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

Synthetic control methods are commonly applied in empirical research to estimate the
effects of treatments or interventions on aggregate outcomes. A synthetic control estimator …

We use traditional and non-traditional data to measure the collapse and partial recovery of
the US labor market from March to early July, contrast this downturn to previous recessions …

We present a new estimator for causal effects with panel data that builds on insights behind
the widely used difference-in-differences and synthetic control methods. Relative to these …

The canonical difference-in-differences (DD) estimator contains two time periods,” pre” and”
post”, and two groups,” treatment” and” control”. Most DD applications, however, exploit …

The two-way linear fixed effects regression (2FE) has become a default method for
estimating causal effects from panel data. Many applied researchers use the 2FE estimator …

Populism at the country level is at an all-time high, with more than 25 percent of nations
currently governed by populists. How do economies perform under populist leaders? We …

Linear panel models, and the “event-study plots” that often accompany them, are popular
tools for learning about policy effects. We discuss the construction of event-study plots and …

This paper presents the staggered difference-in-differences (DiD) method in a manner that is
accessible to a broad accounting research audience, offering insights from the perspective …

The synthetic control method (SCM) is a popular approach for estimating the impact of a
treatment on a single unit in panel data settings. The “synthetic control” is a weighted …