IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp14669.html

COVID-19 Spread in Germany from a Regional Perspective

Author

Listed:
  • Hübler, Olaf

    (Leibniz University of Hannover)

Abstract

This paper investigates the regional differences in the spread of COVID-19 infections in Germany. A machine learning selection procedure is used to reduce variables from a pool of potential influencing variables. The empirical analysis shows that both regional structural variables and regionally aggregated personality traits are significant for the different corona spread. The latter characteristics express differences in mentality between the federal states. The north-east of the country shows a lower degree of affectedness. Regions with a high proportion of migrants show a higher incidence than others. If personality traits are neglected, the migrants' influence is overestimated. With school education and the risk of poverty, two further important regional characteristics are identified. Federal states that have a disproportionately high share of the population with low school education tend to have fewer COVID-19 cases. With regard to poverty, no clear statement can be made. The more the population tends to be responsible towards fellow human beings, the higher is the risk of a more pronounced spread. Where there is a tendency towards altruism, which consists of helping other people, a higher level of COVID-19 infections is revealed. A significant positive correlation between infections and testing is shown by the estimates. The link between vaccinations and the number of infections is less clear. Across the three corona waves,significant changes emerge. This relates in particular to the proportion of migrants and the proportion of families at risk of poverty. The effects decrease over the course of the pandemic.

Suggested Citation

  • Hübler, Olaf, 2021. "COVID-19 Spread in Germany from a Regional Perspective," IZA Discussion Papers 14669, IZA Network @ LISER.
    • Handle: RePEc:iza:izadps:dp14669
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp14669.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cook, Jonathan & Newberger, Noah & Smalling, Sami, 2020. "The spread of social distancing," Economics Letters, Elsevier, vol. 196(C).
    2. Joseph Benitez & Charles Courtemanche & Aaron Yelowitz, 2020. "Racial and Ethnic Disparities in COVID-19: Evidence from Six Large Cities," Journal of Economics, Race, and Policy, Springer, vol. 3(4), pages 243-261, December.
    3. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asuman Ozdaglar, 2024. "Testing, Voluntary Social Distancing, and the Spread of an Infection," Operations Research, INFORMS, vol. 72(2), pages 533-548, March.
    4. A. Belloni & D. Chen & V. Chernozhukov & C. Hansen, 2012. "Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain," Econometrica, Econometric Society, vol. 80(6), pages 2369-2429, November.
    5. Caitlin S. Brown & Martin Ravallion, 2020. "Inequality and the Coronavirus: Socioeconomic Covariates of Behavioral Responses and Viral Outcomes Across US Counties," NBER Working Papers 27549, National Bureau of Economic Research, Inc.
    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. Cantor, Jonathan & Sood, Neeraj & Bravata, Dena M. & Pera, Megan & Whaley, Christopher, 2022. "The impact of the COVID-19 pandemic and policy response on health care utilization: Evidence from county-level medical claims and cellphone data," Journal of Health Economics, Elsevier, vol. 82(C).
    2. R. Jason Faberman & Daniel Hartley, 2020. "The Relationship Between Race, Type of Work, and Covid-19 Infection Rates," Working Paper Series WP2020-18, Federal Reserve Bank of Chicago.
    3. Alexandre Belloni & Victor Chernozhukov & Denis Chetverikov & Christian Hansen & Kengo Kato, 2018. "High-dimensional econometrics and regularized GMM," CeMMAP working papers CWP35/18, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. James P. Ziliak, 2021. "Food Hardship during the COVID‐19 Pandemic and Great Recession," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(1), pages 132-152, March.
    5. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    6. Anil Kumar, 2018. "Do Restrictions on Home Equity Extraction Contribute to Lower Mortgage Defaults? Evidence from a Policy Discontinuity at the Texas Border," American Economic Journal: Economic Policy, American Economic Association, vol. 10(1), pages 268-297, February.
    7. Matthew Goodkin-Gold & Michael Kremer & Christopher M. Snyder & Heidi Williams, 2024. "Optimal Vaccine Subsidies for Epidemic Diseases," The Review of Economics and Statistics, MIT Press, vol. 106(4), pages 895-909, July.
    8. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    9. John McLaren & Su Wang, 2020. "Effects of Reduced Workplace Presence on COVID-19 Deaths: An Instrumental-Variables Approach," NBER Working Papers 28275, National Bureau of Economic Research, Inc.
    10. Bravata, Dena & Cantor, Jonathan & Sood, Neeraj & Whaley, Christopher, 2025. "Back to school: The effect of school visits during COVID-19 on COVID-19 outcomes," Journal of Health Economics, Elsevier, vol. 102(C).
    11. Hakan Yilmazkuday, 2022. "Nonlinear effects of mobility on COVID-19 in the US: targeted lockdowns based on income and poverty," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(1), pages 18-36, April.
    12. Brandily, Paul & Brébion, Clément & Briole, Simon & Khoury, Laura, 2021. "A poorly understood disease? The impact of COVID-19 on the income gradient in mortality over the course of the pandemic," European Economic Review, Elsevier, vol. 140(C).
    13. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    14. Alexandre Belloni & Victor Chernozhukov, 2011. "High Dimensional Sparse Econometric Models: An Introduction," Papers 1106.5242, arXiv.org, revised Sep 2011.
    15. Alexandre Belloni & Victor Chernozhukov & Christian Hansen & Damian Kozbur, 2016. "Inference in High-Dimensional Panel Models With an Application to Gun Control," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(4), pages 590-605, October.
    16. Basu Parantap & Bell Clive & Edwards Terence Huw, 2022. "COVID Social Distancing and the Poor: An Analysis of the Evidence for England," The B.E. Journal of Macroeconomics, De Gruyter, vol. 22(1), pages 211-240, January.
    17. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.
    18. Taisuke Otsu & Myung Hwan Seo, 2014. "Asymptotics for maximum score method under general conditions," STICERD - Econometrics Paper Series 571, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    19. Sauvenier, Mathieu & Van Bellegem, Sébastien, 2023. "Direction Identification and Minimax Estimation by Generalized Eigenvalue Problem in High Dimensional Sparse Regression," LIDAM Discussion Papers CORE 2023005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    20. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

    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:iza:izadps:dp14669. 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: Mark Fallak (email available below). General contact details of provider: https://edirc.repec.org/data/izaaalu.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.