This paper investigates nonlinearities in the relationship between mobility and COVID-19 cases or deaths based on demographic or socioeconomic characteristics, with a special focus on income and poverty.
The formal analysis is achieved by using county-level daily data from the US, where a difference-in-difference design is employed. Nonlinearities in the relationship between mobility and COVID-19 cases or deaths are investigated by regressing weekly percentage changes in COVID-19 cases or deaths on mobility measures, where county fixed effects and daily fixed effects are controlled for. The main innovation is achieved by distinguishing between the coefficients in front of mobility measures across US counties based on their demographic or socioeconomic characteristics.
The results suggest that the positive effects of mobility on COVID-19 cases increase with poverty, per capita income, commuting time or population, whereas they decrease with health insurance or grandparents responsible for grandchildren.
Important policy implications follow regarding where mobility restrictions would work better to fight against COVID-19 through targeted lockdowns.
