope, UN figures also show that immigration has become increasinglyrelevant to Eastern European countries (United Nations, 2010). In light

of this but also taking into account recent efforts by the European Unionto establish common immigration policies regarding new internal andexternal flows of migrants, restricting the analysis to immigrants of

Western European countries seems less and less justifiable. Our analyses

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therefore do not only cover Western Europe but also include a list ofEastern European countries in order to explore labor market outcomes of

migrants in an enlarged Europe.In the following sections, we propose a number of hypotheses about

the determinants of labor market outcomes of various immigrant groups.The main focus of our study is on differences in individual-level correlatesof labor market success between various migrant groups; however, we alsoassess macro-level explanations for different outcomes. To empirically testthese claims, our multilevel analyses draw on European Social Survey(ESS) data from 28 European countries covering the period 20022008

(Norwegian Social Science Data Services, 2008).

DETERMINANTS: MIGRANTS AND LABOR MARKETOUTCOMES

The most prevalent accounts of variegated labor market outcomes refer tothe ways in which differences in human capital (Becker, 1962), socialmobility (e.g., Erikson and Goldthorpe, 1992) and, highly relevant topeople coming from different countries, migration background influencelabor market outcomes (e.g., Farley and Alba, 2002; Waters and Jimenez,2005; Heath and Cheung, 2007).

Human capital, or a persons knowledge and skills, is usuallyregarded as a core contributing factor to a persons productivity and theirlabor market outcomes (Becker, 1962). Migrants, however, face a predica-ment; many of their skills stem from foreign education which might resultin (1) difficulties assessing their human capital from an employers per-spective and (2) disposable knowledge and skills not equally needed in the

destination country (e.g., Cheung and Heath, 2007; Buzdugan and Halli,2009). Hence, foreign human capital may be devalued and payoffs fromeducation and prior experience can often be lower for immigrants thanfor natives. Theories of statistical discrimination add to this that employ-ers may apply different standards when it comes to the allocation of jobsto specific groups such as migrants (e.g., Phelps, 1972; Stiglitz, 1973;Cain, 1986). Drawing on general beliefs about andor prior experiencewith migrant workers, employers might judge migrants capacities based

on group characteristics rather than individual merit, which could trans-late into a devaluation of their education. For example, migrants could begenerally assessed as more mobile and thus more likely to quit a job. Or,migrants could be viewed as less reliable as they come from countries with


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different work cultures and their cultural origin may be, rightly orwrongly, associated with less punctuality or precision in executing job

tasks. Lower labor market outcomes could then be a sort of risk penaltymigrants have to pay for employment in the destination country.

Previous studies lend strong support to such trends, showing thatfirst-generation migrants do not only achieve lower attainment levels butalso fall short of benefitting from higher levels of education to the samedegree as the majority (e.g., Dustmann, 1993; Fleischmann and Dronkers,2007; Heath and Cheung, 2007). Chiswick (1978; Chiswick and Miller,2008) has intensively studied the complexity of this issue and puts forward

three explanations as to why immigrants education may not yield the samepayoffs: (1) self-selection in migration; (2) limited international transfer-ability of skills and (3) general discrimination against immigrants. Accord-ing to Chiswick and Miller (2008), decisions to migrate are morefrequently made by people who are very able and motivated despite theirlow levels of formal education. These migrants then prove to be more pro-ductive in their destination country than their schooling would imply.Therefore, they may achieve labor market outcomes that are substantivelyabove that which could be expected on educational grounds. While self-selection mainly concerns the low-educated, highly skilled migrants oftenface a limited transferability of their skills across national borders. Only incases that these workers find jobs that accord with their skill level, Chiswickand Miller (2008) postulate equal earnings increments for additional edu-cation as compared to the majority. However, more often than not highlyskilled immigrants find themselves in jobs for which they are either overed-ucated or their education is not appropriately recognized, which in turnaccounts for the lower educational payoffs. Rather than general discrimina-

tion, limited transferability in combination with the stronger performanceof low-skilled migrants is thus responsible for the seemingly lower payoffsof education for foreign workers (Chiswick and Miller, 2008).

Following the aforesaid ideas, one would not necessarily expect thatthe second generation would experience similar disadvantages. Self-selec-tion among the highly able can largely be ruled out; over-education mayremain an issue, but it is questionable why this should be more prevalentamong second generation than majority groups. The same patterns may

hold for children of mixed marriage if educated in the host country, but itis less clear how foreign education affects labor market outcomes of thosechildren. These people might, for instance, further benefit from higherability such as the near-perfect knowledge of two languages (if raised


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bilingual) or other valuable cultural and social capabilities gained throughtheir socialization in a context of mixed familial background. However,

because such skills would not have been acquired through formal educa-tion, it remains to be seen whether education plays a lesser role in this case.In accordance with previous results, our first hypothesis then assumes thateducation plays a smaller role in labor market attainment of migrants ascompared to the majority (hypothesis 1) while the education of the second

generation (and children of mixed marriage) should not be devalued(hypothesis 1a).

