arXiv 9 Mar 2026 · Econometrics
arXiv:2603.07914 · PDF · DOI · OpenAlex · Extracted main text
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.
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| 9 | Hernán and Robins (2025) Unpublished manuscript (Chapman & Hall/CRC, 2025) | 0.644 | 2 | 2 | 100% |
| 10 | Roth (2022) Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends | 0.644 | 2 | 2 | 100% |
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| 1 | Compositional Difference-in-Differences | 0.511 | 2 | 1 |