IDEAS home Printed from https://ideas.repec.org/p/boc/bocoec/1067.html
   My bibliography  Save this paper

Drivers of COVID-19 in U.S. counties: A wave-level analysis

Author

Listed:
  • Christopher F Baum

    (Boston College)

  • Andrés Garcia-Suaza

    (Facultad de Economía, Universidad del Rosario)

  • Miguel Henry

    (OnPoint Analytics)

  • Jesús Otero

    (Facultad de Economía, Universidad del Rosario)

Abstract

Since the initial outbreak of COVID-19 in the United States, researchers from a variety of scientific disciplines have sought to understand the factors influencing the evolu- tion of cases and fatalities. This paper proposes a two-stage econometric modeling approach to analyze a range of socioeconomic, demographic, health, epidemiological, climate, pollution, and political factors as potential drivers of the spread of COVID- 19 across waves and counties in the United States. The two-step modeling strategy allows us to (i) accommodate the observed heterogeneity across waves and counties in the transmissibility of the virus, and (ii) assess the relative importance of the cross- sectional measures. We leverage the availability of daily data on confirmed cases and deaths of COVID-19 in counties across the 48 contiguous states and the District of Columbia, spanning a two-year period from March 2020 to March 2022. We find that socioeconomic and demographic factors generally had the greatest influence on the transmissibility of the virus and the associated mortality risk, with health and climate factors playing a lesser role.

Suggested Citation

  • Christopher F Baum & Andrés Garcia-Suaza & Miguel Henry & Jesús Otero, 2024. "Drivers of COVID-19 in U.S. counties: A wave-level analysis," Boston College Working Papers in Economics 1067, Boston College Department of Economics, revised 01 Jun 2025.
    • Handle: RePEc:boc:bocoec:1067
    as

    Download full text from publisher

    File URL: http://fmwww.bc.edu/EC-P/wp1067.pdf
    File Function: main text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chernozhukov, Victor & Kasahara, Hiroyuki & Schrimpf, Paul, 2021. "Causal impact of masks, policies, behavior on early covid-19 pandemic in the U.S," Journal of Econometrics, Elsevier, vol. 220(1), pages 23-62.
    2. Chudik, Alexander & Pesaran, M. Hashem & Sharifvaghefi, Mahrad, 2024. "Variable selection in high dimensional linear regressions with parameter instability," Journal of Econometrics, Elsevier, vol. 246(1).
    3. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    4. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    5. A. Chudik & G. Kapetanios & M. Hashem Pesaran, 2018. "A One Covariate at a Time, Multiple Testing Approach to Variable Selection in High‐Dimensional Linear Regression Models," Econometrica, Econometric Society, vol. 86(4), pages 1479-1512, July.
    6. Christopher F. Baum & Miguel Henry, 2022. "Socio-economic and demographic factors influencing the spatial spread of COVID-19 in the USA," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 12(4), pages 366-380.
    7. Giulietti, Monica & Iregui, Ana María & Otero, Jesús, 2014. "Crude oil price differentials, product heterogeneity and institutional arrangements," Energy Economics, Elsevier, vol. 46(S1), pages 28-32.
    8. Nicholas W. Papageorge & Matthew V. Zahn & Michèle Belot & Eline Broek-Altenburg & Syngjoo Choi & Julian C. Jamison & Egon Tripodi, 2021. "Socio-demographic factors associated with self-protecting behavior during the Covid-19 pandemic," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(2), pages 691-738, April.
    9. Oberhofer, W & Kmenta, J, 1974. "A General Procedure for Obtaining Maximum Likelihood Estimates in Generalized Regression Models," Econometrica, Econometric Society, vol. 42(3), pages 579-590, May.
    10. Paul Haimerl & Tobias Hartl, 2023. "Modeling COVID-19 Infection Rates by Regime-Switching Unobserved Components Models," Econometrics, MDPI, vol. 11(2), pages 1-15, April.
    11. Ho, Paul & Lubik, Thomas A. & Matthes, Christian, 2023. "How to go viral: A COVID-19 model with endogenously time-varying parameters," Journal of Econometrics, Elsevier, vol. 232(1), pages 70-86.
    12. Héctor M. Núñez & Jesús Otero, 2021. "A one covariate at a time, multiple testing approach to variable selection in high‐dimensional linear regression models: A replication in a narrow sense," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(6), pages 833-841, September.
    13. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    14. Hornstein, Abigail S. & Greene, William H., 2012. "Usage of an estimated coefficient as a dependent variable," Economics Letters, Elsevier, vol. 116(3), pages 316-318.
    15. Loann D. Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," AMSE Working Papers 1852, Aix-Marseille School of Economics, France.
    16. Saxonhouse, Gary R, 1976. "Estimated Parameters as Dependent Variables," American Economic Review, American Economic Association, vol. 66(1), pages 178-183, March.
    17. Iregui, Ana María & Núñez, Héctor M. & Otero, Jesús, 2021. "Testing the efficiency of inflation and exchange rate forecast revisions in a changing economic environment," Journal of Economic Behavior & Organization, Elsevier, vol. 187(C), pages 290-314.
    Full references (including those not matched with items on IDEAS)

