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.