Apart from the skills obtained via formal education, it is also well

established that proficiency in the host countrys language has a consider-able impact on labor market attainment (Chiswick, 1978; Braun, 2010).Unless immigrants and their offspring are able to speak the language ofthe country of residence reasonably well, they often find themselves tiedto unfavorable labor market positions (Zhou, 1997b; Portes and Fernan-dez-Kelly, 2008). Among others, Van Tubergen, Maas, and Flaps (2004)study shows that speaking the host language fluently is positively associ-ated with labor force activity and employment. We therefore hypothesizethat migrants with higher levels of language proficiency experience better labormarket outcomes (hypothesis 2). Likewise, we assume that the same holds

for the second generation (hypothesis 2a).Labor market prospects also depend on the social origin of employ-

ees. Erikson and Goldthorpes (1992) work illustrates how parental educa-tion and occupation have long-lasting ramifications on labor marketoutcomes by facilitating or exacerbating upward social mobility acrossgenerations. Numerous studies show that the success of immigrants is lim-ited in these terms (e.g., Crul and Doomernik, 2003; Heath, Rothon, and

Kilpi, 2008; Rumbaut, 2008). Additional studies demonstrate that theimpact of social class origin also varies with ethnicity and that migrants ofparticular origins may suffer from blocked social mobility, or similarly,segmented assimilation (e.g., Zhou, 1997b; Simon, 2003; Platt, 2005;Portes and Fernandez-Kelly, 2008). These studies indicate that migrantsand their offspring often find themselves trapped in low-status occupa-tions for generations or that only migrants from particular origins aresocially upward mobile while other migrant groups are not.

Arguably, there are a number of competing explanations as to whythe education and labor market success of parents might influence futureoutlooks of their children. For instance, low-achieving parents might lackthe funds for their childrens education or they may be unaware, not


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appreciative or insufficiently supportive of inroads into higher education(Konczal and Haller, 2008; Kroneberg, 2008). Migrants frequent own

experience of lower labor market attainment or bleak prospects may alsolead to a loss of belief in their childrens payoffs from investment intohigher education. Therefore, parental education and occupation are consid-ered stronger predictors of migrants and second generations accomplishmentsas compared to the majority (hypothesis 3). Because intermarriage betweenparents can already be taken as a good indication of successful integrationin the destination country (e.g., Qian and Lichter, 2007; Dribe andLundh, 2008; Lucassen and Laarman, 2009), it is also feasible to hypothe-

size that parental background could play a lesser role for children of mixedmarriage than it does for the first and second generation (hypothesis 3a).Social distance between people of different socio-cultural backgrounds

creates additional obstacles, immobility and inequality and further ingrainsdifferences in labor market mechanisms and outcomes (e.g., Van Tubergen,Maas, and Flap, 2004; Bohon, 2005; Fleischmann and Dronkers, 2007).Social distance is especially applicable to immigrants because their lack ofcontacts with the majority as well as more observable differences in race orethnicity might marginalize them, be taken as proxies for diminished pro-ductivity or ignite other discriminatory practices (cf. Phelps, 1972; Arrow,1998). Numerous single and multiple-country studies demonstrate that ori-gin matters greatly for labor market success (e.g., Borjas, 1999; Kalter andGranato, 2002; Fleischmann and Dronkers, 2007). More recent EU policyfurther indicates a sharp distinction between internal migrants, that is, citi-zens of one EU member state moving to another, and people from outsidethe EU, highlighting the differential treatment of immigrants with a viewto political (and socio-cultural) proximity. Hence, we anticipate that immi-

grants who are, or are generally perceived as, more different from the major-ity than others, usually approximated through distant origin of immigrants(non-Western, non-European), face the greatest obstacles to labor market success(hypothesis 4). Following from this, origin should play a more decisive role

for migrants than for the second generation since the latter group is more famil-iar with the cultural life of the destination country (hypothesis 4a). Theparental background of children of mixed marriage may also blur the linesof social and ethnicity-based distinctions. Hence, we would assume that the

origin of the non-native parent has no substantive effect on labor marketoutcomes of children of mixed marriage(hypothesis 4b).Initial social distance and other markers of distinction might,

however, diminish over time because migrants adapt to the destination


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environment, accumulate country-specific human capital or are generallyrecognized as being part of society (cf. Chiswick, 1978). The longer immi-

grants settle in the country, the better is their integration in general and thehigher are their labor market prospects and outcomes (e.g., Van Tubergen,Maas, and Flap, 2004). However, some studies are less conclusive aboutsuch a convergence in European contexts, indicating that duration of resi-dence does not necessarily lead to higher outcomes (e.g., Shields and Price,1998; Husted et al., 2001). Acquiring citizenship could even outdo theduration of residence in its importance for attainment, indicating theimmigrants commitment and efforts to integrate. Studies show that natu-

ralization not only provides access to a wider selection of jobs, especiallyhigher middle-class occupations and public employment, but also promotesother labor market outcomes by, for instance, accelerating wage growth(e.g., Euwals et al., 2010; Bratsberg, Ragan, and Nasir, 2002). Our fifthhypothesis combines both of these arguments about adaptation to the hostsociety over time and postulates that longer residence as well as citizenship

point toward better labor market outcomes.

