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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

    (QuantEcon Research)

  • 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.
  • Handle: RePEc:boc:bocoec:1067
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    References listed on IDEAS

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    1. Alexander Chudik & M. Hashem Pesaran & Mahrad Sharifvaghefi, 2020. "Variable Selection in High Dimensional Linear Regressions with Parameter Instability," Globalization Institute Working Papers 394, Federal Reserve Bank of Dallas, revised 05 Aug 2024.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Liang Chen & Juan J. Dolado & Jesús Gonzalo, 2021. "Quantile Factor Models," Econometrica, Econometric Society, vol. 89(2), pages 875-910, March.
    8. 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.
    9. 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.
    10. 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.
    11. Saxonhouse, Gary R, 1976. "Estimated Parameters as Dependent Variables," American Economic Review, American Economic Association, vol. 66(1), pages 178-183, March.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. Loann D. Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," AMSE Working Papers 1852, Aix-Marseille School of Economics, France.
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    More about this item

    Keywords

    COVID-19; coronavirus; geographic heterogeneity; covariate selection;
    All these 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

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