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Event-Study Designs for Discrete Outcomes under Transition Independence

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  • Young Ahn
  • Hiroyuki Kasahara

Abstract

We develop a new identification strategy for average treatment effects on the treated (ATT) in panel data with discrete outcomes. Standard difference-in-differences (DiD) relies on parallel trends, which is frequently violated in categorical settings due to mean reversion, out-of-bounds counterfactuals, and ill-defined trends for multi-category outcomes. We propose an alternative identification strategy with transition independence: absent treatment, transition dynamics conditional on pre-treatment outcomes are identical between control and treated groups. To capture unobserved heterogeneity, we introduce a latent-type Markov structure delivering type-specific and aggregate treatment effects from short panels. Three empirical applications yield ATT estimates substantially different from conventional DiD.

Suggested Citation

  • Young Ahn & Hiroyuki Kasahara, 2026. "Event-Study Designs for Discrete Outcomes under Transition Independence," Papers 2603.07914, arXiv.org.
    • Handle: RePEc:arx:papers:2603.07914
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