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Socioeconomic Factors influencing the Spatial Spread of COVID-19 in the United States

Author

Listed:
  • Christopher F Baum

    (Boston College
    DIW Berlin
    CESIS)

  • Miguel Henry

    (Greylock McKinnon Associates)

Abstract

As the COVID-19 pandemic has progressed in the U.S., “hotspots” have been shifting geographically over time to suburban and rural counties showing a high prevalence of the disease. We analyze daily U.S. county-level variations in COVID-19 confirmed case counts to evaluate the spatial dependence between neighboring counties. We find strong evidence of county-level socioeconomic factors influencing the spatial spread. We show the potential of combining spatial econometric techniques and socioeconomic factors in assessing the spatial effects of COVID-19 among neighboring counties.

Suggested Citation

  • Christopher F Baum & Miguel Henry, 2020. "Socioeconomic Factors influencing the Spatial Spread of COVID-19 in the United States," London Stata Conference 2020 05, Stata Users Group.
    • Handle: RePEc:boc:usug20:05
    as

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    File URL: http://repec.org/usug2020/Baum_u20.pdf
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    References listed on IDEAS

    as
    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. Theresa Kuchler & Dominic Russel & Johannes Stroebel, 2020. "The Geographic Spread of COVID-19 Correlates with the Structure of Social Networks as Measured by Facebook," NBER Working Papers 26990, National Bureau of Economic Research, Inc.
    4. Persico, Claudia L. & Johnson, Kathryn R., 2020. "Deregulation in a Time of Pandemic: Does Pollution Increase Coronavirus Cases or Deaths?," IZA Discussion Papers 13231, Institute of Labor Economics (IZA).
    5. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    6. Bailey, Michael & Kuchler, Theresa & Russel, Dominic & State, Bogdan & Stroebel, Johannes, 2020. "Social Connectedness in Europe," SocArXiv 3wh67, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics of Welfare > Health Economics > Economics of Pandemics > Specific pandemics > Covid-19 > Health

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    Cited by:

    1. Irene González Rodríguez & Marta Pascual Sáez & David Cantarero Prieto, 2022. "The dynamics of COVID-19: An empirical analysis with a view to spatial health econometrics using macrodata," Working Papers. Collection B: Regional and sectoral economics 2201, Universidade de Vigo, GEN - Governance and Economics research Network.
    2. Héctor López-Mendoza & Antonio Montañés & F. Javier Moliner-Lahoz, 2021. "Disparities in the Evolution of the COVID-19 Pandemic between Spanish Provinces," IJERPH, MDPI, vol. 18(10), pages 1-20, May.
    3. Angelo Cozzubo & Javier Herrera & François Roubaud & Mireille Razafindrakoto, 2021. "El impacto de políticas diferenciadas de cuarentena sobre la mortalidad por COVID-19: el caso de Brasil y Perú," Working Papers DT/2021/05, DIAL (Développement, Institutions et Mondialisation).
    4. Ahumada, Hildegart & Espina, Santos & Navajas, Fernando H., 2020. "COVID-19 with uncertain phases: estimation issues with an illustration for Argentina," MPRA Paper 101466, University Library of Munich, Germany.

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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods
    • R23 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Regional Migration; Regional Labor Markets; Population

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