Estimating heterogeneous effects in static binary response panel data models

This article considers estimating heterogeneous effects in panel data models when the outcome is binary. We argue that a common practice of splitting the sample and performing estimation separately for each subsample results in inconsistent estimators of heterogeneous parameters. The article presents methods that account for a possibility of nonrandom sorting and produce consistent estimators of causal effects in two or more heterogeneous sub-populations. Monte Carlo simulations show that considered methods perform well in finite samples. As an empirical application, the article studies gender differences in job satisfaction by occupation type.