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Thinning out spectators: Did football matches contribute to the second COVID-19 wave in Germany?

Kai Fischer ORCID logo
From the journal German Economic Review

Abstract

The COVID-19 pandemic has decelerated substantial parts of economic and human interaction. This paper estimates football matches’ contribution to the spread of COVID-19 during Germany’s second infection wave in summer and autumn 2020. Exploiting the exogenous fixture schedules of matches across German counties in an event study design, we estimate that one additional match in a county on average raises daily cases by between 0.34 to 0.71 cases per 100,000 inhabitants after three weeks. Hence, this implies an increase of the seven-day incidence per 100,000 inhabitants by around three to seven percent. We do not find qualitatively different results for a subsample of German top league matches with the strictest hygiene regulations or matches with higher occupancy levels. Notably, the found effect is mediated by the incidence level at the day of the match with very few infections for matches at a seven-day incidence below 25. Using mobile phone data, we identify strong increases in the local mobility as an underlying mechanism. We finally show that the ban of away fans successfully limited the spread of COVID-19 beyond county borders. Our results alert that even outdoor mass gatherings can remarkably cause infections.

JEL Classification: I18; H12; Z20; Z21

Funding source: Deutsche Forschungsgemeinschaft

Award Identifier / Grant number: 235577387/GRK1974

Funding statement: This project is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 235577387/GRK1974.

Acknowledgment

I am thankful for very helpful comments by Andreas Lichter, Benedikt Schmal and Simon Schulten as well as for insightful discussions at the Reading Online Sports Economics Seminar 2021 and the Public Health Conference at the International Centre for Economic Analysis (ICEA).

Appendix A

A.1 Extension: Transmission across county borders

We subsequently especially discuss the spatial transmission of potential infections from football matches across county borders. For that, we firstly examine neighbouring counties of treated regions (comparable to Ahammer et al. (2020)). Moreover, we then make use of commuter data provided by the German Employment Agency (Agentur für Arbeit).[33] The data includes information on the interrelation of counties with respect to commuting for work. We use this as proxy of general commuting behavior between counties and network interconnections. The advantage of this data is that the general reduction in mobility during the pandemic cannot totally be reduced in the world of work. Thus, this data should make up a relevant part of the remaining mobility and can also account for spreading beyond close regional borders to for example far distant federal states. Mense and Michelsen (2020) use the same data to investigate interregional interdependences as an infection mitigator in the first wave 2020. They find commuter networks to significantly explain changes in infection rates. While the effect decreased during the first lockdown, it nevertheless remained robustly existent.

To make use of the data, we follow a similar weighting strategy as Mense and Michelsen (2020) and create a treatment variable in the form of

T r a n s m i s s i o n i t = j = 1 , j i 401 [ M a t c h j t × I n c o m m u t e r i j + O u t c o m m u t e r i j P o p u l a t i o n i ]

which equals the sum of the match or visitor indicator (as used above) weighted with the exposure to commuting over all counties. In detail, we calculate the share of people who travel between county i and any other county j of the 401 counties relative to county i’s population which is I n c o m m u t e r i j + O u t c o m m u t e r i j P o p u l a t i o n i . This commuter density is the weight for the exposure to the treatment in county j. Summing this new treatment indicators up over all states gives the respective transmission indicator. As we include the exposure to the treatment in the treated counties j and the commuting behavior, we ensure that higher exposure to risk gives a higher value for T r a n s m i s s i o n i t . The variable can be interpreted as a likelihood or probability of infection spillover in case that football matches contribute to COVID-19 spreading. In comparison to the approach to use neighbouring counties, this procedure will account for example for the bias that the counties’ population is not suitably given by the number of border counties or geographical distance only.

To measure the potential effect of matches on counties which directly border treated counties, potential spillovers will be analyzed here. We plot the estimated coefficients for the effect of matches in a neighbouring county in Figure A1. We do not find any significant effects of a match in the neighbouring county. This, on the one hand, underlines the reliability of the detected patterns – as we hence do not only cover fractions of the development of cases over time – but it also indicates the limited spatial spreading due to less people in the stadiums.[34]

Figure A1 
Effect of Matches and Visitors per 100,000 Inhabitants on Neighbouring Counties.

Figure A1

Effect of Matches and Visitors per 100,000 Inhabitants on Neighbouring Counties.

Figure A2 
Effect of Matches and Visitors per 100,000 Inhabitants on Commuter-Exposed Counties.

Figure A2

Effect of Matches and Visitors per 100,000 Inhabitants on Commuter-Exposed Counties.

Here we now also provide results on our findings with respect to commuting. Applying the created transmission indicator as a treatment indicator in our event study setup should hint at the role of football matches for spatial transmission and can be seen as a robustness check to our findings on the limited transmission to neighbouring counties which are most likely to be locations to which commuting takes place. As can be seen in Figure A2, we cannot find a systematic effect of the transmission indicator on the infection numbers in states with a higher exposure to travel from and to counties with matches with visitors.

A.2 Figures and tables

Table A1

Descriptive Statistics on Included (Semi-)Professional Football Matches.

