Immigrants in the labour markets of France and the United Kingdom: Integration models, institutional variations, and ethnic inequalities

Immigrants in the labour markets of France and the United Kingdom: Integration models,... Abstract Theories of immigrant integration and political-economic institutions starkly contrast the contemporary labour markets of France and the United Kingdom (UK). We draw out predictions from these theories and then, using Labour Force Surveys that we harmonize ourselves, we empirically examine inequalities that immigrants of disadvantaged minority origins face in labour force participation, employment, and earnings in the two societies. The UK labour market attracts immigrants who have a larger skills advantage over natives. Nevertheless, we find inequalities of strikingly similar magnitude in the two labour markets. In the UK, barriers to labour force participation are paramount, whereas in France, barriers to employment among active job-seekers are more important. Earnings inequalities are less significant in both countries. Overall, we conclude that barriers to opportunity are largely similar in the two countries for immigrants of disadvantaged minority origins. 1. Introduction Because of their important differences but also their status as two of the most important former colonial powers, France and the UK have often been included in foundational comparative studies of immigration (Freeman 1979; Hammar 1985; Soysal 1994; Favell 1998). According to this literature, patterns of immigrant integration should be sharply different in France and the UK, stemming from differences in models of citizenship and immigrant integration; and in labour market and welfare state institutions. Nevertheless, these comparative frameworks neglect the possibility that immigrants of disadvantaged minority origins may face similar barriers to socioeconomic opportunity in both countries (Reitz and Breton 1994; Reitz 2002; Heath and Yu 2005), irrespective of public rhetorics and institutional differences. Surprisingly, in terms of socioeconomic inequalities between immigrants and natives, we still know relatively little about how the two countries compare. Some comparative information can be found in Heath and Cheung’s (2007) landmark edited volume, which includes both countries and focuses on labour market inequalities. However, countries are presented in separate chapters, making it difficult to observe the exact contours of cross-national differences. Some other broad comparative studies (Tubergen, Maas and Flap 2004; Kanas and Tubergen 2009) decompose country effects into contextual variables, rendering it difficult to observe patterns of immigrant incorporation in each national context. More recent findings (Algan et al. 2011) compare immigrants and their descendants1 in France, the UK, and Germany, though our analysis differs from theirs in several ways, including focusing on more comparable groups across host countries, examining both labour force participation and employment, and correcting for selection bias in earnings models. Using data from 1990 to 2007, we compare labour force participation, employment, and earnings for first-generation immigrants and natives in France and the UK. We have two strategies for group selection. First, we focus on groups that are similar in their postcolonial ties to the host country, as well as in their size, visibility, and importance: North Africans from Algeria, Morocco and Tunisia in the French case and South Asians from India, Pakistan and Bangladesh in the UK case. Second, we include several groups in each country that are more similar in terms of geographic origin: Western Europeans (who we expect to face little disadvantage compared with non-Europeans), Turks and Sub-Saharan Africans. Our selection strategy allows us to compare groups that are similar in important ways and to observe a range of variation among immigrants (see OECD 2013). We use Oaxaca decompositions to compare the extent of labour market inequality across groups and countries. We observe inequalities of strikingly similar magnitude in the two societies, despite overarching differences in labour market organization and immigrant integration policies. 2. Comparing immigrants in the French and UK labour markets Although France and the UK share many aspects of their histories of postcolonial migration in the second half of the twentieth century, scholars have highlighted distinct ‘national models’, ‘philosophies’, ‘cultures’, or ‘narratives’ of citizenship and immigrant integration in these two countries (Hammar 1985; Schnapper 1991, 1994; Brubaker 1992; Favell 1998; Kastoryano 2002; Koopmans et al. 2005). According to these accounts, France has generally embraced assimilationist politics and policies consistent with its republican tradition and the United Kingdom politics and policies of multiculturalism. According to the French ideal, there is no ethnic or racial differentiation in French society (Simon 2003; Safi 2008). French institutions therefore reject ethnicity, culture, and religion as a basis for political organization, claims-making, and official statistics (Silberman 1992; Simon 1998). Conversely, the UK multiculturalist model has been based on the premise that only through recognition can equality between majority and minority groups be achieved. Scholars highlight the fact that the republican model is powerful rhetoric but rarely translates into concrete policy (Blatt 2000; Guiraudon 2006). Indeed, the invisibility of minority groups in official statistics and discourse may paradoxically allow discrimination to flourish. On the other hand, the UK has, like many countries, experienced a retreat from multiculturalism policies in recent years (Bloemraad, Korteweg and Yurdakul 2008: 160) highlighted by David Cameron’s February 2011 pronouncement that ‘multiculturalism has failed’ (Cameron 2011). Therefore, the extent to which these integration models have a concrete impact on immigrant incorporation in the labour market is questionable (Bertossi, 2011; Bertossi and Duyvendak 2012). Indeed, comparative studies of socioeconomic outcomes frequently come to the conclusion that integration models have little impact (Reitz and Breton 1994; Heath and Brinbaum 2007; Pichler 2011; but see also Koopmans 2010). But France and the UK differ not only in their integration models. For those interested in socioeconomic inequalities, differences in political-economic institutions are also critical. Based on levels of centralization of both labour and capital, Soskice (1999) has distinguished ‘uncoordinated’ market regimes such as the UK from more ‘coordinated’ ones such as France. France has low levels of wage inequality due to coordinated wage bargaining between employers and unions,2 whereas the UK has followed a post-industrial trajectory of increasing earnings inequality, with many high- and low-wage service jobs, the latter disproportionately filled by immigrants (Esping-Andersen 1999; Gallie and Paugam 2000). On the other hand, many scholars suggest that coordinated market economies create more non-employed labour market ‘outsiders’, among whom migrants are over-represented (Alba and Silberman 2002; Meurs, Pailhé and Simon 2006; Rueda 2007; Silberman, Alba and Fournier 2007). So we might expect immigrants, and particularly the low-skilled, to have higher employment gaps with natives in France but higher earnings gaps with natives in the UK (Kogan 2006; 2007). The extent to which there is a trade-off between employment and earnings equality for immigrants is not clearly assessed in empirical studies. As is the case with gender inequality, immigrants’ labour market outcomes may not neatly correspond to the distinction between liberal and coordinated economies (Soskice 2005). Theories of both immigrant integration models and political-economic institutions neglect the possibility that discriminatory barriers could affect certain immigrant groups similarly across countries, resulting in similar inequalities for those groups in different host countries. Summarizing comparative findings of immigrant incorporation across countries, Heath (2007) argues that there is a hierarchy of minorities generally shared by all Western European countries. OECD publications regularly show that immigrants significantly lag behind natives in terms of employment and wages, but this is particularly true for those coming from non-OECD countries (Causa and Sébastien 2007). Many studies show evidence of substantial ethnic discrimination in job access in France and the UK (Aeberhardt and Pouget 2007; Cediey and Foroni 2006; Cheung and Heath 2007; Cédiey, Foroni and Garner 2008; Duguet et al. 2009; Khattab 2009; Wood et al. 2009; Aeberhardt et al. 2010), especially for non-European migrants. The distinction between European and non-European migrants has gained validity and is today widely used in empirical studies (Harrison, Law and Philipps 2005; Wrench, Roolsblad and Kraal 2010; OECD 2013). While this distinction may include cultural or human capital considerations, it is frequently explained by discriminatory barriers that are most severe for racialized minorities. Hypotheses growing out of the frameworks we have just described guide the analysis: H1 (national integration models): If national integration models drive patterns of inequality, we would expect that all labour market outcomes (labour force participation, employment, and earnings) would be systematically better in the country with a more effective integration model. This leads to two directly competing hypotheses. H1A: By neither targeting immigrants in social programs nor officially monitoring ethnic inequalities, the French assimilationist model is less effective at combatting discrimination, generating larger labour market inequalities. H1B: By focusing on ethnic difference and encouraging ethnic differentiation through ethnic targeting and monitoring, the UK multicultural model generates larger labour market inequalities. H2 (political-economic institutions): Greater regulation in the French labour market and more social protection in the French welfare state leads to more labour market outsiders, resulting in larger immigrant/native gaps in labour force participation and employment but smaller gaps in earnings than in the more liberal UK. H3 (discriminatory barriers): Visible, non-European minority groups, such as most of those we focus on in our analysis, face similar discriminatory barriers in the two host countries, leading to similar patterns of labour market inequality across all outcomes. 3. Data and methods 3.1 Data sources Our data come from UK and French Labour Force Surveys (LFS) from 1990 to 2007. These are nationally representative datasets containing key information on demographic and socioeconomic characteristics of interest. For our purposes, it is particularly important that both countries collect information about respondents’ birth countries, allowing us to identify immigrants rather than just foreign nationals.3 It is also important that LFS sample sizes are large enough to identify groups of immigrants by detailed geographic origin. We limit the sample to those who are of working age (16 to 55) and not still in school.4 We consider inequalities between first-generation immigrants and natives in labour force participation, employment, and earnings in the 1990s and 2000s. Though it would be desirable to include the second-generation children of immigrants in our analysis, it is not possible to comparably define this group in the two countries, as the UK LFS does not include information about where respondents’ parents were born.5 For France, this period is one of stability in migration inflows, with an official policy of halting immigration; the size of the foreign-born population remained relatively stable (Boëldieu and Borrel 2000; Insee 2012). In the UK, on the other hand, immigration increased substantially, due largely to the immediate opening of the UK labour market to workers from EU accession states in Eastern Europe (Thierry and Rogers 2004). Despite new flows, the largest groups of immigrants in both countries remained those from former colonies. Economically, the period was one of secularly decreasing unemployment in the UK, whereas France saw increasing unemployment in the 1990s and a decline again in the 2000s. 3.2 Comparative design: which groups? We use two strategies to select immigrant groups for the comparison. First, we focus on groups in each country that are similar in their postcolonial ties to the host country, and in their size and visibility in the host country: North Africans (NAF) from Algeria (DZA), Morocco (MAR), and Tunisia (TUN) in the French case and South Asians (SAS) from India (IND), Pakistan (PAK), and Bangladesh (BAN) in the UK case. Second, we include three groups that are more similar in terms of geographic origin: Western Europeans (WEU), Turks (TUR), and Sub-Saharan Africans (SAF). Western Europeans should be relatively privileged compared with the non-European groups in our analysis and we include them as a point of comparison.6 Sub-Saharan Africans are mostly post-colonial migrants in both countries, even though the former colonies are different for France and the UK. Turks are arguably the most comparable group. Turks are overwhelmingly Muslim in both countries and face stigmatization (Peach 2006a, 2006b; Adida, Laitin and Valfort 2014). In all tables and figures, we also present results for the foreign-born population as a whole in each country, so as to contextualize the results for these specific groups.7 3.3 Statistical analyses We analyse labour force participation, employment, and earnings. Labour force participation indicates whether respondents are in the labour force (which includes those who are unemployed and actively looking for work) versus inactive. Note that the inactive population may include the unemployed who are not actively seeking work. Employment indicates whether respondents are currently working or not. The non-employed include both the inactive and the formally unemployed, so our employment analysis is different from one that looks only at unemployment among the economically active population.8 Our earnings analysis focuses, by necessity, on net earnings, as gross earnings information is not available in the French data.9 The UK earnings