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In Song, Hankuk University of Foreign Studies; Won Lee, University of Minnesota; Steve Ha, Western Carolina University
Prior studies use Blinder-Oaxaca (B-O) decomposition method to examine the racial health and healthcare disparities in the United States. However, B-O method assumes that characteristics of each observations are identical resulting in overestimating the components of the gap attributes. To overcome the limitations of B-O model, this paper proposes an alternative nonparametric decomposition approach using matching algorithm. By adopting this method, the differences in racial health and healthcare differences between two groups - Whites and Blacks - are nonparametrically estimated and decomposed into the portions explained and unexplained gap. Our results show that the nonparametric estimations are consistent with the parametric estimations yet nonparametric analysis results via matching are substantially more accurate.
