Abstract

This paper presents a multivariate decomposition analysis of racial and ethnic differences in children's health insurance using the 2004-2005 Medical Expenditure Panel Survey. We present two methodological contributions. First, we adapt a recently-developed matching decomposition method for use with sample-weighted data. Second, we develop a fully nonparametric approach that implements decomposition through weight adjustments. Accounting for the black-white wealth gap: a nonparametric approach. Journal of the American Statistical Association 97, 663-673]. Differences in observed characteristics explain large percentages of racial and ethnic coverage differences. Important contributors include poverty levels, parent education, family structure (for black children), and immigration-related factors (for Hispanic children). We also examine racial and ethnic differences in parent offers of employer-sponsored insurance and in children's coverage conditional on having a parent offer. Comparison of our linear, nonlinear, and nonparametric results suggests researchers may face a trade-off between robustness and precision when selecting among decomposition methodologies for discrete outcomes.