Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects

TL;DR

This paper identifies limitations of traditional two-way fixed effects regressions in event studies with heterogeneous treatment effects and proposes an alternative estimator to accurately estimate dynamic treatment effects.

Contribution

The paper introduces a new estimator that avoids contamination issues caused by treatment effect heterogeneity in event study analyses.

Findings

Traditional regressions can produce biased estimates due to heterogeneity.

The proposed estimator provides more accurate dynamic treatment effect estimates.

Empirical application demonstrates the shortcomings of existing methods.

Abstract

To estimate the dynamic effects of an absorbing treatment, researchers often use two-way fixed effects regressions that include leads and lags of the treatment. We show that in settings with variation in treatment timing across units, the coefficient on a given lead or lag can be contaminated by effects from other periods, and apparent pretrends can arise solely from treatment effects heterogeneity. We propose an alternative estimator that is free of contamination, and illustrate the relative shortcomings of two-way fixed effects regressions with leads and lags through an empirical application.