IDEAS home Printed from https://ideas.repec.org/p/boc/bocoec/1009.html
   My bibliography  Save this paper

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

Author

Listed:
  • Christopher F Baum

    (Boston College
    DIW Berlin)

  • 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," Boston College Working Papers in Economics 1009, Boston College Department of Economics, revised 02 Oct 2020.
  • Handle: RePEc:boc:bocoec:1009
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. Bailey, Michael & Kuchler, Theresa & Russel, Dominic & State, Bogdan & Stroebel, Johannes, 2020. "Social Connectedness in Europe," SocArXiv 3wh67, Center for Open Science.
    4. 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.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Kunpeng & Lin, Wei, 2024. "Threshold spatial autoregressive model," Journal of Econometrics, Elsevier, vol. 244(1).
    2. Théophile Azomahou, 2008. "Minimum distance estimation of the spatial panel autoregressive model," Cliometrica, Journal of Historical Economics and Econometric History, Association Française de Cliométrie (AFC), vol. 2(1), pages 49-83, April.
    3. Lee, Jungyoon & Robinson, Peter M., 2016. "Series estimation under cross-sectional dependence," Journal of Econometrics, Elsevier, vol. 190(1), pages 1-17.
    4. Ariane Amin & Johanna Choumert, 2015. "Development and biodiversity conservation in Sub-Saharan Africa: A spatial analysis," Economics Bulletin, AccessEcon, vol. 35(1), pages 729-744.
    5. Li, Kunpeng, 2017. "Fixed-effects dynamic spatial panel data models and impulse response analysis," Journal of Econometrics, Elsevier, vol. 198(1), pages 102-121.
    6. Zhang Yuanqing, 2014. "Estimation of Partially Specified Spatial Autoregressive Model," Journal of Systems Science and Information, De Gruyter, vol. 2(3), pages 226-235, June.
    7. Shang, Qingyan & Poon, Jessie P.H. & Yue, Qingtang, 2012. "The role of regional knowledge spillovers on China's innovation," China Economic Review, Elsevier, vol. 23(4), pages 1164-1175.
    8. Philipp Otto & Wolfgang Schmid, 2018. "Spatiotemporal analysis of German real-estate prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 41-72, January.
    9. Jin, Fei & Lee, Lung-fei, 2019. "GEL estimation and tests of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 208(2), pages 585-612.
    10. repec:asg:wpaper:1045 is not listed on IDEAS
    11. Jörg Breitung & Christoph Wigger, 2018. "Alternative GMM estimators for spatial regression models," Spatial Economic Analysis, Taylor & Francis Journals, vol. 13(2), pages 148-170, April.
    12. Liu, Xiaodong & Lee, Lung-fei, 2010. "GMM estimation of social interaction models with centrality," Journal of Econometrics, Elsevier, vol. 159(1), pages 99-115, November.
    13. Lin, Xu & Lee, Lung-fei, 2010. "GMM estimation of spatial autoregressive models with unknown heteroskedasticity," Journal of Econometrics, Elsevier, vol. 157(1), pages 34-52, July.
    14. Luo, Guowang & Wu, Mixia & Pang, Zhen, 2022. "Estimation of spatial autoregressive models with covariate measurement errors," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    15. Sylvain Barde & Rowan Cherodian & Guy Tchuente, 2024. "Moran's I 2-Stage Lasso: for Models with Spatial Correlation and Endogenous Variables," Papers 2404.02584, arXiv.org.
    16. Marina Di Giacomo & Wolfgang Nagl & Philipp Steinbrunner, 2022. "Trump Digs Votes - The Effect of Trump's Coal Campaign on the Presidential Ballot in 2016," CESifo Working Paper Series 9817, CESifo.
    17. Gupta, Abhimanyu & Robinson, Peter M., 2015. "Inference on higher-order spatial autoregressive models with increasingly many parameters," Journal of Econometrics, Elsevier, vol. 186(1), pages 19-31.
    18. Zhenlin Yang & Liangjun Su, 2007. "Instrumental Variable Quantile Estimation of Spatial Autoregressive Models," Working Papers 05-2007, Singapore Management University, School of Economics.
    19. repec:esx:essedp:772 is not listed on IDEAS
    20. Xuan Chen & Yunquan Song, 2025. "Transfer learning for semiparametric varying coefficient spatial autoregressive models," Statistical Papers, Springer, vol. 66(2), pages 1-22, February.
    21. Badi H. Baltagi & Chihwa Kao & Long Liu, 2013. "The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 241-270, September.
    22. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:boc:bocoec:1009. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F Baum (email available below). General contact details of provider: https://edirc.repec.org/data/debocus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.