CONTEXT: MIGRANTS ON EUROPEAN LABOR MARKETS

Theoretic perspectives suggest a number of explanations as to why immi-grants could do better in one country than another. Originating fromresearch in North America, Reitz (2002) proposes a number of interrelatedfeatures of host societies which could determine the prospects of immigrantincorporation. First, pre-existing ethnic relations may affect how new immi-grants fare on the labor market. In a European context, established relationswith immigrants or other ethnic groups may be related to colonialism and

immigration policies after the Second World War. Former imperial coun-tries such as France and Britain have attracted overseas immigrants over alonger period of time. Large-scale immigration to other European countries especially to Germany, Austria or Switzerland only took off after theimplementation of guest-worker programs in the 1950s and 1960s (Castles,2006). Other countries such as Spain or Portugal have only recently chan-ged from being an emigration country to an immigration country (see, forinstance, Feld, 2005; Heath, Rothon, and Kilpi, 2008). Historical experi-

ence thus provides a sound basis for assuming some variation in ethnic rela-tions between majority and migrant groups across Europe.The two features of countries which have attracted most attention in

empirical work are, however, differences in labor market institutions and


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government policies (Reitz, 2002). These accounts appear to be particu-larly relevant to studies of immigrants incorporation in Europe where

countries differ more widely in labor market structures and immigrationpolicies such as naturalization, residence, work permits, social and politicalparticipation but also on general welfare provision. Such direct policiesassist immigrant settlement by establishing equal-opportunity frameworks,anti-discrimination regulations, shaping inter-group relations and provid-ing training (Reitz, 2002). They generally reflect openness and immigra-tion-friendliness of polities. Previous studies then illustrate that variationin immigrant policies plays some role in migrants differential prospects

of incorporation into society across Europe (e.g., Buchel and Frick, 2005;Schain, 2010). Fleischmann and Dronkers (2007), however, find thatquantitative indicators of such integration policies fall somewhat short ofexplaining a meaningful part of cross-national variation in migrants labormarket outcomes. Reitz (2002) reports that the effects of these policiesmay be more indirect and thus more difficult to quantify. We may there-fore assume that more open immigration policies only modestly enhance thelabor market outcomes of migrants in direct ways (hypothesis 6).

Similarly, labor market institutions may affect the outcomes ofmigrant groups. Among others, employment regimes, industrial relationsor unemployment rates frame opportunities of migrants. More often thannot, these institutions constitute barriers to the labor market, which partic-ularly affect immigrants as domestic regulators may be suspicious about theimpact of immigration on their own situation. The interrelations betweenthese mechanisms and other societal institutions, however, necessitate com-plex analyses of these mechanisms, which seem only feasible for two orthree countries at a time (Reitz, 2002). Yet, international studies also report

some effects of employment regulations and welfare institutions on labormarket outcomes of migrants (e.g., Fleischmann and Dronkers, 2007;Kogan, 2007). The findings indicate that more generous welfare regimesand rigid labor market institutions may disadvantage immigrants and hinttoward selective protection of the majority rather than the overall work-force. According to Fleischmann and Dronkers (2007), higher employmentprotection may stimulate statistical discrimination against migrants; Kogan(2007) reports that the highest labor market outcomes of immigrants are

found in liberal welfare regimes. To capture this sort of contextual hetero-geneity, we finally assume that stricter employment regulations negativelyimpact on migrants levels of attainment (hypothesis 7a) and that migrants

fare better in liberal welfare regimes than in other ones(hypothesis 7b).


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DATA AND METHODS

To test our hypotheses, we use data from four rounds of the ESS coveringthe period 20022008 (Norwegian Social Science Data Services, 2008).The ESS is an academically driven, large-scale, repeat cross-sectional socialsurvey, covering over 30 European countries. Its rigorous methodology,relatively large sample size (approximately 2,000 respondents per countryper round) and high data quality standards make the ESS a prime sourcefor comparative survey research across European societies. In this study, werely on 118,295 respondents aged 1565 years, who have been surveyed in

four biennial rounds in 2002, 2004, 2006, and 2008 in 28 countries.2

While the ESS does not particularly address migrants as its main target pop-ulation, it allows a distinction between various migrant groups and their ori-gin. Fleischmann and Dronkers (2007) discuss these important comparativeadvantages over other data sources such as a series of labor force surveysused by Van Tubergen, Maas, and Flap (2004) or studies which focus on asmall number of countries andor migrant groups (e.g., Chiswick, Lee, andMiller, 2003; Barrett and Duffy, 2008; Kasinitz et al., 2008).

We draw on indicators of place of birth to identify four mutuallyexclusive groups: (1) majority, that is, people whose parents have bothbeen born in the country of residence and who do not count themselvestoward any ethnic minority (N = 99,947);3 (2) migrants who have been

2Countries included are: Austria, Belgium, Croatia, Cyprus, Czech Republic, Denmark,Estonia, Germany, Finland, France, Greece, Hungary, Italy, Ireland, Latvia, Luxembourg,Netherlands, Norway, Poland, Portugal, Russian Federation, Slovakia, Slovenia, Spain,Sweden, Switzerland, Ukraine, and United Kingdom. Although data from Bulgaria, Israel,

Romania and Turkey are available, we exclude these respondents from the analyses becauseof issues of data comparability and sample size. For the analysis of labor market outcomesof the second generation, we have further eliminated data from Cyprus, Spain, Finland,Ireland, Italy and Russian Federation because of small sample sizes (N < 25).3The aforesaid sample sizes apply to non-weighted pooled data. Group sizes vary acrosscountries. While different sizes of migrant groups reflect social reality, empirical studiesmay be driven by those countries for which survey data include many respondents withmigration backgrounds. Using hierarchical models in our study, we are partially able todeal with this imbalance in group sizes and account for it in the estimation of fixed andrandom effects respectively. In addition, we have run models excluding data from some

countries (e.g., Estonia and Latvia) because of the contested nature of some ethnic minori-ties andor immigrant groups in these countries. These models, however, do not indicatethat the exclusioninclusion of these respondents has any noteworthy effect on our out-come variables. Results are available from the authors upon request.


