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We investigate intersecting wage gaps by gender and nativity by comparing the wages between immigrant women, immigrant men, native women, and native men based on Western German survey data. Adding to the analytical diversity of the field, we do a full comparison of group wages to emphasize the relationality of privilege and disadvantage, and we use a nonparametric matching decomposition that is well suited to address unique group-specific experiences. We find that wage (dis)advantages associated with the dimensions of gender and nativity are nonadditive and result in distinct decomposition patterns for each pairwise comparison. After accounting for substantial group differences in work attachment, individual resources, and occupational segregation, unexplained wage gaps are generally small for comparisons between immigrant women, immigrant men, and native women, but large when either group is compared to native men. This finding suggests that the often presumed "double disadvantage" of immigrant women is rather a "double advantage" of native men. [ABSTRACT FROM AUTHOR]

Decompositions make it possible to investigate whether gaps between groups in certain outcomes would remain if groups had comparable characteristics. In practice, however, such a counterfactual comparability is difficult to establish in the presence of lacking common support, functional-form misspecification, and insufficient sample size. In this article, the authors show how decompositions can be undermined by these three interrelated issues by comparing the results of a regression-based Kitagawa-Blinder-Oaxaca decomposition and matching decompositions applied to simulated and real-world data. The results show that matching decompositions are robust to issues of common support and functional-form misspecification but demand a large number of observations. Kitagawa-Blinder-Oaxaca decompositions provide consistent estimates also for smaller samples but require assumptions for model specification and, when common support is lacking, for model-based extrapolation. The authors recommend that any decomposition benefits from using a matching approach first to assess potential problems of common support and misspecification. [ABSTRACT FROM AUTHOR]