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Designing Difference in Difference Studies With Staggered Treatment Adoption: Key Concepts and Practical Guidelines

Author

Listed:
  • Seth M. Freedman
  • Alex Hollingsworth
  • Kosali I. Simon
  • Coady Wing
  • Madeline Yozwiak

Abstract

Difference-in-Difference (DID) estimators are a valuable method for identifying causal effects in the public health researcher’s toolkit. A growing methods literature points out potential problems with DID estimators when treatment is staggered in adoption and varies with time. Despite this, no practical guide exists for addressing these new critiques in public health research. We illustrate these new DID concepts with step-by-step examples, code, and a checklist. We draw insights by comparing the simple 2 × 2 DID design (single treatment group, single control group, two time periods) with more complex cases: additional treated groups, additional time periods of treatment, and with treatment effects possibly varying over time. We outline newly uncovered threats to causal interpretation of DID estimates and the solutions the literature has proposed, relying on a decomposition that shows how the more complex DID are an average of simpler 2X2 DID sub-experiments.

Suggested Citation

  • Seth M. Freedman & Alex Hollingsworth & Kosali I. Simon & Coady Wing & Madeline Yozwiak, 2023. "Designing Difference in Difference Studies With Staggered Treatment Adoption: Key Concepts and Practical Guidelines," NBER Working Papers 31842, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:31842
    Note: AG EH
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    Cited by:

    1. Tobias Ruttenauer & Ozan Aksoy, 2024. "When Can We Use Two-Way Fixed-Effects (TWFE): A Comparison of TWFE and Novel Dynamic Difference-in-Differences Estimators," Papers 2402.09928, arXiv.org, revised Apr 2024.

    More about this item

    JEL classification:

    • I0 - Health, Education, and Welfare - - General
    • I1 - Health, Education, and Welfare - - Health

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