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born abroad without any native background in the country of residence(N = 8,315), (3) second generation whose parents have both been born

abroad but who themselves have been born in the country of residence(N = 2,562), and (4) children of mixed marriage, with one indigenousparent and the other born elsewhere (N = 7,471). Country-specific samplesizes by group and gender are presented in Table A1.

To assess labor market outcomes, we first present participation andunemployment rates. These figures are derived from answers to surveyquestions about the main activity during the last 7 days prior to the inter-views. However, the focus of this study is on occupational attainment of

migrants as the ESS provides the most accurate data on this indicator.Following Fleischmann and Dronkers (2007), we derive a measure oflabor market success from the occupational classification scheme EGP,developed by Erikson, Goldthorpe, and Portocarero (1979). In compari-son with alternative measures (participation and unemployment rates,personal andor household incomewage, or ISCOISEI), EGP provides awidely used indicator which also correlates highly with alternative instru-ments. EGPs original 11 categories have then been dichotomized: highoccupational attainment comprises people in controlling positions (higherand lower controllers, or groups I and II respectively); and not-high occu-

pational attainment comprises all remaining groups (such as routinenon-manual, self-employed, skilled, and unskilled manual work).4 Thisdichotomy helps assess the chances of belonging to the higher middle clas-ses as indicated by higher occupational status, which is sometimes seen asan important criterion of social standing in European societies (cf. Fleisch-mann and Dronkers, 2007). While this dichotomization is undoubtedly asimplification, a more complex, multi-group measure of labor market

success would lead to considerably unbalanced sample sizes across the var-ious categories, some problems in statistical estimation using a large num-ber of explanatory variables and a potential blurring of the line ofdemarcation between high and not-high labor market success.

4EGP correlates highly with ISCO 1-digit codes (q = 0.85 in the pooled sample;0.74 q 0.90 in specific countries) and total household net income (in deciles)(q = 0.33 and 0.20 q 0.50 respectively), suggesting the use of EGP as the best avail-able proxy for labor market success (all correlation coefficients calculated using 2008 ESS

data). Fleischmann and Dronkers (2007) study also shows that various indicators of labormarket success, among them a dichotomous EGP-based measure of occupational attain-ment, are highly correlated and produce very similar results in analyses concerning thedeterminants of labor market success of migrants.


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Using hierarchical logistic regression models, we assess the explanatorypower of competing theories of high (coded 1) versus not-highlow (coded

0) occupational attainment separately for each of the four groups. Randomeffects of average attainment take into account cross and within-countrydifferences over the four survey rounds. Independent variables enter themodels as fixed effects because of the small sample size of 28 countries.These models can be expressed as follows:

Yijk b0jk b1HCijk b2SMijk b3MBijk b4Cijk

c1Zjk eijk x1HSijk 1

b0jk c000 u0jk v00k 2

Yijk is the logit-transformed dependent variable pertaining to high occupa-tional attainment of individual i in ESS round j in country k; b0jk includesthe constant c000 and its random within-country variation u0jk and across-country variation m00k (see equation 2); the matrix HC comprises indepen-dent variables referring to human capital, SM includes measures of social

mobility; MB points toward migration background while the matrix Cadds further individual-level control variables, and their respective fixedregression coefficients b1 to b4. Zjk is a matrix including structural variablesat the country level with regression coefficient c1, of which some also varyacross survey round j. eijk is the residual at the individual level.

Following previous studies, our analyses are run for male and femalerespondents separately (cf. Van Tubergen, Maas, and Flap, 2004; Kogan,2006; Fleischmann and Dronkers, 2007). We have also controlled forpotential sample selection bias because the likelihood of entering the labormarket may vary across groups (and gender). Using a Heckman selectionmodel, we have calculated the Inverse Mills Ratio and included it in ourmodels. x1HSijk tries to capture this selection effect.

5 Preliminary multi-level models further show that changes in aggregate labor market out-comes at the survey round level (level 2) are negligible as this variation

5The Heckman selection model regresses a variable indicating either valid or missing dataon occupational attainment on a series of predictors. In these models, we have included

the following independent variables: education, work experience, experience of unemploy-ment, working experience abroad, language spoken at home, working hours, number ofchildren, place of residence, religious denomination, parental education, country of birth,mothers country of birth, citizenship status, and duration of residence.


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only amounts to 3 percent or less of the total variance and can thus beanalytically ignored for our purposes (see Snijders and Bosker, 1999). This

also implies that pooled data do not conceal potential changes in aggre-gate occupational attainment over the period 20022008 in any of thefour groups. We therefore use average scores of macro variables over theperiod 20022008 instead of providing them for each point in time.

