IDEAS home Printed from https://ideas.repec.org/a/oup/emjrnl/v25y2022i1p15-45..html
   My bibliography  Save this article

Large-scale sport events and COVID-19 infection effects: evidence from the German professional football ‘experiment’
[Semiparametric difference-in-differences estimators]

Author

Listed:
  • Philipp Breidenbach
  • Timo Mitze

Abstract

SummaryThis paper studies the effects of large-scale sport events with live spectators on COVID-19 infection trends at the local population level. Specifically, we compare the development of incidence rates in 41 German Nomenclature of Territorial Units for Statistics level 3 (NUTS-3) districts hosting a professional football match with at least 1,000 spectators vis-Ã -vis similar districts without hosting a match. Our empirical analysis builds on difference-in-difference and dynamic event study estimation for panel data. Synthetic control method is applied as a robustness check. While our findings generally do not point to significant treatment effects for the full sample of match locations, we find some noteworthy exceptions. Districts hosting first league matches with spectator attendance above the median (> 6,300 persons) and, particularly, matches without strict face mask requirements experienced a significant relative rise in incidence rates 14Â days after the match. We also find that intra-district mobility increases on match days in treated districts, highlighting the significance of professional football matches as mobility-based infection transmission channel.

Suggested Citation

  • Philipp Breidenbach & Timo Mitze, 2022. "Large-scale sport events and COVID-19 infection effects: evidence from the German professional football ‘experiment’ [Semiparametric difference-in-differences estimators]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 15-45.
    • Handle: RePEc:oup:emjrnl:v:25:y:2022:i:1:p:15-45.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ectj/utab021
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Dave, Dhaval M. & Friedson, Andrew I. & Matsuzawa, Kyutaro & Sabia, Joseph J. & Safford, Samuel, 2020. "Black Lives Matter Protests, Social Distancing, and COVID-19," IZA Discussion Papers 13388, Institute of Labor Economics (IZA).
    2. Mitze, Timo & Kosfeld, Reinhold & Rode, Johannes & Wälde, Klaus, 2020. "Face masks considerably reduce COVID-19 cases in Germany," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 124130, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Isphording, Ingo E. & Lipfert, Marc & Pestel, Nico, 2020. "School Re-Openings after Summer Breaks in Germany Did Not Increase SARS-CoV-2 Cases," IZA Discussion Papers 13790, Institute of Labor Economics (IZA).
    4. Hugo Bodory & Lorenzo Camponovo & Martin Huber & Michael Lechner, 2020. "The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(1), pages 183-200, January.
    5. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
    6. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    7. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    8. Andrew Goodman-Bacon, 2018. "Difference-in-Differences with Variation in Treatment Timing," NBER Working Papers 25018, National Bureau of Economic Research, Inc.
    9. Kurt Schmidheiny & Sebastian Siegloch, 2019. "On Event Study Designs and Distributed-Lag Models: Equivalence, Generalization and Practical Implications," CESifo Working Paper Series 7481, CESifo.
    10. Richard Bluhm & Maxim Pinkovskiy, 2021. "The spread of COVID-19 and the BCG vaccine: A natural experiment in reunified Germany," The Econometrics Journal, Royal Economic Society, vol. 24(3), pages 353-376.
    11. Sebastian Galiani & Brian Quistorff, 2017. "The synth runner package: Utilities to automate synthetic control estimation using synth," Stata Journal, StataCorp LP, vol. 17(4), pages 834-849, December.
    12. Clara von Bismarck-Osten & Kirill Borusyak & Uta Schönberg, 2022. "The role of schools in transmission of the SARS-CoV-2 virus: quasi-experimental evidence from Germany," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 37(109), pages 87-130.
    13. Eduardo Cavallo & Sebastian Galiani & Ilan Noy & Juan Pantano, 2013. "Catastrophic Natural Disasters and Economic Growth," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1549-1561, December.
    14. Kurt Schmidheiny & Sebastian Siegloch, 2023. "On event studies and distributed‐lags in two‐way fixed effects models: Identification, equivalence, and generalization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 695-713, August.
    15. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    16. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    17. Athey, Susan & Imbens, Guido W., 2022. "Design-based analysis in Difference-In-Differences settings with staggered adoption," Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
    18. Sang-Wook (Stanley) Cho, 2020. "Quantifying the impact of nonpharmaceutical interventions during the COVID-19 outbreak: The case of Sweden," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 323-344.
    19. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    20. Andrew Goodman-Bacon & Jan Marcus, 2020. "Difference-in-Differences to Identify Causal Effects of COVID-19 Policies," Discussion Papers of DIW Berlin 1870, DIW Berlin, German Institute for Economic Research.
    21. 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.
    22. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    23. Andrew I. Friedson & Drew McNichols & Joseph J. Sabia & Dhaval Dave, 2021. "Shelter‐In‐Place Orders And Public Health: Evidence From California During The Covid‐19 Pandemic," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(1), pages 258-283, January.
    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. Roxanne Kovacs & Maurice Dunaiski & Janne Tukiainen, 2023. "The effect of compulsory face mask policies on community mobility in Germany," Scandinavian Journal of Economics, Wiley Blackwell, vol. 125(4), pages 1027-1055, October.
    2. Esaka, Taro & Fujii, Takao, 2022. "Quantifying the impact of the Tokyo Olympics on COVID-19 cases using synthetic control methods," Journal of the Japanese and International Economies, Elsevier, vol. 66(C).

