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ABSTRACT
We use data from the Trajectoires et Origines survey to analyze ethnic gaps in education and labor-market outcomes between second-generation immigrants and their French-native counterparts. Our three main findings underscore the importance of family background in explaining lifelong ethnic inequalities. First, second-generation immigrants are on average less likely to experience education success than their native counterparts, with the education gap mainly being rooted in ethnic differences in family backgrounds. Second, while second-generation immigrants have on average a lower probability of employment and lower wages than natives, both gaps are mainly explained by the differences in education. Third, we find considerable heterogeneity across ethnic groups.
Acknowledgments
The authors are very grateful to Caroline Berchet and François-Charles Wolff for their comments on an earlier version of this paper. The authors also thank seminar participants at GAINS (Université du Maine) as well as conference participants at the 2013 Journées de Microéconomie Appliquée (Nice) and the 2013 CREM-INRA Workshop (Rennes).
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes
1. For instance, Dustmann, Machin, and Schönberg (Citation2010) find that while minority groups start off with lower achievement outcomes at school entry, they tend to outperform white British children by age 16.
2. Using the British school census, Dustmann, Machin, and Schönberg (Citation2010) compare the educational outcomes of children from ethnic minority groups and native children from the ages of 5 up to 16. Improved English language proficiency is the most important factor explaining why ethnic minorities improve relative to their British counterparts. Furthermore, language proficiency also affects social integration (see Bleakley and Chin Citation2010).
3. Note that, for convenience, we consider as natives only those respondents whose parents were both born with French nationality, although from a legal point of view, immigrants’ descendants born in France are also French natives as a result of the French jus solis.
4. Note that our data do not allow us to isolate the effect of each of these motives.
5. In November 2005, a wave of violence swept through the suburbs of a number of French cities. Faced with this sudden rise in tension, some commentators underlined long-standing integration problems, including discrimination against minorities and the lack of job opportunities in the suburbs that are mainly populated by immigrants.
6. A similar empirical analysis of ‘Emploi en Continu’ data confirms these findings (see Aeberhardt et al. Citation2010b).
7. Note that we have to be careful in interpreting the role of parents’ education as the diplomas of non-native parents are not always comparable to those of French-native parents. Ideally, one way to control for such differences would be to consider third generation immigrants. Unfortunately, this information was not available in the database.
8. We acknowledge that the answers given by participants to retrospective questions may not be reliable. This question of the reliability of self-reported schooling is a general issue in the literature. Research generally finds the reliability of self-reported education to be about 90% (Angrist and Krueger Citation1991; Miller, Mulvey, and Martin Citation1995; Ashenfelter and Rouse Citation1998). For instance, Angrist and Krueger (Citation1991) conclude that the reliability of self-reported schooling is 85–90%. Griliches (Citation1977) notes that measurement errors in schooling would lead to downward bias in the ordinary least squares (OLS) estimates of the returns to schooling. However, this bias may be partially offset by a possible upward ability bias (Card Citation2001).
9. We exclude the self-employed as we cannot introduce them in our two-step Heckman procedure to measure wage discrimination. By definition, the self-employed are their ‘own boss’ and have no reason to discriminate against themselves in terms of wages. However, we recognize that some individuals may choose self-employment because they are discriminated against by employers. Nevertheless, in our dataset it seems that this is not the case since we find no significant differences in self-employment rates between natives and second-generation immigrants. The self-employed represent 6.24% of second-generation immigrants and 6.57% of natives. A similar concern regards discouraged workers who are not considered here. Although we can identify them in the survey, they are not included in our analysis due to the subjective aspect of their status. Furthermore, they only represent 1.52% of the second-generation and 5.76% of the native inactive population.
10. Following the French Republican egalitarian principle, migrants’ children are not usually visible in national statistics. This was dealt with in the TeO survey by cross-checking with the 2007 French census and local registers to identify first-generation immigrants’ children (in particular from birth certificates).
11. We define the European subgroups as follows: Southern Europe comprises Italy (36.46% of this subgroup), Portugal (32.67%), Spain (30.19%) and Greece (0.68%); Northern Europe Germany (43.02%), Belgium (32.48%), United Kingdom (9.40%), Netherlands (3.42%), Ireland (3.42%), Austria (3.13%), Luxembourg (2.28%), Denmark (1.71%) and Sweden (1.14%); Eastern Europe contains the remainder of the European continent. Second-generation immigrants from America, Oceania and Middle East countries are dropped due to small cell sizes.
12. This assumption is known as the signaling or screening hypothesis (Arrow Citation1973; Spence Citation1974). For instance, suppose that some individuals with low unobserved ability receive lower earnings. If these individuals are more likely to be undereducated, the error in the individual’s occupational selection process will be correlated with the error term in the wage equation. There is thus endogeneity bias in the relationship between schooling and earnings, as ability is not taken into account and is therefore included in the error term. Ideally, we should control for individual ability in the estimates. Unfortunately, in most cases we cannot observe this.
13. IV can suffer from a number of drawbacks. First, IV estimates may be relatively imprecise, making it difficult to establish the size of the ability bias in OLS estimates (see Card Citation2001 for a discussion of this point). In the words of Card (Citation2001), ‘no individual study is likely to be decisive in the debate over the magnitude of ability biases in OLS estimates of the return to schooling’ (1157). A second limitation is the restrictive conditions for the choice of the instrument, which should significantly predict education but not be correlated with any other determinants of the dependent variable such as ability for instance (the exclusion restriction).
