Suchergebnisse

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 methods we find, both theoretically and empirically, that this "synthetic difference-in-differences" estimator has desirable robustness properties, and that it performs well in settings where the conventional estimators are commonly used in practice. We study the asymptotic behavior of the estimator when the systematic part of the outcome model includes latent unit factors interacted with latent time factors, and we present conditions for consistency and asymptotic normality. (JEL C23, H25, H71, I18, L66)

AbstractWe develop front‐door difference‐in‐differences estimators as an extension of front‐door estimators. Under one‐sided noncompliance, an exclusion restriction, and assumptions analogous to parallel trends assumptions, this extension allows identification when the front‐door criterion does not hold. Even if the assumptions are relaxed, we show that the front‐door and front‐door difference‐in‐differences estimators may be combined to form bounds. Finally, we show that under one‐sided noncompliance, these techniques do not require the use of control units. We illustrate these points with an application to a job training study and with an application to Florida's early in‐person voting program. For the job training study, we show that these techniques can recover an experimental benchmark. For the Florida program, we find some evidence that early in‐person voting had small positive effects on turnout in 2008. This provides a counterpoint to recent claims that early voting had a negative effect on turnout in 2008.