Decomposition Analysis of Segregation

Abstract

Although substantive studies on segregation, such as residential or school segregation by race and occupational segregation by gender, are many in sociology, the analytical methodology is almost exclusively focused on measurement issues. The author introduces a set of two statistical models for the decomposition analysis of segregation. These models can be regarded as a tool to analyze whether one dimension of racial or gender inequality is related to another dimension of inequality, because they can assess, for example, the extent to which gender differences in human capital are related to gender segregation in occupation. One of the new models is a simple extension of the DiNardo-Fortin-Lemieux decomposition method of inequality, which implicitly assumes a supply-driven determination of positional status attainment, and another model modifies it to incorporate demand-based macrosocial size constraints on positional status attainment, but both models rely on Rubin’s conception of modeling counterfactual outcomes and inverse-probability-of-treatment weighting on the basis of propensity score. An application focuses on gender segregation in occupation in Japan and leads to a paradoxical result: equalizing human capital and labor supply characteristics between men and women increases, rather than decreases, gender segregation in occupation. Although the underlying behavioral mechanism for gender differences in occupational choice remains to be investigated, the analysis clarifies at least demographically why segregation increases under the counterfactual situation.