Multigroup Segregation Analyses with Covariates

Abstract

The author introduces methods for the decomposition analysis of multigroup segregation measured by the index of dissimilarity, the squared coefficient of variation, and Theil’s entropy measure. Using a new causal framework, the author takes a unified approach to the decomposition analysis by specifying conditions that must be satisfied to decompose segregation into unexplained and explained components. Here, the unexplained component represents the direct effects of the group variable on the conditional probability of acquiring a social position—such as a residential district in an analysis of residential segregation or an occupation in an analysis of occupational segregation—and the explained component represents indirect effects of the group variable on the outcome through covariates. The major merit of this approach is its ability to control individual-level covariates for the decomposition analysis of segregation. Two methods, one for semiparametric outcome models with the identity link function and the other for semiparametric outcome models with the multinomial logit link function, are introduced in this unified framework. The application of these methods focuses on occupational segregation among racial/ethnic groups. Father’s occupation, subject’s educational attainment, and the region of interview are included as covariates, using data from the General Social Surveys.