Addressing Path Dependence and Incorporating Sample Weights in the Nonlinear Blinder-Oaxaca Decomposition Technique for Logit, Probit and Other Nonlinear Models

The Blinder-Oaxaca decomposition technique is widely used to identify and quantify the separate contributions of differences in measurable characteristics to group differences in an outcome of interest. The use of a linear probability model and the standard BlinderOaxaca decomposition, however, can provide misleading estimates when the dependent variable is binary, especially when group differences are very large for an influential explanatory variable. A simulation method of performing a nonlinear decomposition that uses estimates from a logit, probit or other nonlinear model was first developed in a Journal of Labor Economics article (Fairlie 1999). This nonlinear decomposition technique has been used in nearly a thousand subsequent studies published in a wide range of fields and disciplines. In this paper, I address concerns over path dependence in using the nonlinear decomposition technique. I also present a straightforward method of incorporating sample weights in the technique. I thank Eric Aldrich and Ben Jann for comments and suggestions, and Brandon Heck for research assistance.