While most previous research on immigrants’ assimilation refers to the residual disadvantage that remains in empirical analyses of economic outcomes as a general ‘ethnic penalty’, this current paper disentangles the ‘ethnic penalty’ by dividing it into four components: individual characteristics, country characteristics, the social environment in host country, and the policy environment in host country. This study tests the effects of these four components on three economic outcomes: employment, labor force participation, and household income. Data from the European Social Survey, the Migrant Integration Policy Index, the UN, and the World Bank are integrated here. Findings show that the main reasons for immigrants’ disadvantage in terms of labor force participation and household income are both origin and host country characteristics, while the effects of ethnic origins, social exclusion, and policies are weaker. However, ethnic origins and social exclusion actually play a central role in determining unemployment of immigrants.
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Note that there is a problem with comparing coefficients across non-linear models with different independent variables. This is because the unobserved heterogeneity is likely to vary across models as discussed, for example, in the papers of Mood (2010) and Karlson et al. (2012), and several others (see: Allison 1999; Long and Freese 2006; Menard 2011; Williams 2009). Indeed, unlike OLS regressions, the binary dependent variable Y changes its scale in different contexts due to the unobserved residue in the equation.
Some scholars solve this problem by focusing on the average marginal effect (AME). However, this approach cannot be applied where the variable in focus is nominal with multiple levels as it is in this study where the variable in focus is ethnicity. Alternatively, Karlson et al. (2012) have developed the KHB package for STATA. However, this package cannot be applied for ‘xtmelogit’ models. In addition, there is a problem to deal with this issue where both fixed and random effects are at play.
Therefore, I took two other measures in order to address this issue. The first is to follow Mood (2010) in her recommendation to standardize the dependent variable. She suggests that coefficients can be made comparable across models by dividing them with the estimated standard deviation of the latent variable for each model (y-standardization). While this procedure should not apply to comparisons of different groups, it is applicable to comparisons across models estimated on the same sample because one can know the size of the difference in unobserved heterogeneity across models. This procedure produced very similar results to the ones presented and these alternative tables are available upon request.
In addition, the results presented in Table 1, where the household income is the dependent variable in a continuous form, were compared to results where the household income is binary (above/below the median). In this way, one can know if there are significant differences between linear and non-linear estimations. Such comparison shows that the results are very similar. This strengthens the argument that, at least in regards to household income, the bias here is small enough and does not affect the conclusions of this study. Indeed, out of the three economic outcomes examined in this paper, the household income is the only one that can be transformed into a continuous variable. Yet in light of this comparison, there is also less room to suspect that the two other logit models (Tables 2, 3) would have been different by much if estimated without the aforementioned problem of non-linear models. All results and further elaboration on this topic are available upon request from the author.
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See Table 4.
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Kislev, E. Deciphering the ‘Ethnic Penalty’ of Immigrants in Western Europe: A Cross-Classified Multilevel Analysis. Soc Indic Res 134, 725–745 (2017). https://doi.org/10.1007/s11205-016-1451-x
- Western Europe
- Ethnic penalty
- Labor markets