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Ethnic origin matters significantly in education attainment

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Ethnic origin matters significantly in education attainment

    Human capital background and the educational attainment

    #of second-generation immigrants in France

    ***Manon Domingues Dos Santos François-Charles Wolff

    First revision, Economics of Education Review

    November 2010

    Abstract: In this paper, we study the impact of parental human capital background on ethnic educational gaps between second-generation immigrants using a large data set conducted in France in 2003. Estimates from censored random effect ordered Probit regressions show that the skills of immigrants explain in the most part, the ethnic educational gap between their children. Fluency in French and the length of their parents‟ stay in France also matter. The

    impact of the immigrants‟ education on the educational attainment of their children further

    depends on their country of origin, their place of schooling, and their proficiency in French.

Keywords: Immigrants, second generation, educational attainment, France

JEL Classification: I21, J15, J24, J61

     # We would like to thank three anonymous referees and the Associate Editor, Mikael Lindahl, for their very helpful comments and insightful suggestions on a previous draft. We are also indebted to Pierre Cahuc, Arnaud Chevalier, Francis Kramarz and participants at the TEMA seminar (University Paris 1), the Crest seminar (INSEE), the Journées de Microéconomie Appliquée (Fribourg), the Annual Conference of the European Society for Population Economics (Chicago) and the Annual Conference of the European Association of Labour Economics (Oslo) for useful comments. The usual disclaimer applies. * OEP, University Paris-Est Marne-la-Vallée and CREST, INSEE, France.

    Email : manondds@ensae.fr http://www.crest.fr/pageperso/manondds/manondds.htm ** Corresponding author. LEMNA, Université de Nantes ; CNAV and INED, France.

    Email: francois.wolff@univ-nantes.fr http://www.sc-eco.univ-nantes.fr/~fcwolff

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1. Introduction

    In France, 42% of the young men and 27% of the young women whose parents are Northwest Africans leave school without any diploma. Among the children whose parents are natives or Southern Europeans, this proportion is about two times lower amongst men and nearly three times lower among women (Lainé and Okba, 2005). In many countries, significant differences are also observed in the educational achievements of children from different origins (OECD, 2006). What can explain such inequalities? As emphasized in the Chiswick‟s seminal paper (1988), three main hypotheses may explain why different ethnic groups achieve different levels of educational attainment.

     Firstly, some communities are likely to have a greater preference for schooling, which may be due to cultural, religious or historical factors. This particular taste for schooling can lead members of these communities to invest more in the human capital of their descendants. Secondly, ethnic differences in educational attainment may arise from discrimination. During their studies, children from some communities may be discriminated against with regard to access to schooling, quality of schooling, grade retention or tracking decisions (Losen and Orfield, 2002). During their working life, they can face less favorable job conditions. As the returns of their investment in human capital are lower, they would then be less motivated to invest in skills (Coate and Loury, 1993). Thirdly, some ethnic communities may be over-represented in the most disadvantaged socio-economic groups. Following the human capital theory, immigrants of these communities would then invest less in the education of their children.

     Do we have to promote special educational programs in favour of some second-generation youths? Do we have to assist immigrants themselves in their educative mission? Are anti-discriminatory policies which encourage anonymous applications able to narrow the educational gaps? To evaluate the relative efficiency of such policies, it is necessary to rigorously identify the determinants of the educational attainment gaps. In this paper, we investigate how differences in human capital background among immigrant communities explain the educational attainments gaps among their descendants using a data set on immigrants living in France in 2003. We extend the growing literature which intends to understand the role of the family socio-economic background on the educational attainment of the second-generation immigrants in the following ways.

     On the one hand, our contribution is the first study to focus on this issue using French data. Previous empirical studies have dealt with Anglo-Saxon countries, Germany (Gang and Zimmermann, 2000) and the Netherlands (Van Ours and Veenman, 2003). It is not obvious

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    that these results can be readily extrapolated to the situation of immigrants living in other countries. Institutional differences in immigration policies and education systems between Anglo-Saxon and European countries could affect the educational achievement of children of immigrants. On the other hand, the quality of the data allows us to better take the migratory history of the parents of second-generation immigrants into account. For instance, our regressions control for their fluency in the host country‟s language, their length of stay in

    France or their place of study.