variable is originally on a weekly basis, whereas for France it is monthly. We convert the UK variable to monthly earnings, and we adjust earnings in both countries for inflation (to 2007 GBP or EUR), based on a consumer price index. Because the earnings outcome is logged, gaps between groups can be interpreted in percentage terms and there is no need to convert to a single currency. We exclude the self-employed from the earning analyses because income information is not collected on them in the UK. We compare earnings analyses with and without correction for selection bias (Heckman 1990). Accounting for selection is crucial not only because of the traditional problem of endogeneity of the participation decision (especially for women), but also because there are reasons to suspect differential selection bias between immigrant and native populations and among immigrant groups. Selection may differ across groups because of problems with immigrants’ skills recognition, legal entry barriers for immigrants into certain types of jobs, or differences in gender role norms. Also, participation in the informal economy could drive selection into reported earnings and differentially affect immigrants and natives. The exclusion of the self-employed from our earnings analysis may also lead to differential selection, since self-employment may be a strategy to counter discrimination for some groups. Finally, selectivity can result from non-response to income questions. This is a potential problem in the UK data, where non-response to income questions is high. In short, selection bias may affect both the comparison between native and immigrant groups in each country and also the cross-national comparison, so we consider correction for selection bias central to the earnings analysis. Our findings are based on Oaxaca decomposition analyses that compare gaps in labour market attainment between natives and each of the ‘treatment groups’ before and after introducing controls, in each host country separately (Oaxaca 1973).10 Instead of introducing ethnic origin as an independent variable, Oaxaca decomposition is a synthetic presentation of the results of two regression analyses (for immigrants and natives) in terms of the relative share of explained and unexplained gaps. In more formal terms, if YN is the average wage for natives ( YIfor immigrants), XN the set of control variables for natives ( XI for immigrants), and βN the effects of these controls for natives ( βI for immigrants), Oaxaca’s twofold decomposition equation is: EYN-EYI=(EXN-EXI)βN-EXI(βN-βI) The equation refers to decomposition of a continuous outcome like earnings. For dichotomous decompositions for labour force participation and employment, we follow Jann (2008). The first term of the decomposition is referred to as the explained part and the second the unexplained part. The equation shows that if observable characteristics were the same for natives and immigrants, the explained part would be zero. The unexplained part (the differences in return to observables) is comprised of discrimination (Reimers 1983; Cotton 1988; Oaxaca and Ransom 1994; Yamaguchi, 2011) and unobserved characteristics (which in our analysis include language ability and length of stay, among others). The main advantage of Oaxaca decomposition compared with standard regression is that it allows different effects of covariates for each group. However, unlike standard regression analysis, Oaxaca decomposition can only compare two groups at a time. We control for basic contextual variables—year of survey and geographic region—within each country. The region variable has 12 values in the UK and 21 in France. We also control for basic socio-demographic characteristics that are likely to affect labour market outcomes: age, age squared, marital status, children, and education. Marital status has three categories: never married, currently married, or divorced/separated/widowed. We include variables indicating how many pre-school-aged (0–5) and school-aged (6–17) children are in the household. We measure education by age at which highest level of education was completed. This is generally less ideal than level of qualification, but in the case of immigrants, the age-based variable is preferable, because a high proportion of immigrants are categorized as having ‘other’ credentials at an unspecified level of qualification. We distinguish eight education categories: no formal education; and aged 1–14, 15, 16, 17–18, 19–20, 21–25, and 26+ when highest education was completed. We would like to control for years since migration, and although this information is available in the UK data, it is only available from 2003 to 2007 in France, so sample sizes are too small for multivariate analysis. Descriptive statistics (see the Online Appendix) show that Western Europeans have been in the UK longer than Western Europeans in France, whereas the reverse is true for non-European groups. We take this into account when interpreting our results below.11 Finally, in the earnings analysis, we include controls for type of job, including full-time vs. part-time, permanent vs. temporary contract, public vs. private sector, tenure in the current job (less than 1 year, 1–5 years, 6–10 years, and more than 10 years), and the International Socioeconomic Index (ISEI) score of the occupation, which is based on education and earnings of occupational incumbents in prior surveys (Ganzeboom and Treiman 1996). Including the ISEI control helps to account for potential occupational downgrading among immigrants (Dustman et al. 2013). Following previous studies (Blackaby et al. 2002; Mulligan and Rubinstein 2008; Edo 2015) we exclude variables for the number of pre-school-aged and school-aged children in the household from the earnings models, as these are the selection variables in the Heckman models. We assume that these variables affect the reservation wage and therefore labour force participation, but not the wage offer. 4. Differences in human capital, demographics, and job characteristics In both the UK and France, immigrants have characteristics that differ from their native-born counterparts, which may go some way to explaining differences in labour market outcomes. We discuss key differences in human capital and demographic characteristics here, and then turn to differences in job characteristics relevant to the earnings analysis. In these descriptive statistics, we employ sampling weights, though in the multivariate analyses, we do not (Winship and Radbill 1994). 4.1 Human capital In the UK, immigrants as a whole and specific immigrant sub-groups tend to cluster at the high and low ends of the educational distribution, though the pattern is different for men and women. Tables 1 and 2 present the proportion of each group that left education before age 15 and the proportion that continued on with education until an age (21) that suggests tertiary education (there are additional categories in the multivariate analyses). Native men and women are extremely unlikely to have left formal education before the age of 15, whereas for immigrants, especially immigrant women, the figures are as high as 30% (among Pakistani, Bangladeshi and Turkish women). However, native-born men and women are also less likely than immigrants to have left education at age 21+. Among men, even those immigrant groups with the lowest levels of education (Pakistanis, Bangladeshis and Turks) have greater representation at the top education levels than native-born men. This is not the case for Pakistani and Bangladeshi women, however. In sum, immigrants in the UK tend to be educationally polarized, compared with the native-born population. Table 1. Selected demographic and social characteristics of immigrant and native men in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. Table 1. Selected demographic and social characteristics of immigrant and native men in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. Table 2. Selected demographic and social characteristics of immigrant and native women in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. Table 2. Selected demographic and social characteristics of immigrant and native women in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. In France, we see similarities, but also important differences. As in the UK, immigrants are over-represented at the lowest levels of education. Just fewer than 1 in 10 French natives left education before age 15, which is far higher than for natives in the UK, but far lower than for immigrants in France, with the exception of Western European immigrants. For instance, the figures are over 40 per cent for Turkish men and over 50 per cent for Turkish women in France. Immigrants in France are generally also over-represented at the highest levels of education, but that pattern is less consequential than in the UK, because French natives are more likely than UK natives to have high educational attainment. If we examine specific groups, we see a mixed story. Among Turks, we clearly see greater positive educational selectivity in the UK. The percentage of Turkish immigrants in the low educational category is much higher in France than in the UK, while the opposite is true for the high educational category. Sub-Saharan Africans have similar levels of higher education in the two host countries, though far higher representation at the lowest educational level in France than in the UK, so here too we see greater positive educational selectivity in the UK. This is very similar to the pattern we see if we compare South Asians in the UK with North Africans in France. Western European immigrants are more educationally selective in France than in the UK, at both the high and low ends of the educational spectrum. On the whole, we conclude that non-European immigrants are more educationally selective in the UK, while European immigrants are more selective in France. Compared with natives, the picture is complex because UK natives are more concentrated in the middle of the educational spectrum than their counterparts in France. 4.2 Demographics The geographic distribution of immigrants versus natives, especially their concentration in the capital cities, has implications for labour market outcomes. The descriptive statistics in Tables 1 and 2 are based on the London/non-London and Paris/non-Paris (Ile-de-France) dichotomies (the multivariate analyses incorporate more nuanced geographic variation). Immigrants are far more likely to live in London than their native-born counterparts. Less than 10 per cent of UK-born adults in our sample live in London, whereas the figures for all immigrants is over 40 per cent, and among Bangladeshis, Turks, and Sub-Saharan African immigrants, it is a majority. This has mixed implications for labour market outcomes, because while average earnings are higher in London, employment rates are lower there during this time period, so geographic concentration may explain an immigrant disadvantage in employment, but may mute an immigrant disadvantage in earnings. As in the UK, immigrants in France are far more likely to live in the capital region than their native-born counterparts, though the discrepancies between immigrants and natives are not as extreme as in the UK. Around 17 per cent of French-born adults live in Paris, whereas the figure for most immigrant groups is 20–30 per cent. For Sub-Saharan Africans, it is a majority. Employment levels and earnings are higher in Paris than in other regions, so immigrants’ geographic concentration there likely mutes their disadvantage. Another way in which the demographics of immigrant groups and natives differ is in the characteristics of their families. In the UK, immigrants as a whole are more likely to be married than their native-born counterparts, and this is especially true among South Asian groups. More than 80 per cent of Indians, Pakistanis, and Bangladeshis are married (compared with just over 50 per cent among natives). Western European immigrants and Sub-Saharan African women are less likely than natives to be married. In France, there are also dramatic differences in marital status. The proportion of married individuals among the native-born population is similar to the UK. Immigrants are more likely to be married than French natives, though only slightly so for Sub-Saharan African women. The marriage figures are particularly high for Turkish immigrants. Without exception, the immigrant groups in our sample have more children in the household, on average, than natives. (Note that in the multivariate analyses below, we control separately for pre-school and school-aged children, but we present total children here to simplify.) The highest averages are found among Pakistanis and Bangladeshis in the UK. In France, the highest average is among Turkish immigrants. These patterns in family characteristics are likely to affect employment and earnings, though differently for men and women, since being married and especially having children tend to have less detrimental (and even positive) effects on men’s labour market outcomes. 4.3 Job characteristics The characteristics of the jobs that immigrant and native-born workers find in the two countries are quite different, which (like human capital and demographic differences) have implications for earnings inequalities. First, members of some immigrant groups in the UK are relatively unlikely to work full-time. We see no extreme nativity differences in part-time employment for men in France or for women in either country. Differences are less extreme with respect to temporary vs. permanent employment. Not surprisingly, most immigrant groups in both countries are somewhat more likely than native-born workers to have a temporary contract, and in both countries, Turkish women are the group that is most likely to be working on a temporary contract (around 20 per cent). Immigrants as a whole are under-represented in public sector work in both countries. This is particularly true of Turkish men and women in both countries, and of Bangladeshi and Pakistani men in the UK. However, some immigrant groups in both countries are slightly over-represented in the public sector. On average, immigrants and natives have higher-status jobs in the UK than in France, as measured by the ISEI. However, immigrants as a whole in the UK have a slight status advantage over their native-born counterparts, whereas the opposite is true in France. Not surprisingly, Western Europeans have the highest status jobs (higher than natives) in both countries and among men and women. Turks have exceptionally low job status in both countries, with the exception of Turkish women in the UK (among whom selectivity into employment is quite strong). Finally, turning to job tenure, we see that it is generally longer in France, among natives and immigrants. Turks are particularly disadvantaged in both countries in terms of job tenure. 