To assess the effects of human capital on labor market attainment,we first include the number of years of schooling and control for workexperience in years, work experience abroad,6 experience of being unem-ployed and working hours. Language skills are also included in our mod-

els, but the ESS does unfortunately not provide a direct measure oflanguage proficiency. However, we have derived a proxy from a variablethat concerns the language most often spoken at home. We have com-puted a dummy variable that indicates whether the language most oftenspoken at home is an official national language of the country of residenceor not. Arguably, speaking the host countrys language at home pointstoward migrants higher language proficiency (see Fleischmann and Dron-kers, 2007). Second, we test correlates of social mobility, that is, the extentto which parental background may affect labor market success of theiroffspring, by referring to the parents highest educational achievement.Third, we elaborate on migration background of migrants, second genera-tion and children of mixed marriage. Here, we examine influences ofcountry of birth, mothers country of birth, citizenship, and duration ofresidence. We distinguish between 14 regions where migrants come from.Unsurprisingly, most come from within the European Union; but thereare other sizable groups originating from a series of countriesregions suchas Turkey, the Balkans, other non-EU Eastern Europe, Asia, Central- and

South America as well as African regions. After some considerations, wehave only included mothers origins for second generation and children ofmixed marriage as mothers are thought of being more influential insocialization in general (see also Fleischmann and Dronkers, 2007). More-over, parental origins are highly correlated in most of the cases, and

6Unfortunately, the ESS does not further distinguish between years of work experience indifferent countries. In the case of migrants, we can thus not assess how many years they

have worked in the destination labor market and how many in others. While workingabroad for majority members might indicate mobility and valuable foreign experience, inthe case of migrants the same indicator might just reflect employment in the origincountry.


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sample sizes would not suffice to account for interaction effects of specificcombinations of parental origin. Table A2 shows detailed sample sizes of

all 14 origin regions by migrant groups and gender. Finally, we add con-trols for the number of children, place of residence, and religious denomi-nation to account for additional correlates of labor market success asidentified in the broader literature. Selected descriptive statistics of theseand other individual-level variables are presented in Table A3.

At the country level, we include World Bank indicators of GDPgrowth and official participation and unemployment rates which alldescribe the economic and labor market situation in European countries

between 2002 and 2008. Our welfare regime typology extends that ofEsping-Andersen (1990) by including Cyprus, Greece, Italy, Spain, andPortugal as Southern EuropeanMediterranean welfare regimes, which arecharacterized by an extended role of the family in providing welfare (seeFleischmann and Dronkers, 2007). In contrast to previous studies, ouranalysis also comprises a number of Eastern European countries which aremore difficult to include in a welfare typology. Nonetheless, to provide anexhaustive indicator, we have established two other categories for (1)Eastern European countries which have joined the EU (Czech Republic,Estonia, Hungary, Latvia, Poland, Slovakia and Slovenia) or maintain veryclose links to the EU (Croatia) and (2) which have not (Russia andUkraine).

We also test whether employment protection accounts for variableoccupational attainment of migrants in Europe. The OECD provides anindex of employment protection constructed from 21 items on individualdismissals of workers with regular contracts, costs for collective dismissalsand regulations of temporary contracts. Values range from 0 to 4 where

higher values indicate stricter protection regimes.7

Finally, we assess theexplanatory power of indices capturing national immigration policies(Migration Integration Policy Group, 2007). These indices range from 0to 100 where high values indicate immigration integration friendly poli-cies on the dimensions of Access to Nationality, Anti-Discrimination,Family Reunion, Labor Market Access, Long-term Residence andPolitical Participation. For instance, the Labor Market Access Indexreflects the eligibility for jobs, state help for migrants to adjust to the

domestic labor market and others. Anti-Discrimination refers to policies

7See also (accessed November 29,2010).


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concerning definitions and concepts of discrimination on various grounds,for example, ethnicity, religion and nationality, enforcement, and encour-

agement to bring forward a case and so on. Long-term Residence cap-tures the length of time until permanent residence permits are granted,restrictive requirements and associated rights with permanent residence(see Migration Integration Policy Group (2007) for more details). Theeffects of these structural variables on labor market outcomes have to beassessed in separate models because of statistical limitations that exist as aresult of the rather small number of countries included in the analysis.Results will therefore provide insights into alternative accounts rather than

explanatory power of one structural explanation over others. Country-levelstatistics of all structural variables are available in Table A4.

RESULTS

Labor Market Outcomes of Different Groups: Descriptive Findings

Table 1 presents descriptive statistics of labor market outcomes partici-pation and unemployment rates, and occupational attainment by group

and gender. Mean participation rates vary considerably more across groupsfor men than for women. The percentage of men in the majority who areparticipating equals 73 percent; participation is considerably higher among

TABLE 1LABOR MARKET OUTCOMES OF DIFFERENT GROUPS. REPORTED LEVELS OF PARTICIPATION,

UNEMPLOYMENT, AND OCCUPATIONAL ATTAINMENT

Group

Labor Market Outcomes

Participation (%)

Unemployment

(%)

High Attainment

(%) NMean Min Max Mean Min Max Mean Min Max Mean Min Max

MajorityMale 73 62 80 5 0 12 36 21 54 1,957 407 3,829Female 56 46 74 4 0 9 36 19 51 2,159 452 3,795

MigrantMale 78 53 91 7 0 14 28 4 43 161 7 551Female 55 21 73 7 2 18 33 7 51 182 13 654

2nd generationMale 69 42 92 9 0 25 37 4 54 71 12 206Female 57 29 83 6 0 21 39 9 55 74 11 212

Mixed parental backgroundMale 73 54 88 6 0 12 41 22 57 142 17 303Female 56 33 71 4 0 10 41 16 54 160 15 362

Source: European Social Surveys, rounds 14. Data weighted.Note: Country means, minimums, and maximums.


