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

TL;DR

This paper introduces a new difference-in-differences framework that accounts for spillover effects in staggered policy adoption, improving estimation accuracy in network-influenced settings.

Contribution

It develops estimators and inference methods for spillover effects in staggered DID setups, addressing limitations of standard approaches.

Findings

Standard DID estimators can underestimate total effects when spillovers are ignored.

Proposed estimators show small bias and accurate confidence intervals in simulations.

Empirical application reveals significant spillover effects on mortality rates.

Abstract

This paper develops a difference-in-differences framework for staggered policy adoption when units can be affected by other units' adoption. For each treated cohort and event time, the framework separates the effect of own adoption, the spillover effect generated by other adopters, and the total effect under the realized rollout. Identification uses a prespecified summary of spillover exposure and parallel trends comparisons among units with the same exposure at the baseline and target dates. Spillover effects are learned from never-treated units and evaluated for treated cohorts under the exposure distribution they face. We construct estimators for these effects and an inference procedure that allows for spatial dependence. Monte Carlo simulations illustrate that standard DID estimators that ignore spillovers can miss the total effect, whereas the proposed estimators have small bias for these effects and the associated confidence intervals have coverage close to the nominal level. In an empirical study of the Community Health Centers rollout, estimated spillovers account for a substantial share of the effect on older-adult mortality.