Investing in children’s education: are Muslim immigrants different?


Using a unique data set on immigrants living in France in 2003, we investigate whether Muslims invest differently in their children’s education compared to non-Muslims. In particular, we want to assess whether educational inequalities between the children of Muslim and non-Muslim immigrants stem from differences between or within families. After controlling for a broad set of individual and household characteristics, we find no difference in education between children of different religions. However, we do find more within-family inequality in children’s educational achievements among Muslims relative to non-Muslims. The within-family variance is 15 % higher among Muslims relative to Catholics and 45 % higher relative to immigrants with other religions, but the intra-family inequality remains difficult to explain. Overall, our results suggest that Muslim parents tend to redistribute their resources more unequally among their children.

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  1. 1.

    Different sources cited in (Garcia-Munoz and Neuman 2013) indicate that the share of Muslim religious group in the European population is expected to grow from a current 6 to 8 % of the population over the next 20 years. The demographic trend of a growing share of the Muslim populations in Europe is expected to result in a further increase in anti-Moslim taste-based discrimination (Adida et al. 2011).

  2. 2.

    These results are subject to controversy (see Arai et al. 2011; Bisin et al. 2011).

  3. 3.

    One study that investigates the relationship between religion and education is that of Hajj and Panizza (2009). Looking into the gender gap in education between Muslims and Christians households (not immigrants) in Lebanon, these authors find no support that Muslims discriminate against female education.

  4. 4.

    A recent stream of literature, the so-called epidemiological approach summarized by Fernandez (2011), is trying to isolate the cultural traits from the countries of origin that affect the behavior of immigrants in the host countries. The main idea is that culture measured through the persistence of religious traits has an effect on women’s fertility and labor supply (Fernandez and Fogli 2006a, 2006b) as well as on the living arrangements of 18–30-year-olds (Giuliano 2007).

  5. 5.

    For example, differences within family allocations may result in lower outcomes for girls than for boys (when, for instance, the capital markets are incomplete), for higher relative to lower birth-order children (if prices for schooling depend on family size), or for larger than smaller sibship size.

  6. 6.

    The PRI survey was conducted by the Caisse Nationale d’Assurance Vieillesse (CNAV) and the Institut National de la Statistique et des Etudes Economiques (INSEE) from November 2002 to February 2003. The main purpose of the PRI survey was to provide a detailed description of the behavior of older migrants (nearing retirement or already retired) living in France.

  7. 7.

    (Ejrnaes and Portner 2004) also select their sample on the basis of the child’s age. By definition, very young children will not attend school and young children below 16 will be in the secondary school, so they will not have had the opportunity to have obtained a diploma. At the same time, we implicitly account for these young children because they are considered when calculating the number of siblings and other characteristics of the sibship.

  8. 8.

    For the children, the corresponding figures are 44.9 % (Muslims), 37.8 % (Catholics), and 17.4 % (other religions). Other religion category includes the following: Protestant (including Evangelists and Anglicans) (2.8 %), other Christian (Orthodox, Gregorian, Maronite, and other Christian Armenian) (3.1 %), other religion (5.1 %), and also no religion (6.3 %).

  9. 9.

    There is mixed evidence on whether the time since migration is correlated to immigrants’ religiosity. We will return to this issue later.

  10. 10.

    By construction, the rank among siblings is correlated with the size of the sibship: children with many siblings will have higher rank on average. Following (Booth and Kee 2009), we construct an adjusted rank R α among siblings such that \(R_{a} =2\ast \frac {R}{N+1}\), with R as the standard rank and N as the size of the sibship. Whatever the number of siblings, the average adjusted rank is always equal to one at the family level.

  11. 11.

    Results do not change if we do not account for current region of residence. However, one worry here is that some unobservable characteristics that affect the immigrants’ residence choice may also affect the level of parental investment in their children education. Ideally, one would need questions about the families’ location and neighborhood when the children were in school, but such information is not available in our cross-sectional data. In some further specifications, we also account for information about the safety of the neighborhood where the respondents currently live (whether the respondents live in a dangerous place or not). Our main findings are robust to these controls.

