Working Paper: NBER ID: w24857
Authors: Ariella Kahn-Lang; Kevin Lang
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.
Keywords: differences-in-differences; causal inference; empirical economics; 16 and pregnant
JEL Codes: C18; C21; J13
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| initial group differences (C92) | causal interpretation of treatment effects (C22) |
| functional form used in analysis (C29) | estimated treatment effect (C51) |
| pre-treatment trends (C22) | post-treatment trends (C22) |
| potential outcomes in absence of treatment (I12) | causal effects in DiD (C22) |