    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. Demetrescu, Matei & Hosseinkouchack, Mehdi & Rodrigues, Paulo M. M., 2023. "Tests of no cross-sectional error dependence in panel quantile regressions," Ruhr Economic Papers 1041, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Segev, Nimrod, 2020. "Identifying the risk-Taking channel of monetary transmission and the connection to economic activity," Journal of Banking & Finance, Elsevier, vol. 116(C).
    3. Kolasinski, Adam C. & Yang, Nan, 2018. "Managerial myopia and the mortgage meltdown," Journal of Financial Economics, Elsevier, vol. 128(3), pages 466-485.
    4. Dufour, Jean-Marie & Khalaf, Lynda, 2002. "Exact tests for contemporaneous correlation of disturbances in seemingly unrelated regressions," Journal of Econometrics, Elsevier, vol. 106(1), pages 143-170, January.
    5. Balaguer, Jacint & Ripollés, Jordi, 2014. "Are the transport fuel retail markets regionally integrated in Spain? Evidence from price transmission," Economic Modelling, Elsevier, vol. 42(C), pages 323-332.
    6. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    7. Luigi Benfratello & Davide Castellani & Anna D'Ambrosio, 2024. "Migration and the location of MNE activities: Evidence from Italian provinces," Journal of Regional Science, Wiley Blackwell, vol. 64(2), pages 428-461, March.
    8. Thomas Coisnon & Damien Rousselière & Samira Rousselière, 2018. "Information on biodiversity and environmental behaviors: a European study of individual and institutional drivers to adopt sustainable gardening practices," Working Papers SMART 18-02, INRAE UMR SMART.
    9. Mellacher, Patrick, 2023. "The impact of corona populism: Empirical evidence from Austria and theory," Journal of Economic Behavior & Organization, Elsevier, vol. 209(C), pages 113-140.
    10. Guisinger, Amy Y. & Hernandez-Murillo, Ruben & Owyang, Michael T. & Sinclair, Tara M., 2018. "A state-level analysis of Okun's law," Regional Science and Urban Economics, Elsevier, vol. 68(C), pages 239-248.
    11. Matheus Pereira Libório & Petr Iakovlevitch Ekel & Carlos Augusto Paiva Martins, 2023. "Economic analysis through alternative data and big data techniques: what do they tell about Brazil?," SN Business & Economics, Springer, vol. 3(1), pages 1-16, January.
    12. Andrei Zubarev & Maria Kirillova, 2022. "Modeling COVID-19 spread in the Russian Federation using global VAR approach," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 65, pages 117-138.
    13. Hornstein, Abigail S., 2013. "Corporate capital budgeting and CEO turnover," Journal of Corporate Finance, Elsevier, vol. 20(C), pages 41-58.
    14. Samira Rousselière & Gaëlle Petit & Thomas Coisnon & Anne Musson & Damien Rousselière, 2022. "A few drinks behind—Alcohol price and income elasticities in Europe: A microeconometric note," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 301-315, February.
    15. Gehde-Trapp, Monika & Klingler, Linda, 2022. "The effect of sentiment on institutional investors: A gender analysis," CFR Working Papers 22-08, University of Cologne, Centre for Financial Research (CFR).
    16. Difang Huang & Ying Liang & Boyao Wu & Yanyi Ye, 2024. "Estimating the Impact of Social Distance Policy in Mitigating COVID-19 Spread with Factor-Based Imputation Approach," Papers 2405.12180, arXiv.org.
    17. Blanchard, Pierre & Matyas, Laszlo, 1996. "Robustness of tests for error components models to non-normality," Economics Letters, Elsevier, vol. 51(2), pages 161-167, May.
    18. Alessandro Barattieri & Ingo Borchert & Aaditya Mattoo, 2016. "Cross-border mergers and acquisitions in services: The role of policy and industrial structure," Canadian Journal of Economics, Canadian Economics Association, vol. 49(4), pages 1470-1501, November.
    19. Yangfei Xu & Qinghua Zhang & Siqi Zheng & Guozhong Zhu, 2018. "House Age, Price and Rent: Implications from Land-Structure Decomposition," The Journal of Real Estate Finance and Economics, Springer, vol. 56(2), pages 303-324, February.
    20. Mauro Caselli & Andrea Fracasso & Sergio Scicchitano, 2022. "From the lockdown to the new normal: individual mobility and local labor market characteristics following the COVID-19 pandemic in Italy," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1517-1550, October.

    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:1067. 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.