Estimating Geographic Spillover Effects of COVID-19 Policies From Large-Scale Mobility Networks

TL;DR

This paper develops a methodology to estimate the causal geographic spillover effects of COVID-19 policies using mobility networks and regression discontinuity, revealing significant spillovers and proposing optimized regional restrictions.

Contribution

It introduces a novel approach combining mobility network analysis and regression discontinuity to estimate spillover effects of local COVID-19 policies.

Findings

County-level restrictions are 54% as effective as statewide restrictions.

Significant spillover movement occurs in retail, dining, and gyms.

Optimized macro-county restrictions can achieve over 90% of statewide mobility reductions.

Abstract

Many policies in the US are determined locally, e.g., at the county-level. Local policy regimes provide flexibility between regions, but may become less effective in the presence of geographic spillovers, where populations circumvent local restrictions by traveling to less restricted regions nearby. Due to the endogenous nature of policymaking, there have been few opportunities to reliably estimate causal spillover effects or evaluate their impact on local policies. In this work, we identify a novel setting and develop a suitable methodology that allow us to make unconfounded estimates of spillover effects of local policies. Focusing on California's Blueprint for a Safer Economy, we leverage how county-level mobility restrictions were deterministically set by public COVID-19 severity statistics, enabling a regression discontinuity design framework to estimate spillovers between counties. We estimate these effects using a mobility network with billions of timestamped edges and find significant spillover movement, with larger effects in retail, eating places, and gyms. Contrasting local and global policy regimes, our spillover estimates suggest that county-level restrictions are only 54% as effective as statewide restrictions at reducing mobility. However, an intermediate strategy of macro-county restrictions -- where we optimize county partitions by solving a minimum k-cut problem on a graph weighted by our spillover estimates -- can recover over 90% of statewide mobility reductions, while maintaining substantial flexibility between counties.