Causal Estimation of Stay-at-Home Orders on SARS-CoV-2 Transmission

TL;DR

This study uses detailed smartphone location data to quantify the causal impact of stay-at-home orders on reducing movement and SARS-CoV-2 transmission, revealing significant demographic and political differences in compliance.

Contribution

It provides the first causal estimates of stay-at-home orders' effects on social contact reduction and COVID-19 transmission, highlighting demographic and political disparities.

Findings

Stay-at-home orders caused a 25% reduction in movement.

Movement reductions led to a 49% decrease in virus transmission.

Political affiliation significantly influenced compliance with stay-at-home orders.

Abstract

Accurately estimating the effectiveness of stay-at-home orders (SHOs) on reducing social contact and disease spread is crucial for mitigating pandemics. Leveraging individual-level location data for 10 million smartphones, we observe that by April 30th---when nine in ten Americans were under a SHO---daily movement had fallen 70% from pre-COVID levels. One-quarter of this decline is causally attributable to SHOs, with wide demographic differences in compliance, most notably by political affiliation. Likely Trump voters reduce movement by 9% following a local SHO, compared to a 21% reduction among their Clinton-voting neighbors, who face similar exposure risks and identical government orders. Linking social distancing behavior with an epidemic model, we estimate that reductions in movement have causally reduced SARS-CoV-2 transmission rates by 49%.