5. Labour market outcomes We turn now to the decomposition analyses of immigrant/native gaps in labour force participation, employment, and earnings. These analyses help to disentangle the extent to which observed differences in labour market outcomes across groups and countries are due to compositional differences in human capital and demographics (and job characteristics, for the earnings analysis); selectivity into employment (for the earnings analysis); or residual factors including discrimination. We include detailed decomposition results in the Online Appendix, but we focus our discussion on Figures 1 and 2, which display estimates of gaps unconditional and conditional on controls (along with 95 per cent confidence intervals for these estimates) in both countries for men and women respectively. Figure 3 compares how well the model covariates explain unconditional gaps.12 Unconditional gaps are the raw difference in probability of labour force participation or employment or in logged earnings. Conditional gaps control for education, age, age squared, marital status, pre-school and school-aged children, year, and region (in the case of labour force participation and employment) and age, age squared, marital status, year, region, ISEI, full-time vs. part-time work, temporary vs. permanent contract, public vs. private sector, and job tenure (in the case of earnings). Unconditional gaps for earnings are presented with and without the Heckman correction procedure. Note that in all of the decomposition tables and figures, positive gaps indicate that the immigrant group experiences a disadvantage relative to natives, while a negative gap indicates that the immigrant group experiences an advantage. If the unconditional gap is positive, but the conditional gap is null or negative, controls explain away the disadvantage the immigrant group faces relative to natives. Figure 1. View largeDownload slide Unconditional and conditional ethnic penalties among men. Figure 1. View largeDownload slide Unconditional and conditional ethnic penalties among men. Figure 2. View largeDownload slide Unconditional and conditional ethnic penalties among women. Figure 2. View largeDownload slide Unconditional and conditional ethnic penalties among women. Figure 3. View largeDownload slide Proportion of unconditional penalty explained. Figure 3. View largeDownload slide Proportion of unconditional penalty explained. 5.1 Men 5.1.1 Labour force participation and employment Most groups of immigrant men in the analysis face disadvantages in labour force participation and employment in both countries. In the aggregate, foreign-born men face a somewhat larger unconditional employment gap in France than in the UK. However, once individual-level characteristics are controlled, the aggregate employment gap is almost identical in the two countries. This is because immigrants in the UK have more favourable human capital and demographic characteristics for employment than their native-born counterparts, so controlling for these factors magnifies immigrants’ disadvantage. That is, immigrant men enter the labour force in the UK with relatively better characteristics than their immigrant counterparts in France (as we saw in the descriptive statistics about education especially). Despite a similar conditional employment gap in the two countries, its causes are somewhat different. There is a significantly larger gap in labour force participation in the UK, offset by greater difficulty for active immigrant job-seekers in actually finding employment in France. Around half of the conditional employment gap between immigrant and native men in the UK is due to labour force participation while very little of it is in France. Given the potentially ambiguous line between inactivity and unemployment among working-aged men, we hesitate to conclude that immigrant men voluntarily choose not to work in the UK, and we suspect that some inactivity in the UK consists of discouraged jobseekers. For instance, the UK Labour Force Survey includes a question about whether economically inactive respondents would like work, and more than a third of inactive men in our sample report that they would, even though they are not officially classified as unemployed. We do not have comparable evidence for France, though inactivity among men is far lower there. We turn now to a discussion of specific groups of immigrant men. Western Europeans, as we expected, are not particularly disadvantaged in either country. Their unconditional and conditional labour force participation and employment gaps are far smaller than other groups in both countries and the gaps are somewhat smaller in the UK than in France. This can be dramatically contrasted with the very large disadvantages in employment that non-European groups face in both countries. North Africans in France, South Asians in the UK, and Turks and Sub-Saharan Africans in both countries face conditional employment penalties that are many times larger than the penalties faced by Western Europeans. While North Africans in France and South Asians in the UK have statistically indistinguishable employment penalties, Turks and Sub-Saharan Africans fare somewhat better in France. It should be noted, however, that the aggregate grouping of South Asians masks considerable variation across specific groups: Bangladeshis and Pakistanis in the UK fare worse than any of the North African groups in France, while Indians fare somewhat better. But on the whole, after adjusting for individual-level characteristics, non-European groups seem to fare worse in terms of employment in the UK. As with foreign-born workers as a whole, the cause of these employment gaps is different in the two countries: Around half of the employment penalties for non-Europeans in the UK are attributable to differences in labour force participation, whereas in France, they are largely attributable to differences in employment among active job-seekers. We also analyse the different experiences of low- and high-skilled immigrant men from South Asia and North Africa in the UK and France, respectively, as compared with natives with similar skill levels, because we believe that different processes might be operating at the low- and high-end of the labour market. For these analyses, we define ‘low-skilled’ as having left education before age 15 and ‘high-skilled’ as having stayed in education until at least age 21. Among those with low skills, differences in activity and employment rates are more than accounted for in France by differences in individual characteristics. The detailed decomposition results suggest that this is largely because, even among the low-skilled, immigrants are concentrated at the very bottom of the educational spectrum, among those with no formal education. In the UK, there are smaller unconditional penalties that further narrow with individual controls. Conditional employment penalties among this low-skilled group are statistically indistinguishable in the two countries, though they are driven more by inactivity in the UK. Among the high-skilled group, on the other hand, measured characteristics do not account for the activity or employment penalties in either country, and indeed, immigrants have somewhat more favourable characteristics than natives. As among the low-skilled, the difference between France and the UK in the size of the employment penalty among the high-skilled is not substantial, though it is driven more by inactivity in the UK. In both countries, the conditional penalties faced by high-skilled immigrants are somewhat larger than those faced by low-skilled immigrants. Figure 3 summarizes how well our models perform in explaining unconditional penalties in activity and employment among immigrant men. A proportion of 1 means that covariates in the model explain away the gap between the immigrant group and natives in that country—that there would be no gap were immigrants and natives to have the same characteristics. We choose not to display bars in cases where the proportion explained is zero or negative. We see here unequivocally that the covariates in the model explain essentially none of the existing gaps in the UK. In France, activity gaps (small to begin with) are relatively well explained by our models. Men’s overall employment chances in France are, however, poorly explained by model covariates, with the exceptional case of low-skilled North Africans in France, which we discussed above. 5.1.2 Earnings The earnings panel in Figure 1 displays two unconditional gaps: before and after the Heckman correction. These are the top two bars for each group in the earnings figures. By comparing the two, we get a sense of the magnitude and direction of selectivity into earnings. As we noted earlier, selectivity can stem from multiple sources, and can be either positive or negative. In France, we see negative selection into employment (i.e. the estimated gap is more negative once we control for selection) among Western Europeans, Turks and high-skilled North Africans and positive selection (i.e. the estimated gap is more positive once we control for selection) among other groups, including foreign-born men as a whole. However, confidence intervals are large here; in most cases, there is no statistically significant difference between gaps with and without the Heckman correction. Most non-employment among immigrant men in France is in the form of unemployment, so positive selection is to be expected. In the UK, the picture is also widely varying. Among Western Europeans, Pakistanis, and the high-skilled South Asian group, we see positive selection into earnings, though the unconditional gap before and after Heckman correction fails to attain statistical significance, given wide confidence intervals. Among all other groups, including foreign-born men as a whole, we see evidence of negative selection (and for some groups the evidence is statistically significant). That is, those who report no earnings would likely earn more than those who report earnings. This is potentially related to high non-response to income questions in the UK data. At first blush, looking at unconditional gaps before correcting for selection, we see larger earnings gaps in the UK than in France. For example, the most disadvantaged group of immigrant men in France (Turks) earns about 35 per cent less than native-born men, while in the UK, the comparable figure (in this case for Bangladeshi men) is over 70 per cent less. (It should be noted that there are also some groups of immigrant men in both countries, especially Western Europeans, who are not very disadvantaged compared with native-born men in terms of earnings.) However, once we take selection into account and control for individual-level characteristics, any cross-national pattern is less obvious, because estimates are imprecise in both countries. Indeed, for many groups in both countries, we cannot conclude with statistical confidence that there are any earnings gaps once we control for selection. In short, there is no evidence among immigrant men that earnings penalties are significantly different in the two countries. For earnings, we choose not to discuss the proportion of the unconditional gap explained by covariates, because model covariates explain little to none of the unconditional penalties. 5.2 Women 5.2.1 Labour force participation and employment We see in Figure 2 that employment gaps for women are due, more than with men, to differences in labour force activity (i.e. the first and second panels in Figure 2 are quite similar), particularly in the UK. Unlike with men, the covariates in our models explain a substantial portion of observed gaps in labour force activity and employment, which we discuss in more detail below. Focusing here on the size of conditional penalties, we observe, if anything, larger gaps in the UK. The conditional gap for labour force participation among immigrant women in the aggregate is nearly twice as large in the UK as in France, because almost none of the unconditional gap is explained by covariates in the UK. For employment, the cross-national difference is less striking, but still statistically significant: immigrant women in the aggregate in France face a less severe disadvantage vis-à-vis natives than in the UK. As among men, the gap in the UK is due more to non-participation in the labour market, and in France to unemployment. As with men, we have reason to believe that there is a relatively high level of latent unemployment among inactive women in the UK, because nearly a third of them report in the Labour Force Survey that they would like work. Again, we do not have comparable figures for France. The picture for specific immigrant groups is similar. As among men, Western European women are not as disadvantaged as their non-European counterparts, but this is more so in the UK than in France. North African women in France have far smaller penalties in activity than South Asian women in the UK, once individual-level variables are controlled; this is true in the aggregate and for the three specific North African and South Asian origin countries. Furthermore, both Sub-Saharan African women and Turkish women have significantly larger activity penalties in the UK than in France. With employment, the two host countries look more similar. Though Pakistani and Bangladeshi women in the UK still post penalties that are significantly larger than any group of immigrant women in France, Indians in the UK look relatively similar to the various groups in France. Sub-Saharan African and Turkish women have somewhat smaller employment penalties in France, but the cross-national difference is not statistically significant. Our models that separately analyse low- and high-skilled North African and South Asian women