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.