    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. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Mar 2024.
    2. Bonander, Carl & Ekman, Mats & Jakobsson, Niklas, 2022. "Vaccination nudges: A study of pre-booked COVID-19 vaccinations in Sweden," Social Science & Medicine, Elsevier, vol. 309(C).
    3. Roy Cerqueti & Raffaella Coppier & Alessandro Girardi & Marco Ventura, 2022. "The sooner the better: lives saved by the lockdown during the COVID-19 outbreak. The case of Italy [Using synthetic controls: Feasibility, data requirements, and methodological aspects]," The Econometrics Journal, Royal Economic Society, vol. 25(1), pages 46-70.
    4. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    5. Esaka, Taro & Fujii, Takao, 2022. "Quantifying the impact of the Tokyo Olympics on COVID-19 cases using synthetic control methods," Journal of the Japanese and International Economies, Elsevier, vol. 66(C).
    6. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).
    7. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    8. Germain Gauthier, 2021. "On the Use of Two-Way Fixed Effects Models for Policy Evaluation During Pandemics," Papers 2106.10949, arXiv.org.
    9. Kirill Borusyak & Xavier Jaravel & Jann Spiess, 2021. "Revisiting Event Study Designs: Robust and Efficient Estimation," Papers 2108.12419, arXiv.org, revised Jan 2024.
    10. Eli Ben‐Michael & Avi Feller & Jesse Rothstein, 2022. "Synthetic controls with staggered adoption," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(2), pages 351-381, April.
    11. Jianfei Cao & Shirley Lu, 2019. "Synthetic Control Inference for Staggered Adoption: Estimating the Dynamic Effects of Board Gender Diversity Policies," Papers 1912.06320, arXiv.org.
    12. Damian Clarke & Kathya Tapia-Schythe, 2021. "Implementing the panel event study," Stata Journal, StataCorp LP, vol. 21(4), pages 853-884, December.
    13. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    14. Clarke, Damian & Pailañir, Daniel & Athey, Susan & Imbens, Guido W., 2023. "Synthetic Difference-in-Differences Estimation," IZA Discussion Papers 15907, Institute of Labor Economics (IZA).
    15. Ando, Michihito, 2013. "Estimating the effects of nuclear power facilities on local income levels: A quasi-experimental approach," Working Paper Series 2013:3, Uppsala University, Department of Economics.
    16. Dmitry Arkhangelsky & David Hirshberg, 2023. "Large-Sample Properties of the Synthetic Control Method under Selection on Unobservables," Papers 2311.13575, arXiv.org, revised Dec 2023.
    17. Athey, Susan & Imbens, Guido W., 2022. "Design-based analysis in Difference-In-Differences settings with staggered adoption," Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
    18. Apel, Johannes & Rohde, Niklas & Marcus, Jan, 2023. "The effect of a nighttime curfew on the spread of COVID-19," Health Policy, Elsevier, vol. 129(C).
    19. Maximiliano Marzetti & Rok Spruk, 2023. "Long-Term Economic Effects of Populist Legal Reforms: Evidence from Argentina," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 65(1), pages 60-95, March.
    20. Guido W. Imbens & Jeffrey M. Wooldridge, 2009. "Recent Developments in the Econometrics of Program Evaluation," Journal of Economic Literature, American Economic Association, vol. 47(1), pages 5-86, March.

    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:oup:emjrnl:v:25:y:2022:i:1:p:15-45.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/resssea.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.