Working Paper: NBER ID: w27965
Authors: Hunt Allcott; Levi Boxell; Jacob C. Conway; Billy A. Ferguson; Matthew Gentzkow; Benny Goldman
Abstract: We provide new evidence on the drivers of the early US coronavirus pandemic. 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 is modest, and most social distancing is driven by voluntary responses. Moreover, we show that neither policy nor rates of voluntary social distancing explain a meaningful share of geographic variation. The most important predictors of which cities were hardest hit by the pandemic are exogenous characteristics such as population and density.
Keywords: COVID-19; Social Distancing; Policy Interventions; Epidemiological Model
JEL Codes: H7; H79; I1; I12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| fixed differences across CSAs (C80) | variation in transmission rates (C22) |
| stay-at-home orders (H76) | confirmed cases (Y10) |
| stay-at-home orders (H76) | deaths (I12) |
| stay-at-home orders (H76) | social distancing (I14) |
| stay-at-home orders (H76) | economic activity (E20) |
| stay-at-home orders (H76) | contact rates (E43) |
| stay-at-home orders (H76) | contact rate (F16) |