Difference-in-Differences under Bipartite Network Interference: A Framework for Quasi-Experimental Assessment of the Effects of Environmental Policies on Health

TL;DR

This paper develops a new causal inference method for assessing environmental policy impacts on health when interventions and outcomes are geographically disjoint, addressing bipartite network interference in panel data.

Contribution

It introduces a novel difference-in-differences framework tailored for bipartite network interference with staggered treatments, enabling more accurate policy impact evaluation.

Findings

Installation of scrubbers reduced coronary heart disease hospitalizations.

Method provides robust estimates in complex bipartite interference settings.

Analysis shows significant health benefits from emission control technologies.

Abstract

Pollution from coal-fired power plants has been linked to substantial health and mortality burdens in the US. In recent decades, federal regulatory policies have spurred efforts to curb emissions through various actions, such as the installation of emissions control technologies on power plants. However, assessing the health impacts of these measures, particularly over longer periods of time, is complicated by several factors. First, the units that potentially receive the intervention (power plants) are disjoint from those on which outcomes are measured (communities), and second, pollution emitted from power plants disperses and affects geographically far-reaching areas. This creates a methodological challenge known as bipartite network interference (BNI). To our knowledge, no methods have been developed for conducting quasi-experimental studies with panel data in the BNI setting. In this study, motivated by the need for robust estimates of the total health impacts of power plant emissions control technologies in recent decades, we introduce a novel causal inference framework for difference-in-differences analysis under BNI with staggered treatment adoption. We explain the unique methodological challenges that arise in this setting and propose a solution via a data reconfiguration and mapping strategy. The proposed approach is advantageous because analysis is conducted at the intervention unit level, avoiding the need to arbitrarily define treatment status at the outcome unit level, but it permits interpretation of results at the more policy-relevant outcome unit level. Using this interference-aware approach, we investigate the impacts of installation of flue gas desulfurization scrubbers on coal-fired power plants on coronary heart disease hospitalizations among older Americans over the period 2003-2014, finding an overall beneficial effect in mitigating such disease outcomes.