Identifying barriers to Muslim integration in France

Abstract

Is there a Muslim disadvantage in economic integration for second-generation immigrants to Europe? Previous research has failed to isolate the effect that religion may have on an immigrant family's labor market opportunities because other factors, such as country of origin or race, confound the result. This paper uses a correspondence test in the French labor market to identify and measure this religious effect. The results confirm that in the French labor market, anti-Muslim discrimination exists: a Muslim candidate is 2.5 times less likely to receive a job interview callback than is his or her Christian counterpart. A high-n survey reveals, consistent with expectations from the correspondence test, that second-generation Muslim households in France have lower income compared with matched Christian households. The paper thereby contributes to both substantive debates on the Muslim experience in Europe and methodological debates on how to measure discrimination. Following the National Academy of Sciences' 2001 recommendations on combining a variety of methodologies and applying them to real-world situations, this research identifies, measures, and infers consequences of discrimination based on religious affiliation, controlling for potentially confounding factors, such as race and country of origin.

Fig. 1.

Impact of religion and geographic origin on a household's yearly income. This figure is based on results in Table S1. The dependent variable is an ordinal variable ranging from the value “0” if the yearly household income is null to “14” if the yearly household income is greater than 68,000 Euros. The independent variable is whether the respondent is “Muslim,” a binary variable, which takes the value “1” if the head of household is Muslim and “0” otherwise. In model 1, Muslim is significant at the P < 0.01 significance level on a two-tailed test. It is not significant by conventional standards in model 2 and in model 3. As for controls, “Female” is a binary variable, which takes the value “1” if the head of household is female and “0” if the head of household is male. “Age” is a continuous variable equal to the actual age of the head of household. “Education” is an ordinal variable ranging from the value “1” for no schooling to “6” for postsecondary education. “Jewish” is a binary variable, which takes the value “1” if the head of household is Jewish and “0” otherwise. “Asian” is a binary variable, which takes the value “1” if the head of household is Buddhist, Hindu, Shintoist, or Confucianist and “0” otherwise. “Atheist” is a binary variable, which takes the value “1” if the head of household is an atheist and “0” otherwise. The reference group is “Christian,” a binary variable, which takes the value “1” if the head of household is Christian and “0” otherwise. “Yrs France” is a continuous variable equal to the number of years that the head of household has lived in France. SEs in the original probit model are robust. In the figure, the dot represents the regression coefficient, the horizontal line marks the 95% confidence level, and the two vertical lines mark the 90% confidence level. F.E. refers to fixed effects.

Fig. 2.

(A) Substantive effect of Khadija Diouf vs. Marie Diouf. (B) Statistical significance of the Khadija Diouf effect. This figure is based on results in Table S3 from Dataset S1. In B, the dot represents the difference in response rates, the horizontal line marks the 95% confidence level, and the two vertical lines mark the 90% confidence level.

Fig. 3.

Total income effect of Christian vs. Muslim household (Hh). This figure is based on results in Table S4 from Dataset S2 and its codebook (Dataset S3). Predicted probabilities of reaching each income category are calculated for each household. They are then averaged out for Christian households and for Muslim households for cases in which all other independent variables in the model are at their Christian/Muslim household mode. This captures the total effect on household income of being a Christian household vs. a Muslim household. Similar graphs were produced (i) holding all other variables at the sample mode, (ii) holding all other variables at their Christian/Muslim household mean ± 1 SD, and (iii) holding all other variables at the sample mean ± 1 SD. All present the same pattern as that illustrated here.