suggest that, though low-skilled women certainly have larger unconditional penalties in activity and employment than their high-skilled counterparts, this is mostly due to their disadvantageous individual-level characteristics (particularly their concentration at the extreme low end of the educational spectrum and their higher numbers of children). Indeed, conditional gaps are very similar in magnitude between the two skill groups. Both high- and low-skilled groups fare somewhat better in France than in the UK, so the separate analyses by skill level does not change the basic conclusion that employment penalties for non-Europeans are more severe in the UK, driven largely by barriers to labour force participation. We see in Figure 3 that, for immigrant women in both countries, our models do a better job of explaining the activity and employment penalties than was generally the case among men. Covariates explain nearly 100 per cent of some activity gaps in France, and up to 35 per cent in the UK. For employment, covariates explain somewhat less of the gaps in France. In the UK, the explanatory power of covariates in employment and activity models is more similar, because, as we discussed above, employment gaps are driven largely by inactivity. That covariates generally explain more of the gaps in France than in the UK highlights the possibility that institutional rather than individual-level factors drive immigrant women’s lack of employment in the UK. Nonetheless, more than with men, there are also measurable individual-level barriers to employment for immigrant women. So for men and for women, our findings show somewhat larger conditional gaps in employment in the UK than in France among non-Europeans, a pattern driven largely by lower labour force participation in the UK. Lower labour force participation in the UK is somewhat muted but not fully offset by the fact that immigrants in France seem to face higher barriers to employment when they are unemployed according to official criteria. We are cautious in over-emphasizing the cross-national difference, however, because non-European groups have been in France longer on average than non-European groups in the UK (see Online Appendix) and that could reduce the cross-national differences in our analyses. 5.2.2 Earnings We turn now to earnings differences between immigrant and native women. Looking first at the evidence about selection in the earnings panel, we see, as with men, diverse outcomes across groups. Among almost every group in the UK (except high-skilled South Asians), there is some evidence of positive selection into employment: immigrants’ disadvantage appears larger once the Heckman correction is implemented. However, the difference between the size of the gap before and after the correction in no case attains statistical significance. Among almost every group of women in France (except high-skilled North Africans), on the other hand, there is, if anything, evidence of negative selection—that is, those who report earnings likely earn less than would those who report no earnings. But again, rarely does this negative selection attain statistical significance, given very wide confidence intervals. The heterogeneity of selectivity across groups shows that it is important to take it into account as we do. As with immigrant men, confidence intervals are extremely wide for conditional earnings gaps, which are in many cases not significantly different from zero. The exceptions are Western Europeans, Turks and low-skilled North Africans in France (who have a significant earnings premium) and South Asians (driven by Indians and the high-skilled) and Sub-Saharan Africans in the UK (who experience earnings penalties). Earnings penalties for immigrant women appear to be somewhat larger in the UK. For instance, South Asian women in the UK have a significantly larger conditional earnings penalty than North African women in France, though once we break this down by skill level, the cross-national difference disappears. Western European, Turkish, and Sub-Saharan African women also seem to fare somewhat better in terms of conditional earning penalties in France than they do in the UK. (Indeed, Western European and Turkish women have a significant earnings premium in France, and Sub-Saharan African women in France experience no significant earnings penalty.) Confidence intervals for earnings are large, so conclusions should be cautious, but the evidence here suggests somewhat better earnings outcomes for immigrant women relative to natives in France than in the UK. Though we again note that non-European groups have been in France longer on average than non-European groups in the UK, the job tenure variable in the earnings analysis should at least partly capture these differences. 6. Conclusion This article empirically assesses the extent to which theoretical frameworks used in comparative research on immigrant incorporation and political-economic institutions are useful for interpreting patterns of immigrant/native inequality in the French and UK labour markets. Our findings show that the UK labour market attracts immigrants who are more highly skilled and have other advantageous individual-level characteristics. But beyond this, our evidence suggests little advantage for non-European immigrants in the UK labour market in terms of employment. It thus does not seem to be the case (as H2 might suggest) that the UK’s liberal welfare state and labour market institutions seem to generate significantly better employment outcomes for non-European immigrants. Indeed, the UK actually looks substantially worse than France in terms of inequalities between non-European immigrants and natives in labour force participation for both men and women. Though this does seem to be somewhat offset by the greater difficulty that active job-seeking immigrants have in finding employment in France (a pattern that proponents of the UK’s institutional features would highlight), it is still the case that employment gaps tend to be, if anything, somewhat larger in the UK for non-European immigrants. This lends tentative support to H1B. Among Western European immigrants, on the other hand, activity and employment outcomes seem to be relatively better in the UK, so the issue appears to be an interaction between immigrant groups’ characteristics (whether demographic, religious, cultural, physical appearance, etc.) and host country institutions and policies. Because the integration of Western European immigrants is generally not considered problematic and outcomes for this group are quite good in both countries, we place greater emphasis on the findings about non-European groups. Some might dismiss labour force participation as an outcome driven largely by individual choices and not relevant to considerations of immigrants’ labour market disadvantages, but we disagree. Immigrants’ labour market participation cannot be assumed to be a purely individual choice, because formal barriers to entering some jobs and problems with skills recognition can affect immigrants’ participation in the labour market and result in higher numbers of discouraged workers who cease (or never begin) to actively look for work. Indeed, just under a third of inactive women and more than a third of inactive men in the UK data report that they would like to work. For women, child care and other family-related welfare policies, which are quite different in the two countries, form an important institutional context for participation decisions, and such institutions may disproportionately affect immigrant women, who are more likely than their native-born counterparts to have children. Our evidence suggests that improving immigrant employment outcomes depends on considering barriers to both labour force participation and employment among active job-seekers. Unlike some previous analyses of earnings, ours corrects for selection bias into employment. Though the evidence for earnings is weaker than for labour force participation and employment, it seems to be the case that at least among immigrant women, earnings outcomes are also relatively more positive in France than in the UK, which is consistent with political-economic theories that highlight the UK’s higher earnings inequality and its potential impact on immigrants (H2). Nonetheless, given that cross-national evidence is stronger for labour force participation and employment, our evidence seems somewhat more consistent with H1B, if we are to conclude that there are any notable cross-national differences at all. But most striking to us is that inequalities are in many cases so similar in the two countries (H3), particularly once we consider that the non-European groups in our analysis have been in France longer on average than in the UK. Though our analysis pinpoints cross-national differences in the underlying mechanisms generating employment inequalities between immigrants and natives in the two countries (relating more to labour force participation in the UK and to barriers in job-seeking in France), the end result is actually relatively similar, and inequalities in earnings between immigrants and natives in the two countries are also remarkably similar. Therefore, we conclude that despite differences in immigrant incorporation models and political-economic institutions, immigrants with disadvantaged non-European origins seem to face barriers to equal opportunity that are remarkably similar in magnitude in the French and UK labour markets. Funding This research received no external funding, but was supported by the institutions with which the authors are currently affiliated, noted above, as well as by the institutions with which Kesler has previously been affiliated (Barnard College of Columbia University and Nuffield College of the University of Oxford). Footnotes 1. Though the second generation is included in their study, it is defined differently in each country, making the comparative results difficult to interpret. 2. While union membership is low in France (10 per cent compared with 30 per cent in the UK), collective agreements cover many non-members, making France comparable in terms of union coverage to countries such as Sweden and Austria (OECD 2004). 3. This is important because of both countries’ high levels of naturalization among immigrants. Unfortunately, we cannot uniformly identify return colonial migrants (those who are born abroad with French or UK nationality at birth) in our data, though in the UK case, we have information on self-classified ethnicity. We conclude from looking at this variable that only a small proportion of immigrants in our sample are likely to be return migrants. For example, less than 3 per cent of respondents born in South Asian countries consider their ethnicity to be white, and so our decision about whether to include them is not consequential to the findings. (We do exclude them from the UK data.) 4. Both datasets contain short panels of individuals. Individuals stay in the sample for 3 years until 2003 in France, then for six quarters; and for five quarters in the UK. We include one observation, from the first time a respondent is interviewed. We exclude workers who are older than 55, because policies related to retirement age are different in the two countries, and this is beyond the scope of our analysis. 5. Because the second generation cannot be uniformly identified, it is by default included in the native-born baseline group. This is unlikely to have a significant impact on our findings, because the second generation is so small relative to the total native-born population. 6. This group includes those born in EU-15 countries plus Norway and Switzerland. 7. We do not include Afro-Caribbeans in our analysis, because in France, citizens of overseas territories cannot be further differentiated by territory or even world region. 8. We conduct the analysis this way to avoid the selection bias in an analysis that includes only the economically active. 9. The gross earnings variable for the UK yields similar results. In the UK data, earnings information is available only from 1997 onward. 10. 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Immigrants in the labour markets of France and the United Kingdom: Integration models, institutional variations, and ethnic inequalities

Migration Studies , Volume Advance Article (2) – Jul 9, 2017

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© The Authors 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
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Abstract

