Event-Study Designs for Discrete Outcomes under Transition Independence
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.
Cite
@article{arxiv.2603.07914,
title = {Event-Study Designs for Discrete Outcomes under Transition Independence},
author = {Young Ahn and Hiroyuki Kasahara},
journal= {arXiv preprint arXiv:2603.07914},
year = {2026}
}