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migrants (78 percent) and is somewhat lower among second-generationmen (69 percent). Among women, participation rates are more or less

equal across groups and amount to approximately 56 percent. We alsofind substantial cross-national variation indicated by minimum and maxi-mum participation rates given in Table 1. Unemployment rates also varyacross groups, with the highest level of unemployment reported by thesecond generation. As for high occupational attainment, 36 percent ofmajority men and women report high occupational status. A lower pro-portion of migrant men achieve the highest occupational status (28 per-cent) than women do (33 percent). However, the second generation and

the children of mixed marriage report higher levels of occupational attain-ment than majority. For 37 percent of second-generation men and 39percent of women, high occupational attainment is indicated. Percentagesof those with high attainment levels rise further still to 41 percent forboth genders among the children of mixed marriage. However, we alsoobserve larger cross-national variation in these figures which raises thequestion of whether these differences may be brought about by compo-sitional effects, that is, that different countries may attract differentlyqualified migrants or whether there are country-level explanations fordifferent attainment across groups and gender (see Van Tubergen, Maas,and Flap, 2004).

Figure I shows a more detailed comparison of rates of high attain-ment between majority (x-axis) and migrants (y-axis) by gender. The 45degree line indicates equal outcomes for both groups in each of the charts.In most countries, migrant men report equal or higher participation ratesthan majority men with the exceptions of Czech Republic, Slovakia, andLatvia. For women, however, such a trend does not appear to exist. While

migrant women participate considerably less often than the majority inPoland and Finland, migrant women participate comparatively more oftenin many other countries. Unequal labor market outcomes are much morepronounced in the case of unemployment where both migrant men andwomen report higher levels than the majority. This holds for almost allcountries under study with the exception of migrant men in Russia, Hungary,and Italy, where none of the sampled men report unemployment (butnote the smaller sample sizes shown in Table A1).

A very similar picture arises from the comparison of occupationalattainment. In most countries, migrants of both genders less often reporthigh attainment than majority. This is particularly true for men inGreece but also in the Netherlands, Denmark, Germany and some other


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FigureI.

LaborMarketOutcomes

ofMajorityandFirst-generation

MigrantMale(Top)andFemale(Bottom)Populationsin28C

oun-

tries.LaborMarketParticipation,

UnemploymentRateandOccupationalAttainment(Proportions)

Source:

EuropeanSocial

Surveys,roun

ds14.Dataw

eighted.

Notes:

AT,Au

stria;

BE,

Belgium;

CH,

Switzerlan

d;C

Y,

Cyprus;

CZ,

Czech

Repub

lic;

DE,

Germany;

DK,

Denmar

k;EE,

Eston

ia;

ES,Spain;FI,Finland;FR,

France;U

K,

United

Kingdom;

GR

,Greece;

HR,

Croat

ia;

HU,

Hungary;

IE,

Irelan

d;

IT,

Italy;

LU,

Luxem

bour

g;LV,

Latvia;

NL,

Net

herlan

ds;

NO,Norway;

PL,

Polan

d;PT,

Portuga

l;RU

,Russian

Federat

ion;SE

,Sweden;

SI,

Sloven

ia;

SK,

Slovak

ia;U

KR,

Ukraine.


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countries, where differences in occupational attainment between migrantsand the majority are largest. It is also true for women in most of these

countries, although country-level patterns of high occupational attainmentamong women do not necessarily run in parallel to those among men.This pattern can be illustrated with data from Austria where migrantwomen report relatively better labor market outcomes than the majority,while migrant men in this case report considerably lower levels of attain-ment. The aforementioned findings should, however, be interpreted withsome caution because of the variable sample sizes of migrants across coun-tries and the fact that different countries attract migrants from different

origins; indeed, we have not yet considered any analytical explanation forthese findings.To provide a fuller picture, we first discuss so-called baseline multi-

level models (not shown), in which we partition the variance in labormarket attainment at the country, survey round and individual levels aftercontrolling for selection bias. As has already been noted, our baselinemodels indicate an absence of variability of occupational attainment acrosssurvey rounds within countries (level 2). More importantly, however, thebaseline models also identify some countries which show dissimilar pat-terns of labor market outcomes for some groups (see bottom of Table 2for a list of these countries). After isolating these countries, that is, remov-ing them from the random part, country-level variation has unsurprisinglydecreased and now only accounts for up to 5 percent in the case of malemigrants after controlling for sample selection bias in baseline models.Figure II presents boxplots of odds for high occupational attainmentobtained from these baseline models. It can be easily seen that secondgeneration and migrant males fare, on average, considerably worse than

majority males, while the sons of parents with mixed background dobetter. Although there is a similar trend among women, the daughters ofmixed marriages do not report higher average levels of occupationalattainment than majority women.