     We show that skills of immigrants explain, in the most part, the ethnic educational gap between their descendants in France. French fluency and the length of stay in France of parents also matter. The impact of the immigrants‟ education on the educational attainment of

    their children further depends on their country of origin, their place of schooling and their proficiency in French. The remainder of our paper is organized as follows. In Section 2, we briefly review the literature on human capital background and educational attainment of second-generation immigrants. We describe the French data used for our empirical analysis in Section 3 and provide some descriptive statistics in Section 4. We present our econometric strategy in Section 5 and discuss our results in Section 6. Finally, Section 7 concludes.

    2. Background on the educational attainment of second-generation immigrants

     According to the Programme for International Students Assessment (OECD, 2006), in many OECD countries the second-generation students defined as those who are born in the host country and those who have arrived as children before school age perform at a lower level with respect to mathematical literacy, reading literacy and scientific literacy than their native peers. Understanding the determinants of this achievement gap is a key challenge. According to previous studies, the educational attainment of parents, language spoken at home and the age at immigration of children significantly influence the educational achievement of the second generation.

    As pointed out in Haveman and Wolfe (1995), the main determinant of the educational attainment of children is the educational attainment of their parents. The PISA surveys show that in OECD countries the parents of first-generation and second-generation students have, on average, completed fewer years of schooling than the parents of native students. A simple explanation of the educational gap between second-generation immigrants and their native-parentage peers is hence that parents of the former group are on average less skilled than those of the latter.

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     Following Chiswick‟s (1988) seminal contribution, Borjas (1995) finds a positive

    correlation between parental skills and the skills of children in the US, but this correlation is not sufficiently high to remove ethnic skill differentials. This result is also confirmed by Card et al. (2000). Nevertheless, both studies are based on data that do not directly link the skills of a given child with the skills of its own parents. They only exhibit correlations between the average skills of a cohort of immigrants and the average skills of a cohort of immigrants‟

    children with respect to groups of different national origins and do not distinguish between second-generation immigrants by country of origin.

     Focusing on the largest groups of immigrants living in Germany (Turkish, Yugoslav, Greek, Italian, Spanish), Gang and Zimmermann (2000) showed that Turkish and Yugoslav pupils obtain less favorable results than German pupils. Parental schooling plays no role in the educational attainment of foreign born children, whereas it has a major role in the educational attainment of the native parentage children. In the Netherlands, Van Ours and Veenman (2003) found that differences in parental education explain differences in educational attainment between ethnic groups. Turkish, Moroccan and Antilleanpupils perform worse than Dutch pupils, but there is no difference between the school attendance of second-generation immigrants (whatever their ethnic origin) and natives once the educational level of parents is taken into account.

     So, the intergenerational transferability of skills may differ not only between natives and immigrants, but also between various ethnic communities. Several studies have further stressed that the returns to foreign experience and education were lower than those obtained domestically (Chiswick and Miller, 1985, Kossoudji, 1989, Schaafsma and Sweetman, 2001). As long as immigrants are unable to completely transfer the human capital accumulated in their home country to the labor market of the host country, it is also plausible that the impact of their skills on the educational attainment of their children depends on the place where these skills have been acquired. This suggests that the effect of the human capital of immigrants on the educational achievement of their children should further be affected by the immigrants‟

    place of schooling as well as by their length of stay in the host country.

    Language spoken at home should also affect the educational achievement of children of foreign origin. Taking into account parents‟ educational and occupational status, several

    contributions have shown that educational attainment was lower among young people who did not speak the language of the host country at home (Jones, 1987, Schaafsma and Sweetman, 2001). Educational learning will be less easy for children not fluent in the host country‟s language, as they will face more difficulties revising their lessons and doing their

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    schoolwork. Language spoken at home is not only an instrument for transmitting

    1intergenerational effects, it will also indicate the degree of assimilation of parents. At the

    same time, the effect of language is a little bit more complex in France since there are immigrants from former French colonies, where French is still a dominant language.

