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

A-Tier
Journal: Journal of Business & Economic Statistics
Year: 2020
Volume: 38
Issue: 3
Pages: 613-620

Authors (2)

Ariella Kahn-Lang (not in RePEc) Kevin Lang (Boston University)

Score contribution per author:

2.011 = (α=2.01 / 2 authors) × 2.0x A-tier

α: calibrated so average coauthorship-adjusted count equals average raw count

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.

Technical Details

RePEc Handle
repec:taf:jnlbes:v:38:y:2020:i:3:p:613-620
Journal Field
Econometrics
Author Count
2
Added to Database
2026-01-25