Coronavirus Infections and Deaths by Poverty Status: Time Trends and Patterns

We study the spread of SARS-CoV-2 infections and COVID-19 deaths by county poverty level in the US. We first document a U-shaped relationship between county groupings by poverty level and the intensity of coronavirus events defined as either coronavirus infections or COVID-19 related deaths. The U-shaped relationship prevails for counties with high population density while in counties with low population density, poorer counties exhibit much higher numbers in coronavirus cases, both in infections and deaths. Second, we investigate the patterns of coronavirus events following the announcements of state level stay-at-home mandates. We distinguish between four groups of states: First, Second, Third and Late Movers. Among First Movers—also the states with the largest share of infections—we observe a decrease in the average number of weekly new cases in rich and poor counties two weeks following the mandate announcement. The average numbers of cases per week in richer counties then quickly converges to the number reported in middle income counties, while the poorer counties show a much slower decrease in coronavirus cases. This pattern is accompanied by a dramatic reduction in mobility in all county groupings. Third, comparing counties in Second and Third Mover states, we show that a few days of delay in non-pharmaceutical interventions (NPIs) results in significantly larger numbers of coronavirus cases compared to states that introduce a mandate quicker. Finally, we use weather shocks as instruments to address endogeneity of the announcement date of stay-at-home mandates and establish causality.