Nonlinear Effects of Mobility on COVID-19 in the U.S.: Targeted Lockdowns Based on Income and Poverty
One sentence summary: The positive effects of mobility on COVID-19 increase with certain demographic or socioeconomic characteristics.
The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Economic Studies.
The working paper version is available here.
Abstract
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.
Non-technical Summary
The relationship between the spread of COVID-19 and social interactions through mobility is well established. Accordingly, several governments have employed lockdowns to slow down the spread of COVID-19. However, this relationship by itself does not suggest anything related to targeted lockdowns that can be useful when policy makers face trade-offs between health-related concerns and economic slowdown as certain group of people or certain communities can be more vulnerable to the spread of COVID-19.
This paper investigates how the relationship between mobility and the COVID-19 spread changes with demographic or socioeconomic characteristics. The formal investigation is achieved by using daily county-level data from the U.S., where a difference-in-difference approach is employed. The nonlinear relationship between mobility and COVID-19 cases or deaths is 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; accordingly, county-specific factors that are constant over time and day-specific factors that are common across U.S. counties are already 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 that we utilize as threshold variables.
Several demographic or socioeconomic characteristics of U.S. counties are considered for investigating the nonlinear relationship between mobility and the COVID-19 spread. These include 45 different variables based on the categories of population characteristics, economic variables, occupations, employment in industries, school attendance, educational attainment, and race. The motivation behind including these potential threshold variables comes from the existing literature, where several studies have shown how the spread of COVID-19 is related to these demographic or socioeconomic characteristics.
The results of the nonlinear investigation 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. Since mobility restrictions to fight against COVID-19 would work better in counties where the positive effects of mobility on COVID-19 cases or deaths are bigger, it is implied that policy makers can consider targeted lockdowns based on the threshold variables identified in this paper.
The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Economic Studies.
The working paper version is available here.