This paper synthesizes recent advances in the econometrics of difference-in-differences
(DiD) and provides concrete recommendations for practitioners. We begin by articulating a …

We study treatment-effect estimation using panel data. The treatment may be non-binary,
non-absorbing, and the outcome may be affected by treatment lags. We make a parallel …

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 …

In this article, we consider identification, estimation, and inference procedures for treatment
effect parameters using Difference-in-Differences (DiD) with (i) multiple time periods,(ii) …

This paper proposes tools for robust inference for difference-in-differences and eventstudy
designs. Instead of requiring that the parallel trends assumption holds exactly, we impose …

Evaluating the impacts of population-level interventions (eg, changes to state legislation)
can be challenging as conducting randomized experiments is often impractical and …

This paper assesses when the validity of difference‐in‐differences depends on functional
form. We provide a novel characterization: the parallel trends assumption holds under all …

Little is known about the fiscal costs of natural disasters, especially regarding social safety
nets that do not specifically target extreme weather events. This paper shows that US …

We discuss three important properties of panel data event study designs. First, assuming
constant treatment effects before and/or after some event time, also known as binning, is a …

We study estimation of causal effects in staggered-rollout designs—that is, settings where
there is staggered treatment adoption and the timing of treatment is as good as randomly …