     Place of birth and age at arrival affect the academic success of immigrants‟ children,

    as shown by Gonzales (2003), Chiswick and DebBurman (2004), Van Ours and Veenman (2006) and Böhlmark (2008). Children who arrive in their teenage years achieve a much lower educational attainment than those who arrive at a very young age. It is easier for younger children to assimilate the culture of the host country. Age at immigration also influences the ability to acquire destination language skills because of the greater exposure to the destination language at school and the greater ability of children to learn a new language (Bleakley and Chin, 2004, Newport, 2002). As there are large cultural differences among immigrants depending on their country of origin, age at immigration and place of birth are likely to affect children from different origins differently.

    To summarize, the educational achievement of children belonging to different ethnic groups should depend not only on their parents‟ level of education, but also on the migration history itself through the place of parents‟ schooling, the length of stay in the host country and host language proficiency. In what follows, we consider a unique data set containing detailed characteristics of both immigrant parents and their children to study the educational attainment of the second-generation immigrants in France.

3. The Passage to Retirement of Immigrants survey

     For our purpose, we consider a French data set entitled Passage to Retirement of Immigrants survey (PRI hereafter). This survey was conducted by the Caisse Nationale d‟Assurance Vieillesse and the Institut National de la Statistique et des Etudes Economiques

    from November 2002 to February 2003. The sample comprises foreign respondents born in a foreign country between 1932 and 1957 and living in France at the date of the survey. The survey is representative of the different nationalities of the first-generation of immigrants living in France in 2002-2003 and whose age is between 45 and 70 (for a detailed description, see Attias-Donfut et al., 2006).

     Given the focus of the PRI survey on older migrants, first-generation immigrants between 35 and 44 years of age are by definition excluded from our empirical analysis. These

     1 Chiswick et al. (2004) find a large positive correlation in the unmeasured determinants of proficiency between parents and children after taking into account age, family status and years of schooling.

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    younger immigrants represent approximately 20% of the migrant population living in France,

    2meaning that a significant proportion of immigrants is not covered by our survey. However,

    the problem is perhaps less severe than it seems. Indeed, younger first-generation immigrants are expected to have, on average, young children themselves. Among those who are old enough to attend school, many of them are likely to be enrolled and to attend either primary or secondary schools. There is unfortunately little to learn about educational attainment when considering these young children: we just know that they will complete additional schooling.

    Among other topics, the PRI survey contains basic information on demographic and socioeconomic characteristics like gender, age, family status, education, financial status as well as work trajectories. It also includes original information on the migration history of the respondent, like economic status in the country of origin, date of immigration and level of proficiency in French. A unique feature of this survey is that it provides detailed information on the respondent‟s extended family.

     For each child, we have information on gender, year of birth, country of birth, citizenship, current place and, when relevant, year of arrival in France. The survey includes

    3several additional questions only when the children are at least 16 years old. In particular, we

    know whether each child is still enrolled in school or is a student as well as their highest level of diploma according to the following categories: „no education‟, „primary or secondary schooling‟, „vocational school‟, „high school‟, „undergraduate studies‟, „graduate studies‟, and „postgraduate studies‟.

    The core sample of the survey includes 6211 respondents. From this „parent‟ sample,

    we constructed a „child‟ sample where each child is counted as one observation. This leads to

    a sample of N=19,285 parent-child pairs. Among them, 83.3% are at least 16 years old. We deleted the younger children (N=3,234) since we have no information on their schooling in the survey. The only thing we know is that all these children should be enrolled since education is mandatory until the age of 16 in France (since January 1959). However, leaving out the younger children does not lead to selection bias as the selection is based on age, which is an exogenous covariate (see the discussion in Ejrnaes and Pörtner, 2004).

    As older children may have experienced very different educational conditions, we dropped a small number of children aged above 50 (N=29) from the sample. A shortcoming of the survey is that we do not know where child‟s schooling was completed. For that reason,

     2 Immigrants more than 45 years old represent about 50% of the immigrants living in France. 3 As this is a recollection date from the parents, then the data about the children may suffer from measurement errors. Controlling for unobserved heterogeneity using family specific effects will reduce the underlying bias.

