This paper investigates nonlinearities in the relationship between mobility and COVID-19 cases or deaths. The formal analysis is achieved by using county-level daily data from the U.S., 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 U.S. counties based on their demographic or socioeconomic characteristics. The results suggest that the positive effects of mobility on COVID-19 cases or deaths increase with population, per capita income, or commuting time as well as with having certain occupations, working in certain industries, attending certain schools, or having certain educational attainments. Important policy implications follow regarding where mobility restrictions would work better to fight against COVID-19 through targeted lockdowns.