  12. 12.

    Our results are not affected by excluding respondents with no religion. These results are available upon request.

  13. 13.

    To further understand these effects, we make use of another variable in our survey—date of entry in France for each child (we already control for the duration since migration, but it is not always the case that all family migrate at one point in time). Unfortunately, there is a large proportion of missing values that prevents us from using it in our main regressions. However, we have looked into our results on a restricted sample of observations for which we have the information, and we find indeed lower education for those children that entered France the latest, while the dummy born abroad, living in France is not significant anymore. All other results, including the religion indicator, are very similar to those in column 3. These results are available upon request.

  14. 14.

    The number of children of the Muslim sample is lower than that of the Catholic sample because we only consider children aged at least 24 years old and not enrolled at the date of the survey (recall that Muslim children are younger on average and have a higher probability to attend school).

  15. 15.

    Additionally, we have also looked into our results excluding all children born abroad and living abroad, as this group may be very different than those living in France. Our results, available upon request, are largely in line with those in Table 2.

  16. 16.

    We believe that it is difficult to assess the importance of this variable for other religion immigrants, because this is a highly heterogeneous group.

  17. 17.

    All these additional estimates are available upon request.

  18. 18.

    As shown in Table 1, Muslim children are, for instance, younger on average and more often enrolled.

  19. 19.

    Here, we suppose that censoring is exogenous in the sense that it is not affected by family characteristics.

  20. 20.

    Even though it remains difficult to explain, we also attempted to put a number on the how much of the within inequality is due to some of our explanatory variables. For each Muslim household, we regress the explained within variance by the standard deviation of the following characteristics: gender, age, raised by both head and spouse until age of 12, born abroad and living in France, and born abroad and living abroad. To assess the contribution of each variable to the within inequality, we perform a Fields’ decomposition of factor contribution. We find that around 18 % of within inequality is explained by the gap in age at the sibship level, 23.5 % by change in raised by both parents, and around 37 % by place of birth. The variation in gender at the sibship level explains less than 0.001 % of the variation.

  21. 21.

    We also only consider families with at most four children satisfying the previous criteria, as at most four transfers to children are recorded in the PRI survey.

  22. 22.

    Both in the education and transfer regressions, we include a set of dummies for the region of origin (instead of countries of origin) with the following categories: Northern Europe, Eastern Europe, Southern Europe, Northern Africa, Southern Africa, America, Middle East, and Asia. We choose to group countries by regions as there were very few children for some countries. For private transfers, we have also estimated a regression pooling all religions. We find that children with Muslim parents are significantly less likely to receive money, while there is no difference between Christian and other religions (see alsoWolff et al. 2007).

  23. 23.

    Note that this variable does not vary a lot at the family level for our sample.

  24. 24.

    According to our data, the proportion of transfers related to family events (like marriage or birth) is extremely low among Muslims (3.2 %). The lower probability of transfers for girls relative to boys in Muslim vs. non-Muslim households may also be a compensation mechanism if the family would have invested more in terms of education for girls than for boys or if girls perform better at school.

  25. 25.

    In 2003, the year of the survey, public spending on education in France was 5.9 % of GDP, while private education expenditures represented 0.5 of GDP.


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We would like to thank our editor Klaus F. Zimmermann, two anonymous referees, Christian Dustmann, and seminar participants at the Conference in Economics of the Family in the honor of G. Becker, Paris, for their very helpful comments and suggestions. Andreea Mitrut gratefully acknowledges support from Jan Wallanders and Tom Hedelius Fond and from the Scientific Council of Sweden and the European Research Council (through Mikael Lindahl’s) ERC 2009 starting grant 24116. Any remaining errors are ours.

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Correspondence to Andreea Mitrut.

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Mitrut, A., Wolff, FC. Investing in children’s education: are Muslim immigrants different?. J Popul Econ 27, 999–1022 (2014).

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  • Immigrants
  • Religion
  • Education
  • Intra-household inequality
  • France

JEL Classification

  • J15
  • D13
  • Z12