Three's a crowd: Identification challenges in the triple difference model with spillover effects

TL;DR

This paper examines the challenges of identifying treatment and spillover effects in triple-difference models with spillover contamination, proposing a new model to address these issues and validating it through simulations and an empirical case study.

Contribution

It introduces the double-triple-difference model that enables consistent identification of effects under spillover interference, extending the triple-difference methodology.

Findings

Conventional triple-difference models fail under spillover contamination.

The proposed double-triple-difference model achieves consistent effect identification.

Simulation results demonstrate the model's effectiveness in various spillover scenarios.

Abstract

The paper studies identification in triple-difference designs when spillover effects contaminate one or more control groups. We show that, under conventional identifying assumptions, the triple-difference model fails to identify both the treatment effect and the spillover effect under such interference. To overcome this limitation, we propose an alternative specification, the double-triple-difference model, and explicitly formalize identifying assumptions and spillover structures required for consistent identification of both effects. We derive formal identification results and assess the performance of the proposed model through Monte Carlo simulations. An empirical application evaluating a Special Economic Zone in Italy is provided.