We offer a two-stage (time-series and cross-section) econometric modeling approach to examine the drivers behind the spread of COVID-19 deaths across counties in the United States. Our empirical strategy exploits the availability of two years (January 2020 through January 2022) of daily data on the number of conKrmed deaths and cases of COVID-19 in the 3,000 U.S. counties of the 48 contiguous states and the District of Columbia. In the Krst stage of the analysis, we use daily time-series data on COVID-19 cases and deaths to Kt mixed models of deaths against lagged conKrmed cases for each county. Because the resulting coeffcients are county specifc, they relax the homogeneity assumption that is implicit when the analysis is performed using geographically aggregated cross-section units. In the second stage of the analysis, we assume that these county estimates are a function of economic and sociodemographic factors that are taken as Kxed over the course of the pandemic. Here we employ the novel one-covariate-at-a- time variable-selection algorithm proposed by Chudik et al. (2018) to guide the choice of regressors.