Figure II also indicates substantial country-level variation in occupa-tional achievement. In this respect, occupational attainment most stronglyvaries across countries for the majority suggesting general structuraldifferences between European labor markets. This variation, however,

appears less pronounced for male immigrants and even lesser so for thesecond generation, where the rather short whiskers illustrate smallercross-national variation in attainment. This also suggests a rather similarexperience of these groups across Europe with the exceptions of Great


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TABLE2

RESULTSFROM

LOGISTICREGRESSIONSON

OCCUPATIONALATTAINMENT(FOR1565-YEAR-OLDS)

Indicator

Major

ity

Migrant

2ndGenerat

ion

Childrenof

Mixed

Parental

Bac

kground

Male

Fema

le

Male

Fem

ale

Male

Female

Male

F

emale

OR

p

OR

p

OR

p

OR

p

OR

p

OR

p

OR

p

OR

p

Constant

0.67

***

0.65

***

1.17

0.67

*

0.80

0.63

0.83

0.74

*

Educat

ion(ft)

inyears

1.39

***

1.36

***

1.30

***

1.31

***

1.47

***

1.32

***

1.37

***

1.28

***

Wor

kexperience

(years)

1.03

***

1.02

***

1.01

1.01

*

1.03

**

1.02

*

1.03

***

1.01

*

Exper

ienceunemployment

Recentshort

0.59

***

0.55

***

0.47

***

0.69

*

0.52

*

0.62

0.52

***

0.72

*

Recentlon

g

0.49

***

0.49

***

0.59

***

0.73

*

0.54

*

0.59

*

0.45

***

0.65

**

Previous

short

0.79

***

0.71

***

0.89

0.9

0.91

0.71

0.75

*

0.89

Previous

long

0.58

***

0.61

***

0.79

0.83

Noobs

0.60

0.83

0.62

**

Exper

ience

abroad

(no)

1.14

**

1.13

*

0.96

1.44

**

1.23

1.18

1.05

1.72

**

Wor

king

hours

week

1.00

**

1.01

***

1.00

1.02

***

1.01

1.03

***

1.00

1.02

***

Language

(major

ity)

NA

NA

0.73

**

0.72

***

1.32

0.55

*

0.85

0.77

Parentaleducation

(uppersecondary)

Not co

mpleted

primary

0.62

***

0.64

***

0.60

**

0.62

**

0.59

0.72

0.40

**

0.53

*

Primary

0.70

***

0.71

***

0.62

**

0.84

0.60

0.91

0.61

**

0.73

*

Lower

secondary

0.78

***

0.86

***

0.64

**

0.84

1.04

0.97

0.80

0.70

**

Post-

secondary

1.14

*

1.14

*

1.12

1.49

*

1.98

1.20

1.05

1.07


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TABLE2(CONTINUED)

RESULTSFROM

LOGISTIC

REGRESSIONSON

OCCUPATIONALATTAINMENT(FOR1565-YEAR-OLDS)

Indicator

Major

ity

Migrant

2ndGeneration

Childrenof

Mixed

Parental

Bac

kground

Male

Female

Male

Female

Male

Female

Male

F

emale

OR

p

OR

p

OR

p

O

R

p

OR

p

O

R

p

OR

p

OR

p

Firststage

tertiary

1.49

***

1.37

***

1.71

***

1.4

3

**

1.47

1.2

4

1.26

1.45

**

Secondsta

ge

tertiary

1.57

***

1.54

***

2.88

***

1.2

5

1.78

1.4

1

1.59

*

2.05

***

Countryofb

irth(EU15)

New

EU12

NA

NA

0.41

***

0.7

6

NA

N

A

NA

NA

Westernn

on-

EUEuro

pe

NA

NA

1.78

0.5

5

NA

N

A

NA

NA

Easternno

n-E

U

Europe

NA

NA

0.34

***

0.5

8

**

NA

N

A

NA

NA

Bal

kan

NA

NA

0.47

***

0.6

1

**

NA

N

A

NA

NA

Turkey

NA

NA

0.54

*

0.6

6

NA

N

A

NA

NA

Western

non-Euro

pe

NA

NA

0.89

1.0

5

NA

N

A

NA

NA

MiddleEa

st

NA

NA

0.62

0.7

2

NA

N

A

NA

NA

India,

Pak

istan,

Bangladesh

NA

NA

0.42

**

1.0

1

NA

N

A

NA

NA

China

NA

NA

0.57

1.4

2

NA

N

A

NA

NA

Other

Asia

NA

NA

0.43

***

0.4

5

***

NA

N

A

NA

NA

Centralan

d

Sout

h

Amer

ica,

Car

ibbean

NA

NA

0.34

***

0.6

9

*

NA

N

A

NA

NA

NorthAfrica

NA

NA

0.58

*

0.6

5

NA

N

A

NA

NA

Other

Africa

NA

NA

0.71

0.4

7

**

NA

N

A

NA

NA

Mot

her

scou

ntryof

birth(nat

ive)

OldEU15

NA

NA

NA

N

A

Ref

Ref

0.95

1.04

New

EU12

NA

NA

NA

N

A

0.41

*

0.5

9

0.67

*

0.70

*


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TABLE2(CONTINUED)

