Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey

TL;DR

This paper surveys the issues with two-way fixed effects and differences-in-differences methods when treatment effects are heterogeneous, highlighting recent developments and alternative estimators that address these challenges.

Contribution

It reviews recent literature on heterogeneous treatment effects in fixed effects models and demonstrates the use of alternative estimators through a reanalysis of Wolfers (2006).

Findings

Traditional fixed effects models can be misleading with heterogeneous effects

Alternative estimators provide more robust estimates in such settings

Reanalysis of Wolfers (2006) illustrates the impact of these methods

Abstract

Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been shown that those regressions may produce misleading estimates, if the policy's effect is heterogeneous between groups or over time, as is often the case. This survey reviews a fast-growing literature that documents this issue, and that proposes alternative estimators robust to heterogeneous effects. We use those alternative estimators to revisit Wolfers (2006).