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    we choose to exclude all the children who are not living in France at the date of the survey as they may have completed their studies in a foreign country (N=1,735). Since the conventional definition of second generation refers to children born in the host country or immigrated before school age, we deleted those who arrived in France after six years (N=2,718). Finally,

     deleted children with missing information on the educational attainment of both the father we

    and the mother (N=1,634) along with the few children with missing educational outcomes (N=31).

     These different selections leave us with a full sample of N=9,904 children belonging to N=4,118 families. About 83% of the respondents have at most three children. The proportion is 28% for parents with one child satisfying the selection criteria, 33.8% for parents with two children, and 21% for parents with three children. Since there are multiple observations per family in many cases, we will be able to control for unobserved heterogeneity at the family level through the use of family specific effect models. Actually, many unobserved factors associated with the child‟s educational attainment (like parental

    4altruism or parental ability) are presumably highly correlated within siblings.

     We present the distribution of education among the second generation in Figure 1. The proportion of children having achieved more than high school is equal to 25.7%. We note that there are large gender differences in education, the former proportion being respectively 30.2% among girls instead of 21.6% among boys. A significant proportion of children were

    5enrolled at the date of the survey (29.7%). These are censored observations, since by

    definition we do not observe their final level of education. Given their importance, all the children (either enrolled or not) will be included in our regressions when explaining the educational attainment of the immigrants‟ children.

    Insert Figure 1 here

4. Descriptive statistics

    We present some descriptive statistics in Table 1, both for the parents and the second-generation children. Concerning the former, the distribution with respect to their country of origin reflects the French immigration pattern. The Southern European community (essentially Portuguese, Spanish and Italian respondents) and the Northern African community (Algerian, Moroccan and Tunisian migrants) represent more than 75% of the

     4 In a recent contribution, Böhlmark (2008) also exploits within-family variation to study the role of age at immigration for the school performance gap between native and immigrant pupils in Sweden. 5 Again, the proportion of children is much higher among girls (32.4%) than among boys (27.1%).

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    immigrant population living in France. Migrants from Asia and Turkey are much less numerous, although their fraction increases over time.

    Insert Table 1 here

     The sample shows important differences between communities with respect to skills. Female immigrants are on average less educated than male immigrants. However, differences in skill acquisition between the women of two communities are rather similar to the ones between the men of the same two communities. Immigrants coming from Northern Europe and America are more educated, many of them having graduated. On the opposite, very few North Africans have graduated and more than one-third of this population has never been enrolled. The proportion of parents with no education is 10% among the Middle Eastern community, but only 5% among the Southern European population. Finally, the Asian community is more heterogeneous, 40% of Asians having graduated and one quarter of them having no diploma.

     With respect to proficiency in French, nearly one-fifth of Northern Europeans and Americans have difficulties in speaking or writing French, whereas nearly half of North Africans are concerned. Comparing Asians to Southern Europeans, we note the particularly high fraction of Asians facing difficulties in French in spite of an education pattern close to the education pattern of Southern Europeans. The younger mean age at arrival of Southern Europeans and their longer length of stay in France could contribute to explaining their better proficiency in French.

     Regarding the socioeconomic status of immigrants in their home country, Southern European and North African immigrants have a higher propensity to have grown up in a small town or a village as well as in a poor or very poor financial context than other immigrants. These findings are of course related to differences in economic development among the various countries of origin. Finally, we observe significant differences in the number of children. North Africans have on average three children above 16, while Northern and Southern Europeans, as well as Americans, have less than two children.

     Concerning the children, significant differences appear between communities with respect to their educational attainment. About 40% of children originating from Northern and Eastern Europe, America and Asia have completed more than high school education, while this proportion reaches 30% among Southern Europeans, 19% among Northern Africans and 18% among Middle Eastern children. Conversely, the proportion of children with no education or primary school is much higher in these ethnic groups. This proportion is

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    respectively 35% and 43% when parents are from North Africa and from other African countries, while the average rate for the whole sample of children is 29%.