RESULTSFROM

LOGISTICREGRESSIONSON

OCCUPATIONALATTAINMENT(FOR1565-Y

EAR-OLDS)

Indicator

Major

ity

Migrant

2ndGeneration

Childrenof

Mixed

Parental

Bac

kground

Male

F

emale

Male

F

emale

Male

Female

Male

Female

OR

p

OR

p

OR

p

OR

p

OR

p

OR

p

OR

p

OR

p

Westernn

on-

EUEurope

NA

NA

NA

NA

0.11

Noobs

1.20

1.3

2

Easternno

n-E

U

Europe

NA

NA

NA

NA

0.29

**

0.6

2

0.95

0.9

5

Bal

kan

NA

NA

NA

NA

1.04

0.8

4

0.57

1.1

1

Turkey

NA

NA

NA

NA

0.55

1.5

9

2.53

3.0

6

Western

non-Euro

pe

NA

NA

NA

NA

0.07

Noobs

1.26

0.6

3

MiddleEa

st

NA

NA

NA

NA

2.18

Noobs

0.24

1.9

7

India,

Pakistan,

Bangladesh

NA

NA

NA

NA

1.13

0.8

1

1.10

5.1

5

China

NA

NA

NA

NA

8.18

Noobs

Noobs

No

obs

Other

Asia

NA

NA

NA

NA

0.79

1.3

5

1.99

1.2

5

Centralan

d

Sout

h

Amer

ica,

Car

ibbean

NA

NA

NA

NA

0.61

0.3

9

1.75

1.1

9

NorthAfrica

NA

NA

NA

NA

0.34

*

1.8

5

1.75

0.6

4

Other

Africa

NA

NA

NA

NA

0.40

1.7

8

1.66

1.2

2

Citizensh

ip(yes)

NA

NA

0.74

**

0.9

8

0.89

0.7

8

0.63

0.8

2

Durationofresi

dence

(bornincountry)

Morethan

20years

NA

NA

Ref

Ref

NA

NA

0.84

0.8

4

112

0years

NA

NA

0.81

1.0

9

NA

NA

0.69

0.4

8

**

610years

NA

NA

0.88

0.8

5

NA

NA

1.28

0.9

9

15years

NA

NA

1.01

1.1

1

NA

NA

0.83

0.6

0

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TABLE2(CONTINUED)

RESULTSFROM

LOGISTIC

REGRESSIONSON

OCCUPATIONALATTAINMENT(FOR1565-Y

EAR-OLDS)

Indicator

Major

ity

Migrant

2ndGeneration

Childrenof

Mixed

Parental

Bac

kground

Male

Female

Male

Female

Male

Female

Male

F

emale

OR

p

OR

p

OR

p

O

R

p

OR

p

O

R

p

OR

p

OR

p

Dom

icile(bigcity)

Suburbs

0.96

1.0

1

0.79

1.03

1.12

1.00

0.94

1.1

5

Smal

lcity

0.80

***

0.9

1

**

0.83

0.76

**

0.78

0.90

0.80

0.9

1

Countryv

illage

0.64

***

0.7

9

***

0.74

*

0.82

0.85

0.85

0.78

*

0.8

9

Countrysi

de

0.42

***

0.7

0

***

0.51

*

0.81

Noobs

No

obs

0.65

0.7

8

Rel

igious

denom

inat

ion(none)

Roman

Cat

holic

1.06

1.0

3

1.07

0.85

0.86

0.97

0.91

1.1

7

Protestant

1.01

1.0

0

0.88

1.01

1.39

1.55

0.90

0.7

8

*

EasternOrthodox

0.79

**

1.1

0

0.90

0.97

0.83

1.38

0.56

**

0.9

6

Islam

0.65

0.6

1

0.91

0.79

0.96

0.77

0.29

1.1

0

Samplese

lection

control

(IM

R)

0.311

***

0.401

**

0.215

**

0.7

73

0.174

*

0.6

10

0.332

*

0.344

*

Countries(fixe

d)

Great

Britain

1.009

Greece

0.222

**

Luxem

bou

rg

1.828

Net

herlands

1.857

2.363

3.152

*

1.868

2.392

**

Switzerland

2.807

*

2.878

*

2.117

**

Ran

domparts

variat

ion

Country

variat

ion

0.178

**

0.098

**

0.103

0.2

14

**

0.000

0.2

31

*

0.180

**

0.036

Source:

EuropeanSocial

Survey,

roun

ds14.Twenty-e

ightcountries

(22inthecaseof

2nd

generation

).

Notes:

Oddsrat

ios

forhighlabormar

ketattainment

andleve

lsofstat

istica

lsign

ificance.M

odelscontrol

forsamplese

lect

ionbias

(Hec

kmanse

lect

ionmodel

).Reference

categories

inparentheses,continuouspredictorscentered

.Missingan

dunreliablecases

dummie

dout.Inlogist

icregressionmodels,th

eindividua

l-leve

lvarianceofthe

depen

dentvari-

ableisconstantan

dequa

lsp

2

3=

3.29.

Tables

includ

ing

logitsan

dorstan

darderrorsareavai

lable

fromtheauthorsupon

request.

OR,od

dsratio

;p,

p-value;

NA,

notapplicab

le;

Noo

bs,

noobservat

ions;

Ref,

referencecategory.

***p

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