The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications

By Ariella Kahn-Lang, Kevin Lang. Journal of business & economic statistics, 2018

332 citations recorded by the source corpus.

Abstract We use the exchange between Kearney/Levine and Jaeger/Joyce/Kaestner on 16 and Pregnant to reexamine the use of DiD as a response to the failure of nature to properly design an experiment for us. We argue that (1) any DiD paper should address why the original levels of the experimental and control groups differed, and why this would not impact trends, (2) the parallel trends argument requires a justification of the chosen functional form and that the use of the interaction coefficients in probit and logit may be justified in some cases, and (3) parallel trends in the period prior to treatment is suggestive of counterfactual parallel trends, but parallel pre-trends is neither necessary nor sufficient for the parallel counterfactual trends condition to hold. Importantly, the purely statistical approach uses pretesting and thus, generates the wrong standard errors. Moreover, we underline the dangers of implicitly or explicitly accepting the null hypothesis when failing to reject the absence of a differential pre-trend.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7dde603cf615473d67ff0e02a3bb0d24220e444c

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Citations in the corpus

On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data (2020, background): “We emphasize that this assumption may not be credible in some settings (see, e.g., Bilinski and Hatfield 2018; Kahn-Lang and Lang 2019; Rambachan and Roth 2019).”
Pre-Event Trends in the Panel Event-Study Design (2019, unknown): “4See also Kahn-Lang and Lang (2018).”