What explains temporal and geographic variation in the early US COVID-19 pandemic?

Hunt Allcott (), Levi Boxell (), Jacob Conway, Billy Ferguson (), Matthew Gentzkow () and Benny Goldman ()
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Hunt Allcott: Stanford University
Billy Ferguson: Stanford University
Matthew Gentzkow: Stanford University
Benny Goldman: Cornell University

Review of Economic Design, 2025, vol. 29, issue 1, No 3, 45-85

Abstract: Abstract We provide new evidence on the drivers of the early US COVID-19 pandemic and develop a methodology that future researchers can use to similarly analyze the outbreaks of new diseases. We combine an epidemiological model of disease transmission with quasi-random variation arising from the timing of stay-at-home-orders to estimate the causal roles of policy interventions and voluntary social distancing. We then relate the residual variation in disease transmission rates to observable features of cities. We estimate significant impacts of policy and social distancing responses, but we show that the magnitude of policy effects was modest, and most social distancing was driven by voluntary responses. Moreover, we show that neither policy nor rates of voluntary social distancing explained a meaningful share of geographic variation. The most important predictors of which cities were hardest hit by the pandemic were exogenous characteristics such as population and density.

Keywords: COVID-19; Epidemics; Public policy; Health behaviors; Disease transmission; H12; H75; I12; I18 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10058-024-00375-9

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