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What's Trending in Difference-in-Differences? A Synthesis of the Recent Econometrics Literature
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abstract
This paper synthesizes recent advances in the econometrics of difference-in-differences (DiD) and provides concrete recommendations for practitioners. We begin by articulating a simple set of ``canonical'' assumptions under which the econometrics of DiD are well-understood. We then argue that recent advances in DiD methods can be broadly classified as relaxing some components of the canonical DiD setup, with a focus on $(i)$ multiple periods and variation in treatment timing, $(ii)$ potential violations of parallel trends, or $(iii)$ alternative frameworks for inference. Our discussion highlights the different ways that the DiD literature has advanced beyond the canonical model, and helps to clarify when each of the papers will be relevant for empirical work. We conclude by discussing some promising areas for future research.
Forward citations
Cited by 2 Pith papers
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Identification of dynamic treatment effects when treatment histories are partially observed
A robust DID estimator recovers path-dependent treatment effects with partially missing treatment histories whenever any two of outcome, propensity, and missingness models are correct.
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Good Controls Gone Bad: Difference-in-Differences with Covariates
The paper introduces the common causal covariates (CCC) assumption and a saturated 'DID-INT' estimator that is unbiased when covariate effects vary by group and time, but the heterogeneity claim is not proven.
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