Better explicating the strengths and shortcomings of these models will help refine projections and improve transparency in the years ahead.
Image credit: Witsawat.S.
Article Figures & SI
Figures
- 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.
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Identifying barriers to Muslim integration in France
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