Disparities in COVID-19 risk exposure: Evidence from geolocation data

We document that racial disparities in COVID-19 in New York City stem from patterns of commuting and housing crowding. During the initial wave of the pandemic, out- of-home activity related to commuting is strongly associated with COVID-19 cases at the ZIP Code level and hospitalization at an individual level. After layoffs of essential workers decreased commuting, case growth continued through household crowding. A larger share of individuals in crowded housing or commuting to essential work are Black, Hispanic, and lower-income. As a result, structural inequalities, rather than population density, help determine the cross-section of COVID-19 risk exposure in urban areas.

[1]  I. Ellen,et al.  Demons of Density Do Higher-Density Environments Put People at Greater Risk of Contagious Disease? , 2022, Journal of Housing Economics.

[2]  S. Athey,et al.  Estimating experienced racial segregation in US cities using large-scale GPS data , 2021, Proceedings of the National Academy of Sciences.

[3]  Kevin R. Williams,et al.  JUE Insight: Measuring movement and social contact with smartphone data: a real-time application to COVID-19 , 2021, Journal of Urban Economics.

[4]  Leora I. Horwitz,et al.  Assessment of Racial/Ethnic Disparities in Hospitalization and Mortality in Patients With COVID-19 in New York City , 2020, JAMA network open.

[5]  E. Glaeser,et al.  JUE Insight: How much does COVID-19 increase with mobility? Evidence from New York and four other U.S. cities , 2020, Journal of Urban Economics.

[6]  P. Karaca-Mandic,et al.  Assessment of COVID-19 Hospitalizations by Race/Ethnicity in 12 States. , 2020, JAMA internal medicine.

[7]  S. Provenzano,et al.  Urban Density and Covid-19 , 2020, SSRN Electronic Journal.

[8]  Elisa F. Long,et al.  Nursing home staff networks and COVID-19 , 2020, Proceedings of the National Academy of Sciences.

[9]  Milena Almagro,et al.  JUE Insight: The determinants of the differential exposure to COVID-19 in New York city and their evolution over time , 2020, Journal of Urban Economics.

[10]  C. Avery,et al.  Economic Vulnerability of Households With Essential Workers. , 2020, JAMA.

[11]  L. Fang,et al.  Household secondary attack rate of COVID-19 and associated determinants in Guangzhou, China: a retrospective cohort study , 2020, The Lancet Infectious Diseases.

[12]  J. McLaren.  Racial Disparity in COVID-19 Deaths: Seeking Economic Roots with Census Data , 2020, The B.E. Journal of Economic Analysis & Policy.

[13]  Chad Syverson,et al.  Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020 , 2020, Journal of Public Economics.

[14]  E. Price-Haywood,et al.  Hospitalization and Mortality among Black Patients and White Patients with Covid-19 , 2020, The New England journal of medicine.

[15]  G. Duranton,et al.  The Economics of Urban Density , 2020, Journal of Economic Perspectives.

[16]  Jonathan I. Dingel,et al.  How many jobs can be done at home?☆ , 2020, Journal of Public Economics.

[17]  G. Borjas,et al.  Demographic Determinants of Testing Incidence and Covid-19 Infections in New York City Neighborhoods , 2020, SSRN Electronic Journal.

[18]  Devin G. Pope,et al.  Racial Disparities in Voting Wait Times: Evidence from Smartphone Data , 2019, Review of Economics and Statistics.

[19]  E. Oster.  Unobservable Selection and Coefficient Stability: Theory and Evidence , 2019 .

[20]  M. K. Chen,et al.  The effect of partisanship and political advertising on close family ties , 2017, Science.

[21]  A. Zaslavsky,et al.  Trends in the quality of care and racial disparities in Medicare managed care. , 2005, The New England journal of medicine.

[22]  Susan L Ettner,et al.  Contribution of major diseases to disparities in mortality. , 2002, The New England journal of medicine.