Two-stage differences in differences
By J. Gardner. 2022
201 citations recorded by the source corpus.
A recent literature has shown that when adoption of a treatment is staggered and average treatment effects vary across groups and over time, difference-in-differences regression does not identify an easily interpretable measure of the typical effect of the treatment. In this paper, I extend this literature in two ways. First, I provide some simple underlying intuition for why difference-in-differences regression does not identify a group × period average treatment effect. Second, I propose an alternative two-stage estimation framework, motivated by this intuition. In this framework, group and period effects are identified in a first stage from the sample of untreated observations, and average treatment effects are identified in a second stage by comparing treated and untreated outcomes, after removing these group and period effects. The two-stage approach is robust to treatment-effect heterogeneity under staggered adoption, and can be used to identify a host of different average treatment effect measures. It is also simple, intuitive, and easy to implement. I establish the theoretical properties of the two-stage approach and demonstrate its effectiveness and applicability using Monte-Carlo evidence and an example from the literature.
Subfields: Economics and Econometrics, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/8c98f4e69eb0f287ea4ab0b9d8adfa240feb7ed6
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Citations in the corpus
- What’s trending in difference-in-differences? A synthesis of the recent econometrics literature (2022, unknown): “Gardner (2021) shows that this approach estimates a convex weighted average of the ATT ( g , t ) under parallel trends and no anticipation, although the weights are determined by the number of treated units and variance of treatment within each stacked event, rather than by economic considerations.”
- Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, methodology): “We start by reviewing Borusyak et al. (2021), before discussing the connection between their results and those in Gardner (2021) and Liu et al. (2021).”