Semiparametric Estimation of Selection Models: Some Empirical Results

TLDR: In this paper, the authors used semiparametric methods to reanalyze data on the labor supply of married women first studied by Thomas Mroz (1987) using parametric methods.

Abstract: Among the central theoretical developments in the econometric analysis of nonexperimental microeconomic data has been the analysis of selectivity bias. Following the work of James Heckman (1974), statistical techniques were developed in the 1970s to consistently estimate the parameters of these models. One potential drawback to the application of these techniques is their sensitivity to the assumed parametric distribution of the unobservable error terms in the model. In recent years, a number of estimation methods for selection models have been developed which do not impose parametric forms on error distributions; these methods are termed "semiparametric," since only part of the model of interest (the regression function) is parametrically specified. While the statistical theory of these semiparametric estimators has received much attention, practical applications of the methods are lacking (an exception being the paper by Joel Horowitz and George Neumann, 1987). In this paper, we use semiparametric methods to reanalyze data on the labor supply of married women first studied by Thomas Mroz (1987) using parametric methods. The object of this reanalysis is to determine whether Mroz's results are sensitive to his parametric assumptions.