Event-Study Designs for Discrete Outcomes under Transition Independence

Young Ahn and Hiroyuki Kasahara

No 2026-02, CEI Working Paper Series from Center for Economic Institutions, Institute of Economic Research, Hitotsubashi University

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.

Keywords: Difference-in-differences; discrete outcomes; latent heterogeneity; treatment effects; finite mixture models (search for similar items in EconPapers)
JEL-codes: C14 C23 C33 (search for similar items in EconPapers)
Pages: 73 pages
Date: 2026-06
New Economics Papers: this item is included in nep-dem
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