Estimating the effects of staggered interventions with count and binary outcomes: a simulation study

Difference-in-Differences and Event-Study regression estimators are commonly used to estimate treatment effects. However, when effects are heterogeneous, the standard two-way fixed effects (TWFE) regression can provide biased estimates. Recent literature proposes alternative estimators to overcome the so-called “bad comparisons” problem, allowing unbiased estimates of the treatment effect to be recovered. To date, attention has primarily focused on linear models despite the prevalence of other outcome types in applied research. In this paper, we address this gap by extending five of these alternative estimators for estimating treatment effects on count and binary outcomes and exploring their relative performance within a simulation exercise where true effects are known. Our simulation results indicate that while these estimators can produce unbiased estimates for linear outcomes, they may fail to recover the true treatment effect for nonlinear outcomes if used ‘straight out of the box’. We show that Interaction-weighted, IPW, and Extended-TWFE estimators can be readily adjusted to provide unbiased estimates. Finally, we apply these estimators to prior published work examining the effect of star coauthorship on peers’ productivity using citation data, revealing moderate differences in the magnitude of the effects across the estimators.