Unequal Welfare Costs of Staying at Home across Socioeconomic and Demographic Groups
One sentence summary: There is evidence for unequal welfare costs of staying at home across socioeconomic and demographic groups.
The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Journal of Urban Sciences.
The working paper version is available here.
Abstract
Using daily census block group level data from the U.S., this paper investigates the welfare costs of staying at home due to COVID-19 across socioeconomic and demographic groups. The investigation is based on an economic model of which implications suggest that the welfare costs of staying at home increase with the stay-at-home probabilities of individuals. The empirical results provide evidence for significant heterogeneity across census block groups regarding the welfare effects of staying at home. This heterogeneity is further used to obtain measures of welfare changes for different socioeconomic and demographic groups at the national level.
Non-technical Summary
Staying at home is considered as one of the most effective ways to fight against COVID-19. Accordingly, several layers of government around the world have implemented lockdowns to mitigate the spread of COVID-19. Individuals have also reduced their mobility to protect themselves from COVID-19 in a voluntary way. Despite its success in reducing the spread of COVID-19, staying at home has resulted in many individuals having economic and psychological problems. Moreover, as individuals belonging to different socioeconomic and demographic groups have access to different employment, consumption or health-related opportunities, they have stayed at home in different amounts of time during COVID-19, suggesting that they might have been affected differently from COVID-19.
This paper attempts to measure the welfare implications of staying at home across alternative socioeconomic and demographic groups. The investigation is achieved by using the implications of an economic model, where both direct and indirect welfare effects of COVID-19 are considered. Specifically, the direct welfare effects are captured by the standard economic measures, namely the amount of consumption versus the amount of labor supplied, whereas the indirect welfare effects are captured by idiosyncratic benefits of being in another location (outside of home) versus the corresponding costs of mobility. In this context, idiosyncratic benefits of being in another location capture the welfare effects of having social interactions, whereas their absence captures the effects of mental distress, anxiety, worry, disinterest, depression, increased risks of suicide, domestic violence, obesity or poor general health perception. The corresponding costs of mobility capture not only the standard measures of traffic or the opportunity cost of time but also the effects of COVID-19 (e.g., the probability of getting sick) that is essential to measure the welfare effects of COVID-19.
In equilibrium, the model implies that the overall welfare effects of COVID-19 (discussed so far) can be captured by the changes in stay-at-home probabilities of individuals. This implication is used to measure the daily welfare changes in the U.S. at the census block group level. The measurement of mobility is achieved by using SafeGraph cellphone location data for each census block group (220,115 of them) for the daily period between January 1st and December 31st, 2020. The period between January 1st and February 29th, 2020 is considered as the pre-COVID-19 period, whereas the period between March 1st and December 31st, 2020 is considered as the COVID-19 period. The empirical results show that the median census block group has experienced a welfare loss of about 7.1% during the COVID-19 period. The corresponding nationwide welfare costs of COVID-19 (calculated as the weighted average across census block groups) is as much as 6.4%, with a daily average of about 2.1% during the sample period.
The empirical results also provide evidence for significant heterogeneity across census block groups regarding the welfare effects of staying at home due to COVID-19. This heterogeneity is further used to obtain measures of welfare changes for alternative socioeconomic and demographic groups at the national level, where the American Community Survey data on socioeconomic and demographic characteristics (at the census block group level) are used to aggregate across census block groups. The corresponding results based on race/ethnicity show that the average (across days) welfare costs have been experienced by the Asian population, followed by the Hispanic population, the white population, the black population and the native population. The results based on education level suggest that the average (across days) welfare costs have been experienced by the master's degree holders, followed by bachelor's degree holders, doctorate degree holders, elementary school graduates, high school graduates, and middle school graduates. Finally the results also that the average (across days) welfare costs have increased by the income level of individuals.
The results can be explained by individuals belonging to different socioeconomic and demographic groups having access to different employment opportunities. Specifically, the heterogeneity in welfare changes due to COVID-19 based on race/ethnicity can be explained by the Hispanic and black populations not being able to work from home compared to the white or Asian populations. This is reflected in welfare calculations of this paper as the white or Asian populations staying at home more compared to the Hispanic and black populations and thus experiencing higher welfare costs of COVID-19. Similarly, the heterogeneity in welfare changes due to COVID-19 based on education or income levels can also be explained by higher-educated or higher-income individuals being able to work from home. This is reflected in welfare calculations of this paper as higher-educated or higher-income individuals staying at home more compared to lower-educated or lower-income individuals and thus having experiencing welfare costs of COVID-19.
The corresponding academic paper by Hakan Yilmazkuday has been accepted for publication at International Journal of Urban Sciences.