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Efficient Estimation for Staggered Rollout Designs

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  • Jonathan Roth
  • Pedro H. C. Sant’Anna

Abstract

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.

Suggested Citation

  • Jonathan Roth & Pedro H. C. Sant’Anna, 2023. "Efficient Estimation for Staggered Rollout Designs," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 1(4), pages 669-709.
    • Handle: RePEc:ucp:jpemic:doi:10.1086/726581
    DOI: 10.1086/726581
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    17. Clément de Chaisemartin & Xavier D’Haultfœuille, 2023. "Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: a survey," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 1-30.
    18. Dmitry Arkhangelsky & Guido W. Imbens & Lihua Lei & Xiaoman Luo, 2021. "Design-Robust Two-Way-Fixed-Effects Regression For Panel Data," Papers 2107.13737, arXiv.org, revised Mar 2024.
    19. Jaworski, Krystian & Olipra, Jakub, 2025. "Cutting VAT rate on food products in a high-inflation environment. Does it work out?," Food Policy, Elsevier, vol. 131(C).
    20. Lihua Lei & Timothy Sudijono, 2024. "Inference for Synthetic Controls via Refined Placebo Tests," Papers 2401.07152, arXiv.org, revised Apr 2025.

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