Abstract Theories of immigrant integration and political-economic institutions starkly contrast the contemporary labour markets of France and the United Kingdom (UK). We draw out predictions from these theories and then, using Labour Force Surveys that we harmonize ourselves, we empirically examine inequalities that immigrants of disadvantaged minority origins face in labour force participation, employment, and earnings in the two societies. The UK labour market attracts immigrants who have a larger skills advantage over natives. Nevertheless, we find inequalities of strikingly similar magnitude in the two labour markets. In the UK, barriers to labour force participation are paramount, whereas in France, barriers to employment among active job-seekers are more important. Earnings inequalities are less significant in both countries. Overall, we conclude that barriers to opportunity are largely similar in the two countries for immigrants of disadvantaged minority origins. 1. Introduction Because of their important differences but also their status as two of the most important former colonial powers, France and the UK have often been included in foundational comparative studies of immigration (Freeman 1979; Hammar 1985; Soysal 1994; Favell 1998). According to this literature, patterns of immigrant integration should be sharply different in France and the UK, stemming from differences in models of citizenship and immigrant integration; and in labour market and welfare state institutions. Nevertheless, these comparative frameworks neglect the possibility that immigrants of disadvantaged minority origins may face similar barriers to socioeconomic opportunity in both countries (Reitz and Breton 1994; Reitz 2002; Heath and Yu 2005), irrespective of public rhetorics and institutional differences. Surprisingly, in terms of socioeconomic inequalities between immigrants and natives, we still know relatively little about how the two countries compare. Some comparative information can be found in Heath and Cheung’s (2007) landmark edited volume, which includes both countries and focuses on labour market inequalities. However, countries are presented in separate chapters, making it difficult to observe the exact contours of cross-national differences. Some other broad comparative studies (Tubergen, Maas and Flap 2004; Kanas and Tubergen 2009) decompose country effects into contextual variables, rendering it difficult to observe patterns of immigrant incorporation in each national context. More recent findings (Algan et al. 2011) compare immigrants and their descendants1 in France, the UK, and Germany, though our analysis differs from theirs in several ways, including focusing on more comparable groups across host countries, examining both labour force participation and employment, and correcting for selection bias in earnings models. Using data from 1990 to 2007, we compare labour force participation, employment, and earnings for first-generation immigrants and natives in France and the UK. We have two strategies for group selection. First, we focus on groups that are similar in their postcolonial ties to the host country, as well as in their size, visibility, and importance: North Africans from Algeria, Morocco and Tunisia in the French case and South Asians from India, Pakistan and Bangladesh in the UK case. Second, we include several groups in each country that are more similar in terms of geographic origin: Western Europeans (who we expect to face little disadvantage compared with non-Europeans), Turks and Sub-Saharan Africans. Our selection strategy allows us to compare groups that are similar in important ways and to observe a range of variation among immigrants (see OECD 2013). We use Oaxaca decompositions to compare the extent of labour market inequality across groups and countries. We observe inequalities of strikingly similar magnitude in the two societies, despite overarching differences in labour market organization and immigrant integration policies. 2. Comparing immigrants in the French and UK labour markets Although France and the UK share many aspects of their histories of postcolonial migration in the second half of the twentieth century, scholars have highlighted distinct ‘national models’, ‘philosophies’, ‘cultures’, or ‘narratives’ of citizenship and immigrant integration in these two countries (Hammar 1985; Schnapper 1991, 1994; Brubaker 1992; Favell 1998; Kastoryano 2002; Koopmans et al. 2005). According to these accounts, France has generally embraced assimilationist politics and policies consistent with its republican tradition and the United Kingdom politics and policies of multiculturalism. According to the French ideal, there is no ethnic or racial differentiation in French society (Simon 2003; Safi 2008). French institutions therefore reject ethnicity, culture, and religion as a basis for political organization, claims-making, and official statistics (Silberman 1992; Simon 1998). Conversely, the UK multiculturalist model has been based on the premise that only through recognition can equality between majority and minority groups be achieved. Scholars highlight the fact that the republican model is powerful rhetoric but rarely translates into concrete policy (Blatt 2000; Guiraudon 2006). Indeed, the invisibility of minority groups in official statistics and discourse may paradoxically allow discrimination to flourish. On the other hand, the UK has, like many countries, experienced a retreat from multiculturalism policies in recent years (Bloemraad, Korteweg and Yurdakul 2008: 160) highlighted by David Cameron’s February 2011 pronouncement that ‘multiculturalism has failed’ (Cameron 2011). Therefore, the extent to which these integration models have a concrete impact on immigrant incorporation in the labour market is questionable (Bertossi, 2011; Bertossi and Duyvendak 2012). Indeed, comparative studies of socioeconomic outcomes frequently come to the conclusion that integration models have little impact (Reitz and Breton 1994; Heath and Brinbaum 2007; Pichler 2011; but see also Koopmans 2010). But France and the UK differ not only in their integration models. For those interested in socioeconomic inequalities, differences in political-economic institutions are also critical. Based on levels of centralization of both labour and capital, Soskice (1999) has distinguished ‘uncoordinated’ market regimes such as the UK from more ‘coordinated’ ones such as France. France has low levels of wage inequality due to coordinated wage bargaining between employers and unions,2 whereas the UK has followed a post-industrial trajectory of increasing earnings inequality, with many high- and low-wage service jobs, the latter disproportionately filled by immigrants (Esping-Andersen 1999; Gallie and Paugam 2000). On the other hand, many scholars suggest that coordinated market economies create more non-employed labour market ‘outsiders’, among whom migrants are over-represented (Alba and Silberman 2002; Meurs, Pailhé and Simon 2006; Rueda 2007; Silberman, Alba and Fournier 2007). So we might expect immigrants, and particularly the low-skilled, to have higher employment gaps with natives in France but higher earnings gaps with natives in the UK (Kogan 2006; 2007). The extent to which there is a trade-off between employment and earnings equality for immigrants is not clearly assessed in empirical studies. As is the case with gender inequality, immigrants’ labour market outcomes may not neatly correspond to the distinction between liberal and coordinated economies (Soskice 2005). Theories of both immigrant integration models and political-economic institutions neglect the possibility that discriminatory barriers could affect certain immigrant groups similarly across countries, resulting in similar inequalities for those groups in different host countries. Summarizing comparative findings of immigrant incorporation across countries, Heath (2007) argues that there is a hierarchy of minorities generally shared by all Western European countries. OECD publications regularly show that immigrants significantly lag behind natives in terms of employment and wages, but this is particularly true for those coming from non-OECD countries (Causa and Sébastien 2007). Many studies show evidence of substantial ethnic discrimination in job access in France and the UK (Aeberhardt and Pouget 2007; Cediey and Foroni 2006; Cheung and Heath 2007; Cédiey, Foroni and Garner 2008; Duguet et al. 2009; Khattab 2009; Wood et al. 2009; Aeberhardt et al. 2010), especially for non-European migrants. The distinction between European and non-European migrants has gained validity and is today widely used in empirical studies (Harrison, Law and Philipps 2005; Wrench, Roolsblad and Kraal 2010; OECD 2013). While this distinction may include cultural or human capital considerations, it is frequently explained by discriminatory barriers that are most severe for racialized minorities. Hypotheses growing out of the frameworks we have just described guide the analysis: H1 (national integration models): If national integration models drive patterns of inequality, we would expect that all labour market outcomes (labour force participation, employment, and earnings) would be systematically better in the country with a more effective integration model. This leads to two directly competing hypotheses. H1A: By neither targeting immigrants in social programs nor officially monitoring ethnic inequalities, the French assimilationist model is less effective at combatting discrimination, generating larger labour market inequalities. H1B: By focusing on ethnic difference and encouraging ethnic differentiation through ethnic targeting and monitoring, the UK multicultural model generates larger labour market inequalities. H2 (political-economic institutions): Greater regulation in the French labour market and more social protection in the French welfare state leads to more labour market outsiders, resulting in larger immigrant/native gaps in labour force participation and employment but smaller gaps in earnings than in the more liberal UK. H3 (discriminatory barriers): Visible, non-European minority groups, such as most of those we focus on in our analysis, face similar discriminatory barriers in the two host countries, leading to similar patterns of labour market inequality across all outcomes. 3. Data and methods 3.1 Data sources Our data come from UK and French Labour Force Surveys (LFS) from 1990 to 2007. These are nationally representative datasets containing key information on demographic and socioeconomic characteristics of interest. For our purposes, it is particularly important that both countries collect information about respondents’ birth countries, allowing us to identify immigrants rather than just foreign nationals.3 It is also important that LFS sample sizes are large enough to identify groups of immigrants by detailed geographic origin. We limit the sample to those who are of working age (16 to 55) and not still in school.4 We consider inequalities between first-generation immigrants and natives in labour force participation, employment, and earnings in the 1990s and 2000s. Though it would be desirable to include the second-generation children of immigrants in our analysis, it is not possible to comparably define this group in the two countries, as the UK LFS does not include information about where respondents’ parents were born.5 For France, this period is one of stability in migration inflows, with an official policy of halting immigration; the size of the foreign-born population remained relatively stable (Boëldieu and Borrel 2000; Insee 2012). In the UK, on the other hand, immigration increased substantially, due largely to the immediate opening of the UK labour market to workers from EU accession states in Eastern Europe (Thierry and Rogers 2004). Despite new flows, the largest groups of immigrants in both countries remained those from former colonies. Economically, the period was one of secularly decreasing unemployment in the UK, whereas France saw increasing unemployment in the 1990s and a decline again in the 2000s. 3.2 Comparative design: which groups? We use two strategies to select immigrant groups for the comparison. First, we focus on groups in each country that are similar in their postcolonial ties to the host country, and in their size and visibility in the host country: North Africans (NAF) from Algeria (DZA), Morocco (MAR), and Tunisia (TUN) in the French case and South Asians (SAS) from India (IND), Pakistan (PAK), and Bangladesh (BAN) in the UK case. Second, we include three groups that are more similar in terms of geographic origin: Western Europeans (WEU), Turks (TUR), and Sub-Saharan Africans (SAF). Western Europeans should be relatively privileged compared with the non-European groups in our analysis and we include them as a point of comparison.6 Sub-Saharan Africans are mostly post-colonial migrants in both countries, even though the former colonies are different for France and the UK. Turks are arguably the most comparable group. Turks are overwhelmingly Muslim in both countries and face stigmatization (Peach 2006a, 2006b; Adida, Laitin and Valfort 2014). In all tables and figures, we also present results for the foreign-born population as a whole in each country, so as to contextualize the results for these specific groups.7 3.3 Statistical analyses We analyse labour force participation, employment, and earnings. Labour force participation indicates whether respondents are in the labour force (which includes those who are unemployed and actively looking for work) versus inactive. Note that the inactive population may include the unemployed who are not actively seeking work. Employment indicates whether respondents are currently working or not. The non-employed include both the inactive and the formally unemployed, so our employment analysis is different from one that looks only at unemployment among the economically active population.8 Our earnings analysis focuses, by necessity, on net earnings, as gross earnings information is not available in the French data.9 The UK earnings variable is originally on a weekly basis, whereas for France it is monthly. We convert the UK variable to monthly earnings, and we adjust earnings in both countries for inflation (to 2007 GBP or EUR), based on a consumer price index. Because the earnings outcome is logged, gaps between groups can be interpreted in percentage terms and there is no need to convert to a single currency. We exclude the self-employed from the earning analyses because income information is not collected on them in the UK. We compare earnings analyses with and without correction for selection bias (Heckman 1990). Accounting for selection is crucial not only because of the traditional problem of endogeneity of the participation decision (especially for women), but also because there are reasons to suspect differential selection bias between immigrant and native populations and among immigrant groups. Selection may differ across groups because of problems with immigrants’ skills recognition, legal entry barriers for immigrants into certain types of jobs, or differences in gender role norms. Also, participation in the informal economy could drive selection into reported earnings and differentially affect immigrants and natives. The exclusion of the self-employed from our earnings analysis may also lead to differential selection, since self-employment may be a strategy to counter discrimination for some groups. Finally, selectivity can result from non-response to income questions. This is a potential problem in the UK data, where non-response to income questions is high. In short, selection bias may affect both the comparison between native and immigrant groups in each country and also the cross-national comparison, so we consider correction for selection bias central to the earnings analysis. Our findings are based on Oaxaca decomposition analyses that compare gaps in labour market attainment between natives and each of the ‘treatment groups’ before and after introducing controls, in each host country separately (Oaxaca 1973).10 