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arXiv · 2609.01467

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

Nichole Austin·Sunny R. Karim·Erin Strumpf·Matthew D. Webb
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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.

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Nichole Austin, Sunny R. Karim, Erin Strumpf, Matthew D. Webb. 2026-09-01. Which Policy Works, and Where? Estimation and Inference for State-Level Treatment Effects in Difference-in-Differences. https://arxiv.org/abs/2609.01467

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