🤖 AI Summary
This study addresses the limitations of conventional difference-in-differences (DiD) methods in discrete-outcome panel data, where violations of the parallel trends assumption—such as mean reversion, counterfactual extrapolation beyond support, and ill-defined trends across multiple outcome categories—can induce substantial bias. The authors propose a novel identification strategy grounded in transition independence: absent treatment, the state transition dynamics of the treated and control groups are identical conditional on prior outcomes. Integrating this assumption with a latent class Markov model, the approach identifies latent individual types from short panels and consistently estimates the average treatment effect on the treated (ATT), effectively accounting for unobserved heterogeneity. Empirical results demonstrate that the estimated ATT under the proposed method differs markedly from conventional DiD estimates, confirming its validity and robustness.
📝 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.