Instead of introducing ethnic origin as an independent variable, Oaxaca decomposition is a synthetic presentation of the results of two regression analyses (for immigrants and natives) in terms of the relative share of explained and unexplained gaps. In more formal terms, if YN is the average wage for natives ( YIfor immigrants), XN the set of control variables for natives ( XI for immigrants), and βN the effects of these controls for natives ( βI for immigrants), Oaxaca’s twofold decomposition equation is: EYN-EYI=(EXN-EXI)βN-EXI(βN-βI) The equation refers to decomposition of a continuous outcome like earnings. For dichotomous decompositions for labour force participation and employment, we follow Jann (2008). The first term of the decomposition is referred to as the explained part and the second the unexplained part. The equation shows that if observable characteristics were the same for natives and immigrants, the explained part would be zero. The unexplained part (the differences in return to observables) is comprised of discrimination (Reimers 1983; Cotton 1988; Oaxaca and Ransom 1994; Yamaguchi, 2011) and unobserved characteristics (which in our analysis include language ability and length of stay, among others). The main advantage of Oaxaca decomposition compared with standard regression is that it allows different effects of covariates for each group. However, unlike standard regression analysis, Oaxaca decomposition can only compare two groups at a time. We control for basic contextual variables—year of survey and geographic region—within each country. The region variable has 12 values in the UK and 21 in France. We also control for basic socio-demographic characteristics that are likely to affect labour market outcomes: age, age squared, marital status, children, and education. Marital status has three categories: never married, currently married, or divorced/separated/widowed. We include variables indicating how many pre-school-aged (0–5) and school-aged (6–17) children are in the household. We measure education by age at which highest level of education was completed. This is generally less ideal than level of qualification, but in the case of immigrants, the age-based variable is preferable, because a high proportion of immigrants are categorized as having ‘other’ credentials at an unspecified level of qualification. We distinguish eight education categories: no formal education; and aged 1–14, 15, 16, 17–18, 19–20, 21–25, and 26+ when highest education was completed. We would like to control for years since migration, and although this information is available in the UK data, it is only available from 2003 to 2007 in France, so sample sizes are too small for multivariate analysis. Descriptive statistics (see the Online Appendix) show that Western Europeans have been in the UK longer than Western Europeans in France, whereas the reverse is true for non-European groups. We take this into account when interpreting our results below.11 Finally, in the earnings analysis, we include controls for type of job, including full-time vs. part-time, permanent vs. temporary contract, public vs. private sector, tenure in the current job (less than 1 year, 1–5 years, 6–10 years, and more than 10 years), and the International Socioeconomic Index (ISEI) score of the occupation, which is based on education and earnings of occupational incumbents in prior surveys (Ganzeboom and Treiman 1996). Including the ISEI control helps to account for potential occupational downgrading among immigrants (Dustman et al. 2013). Following previous studies (Blackaby et al. 2002; Mulligan and Rubinstein 2008; Edo 2015) we exclude variables for the number of pre-school-aged and school-aged children in the household from the earnings models, as these are the selection variables in the Heckman models. We assume that these variables affect the reservation wage and therefore labour force participation, but not the wage offer. 4. Differences in human capital, demographics, and job characteristics In both the UK and France, immigrants have characteristics that differ from their native-born counterparts, which may go some way to explaining differences in labour market outcomes. We discuss key differences in human capital and demographic characteristics here, and then turn to differences in job characteristics relevant to the earnings analysis. In these descriptive statistics, we employ sampling weights, though in the multivariate analyses, we do not (Winship and Radbill 1994). 4.1 Human capital In the UK, immigrants as a whole and specific immigrant sub-groups tend to cluster at the high and low ends of the educational distribution, though the pattern is different for men and women. Tables 1 and 2 present the proportion of each group that left education before age 15 and the proportion that continued on with education until an age (21) that suggests tertiary education (there are additional categories in the multivariate analyses). Native men and women are extremely unlikely to have left formal education before the age of 15, whereas for immigrants, especially immigrant women, the figures are as high as 30% (among Pakistani, Bangladeshi and Turkish women). However, native-born men and women are also less likely than immigrants to have left education at age 21+. Among men, even those immigrant groups with the lowest levels of education (Pakistanis, Bangladeshis and Turks) have greater representation at the top education levels than native-born men. This is not the case for Pakistani and Bangladeshi women, however. In sum, immigrants in the UK tend to be educationally polarized, compared with the native-born population. Table 1. Selected demographic and social characteristics of immigrant and native men in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. Table 1. Selected demographic and social characteristics of immigrant and native men in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.4 6.6 9.6 8.8 6.1 10.7 11.5 29.4 4.6 % Left education at 21+ 14.7 35.3 30.0 29.9 40.1 22.8 20.2 18.4 43.6 % Currently married 52.7 63.8 51.3 83.3 84.1 85.2 78.5 72.6 53.1 Average number of children at home 0.7 1.0 0.7 1.6 1.2 1.9 1.8 1.0 1.0 % Living in London 9.6 43.0 35.2 35.0 37.3 20.4 54.3 68.0 64.0 % Full-time 96.0 91.0 95.2 86.2 95.3 78.9 72.3 67.1 84.4 % Permanent contract 95.9 91.5 93.7 92.5 92.3 91.7 94.1 89.7 86.6 % Public sector 19.2 18.2 18.6 15.8 21.0 10.6 9.3 8.8 23.8 Mean ISEI 47.7 48.7 52.8 44.6 48.6 41.3 38.6 36.7 41.7 % Job tenure less than 1 year 11.2 16.2 13.4 14.5 11.6 17.1 18.7 16.5 17.6 % Job tenure more than 10 years 27.8 15.2 17.3 15.5 19.2 13.7 8.4 4.0 7.7 N (employment analysis) 375,375 35,826 7,537 7,815 3,505 2,802 1,508 541 2,638 N (earnings analysis) 131,045 11,827 2,634 2,164 1,160 622 382 116 932 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 9.1 22.8 4.8 21.1 19.1 23.7 21.4 41.5 16.5 % Left education at 21+ 27.1 28.6 49.3 27.0 25.7 30.8 22.8 9.8 45.6 % Currently married 49.5 67.0 56.7 69.8 68.0 70.3 74.2 80.5 57.3 Average number of children at home 0.8 1.1 0.9 1.3 1.2 1.4 1.2 1.5 1.2 % Living in Paris 17.2 38.5 22.3 32.3 32.2 30.7 36.1 27.5 54.6 % Full-time 96.6 95.2 95.1 95.7 95.7 95.7 95.5 96.8 91.7 % Permanent contract 92.7 91.1 94.3 90.6 92.0 87.8 92.2 84.9 85.2 % Public sector 26.0 19.5 25.6 24.0 27.4 20.8 20.3 8.4 25.5 Mean ISEI 43.1 40.4 50.8 41.0 41.9 39.7 40.9 32.1 40.2 % Job tenure less than 1 year 13.1 16.4 16.6 15.1 14.3 17.0 13.6 33.3 19.3 % Job tenure more than 10 years 41.2 36.3 34.8 42.2 45.2 38.0 41.9 17.0 26.6 N (employment analysis) 225,689 30,626 2,201 12,663 6,268 4,426 1,969 1,410 3,294 N (earnings analysis) 158,715 19,273 1,428 7,581 3,778 2,636 1,167 786 1,979 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. Table 2. Selected demographic and social characteristics of immigrant and native women in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. Table 2. Selected demographic and social characteristics of immigrant and native women in the United Kingdom and France United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 United Kingdom NB FB WEU SAS IND PAK BAN TUR SAF % Left education before 15 1.2 9.5 7.6 20.5 11.9 27.9 27.0 36.9 10.3 % Left education at 21+ 13.1 28.7 27.9 18.3 29.2 9.7 8.6 16.6 29.8 % Currently married 54.8 62.9 53.7 82.7 85.0 80.9 80.9 65.6 42.9 Average number of children at home 0.9 1.1 0.8 1.6 1.2 2.0 2.1 1.2 1.4 % Living in London 9.5 43.1 33.5 35.2 39.6 17.7 54.3 80.7 69.8 % Full-time 61.2 69.9 68.1 68.8 72.3 54.5 67.3 56.3 69.7 % Permanent contract 94.5 90.2 91.4 91.5 90.5 92.3 97.7 80.9 88.8 % Public sector 35.8 32.8 31.4 34.7 33.3 40.2 36.1 19.0 38.3 Mean ISEI 45.9 48.1 49.5 42.2 42.7 40.0 42.3 45.8 38.8 % Job tenure less than 1 year 11.3 16.0 14.6 14.8 14.8 14.9 14.8 27.2 14.9 % Job tenure more than 10 years 22.0 13.6 14.8 14.1 15.4 11.3 8.5 7.3 9.1 N (employment analysis) 400,378 43,355 9,885 9,032 4,115 3,152 1,765 487 3,452 N (earnings analysis) 40,715 12,254 3,345 1,330 988 228 114 76 1,016 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 France NB FB WEU NAF DZA MAR TUN TUR SAF % Left education before 15 8.0 26.7 3.7 29.3 25.1 35.3 28.9 53.7 22.0 % Left education at 21+ 29.1 26.3 43.5 22.8 22.3 24.5 20.5 6.3 33.3 % Currently married 54.5 69.0 61.3 70.8 68.6 71.7 76.0 85.5 57.0 Average number of children at home 0.9 1.2 0.9 1.3 1.2 1.4 1.3 1.6 1.5 % Living in Paris 17.5 39.2 23.2 32.6 31.4 32.9 35.9 25.6 54.9 % Full-time 73.1 68.6 68.9 70.1 70.7 68.2 72.5 76.0 66.8 % Permanent contract 91.3 90.4 91.6 89.9 90.7 88.2 90.2 78.7 87.4 % Public sector 37.8 30.3 32.8 38.6 41.3 33.4 39.8 20.0 32.5 Mean ISEI 42.9 37.9 48.1 39.7 40.2 38.1 40.9 33.9 35.3 % Job tenure less than 1 year 14.0 16.4 18.5 14.8 13.8 16.6 14.4 24.5 19.9 % Job tenure more than 10 years 39.7 33.0 29.2 38.7 41.9 33.4 38.3 8.9 21.9 N (employment analysis) 232,396 32,666 2,933 12,910 6,536 4,655 1,717 1,256 3,525 N (earnings analysis) 142,038 14,572 1,546 5,012 2,757 1,566 689 210 1,538 Note: Full-time, permanent contract, public sector, mean ISEI, and job tenure, and logged earnings pertain only to those who are currently employed. NB, native-born; FB, foreign-born; WEU, Western European; SAS, South Asian; NAF, North African; BAN, Bangladeshi; IND, Indian; PAK, Pakistani; DZA, Algerian; MAR, Moroccan; TUN, Tunisian; TUR, Turkish; SAF, Sub-Saharan African. In France, we see similarities, but also important differences. As in the UK, immigrants are over-represented at the lowest levels of education. Just fewer than 1 in 10 French natives left education before age 15, which is far higher than for natives in the UK, but far lower than for immigrants in France, with the exception of Western European immigrants. For instance, the figures are over 40 per cent for Turkish men and over 50 per cent for Turkish women in France. Immigrants in France are generally also over-represented at the highest levels of education, but that pattern is less consequential than in the UK, because French natives are more likely than UK natives to have high educational attainment. If we examine specific groups, we see a mixed story. Among Turks, we clearly see greater positive educational selectivity in the UK. The percentage of Turkish immigrants in the low educational category is much higher in France than in the UK, while the opposite is true for the high educational category. Sub-Saharan Africans have similar levels of higher education in the two host countries, though far higher representation at the lowest educational level in France than in the UK, so here too we see greater positive educational selectivity in the UK. This is very similar to the pattern we see if we compare South Asians in the UK with North Africans in France. Western European immigrants are more educationally selective in France than in the UK, at both the high and low ends of the educational spectrum. On the whole, we conclude that non-European immigrants are more educationally selective in the UK, while European immigrants are more selective in France. Compared with natives, the picture is complex because UK natives are more concentrated in the middle of the educational spectrum than their counterparts in France. 4.2 Demographics The geographic distribution of immigrants versus natives, especially their concentration in the capital cities, has implications for labour market outcomes. The descriptive statistics in Tables 1 and 2 are based on the London/non-London and Paris/non-Paris (Ile-de-France) dichotomies (the multivariate analyses incorporate more nuanced geographic variation). Immigrants are far more likely to live in London than their native-born counterparts. Less than 10 per cent of UK-born adults in our sample live in London, whereas the figures for all immigrants is over 40 per cent, and among Bangladeshis, Turks, and Sub-Saharan African immigrants, it is a majority. This has mixed implications for labour market outcomes, because while average earnings are higher in London, employment rates are lower there during this time period, so geographic concentration may explain an immigrant disadvantage in employment, but may mute an immigrant disadvantage in earnings. As in the UK, immigrants in France are far more likely to live in the capital region than their native-born counterparts, though the discrepancies between immigrants and natives are not as extreme as in the UK. Around 17 per cent of French-born adults live in Paris, whereas the figure for most immigrant groups is 20–30 per cent. For Sub-Saharan Africans, it is a majority. Employment levels and earnings are higher in Paris than in other regions, so immigrants’ geographic concentration there likely mutes their disadvantage. Another way in which the demographics of immigrant groups and natives differ is in the characteristics of their families. In the UK, immigrants as a whole are more likely to be married than their native-born counterparts, and this is especially true among South Asian groups. More than 80 per cent of Indians, Pakistanis, and Bangladeshis are married (compared with just over 50 per cent among natives). Western European immigrants and Sub-Saharan African women are less likely than natives to be married. In France, there are also dramatic differences in marital status. The proportion of married individuals among the native-born population is similar to the UK. Immigrants are more likely to be married than French natives, though only slightly so for Sub-Saharan African women. The marriage figures are particularly high for Turkish immigrants. Without exception, the immigrant groups in our sample have more children in the household, on average, than natives. (Note that in the multivariate analyses below, we control separately for pre-school and school-aged children, but we present total children here to simplify.) The highest averages are found among Pakistanis and Bangladeshis in the UK. In France, the highest average is among Turkish immigrants. These patterns in family characteristics are likely to affect employment and earnings, though differently for men and women, since being married and especially having children tend to have less detrimental (and even positive) effects on men’s labour market outcomes. 