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Which Policy Works, and Where? Estimation and Inference for State-Level Treatment Effects in Difference-in-Differences

Author

Listed:
  • Nichole Austin
  • Sunny R. Karim
  • Erin Strumpf
  • Matthew D. Webb

Abstract

Policies with a common objective and implementation date may differ in details or context. We distinguish the aggregate average treatment effect on the treated (ATT) from sub-aggregate ATTs defined by implementation cohort, jurisdiction, period, or policy type. UN-DID and DID-INT, two DiD estimators that construct jurisdiction-by-time effects, estimate these ATTs under parallel-trends conditions matched to the aggregation. In CPS placebo-law simulations, randomization inference is generally well-sized, though some jurisdiction-specific tests are conservative. The jackknife can be undefined for sub-aggregate ATTs; when defined, it over-rejects with few treated or comparison jurisdictions. Estimands and inference methods should match the policy question and implementation setting.

Suggested Citation

  • Nichole Austin & Sunny R. Karim & Erin Strumpf & Matthew D. Webb, 2026. "Which Policy Works, and Where? Estimation and Inference for State-Level Treatment Effects in Difference-in-Differences," Papers 2609.01467, arXiv.org.
    • Handle: RePEc:arx:papers:2609.01467
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    File URL: https://arxiv.org/pdf/2609.01467
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