     There are more boys than girls in our sample. The proportion of girls is 47% and 46% when the parents originate from Southern Europe and Middle East respectively. As we only focus on children currently living in France, this difference in the gender composition is due

    6to the fact that girls are more likely to live in the country of origin. On average, children from

    non-European countries are younger. Part of this age gap stems from differences in fertility rates, as parents from Africa, the Middle East and to a lesser extent from Asia have more children than other parents. The age pattern explains why children from Africa are more likely to be enrolled. Nevertheless, differences in enrolment rates are also due to the increased propensity of some groups to invest in the human capital of their children, which may explain the high rate observed among Asians.

     Finally, when considering the place of birth, almost all children originating from Southern Europe are born in France, while about 30% of Asian and Middle Eastern children are foreign-born. Migratory legislations concerning the free movement area and the conditions under which immigrants have the right to have their family with them could partly explain these disparities.

5. Econometric strategy

     Since a large proportion of children were still in education at the time of the survey (about 30% according to Table 1), we considered both children who were no longer enrolled and children who had not yet completed schooling at the time of the survey in our econometric analysis. The three following features of the PRI data have to be taken into account when attempting to explain educational attainment. First, the survey provides ordered categorical information for the level of schooling. Specifically, the diploma pattern denoted by is defined in the following way: for „no education‟, for „primary or ee0e1

    secondary level‟, for „vocational studies‟, for „high school‟, for e2e3e4

    „undergraduate studies‟, for „graduate studies‟ and for „postgraduate studies‟. We e5e6

    *assume that a continuous latent variable associated to the educational outcome exists, e

    Xwhich we express as a linear function of a set of family characteristics , a vector of

    coefficients and a residual : (

     6 When considering the whole sample of children (no restriction on age), 10.2% of girls are living in the country of origin while the proportion is 9.1% for boys. As Dustmann (2003) points out, the welfare of the offspring perceived by the parent may vary depending on the location of the child. Concerns about preserving traditions may be more influential for female offspring than for male offspring.

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    *(1) eX(

    ** Given the various categories, we assume that when , when e?e?e0112

    **, when , .., and when , being normalized to . e?ee1e2e602316

    Assuming that the random perturbation is normally distributed, the corresponding model is

    a standard ordered Probit regression. The different parameters are a set of threshold levels j

    which have to be estimated jointly with the vector of coefficients. (

     The second feature is that many children are still enrolled. Consider first the case of a child who has completed schooling. From the definition of the ordered Probit model, the probability that a child has the diploma is (with ): jPr(ej)j1,...,6

     (2) Pr(ej)(X()(X()j1j

    The case of children who are still enrolled is a little bit different. From the data, we know that these children will end their schooling with at least the same diploma as they currently have (this would occur in case of educational failure), and presumably with a higher diploma. For these censored observations, the probability may hence be expressed as: Pr(ej)

     (3) Pr(ej)1(X()j

    In the general case, we thus get the following expression for a given level of education:

    (4) ;;;; Pr(ej)c*(X()(X()(1c)*1(X()j1jj

    with a dummy variable equal to one when education is completed (uncensored observation) c

    and to zero otherwise (censored observation).

     The last concern with the data is that we have information on several children for many families. While siblings may be treated as independent observations, this assumption is clearly unlikely to hold. Indeed, since children from a given family have the same parents, their different educational levels are likely to be strongly correlated. Formally, this means that the model we seek to estimate should have the following form:

    *(5) eX(fififfi

    where and refer respectively to the family and to the child. In (5), is an unobserved fif

    family heterogeneity term. These family specific perturbations are supposed to be normally

    2distributed, with mean and variance . We assume that the disturbances follow a ~0fi

    2Xnormal distribution, with mean and variance, and that , and are independent. ~0

    The likelihood function for the above model involves multivariate normal integrals, but one can rely on quadrature techniques to estimate the corresponding random effect ordered Probit

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