4.3 Job characteristics The characteristics of the jobs that immigrant and native-born workers find in the two countries are quite different, which (like human capital and demographic differences) have implications for earnings inequalities. First, members of some immigrant groups in the UK are relatively unlikely to work full-time. We see no extreme nativity differences in part-time employment for men in France or for women in either country. Differences are less extreme with respect to temporary vs. permanent employment. Not surprisingly, most immigrant groups in both countries are somewhat more likely than native-born workers to have a temporary contract, and in both countries, Turkish women are the group that is most likely to be working on a temporary contract (around 20 per cent). Immigrants as a whole are under-represented in public sector work in both countries. This is particularly true of Turkish men and women in both countries, and of Bangladeshi and Pakistani men in the UK. However, some immigrant groups in both countries are slightly over-represented in the public sector. On average, immigrants and natives have higher-status jobs in the UK than in France, as measured by the ISEI. However, immigrants as a whole in the UK have a slight status advantage over their native-born counterparts, whereas the opposite is true in France. Not surprisingly, Western Europeans have the highest status jobs (higher than natives) in both countries and among men and women. Turks have exceptionally low job status in both countries, with the exception of Turkish women in the UK (among whom selectivity into employment is quite strong). Finally, turning to job tenure, we see that it is generally longer in France, among natives and immigrants. Turks are particularly disadvantaged in both countries in terms of job tenure. 5. Labour market outcomes We turn now to the decomposition analyses of immigrant/native gaps in labour force participation, employment, and earnings. These analyses help to disentangle the extent to which observed differences in labour market outcomes across groups and countries are due to compositional differences in human capital and demographics (and job characteristics, for the earnings analysis); selectivity into employment (for the earnings analysis); or residual factors including discrimination. We include detailed decomposition results in the Online Appendix, but we focus our discussion on Figures 1 and 2, which display estimates of gaps unconditional and conditional on controls (along with 95 per cent confidence intervals for these estimates) in both countries for men and women respectively. Figure 3 compares how well the model covariates explain unconditional gaps.12 Unconditional gaps are the raw difference in probability of labour force participation or employment or in logged earnings. Conditional gaps control for education, age, age squared, marital status, pre-school and school-aged children, year, and region (in the case of labour force participation and employment) and age, age squared, marital status, year, region, ISEI, full-time vs. part-time work, temporary vs. permanent contract, public vs. private sector, and job tenure (in the case of earnings). Unconditional gaps for earnings are presented with and without the Heckman correction procedure. Note that in all of the decomposition tables and figures, positive gaps indicate that the immigrant group experiences a disadvantage relative to natives, while a negative gap indicates that the immigrant group experiences an advantage. If the unconditional gap is positive, but the conditional gap is null or negative, controls explain away the disadvantage the immigrant group faces relative to natives. Figure 1. View largeDownload slide Unconditional and conditional ethnic penalties among men. Figure 1. View largeDownload slide Unconditional and conditional ethnic penalties among men. Figure 2. View largeDownload slide Unconditional and conditional ethnic penalties among women. Figure 2. View largeDownload slide Unconditional and conditional ethnic penalties among women. Figure 3. View largeDownload slide Proportion of unconditional penalty explained. Figure 3. View largeDownload slide Proportion of unconditional penalty explained. 5.1 Men 5.1.1 Labour force participation and employment Most groups of immigrant men in the analysis face disadvantages in labour force participation and employment in both countries. In the aggregate, foreign-born men face a somewhat larger unconditional employment gap in France than in the UK. However, once individual-level characteristics are controlled, the aggregate employment gap is almost identical in the two countries. This is because immigrants in the UK have more favourable human capital and demographic characteristics for employment than their native-born counterparts, so controlling for these factors magnifies immigrants’ disadvantage. That is, immigrant men enter the labour force in the UK with relatively better characteristics than their immigrant counterparts in France (as we saw in the descriptive statistics about education especially). Despite a similar conditional employment gap in the two countries, its causes are somewhat different. There is a significantly larger gap in labour force participation in the UK, offset by greater difficulty for active immigrant job-seekers in actually finding employment in France. Around half of the conditional employment gap between immigrant and native men in the UK is due to labour force participation while very little of it is in France. Given the potentially ambiguous line between inactivity and unemployment among working-aged men, we hesitate to conclude that immigrant men voluntarily choose not to work in the UK, and we suspect that some inactivity in the UK consists of discouraged jobseekers. For instance, the UK Labour Force Survey includes a question about whether economically inactive respondents would like work, and more than a third of inactive men in our sample report that they would, even though they are not officially classified as unemployed. We do not have comparable evidence for France, though inactivity among men is far lower there. We turn now to a discussion of specific groups of immigrant men. Western Europeans, as we expected, are not particularly disadvantaged in either country. Their unconditional and conditional labour force participation and employment gaps are far smaller than other groups in both countries and the gaps are somewhat smaller in the UK than in France. This can be dramatically contrasted with the very large disadvantages in employment that non-European groups face in both countries. North Africans in France, South Asians in the UK, and Turks and Sub-Saharan Africans in both countries face conditional employment penalties that are many times larger than the penalties faced by Western Europeans. While North Africans in France and South Asians in the UK have statistically indistinguishable employment penalties, Turks and Sub-Saharan Africans fare somewhat better in France. It should be noted, however, that the aggregate grouping of South Asians masks considerable variation across specific groups: Bangladeshis and Pakistanis in the UK fare worse than any of the North African groups in France, while Indians fare somewhat better. But on the whole, after adjusting for individual-level characteristics, non-European groups seem to fare worse in terms of employment in the UK. As with foreign-born workers as a whole, the cause of these employment gaps is different in the two countries: Around half of the employment penalties for non-Europeans in the UK are attributable to differences in labour force participation, whereas in France, they are largely attributable to differences in employment among active job-seekers. We also analyse the different experiences of low- and high-skilled immigrant men from South Asia and North Africa in the UK and France, respectively, as compared with natives with similar skill levels, because we believe that different processes might be operating at the low- and high-end of the labour market. For these analyses, we define ‘low-skilled’ as having left education before age 15 and ‘high-skilled’ as having stayed in education until at least age 21. Among those with low skills, differences in activity and employment rates are more than accounted for in France by differences in individual characteristics. The detailed decomposition results suggest that this is largely because, even among the low-skilled, immigrants are concentrated at the very bottom of the educational spectrum, among those with no formal education. In the UK, there are smaller unconditional penalties that further narrow with individual controls. Conditional employment penalties among this low-skilled group are statistically indistinguishable in the two countries, though they are driven more by inactivity in the UK. Among the high-skilled group, on the other hand, measured characteristics do not account for the activity or employment penalties in either country, and indeed, immigrants have somewhat more favourable characteristics than natives. As among the low-skilled, the difference between France and the UK in the size of the employment penalty among the high-skilled is not substantial, though it is driven more by inactivity in the UK. In both countries, the conditional penalties faced by high-skilled immigrants are somewhat larger than those faced by low-skilled immigrants. Figure 3 summarizes how well our models perform in explaining unconditional penalties in activity and employment among immigrant men. A proportion of 1 means that covariates in the model explain away the gap between the immigrant group and natives in that country—that there would be no gap were immigrants and natives to have the same characteristics. We choose not to display bars in cases where the proportion explained is zero or negative. We see here unequivocally that the covariates in the model explain essentially none of the existing gaps in the UK. In France, activity gaps (small to begin with) are relatively well explained by our models. Men’s overall employment chances in France are, however, poorly explained by model covariates, with the exceptional case of low-skilled North Africans in France, which we discussed above. 5.1.2 Earnings The earnings panel in Figure 1 displays two unconditional gaps: before and after the Heckman correction. These are the top two bars for each group in the earnings figures. By comparing the two, we get a sense of the magnitude and direction of selectivity into earnings. As we noted earlier, selectivity can stem from multiple sources, and can be either positive or negative. In France, we see negative selection into employment (i.e. the estimated gap is more negative once we control for selection) among Western Europeans, Turks and high-skilled North Africans and positive selection (i.e. the estimated gap is more positive once we control for selection) among other groups, including foreign-born men as a whole. However, confidence intervals are large here; in most cases, there is no statistically significant difference between gaps with and without the Heckman correction. Most non-employment among immigrant men in France is in the form of unemployment, so positive selection is to be expected. In the UK, the picture is also widely varying. Among Western Europeans, Pakistanis, and the high-skilled South Asian group, we see positive selection into earnings, though the unconditional gap before and after Heckman correction fails to attain statistical significance, given wide confidence intervals. Among all other groups, including foreign-born men as a whole, we see evidence of negative selection (and for some groups the evidence is statistically significant). That is, those who report no earnings would likely earn more than those who report earnings. This is potentially related to high non-response to income questions in the UK data. At first blush, looking at unconditional gaps before correcting for selection, we see larger earnings gaps in the UK than in France. For example, the most disadvantaged group of immigrant men in France (Turks) earns about 35 per cent less than native-born men, while in the UK, the comparable figure (in this case for Bangladeshi men) is over 70 per cent less. (It should be noted that there are also some groups of immigrant men in both countries, especially Western Europeans, who are not very disadvantaged compared with native-born men in terms of earnings.) However, once we take selection into account and control for individual-level characteristics, any cross-national pattern is less obvious, because estimates are imprecise in both countries. Indeed, for many groups in both countries, we cannot conclude with statistical confidence that there are any earnings gaps once we control for selection. In short, there is no evidence among immigrant men that earnings penalties are significantly different in the two countries. For earnings, we choose not to discuss the proportion of the unconditional gap explained by covariates, because model covariates explain little to none of the unconditional penalties. 