Working Paper: NBER ID: w27329
Authors: Klaus Desmet; Romain Wacziarg
Abstract: We analyze the correlates of COVID-19 cases and deaths across US counties. We consider a wide range of correlates - population density, public transportation, age structure, nursing home residents, connectedness to source countries, etc. - finding that these variables are important predictors of variation in disease severity. Many of the effects are persistent - even increasing - through time. We also show that there are fewer deaths and cases in counties where Donald Trump received a high share of the vote in 2016, partly explaining the emerging political divide over lockdown and reopening policies, but that this correlation is reversed when controlling for shares of minority groups. The patterns we identify are meant to improve our understanding of the drivers of the spread of COVID-19, with an eye toward helping policymakers design responses that are sensitive to the specificities of different locations.
Keywords: COVID-19; spatial variation; public health; policy responses
JEL Codes: I18; R1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Higher population density (R23) | Increase in COVID-19 cases and deaths (I12) |
| Higher share of Trump voters (F62) | Fewer COVID-19 cases and deaths (I14) |
| Counties with a larger share of elderly individuals (J14) | Higher mortality rates (I12) |
| Length of stay-at-home orders (C41) | Fewer deaths (I12) |
| Higher percentage of nursing home residents (I18) | Higher deaths (I12) |
| Higher share of African American and Hispanic populations (R23) | Higher COVID-19 severity (I12) |