Efficient Estimation for Staggered Rollout Designs

By Jonathan Roth, Pedro H. C. Sant’Anna. Journal of Political Economy Microeconomics, 2021

81 citations recorded by the source corpus.

We study estimation of causal effects in staggered-rollout designs—that is, settings where there is staggered treatment adoption and the timing of treatment is as good as randomly assigned. We derive the most efficient estimator in a class of estimators that nests several popular generalized difference-in-differences methods. A feasible plug-in version of the efficient estimator is asymptotically unbiased, with efficiency (weakly) dominating that of existing approaches. We provide both t-based and permutation-test-based methods for inference. In an application to a training program for police officers, confidence intervals for the proposed estimator are as much as eight times shorter than those for existing approaches.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f7c531477868020cba006c7a9341df20e4144bd6

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In Statistics and Probability

1. C. Chaisemartin
2. X. d'Haultfoeuille
3. Pedro H. C. Sant’Anna
4. Brantly Callaway

Citations in the corpus

• Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey (2021, background): “In such instances, Roth (2021) notes that leveraging earlier pre-treatment periods increases the bias of a DID estimator, since one makes comparisons from earlier periods.”