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Hayato Tagawa

Publications and source records attributed to Hayato Tagawa.

2 recordsLinked to original sources
arXiv2026

Identification and Estimation of Staggered Difference-in-Differences with Network Spillovers

This paper studies staggered difference-in-differences designs in which treated and comparison units can be exposed to other units' adoption. Using an exposure mapping specified by the researcher, we define realized and counterfactual exposure states and decompose the total effect into a switching effect of own adoption and a spillover effect under no own adoption. Conditional parallel trends identify the total and switching effects by matching, respectively, the counterfactual and realized exposure states of the treated cohort to the same states among units that never adopt. The difference between these effects identifies the spillover effect for the treated cohort. We construct outcome regression estimators for these effects and doubly robust estimators for the total and switching effects, and establish their asymptotic normality under spatial dependence. Monte Carlo simulations assess finite-sample bias, RMSE, pointwise coverage, and robustness to outcome regression misspecification. In an application to Community Health Centers, the estimated total and spillover effects on mortality are negative after treatment. We also find that the spillover estimates for never-treated counties are negative at some event times.

econ.EM↗
arXiv2024

Identification and Bayesian Inference for Synthetic Control Methods with Spillover Effects

The synthetic control method (SCM) is widely used for causal inference with panel data, particularly when the number of treated units is small. It relies on the stable unit treatment value assumption (SUTVA), ruling out spillover effects. However, interventions often affect not only treated but also untreated units. This study proposes a novel panel data method that extends standard SCM to account for spillovers and estimate both treatment and spillover effects. The approach extends the SCM framework by incorporating a spatial autoregressive (SAR) panel data model that captures spillover patterns across units. We also develop a Bayesian inference procedure using horseshoe priors for regularization. We apply the proposed method to two empirical studies: (i) evaluating the effect of the California tobacco tax on cigarette consumption, and (ii) assessing the economic impact of the 2011 Sudan division on GDP per capita.

econ.EM↗