Blinder-Oaxaca decomposition for Tobit models

A study derives a decomposition method for Tobit models, which enables the decomposition of the differences in outcome variables between two groups into two parts, respectively attributed to differences in characteristics and in estimated coefficients. Monte Carlo simulations confirm that the proposed method is more reliable than the conventional Blinder-Oaxaca decomposition for linear regression models.