14. These figures should be interpreted with caution since the age structure varies widely between ethnic groups.
15. Recently, economists have shown that the positive relationship between parental income and children’s school outcomes may be biased if parental ability is ignored. Plug and Vijverberg (Citation2005) note, however, that this bias may be overestimated. They appeal to a sample of adopted children, which produces genetically unbiased estimates, and find that family income still plays a significant role.
16. Following most existing work, we use ordered probit models to estimate the highest grade attained (e.g. Drèze and Kingdon Citation2001). A possible limitation of this approach is that the errors are supposed to be homoscedastic, which does not sit comfortably with the possibility of heterogeneous responses that depend on unobservables. Alternatively, we could run a multinomial choice model over the six education levels. However, such models are computationally more complex and require the IIA property, which is not necessarily the case. We compare our ordered probit findings to those from a simple probit on ‘schooling success/failure’. We also run OLS, ordered logit and logit models (available upon request): these produce very similar findings.
17. For computational reasons, we consider here schooling failure/success rather than education attainment.
18. These results suggest that the ethnicity of both the teacher and the pupil seems to matter. Unfortunately, the TeO survey does not include data on teacher characteristics. However, one may reasonably assume that the majority of teachers belong to the native group.
19. Note that they first use this method to decompose the ethnic employment gap, and that we will also do so in Section 3.2.2.
20. For instance, when considering North Africans, who earn the lowest wages, the average monthly wage differential amounts to 261 Euros.
21. Weighted hourly wage means are compared via a t-test. We use the Rao and Scott (Citation1984) second-order correction of the Pearson χ2-test to analyze employment differences. We here consider the 10% significance threshold.
22. We use the pweight command in Stata10 to include weights in our models.
23. We use the same covariates as in , apart for the ethnic group dummies.
24. A number of authors suggest that using dummies may reduce measurement error as the errors in reported schooling are probably mean regressive (see Card Citation1999 for a discussion; also Kane et al. Citation1997, and Psacharopoulos and Patrinos Citation2004).
25. We appeal to maximum-likelihood estimators, which are known to be more efficient than the original two-step procedure (Puhani Citation2000).
26. Controlling for occupation allows us to capture any occupational particularity; however, it may lead to an underestimate of discrimination if access to certain occupations is itself discriminatory.
27. That the inverse Mills ratio is insignificant in some estimates does not necessarily imply that there is no selection bias within each ethnic group. This is due to the selection bias being different in both extent and nature between natives and second-generation immigrants. When we run regressions for each subgroup, we obtain a Mills ratio that is significantly positive for natives and significantly negative for second-generation immigrants, which is why we observe an insignificant Mills ratio in the whole-sample estimation.
28. Despite the advantages of the Blinder-Oaxaca approach, this method may have some limitations. We thank an anonymous referee for this helpful remark. A first is that the wage gap is estimated around the mean value of the total population and thus provides no information about the distribution of earnings differences (e.g. Jenkins Citation1994). A second is that the relationship between characteristics and earnings is not necessarily linear (e.g. Heckman, Lochner, and Todd Citation2003). Another is that it may underestimate labor-market discrimination by not taking into account feedback effects from labor-market discrimination on individual characteristics, (Oaxaca Citation1973, 708): some individual characteristics such as human capital may themselves reflect discrimination (Oaxaca Citation1973). Individuals may also invest less in human capital if they anticipate lower returns, which themselves show current discrimination. Discrimination may also be blurred by omitted-variable bias, making it difficult to identify accurately. Finally, the Blinder-Oaxaca approach does not restrict the comparison to comparable individuals, which likely upwardly biases the unexplained pay difference estimate (e.g. Barsky et al. Citation2002).
29. Greater family size may negatively affect child outcomes through resource dilution (Becker Citation1960), although this can be partly offset by larger families helping to stabilize marriages and thus reduce the probability of education failure. The empirical evidence leans toward a negative correlation between family size and education. This is the case in Black, Devereux, and Salvanes (Citation2005) in data on the entire population of Norway. However, this correlation turns insignificant when the authors include indicators for birth order and use twin births as an instrument. The authors also find a negative significant birth order effect on child education.
30. We also ran separate wage estimates by success/failure at school (available upon request). These indicate a wage premium relative to natives only for Turkish second-generation immigrants who succeeded at school.
31. In these models, negative prior beliefs about members of a particular group may become self-fulfilling in equilibrium (Lundberg and Startz Citation1983). This may occur for example if individuals of a particular group under-invest in human capital due to anticipated discriminatory treatment and therefore a lower return to education.
32. This interpretation of the residual employment gap requires caution, as the latter may reflect not only discrimination, but also unobserved differences in ability, attitudes or preferences. One limitation of our work here is that we cannot disentangle these explanations. This is a general difficulty in most survey data without information on discrimination at the hiring stage. Recent developments have emphasized the value of field and laboratory experiments in circumventing this difficulty (see Riach and Rich Citation2002, for an exhaustive survey of field experiments).