Nonlinear Effects of Mobility on COVID-19 in the U.S.: Targeted Lockdowns Based on Income and Poverty

 Follow @yilmazkuday

 

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.

 

Welfare Costs of COVID-19: Evidence from U.S. Counties

 Follow @yilmazkuday

 

Welfare Costs of COVID-19: Evidence from U.S. Counties

One sentence summary: The average (across days) welfare reduction during COVID-19 is about 11% for the average U.S. county and up to about 46% across counties.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Regional Science.

The working paper version is available here.

 
Abstract

Using daily U.S. county-level data on consumption, employment, mobility and the coronavirus disease 2019 (COVID-19) cases, this paper investigates the welfare costs of COVID-19. The investigation is achieved by using implications of a model, where there is a trade-off between consumption and COVID-19 cases that are both determined by the optimal mobility decision of individuals. The empirical results show evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December, 2020 for the average county. There is also evidence for heterogeneous welfare costs across U.S. counties and days, where certain counties have experienced welfare reductions up to 46% on average across days and up to 97% in late March, 2020 that are further connected to the socioeconomic characteristics of the U.S. counties.

 

 

  

Non-technical Summary

The coronavirus disease 2019 (COVID-19) has resulted in not only numerous casualties but also unprecedented reductions in economic activity. Since both COVID-19 cases and economic activity are positively related to mobility, individuals have faced trade-offs regarding the optimal amount of mobility that they should have. It is implied that investigating the welfare changes due to COVID-19 requires taking into account the mobility of individuals.

Based on this background, this paper investigates the welfare costs of COVID-19 by considering the interaction between COVID-19 cases, economic activity and mobility of individuals. A multi-region model is introduced to motivate the empirical investigation, where individuals optimally decide on their mobility that further determines their current consumption and future COVID-19 cases. The parameters and unknown variables of the model are estimated by using daily U.S. county-level data on consumption, employment, mobility and COVID-19 cases.

The estimation results confirm that economic activity (measured by either consumption or employment) increases with mobility of individuals. The estimation results also confirm the positive relationship between mobility and COVID-19 cases. These results are robust to the consideration of county-specific factors that are constant over time and time-varying nationwide factors that are common across counties.
 

The implications of the model are further used to investigate welfare costs of COVID-19 and its components based on economic activity and COVID-19 cases. The corresponding model implications suggest evidence for about 11% of an average (across days) reduction of welfare during the sample period between February and December, 2020 for the average U.S. county. 

 

When welfare costs are decomposed into those due to each model component, it is shown that COVID-19 cases contribute the most to welfare reductions in early months of COVID-19, whereas they have similar contributions with consumption/employment starting from about May 2020. Mobility contributes negatively to welfare in a steady way during the sample period, whereas other factors have been more effective in early months of COVID-19. In terms of the contribution of each welfare component as an average across days, increases in COVID-19 cases reduce welfare by about 6.7% for the average county (up to 14.2% across counties), whereas consumption reductions contribute to welfare costs by about 3.7% for the average county (up to 43.2% across counties). The contribution of mobility (with respect to other factors) is much more on average (across days) during the sample period.

 

 

 

The empirical results of this paper also provide evidence for heterogeneous welfare costs across U.S. counties and days, where certain counties have experienced welfare reductions up to 46% on average across days and up to 97% in late March, 2020. These results are robust to the consideration of alternative data sets as well as alternative parameter values considered in the model.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Journal of Regional Science.

The working paper version is available here.

  

COVID-19 and Housing Prices: Evidence from U.S. County-Level Data

 Follow @yilmazkuday

 

COVID-19 and Housing Prices: Evidence from U.S. County-Level Data

One sentence summary: The effects of COVID-19 cases on housing prices are negative and significant after controlling for other factors.

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Review of Regional Research.

The working paper version is available here.

 
Abstract

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on housing prices at the U.S. county level. The effects of COVID-19 cases on housing prices are formally investigated by using a two-way fixed effects panel regression, where county-specific factors, time-specific factors, and mobility measures of individuals are controlled for. The benchmark results show evidence for negative and significant effects of COVID-19 cases on housing prices, robust to the consideration of several permutation tests, where the negative effects are more evident in counties with higher poverty rates. Exclusion tests further suggest that U.S. counties in the state of California or the month of May 2020 are more responsible for the empirical results, although the results based on other counties and months are still in line with the benchmark results.

  

Non-technical Summary

The coronavirus disease 2019 (COVID-19) has resulted in not only a health crisis through its direct effects but also an economic one through its indirect effects. These indirect effects are reflected in housing prices within the U.S. in an unequal way across counties, where housing prices have increased by about $1,408 on average across counties on a monthly basis (between February 2020 and August, 2021), with a range between $1,979 of a reduction and $14,963 of an increase. Within this context, what is the contribution of COVID-19 cases on this heterogeneity representing unequal changes in housing prices across U.S. counties? The answer to this equation depends on several channels that affect the housing market at the local (U.S. county) level.

This paper investigates this heterogeneity representing unequal changes in housing prices across U.S. counties due to COVID-19 cases. The formal investigation is achieved by using a two-way fixed effects panel regression, where county-specific and time-specific factors are controlled for. Several mobility measures of individuals are also considered as control variables as they not only represent the overall economic activity at the U.S. county level over time but also the developments in the housing sector at the U.S. county level over time due to staying at home as a housing-demand shifter.

The benchmark empirical results show evidence for negative and significant effects of COVID-19 cases on housing prices, and they have been confirmed by several robustness checks based on permutation tests, exclusion tests, or interactions with other variables. 

 

When the channels of causality are further investigated, poverty is shown to be an important factor. Specifically, the U.S. counties with higher rates of poverty have experienced more reductions in housing prices due to COVID-19, whereas those with lower rates of poverty have experience almost no changes (or sometimes increases) in housing prices. Therefore, there is evidence for unequal effects of COVID-19 on housing prices across U.S. counties due to poverty differences.

 

The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at Review of Regional Research.

The working paper version is available here.