5.2 Women 5.2.1 Labour force participation and employment We see in Figure 2 that employment gaps for women are due, more than with men, to differences in labour force activity (i.e. the first and second panels in Figure 2 are quite similar), particularly in the UK. Unlike with men, the covariates in our models explain a substantial portion of observed gaps in labour force activity and employment, which we discuss in more detail below. Focusing here on the size of conditional penalties, we observe, if anything, larger gaps in the UK. The conditional gap for labour force participation among immigrant women in the aggregate is nearly twice as large in the UK as in France, because almost none of the unconditional gap is explained by covariates in the UK. For employment, the cross-national difference is less striking, but still statistically significant: immigrant women in the aggregate in France face a less severe disadvantage vis-à-vis natives than in the UK. As among men, the gap in the UK is due more to non-participation in the labour market, and in France to unemployment. As with men, we have reason to believe that there is a relatively high level of latent unemployment among inactive women in the UK, because nearly a third of them report in the Labour Force Survey that they would like work. Again, we do not have comparable figures for France. The picture for specific immigrant groups is similar. As among men, Western European women are not as disadvantaged as their non-European counterparts, but this is more so in the UK than in France. North African women in France have far smaller penalties in activity than South Asian women in the UK, once individual-level variables are controlled; this is true in the aggregate and for the three specific North African and South Asian origin countries. Furthermore, both Sub-Saharan African women and Turkish women have significantly larger activity penalties in the UK than in France. With employment, the two host countries look more similar. Though Pakistani and Bangladeshi women in the UK still post penalties that are significantly larger than any group of immigrant women in France, Indians in the UK look relatively similar to the various groups in France. Sub-Saharan African and Turkish women have somewhat smaller employment penalties in France, but the cross-national difference is not statistically significant. Our models that separately analyse low- and high-skilled North African and South Asian women suggest that, though low-skilled women certainly have larger unconditional penalties in activity and employment than their high-skilled counterparts, this is mostly due to their disadvantageous individual-level characteristics (particularly their concentration at the extreme low end of the educational spectrum and their higher numbers of children). Indeed, conditional gaps are very similar in magnitude between the two skill groups. Both high- and low-skilled groups fare somewhat better in France than in the UK, so the separate analyses by skill level does not change the basic conclusion that employment penalties for non-Europeans are more severe in the UK, driven largely by barriers to labour force participation. We see in Figure 3 that, for immigrant women in both countries, our models do a better job of explaining the activity and employment penalties than was generally the case among men. Covariates explain nearly 100 per cent of some activity gaps in France, and up to 35 per cent in the UK. For employment, covariates explain somewhat less of the gaps in France. In the UK, the explanatory power of covariates in employment and activity models is more similar, because, as we discussed above, employment gaps are driven largely by inactivity. That covariates generally explain more of the gaps in France than in the UK highlights the possibility that institutional rather than individual-level factors drive immigrant women’s lack of employment in the UK. Nonetheless, more than with men, there are also measurable individual-level barriers to employment for immigrant women. So for men and for women, our findings show somewhat larger conditional gaps in employment in the UK than in France among non-Europeans, a pattern driven largely by lower labour force participation in the UK. Lower labour force participation in the UK is somewhat muted but not fully offset by the fact that immigrants in France seem to face higher barriers to employment when they are unemployed according to official criteria. We are cautious in over-emphasizing the cross-national difference, however, because non-European groups have been in France longer on average than non-European groups in the UK (see Online Appendix) and that could reduce the cross-national differences in our analyses. 5.2.2 Earnings We turn now to earnings differences between immigrant and native women. Looking first at the evidence about selection in the earnings panel, we see, as with men, diverse outcomes across groups. Among almost every group in the UK (except high-skilled South Asians), there is some evidence of positive selection into employment: immigrants’ disadvantage appears larger once the Heckman correction is implemented. However, the difference between the size of the gap before and after the correction in no case attains statistical significance. Among almost every group of women in France (except high-skilled North Africans), on the other hand, there is, if anything, evidence of negative selection—that is, those who report earnings likely earn less than would those who report no earnings. But again, rarely does this negative selection attain statistical significance, given very wide confidence intervals. The heterogeneity of selectivity across groups shows that it is important to take it into account as we do. As with immigrant men, confidence intervals are extremely wide for conditional earnings gaps, which are in many cases not significantly different from zero. The exceptions are Western Europeans, Turks and low-skilled North Africans in France (who have a significant earnings premium) and South Asians (driven by Indians and the high-skilled) and Sub-Saharan Africans in the UK (who experience earnings penalties). Earnings penalties for immigrant women appear to be somewhat larger in the UK. For instance, South Asian women in the UK have a significantly larger conditional earnings penalty than North African women in France, though once we break this down by skill level, the cross-national difference disappears. Western European, Turkish, and Sub-Saharan African women also seem to fare somewhat better in terms of conditional earning penalties in France than they do in the UK. (Indeed, Western European and Turkish women have a significant earnings premium in France, and Sub-Saharan African women in France experience no significant earnings penalty.) Confidence intervals for earnings are large, so conclusions should be cautious, but the evidence here suggests somewhat better earnings outcomes for immigrant women relative to natives in France than in the UK. Though we again note that non-European groups have been in France longer on average than non-European groups in the UK, the job tenure variable in the earnings analysis should at least partly capture these differences. 6. Conclusion This article empirically assesses the extent to which theoretical frameworks used in comparative research on immigrant incorporation and political-economic institutions are useful for interpreting patterns of immigrant/native inequality in the French and UK labour markets. Our findings show that the UK labour market attracts immigrants who are more highly skilled and have other advantageous individual-level characteristics. But beyond this, our evidence suggests little advantage for non-European immigrants in the UK labour market in terms of employment. It thus does not seem to be the case (as H2 might suggest) that the UK’s liberal welfare state and labour market institutions seem to generate significantly better employment outcomes for non-European immigrants. Indeed, the UK actually looks substantially worse than France in terms of inequalities between non-European immigrants and natives in labour force participation for both men and women. Though this does seem to be somewhat offset by the greater difficulty that active job-seeking immigrants have in finding employment in France (a pattern that proponents of the UK’s institutional features would highlight), it is still the case that employment gaps tend to be, if anything, somewhat larger in the UK for non-European immigrants. This lends tentative support to H1B. Among Western European immigrants, on the other hand, activity and employment outcomes seem to be relatively better in the UK, so the issue appears to be an interaction between immigrant groups’ characteristics (whether demographic, religious, cultural, physical appearance, etc.) and host country institutions and policies. Because the integration of Western European immigrants is generally not considered problematic and outcomes for this group are quite good in both countries, we place greater emphasis on the findings about non-European groups. Some might dismiss labour force participation as an outcome driven largely by individual choices and not relevant to considerations of immigrants’ labour market disadvantages, but we disagree. Immigrants’ labour market participation cannot be assumed to be a purely individual choice, because formal barriers to entering some jobs and problems with skills recognition can affect immigrants’ participation in the labour market and result in higher numbers of discouraged workers who cease (or never begin) to actively look for work. Indeed, just under a third of inactive women and more than a third of inactive men in the UK data report that they would like to work. For women, child care and other family-related welfare policies, which are quite different in the two countries, form an important institutional context for participation decisions, and such institutions may disproportionately affect immigrant women, who are more likely than their native-born counterparts to have children. Our evidence suggests that improving immigrant employment outcomes depends on considering barriers to both labour force participation and employment among active job-seekers. Unlike some previous analyses of earnings, ours corrects for selection bias into employment. Though the evidence for earnings is weaker than for labour force participation and employment, it seems to be the case that at least among immigrant women, earnings outcomes are also relatively more positive in France than in the UK, which is consistent with political-economic theories that highlight the UK’s higher earnings inequality and its potential impact on immigrants (H2). Nonetheless, given that cross-national evidence is stronger for labour force participation and employment, our evidence seems somewhat more consistent with H1B, if we are to conclude that there are any notable cross-national differences at all. But most striking to us is that inequalities are in many cases so similar in the two countries (H3), particularly once we consider that the non-European groups in our analysis have been in France longer on average than in the UK. Though our analysis pinpoints cross-national differences in the underlying mechanisms generating employment inequalities between immigrants and natives in the two countries (relating more to labour force participation in the UK and to barriers in job-seeking in France), the end result is actually relatively similar, and inequalities in earnings between immigrants and natives in the two countries are also remarkably similar. Therefore, we conclude that despite differences in immigrant incorporation models and political-economic institutions, immigrants with disadvantaged non-European origins seem to face barriers to equal opportunity that are remarkably similar in magnitude in the French and UK labour markets. Funding This research received no external funding, but was supported by the institutions with which the authors are currently affiliated, noted above, as well as by the institutions with which Kesler has previously been affiliated (Barnard College of Columbia University and Nuffield College of the University of Oxford). Footnotes 1. Though the second generation is included in their study, it is defined differently in each country, making the comparative results difficult to interpret. 2. While union membership is low in France (10 per cent compared with 30 per cent in the UK), collective agreements cover many non-members, making France comparable in terms of union coverage to countries such as Sweden and Austria (OECD 2004). 3. This is important because of both countries’ high levels of naturalization among immigrants. Unfortunately, we cannot uniformly identify return colonial migrants (those who are born abroad with French or UK nationality at birth) in our data, though in the UK case, we have information on self-classified ethnicity. We conclude from looking at this variable that only a small proportion of immigrants in our sample are likely to be return migrants. For example, less than 3 per cent of respondents born in South Asian countries consider their ethnicity to be white, and so our decision about whether to include them is not consequential to the findings. (We do exclude them from the UK data.) 4. Both datasets contain short panels of individuals. Individuals stay in the sample for 3 years until 2003 in France, then for six quarters; and for five quarters in the UK. We include one observation, from the first time a respondent is interviewed. We exclude workers who are older than 55, because policies related to retirement age are different in the two countries, and this is beyond the scope of our analysis. 5. Because the second generation cannot be uniformly identified, it is by default included in the native-born baseline group. This is unlikely to have a significant impact on our findings, because the second generation is so small relative to the total native-born population. 6. This group includes those born in EU-15 countries plus Norway and Switzerland. 7. We do not include Afro-Caribbeans in our analysis, because in France, citizens of overseas territories cannot be further differentiated by territory or even world region. 8. We conduct the analysis this way to avoid the selection bias in an analysis that includes only the economically active. 9. The gross earnings variable for the UK yields similar results. In the UK data, earnings information is available only from 1997 onward. 10. 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Migration StudiesOxford University Press

Published: Jul 9, 2017

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