Competition # Matches ...of which no Ghost Games Mean # Visitors Mean Occupancy
Bundesliga 108 33 4809.3 11.97 %
2. Bundesliga 108 41 2515.3 10.41 %
3. Liga 149 44 2777.4 13.47 %
Regionalliga West 194 100 437.8 5.62 %
Regionalliga Suedwest 137 109 481.4 5.55 %
Regionalliga Nordost 121 116 751.4 9.73 %
Regionalliga Nord 98 71 431.9 10.23 %
Regionalliga Bayern 27 21 283.0 7.31 %
DFB-Pokal 46 21 1640.1 8.90 %
Others 110 52 695.0 6.31 %
Women’s Bundesliga 65 28 377.6 5.50 %
Women’s DFB-Pokal 40 24 189.6 6.87 %
1203 660 1045.1 8.26 %

  1. Note: “Others” include matches from the following competitions: Champions League, Europa League, State Cup finals (“Landespokal”), national team matches and friendlies. “Average # Visitors” and “Average Occupancy” give the respective values for the subsample of matches which were played in front of spectators. Match dates range from 2020-08-01 and 2020-12-23.

Table A2

Socio-Demographic and Economic Differences Between Treated and Untreated Counties.

N = 401 counties

127 treated, 274 untreated
(1 = Treated, 0 = Untreated)

(1) (2)
ln(Inhabitants) 0.322*** 0.356***
(0.052) (0.068)
Share Age ≥ 65 −4.096** −4.873**
(1.667) (2.100)
Population Density 0.015 0.016
(0.010) (0.010)
Share Foreigners −1.454* −0.760
(0.865) (1.232)
ln(Available Income) −0.594** −0.634*
(0.296) (0.373)
Share Protestants −0.013 0.341
(0.216) (0.572)
Share Catholics 0.087 0.406
(0.195) (0.508)
Share Households with Children −0.017 −0.022
(0.023) (0.026)
Average Household Size 0.071 0.173
(0.709) (0.779)
Hospital Density −1.256 −2.098
(2.507) (2.681)
Hospital Bed Density 0.019 0.020
(0.012) (0.013)
State FE No Yes
Observations 401 401
McFadden (Pseudo-)R2 0.294 0.315

  1. Note: The table gives marginal effects at the variables’ means of socio-demographic and economic differences between the treated and untreated counties estimated in probit regressions with heteroskedasticity-robust standard errors. Treated counties are all counties in which at least one match took place which is included in the sample. There are only three out of the 127 counties which did not have a match where visitors were allowed to attend but only ghost games.

Table A3

Absorbing Dummy Difference-in-Differences.

(Daily Cases per 100,000 Inh.)it (Daily Deaths per 100,000 Inh.)it


(1) (2) (3) (4)
1[Post First Match with Spectators] 0.645* 0.035 −0.003 0.002
(0.358) (0.231) (0.008) (0.005)
1[Post First Match with Spectators] × 1[≥ 10 Days Post First Match] 0.843*** −0.004
(0.315) (0.008)
County FE Yes Yes Yes Yes
Date FE Yes Yes Yes Yes
State × Week FE Yes Yes Yes Yes
Observations 36,491 36,491 36,491 36,491
Adjusted R2 0.658 0.658 0.135 0.135

  1. Note: *p < 0.1; **p < 0.05; ***p < 0.01. Heteroskedasticity-robust standard errors clustered on the county level.

Figure A3 
Development of NPI Density Across Counties.

Figure A3

Development of NPI Density Across Counties.

Figure A4 
Registered COVID-19 Cases and Deaths as of Registration Day in Germany.

Figure A4

Registered COVID-19 Cases and Deaths as of Registration Day in Germany.

Figure A5 
Distribution of Cases Across Age Groups and Over Time.

Figure A5

Distribution of Cases Across Age Groups and Over Time.

Figure A6 
Effect of Football Matches per 100,000 Inhabitants of Respective Age Groups.

Figure A6

Effect of Football Matches per 100,000 Inhabitants of Respective Age Groups.

Figure A7 
Effect of Visitors per 100,000 Inhabitants of Respective Age Groups.

Figure A7

Effect of Visitors per 100,000 Inhabitants of Respective Age Groups.

Figure A8 
Effect of Matches per 100,000 Inhabitants Under Different Incidence Levels.

Figure A8

Effect of Matches per 100,000 Inhabitants Under Different Incidence Levels.

Figure A9 
Attendance Depending on Incidence Level on Match Day.

Figure A9

Attendance Depending on Incidence Level on Match Day.

Figure A10 
Effect of Matches per 100,000 Inhabitants on Away Team Counties.

Figure A10

Effect of Matches per 100,000 Inhabitants on Away Team Counties.

Figure A11 
Effect of Matches per 100,000 Inhabitants (Occupancy Level).

Figure A11

Effect of Matches per 100,000 Inhabitants (Occupancy Level).

Figure A12 
Effect of Visitors per 100,000 Inhabitants (Occupancy Level).

Figure A12

Effect of Visitors per 100,000 Inhabitants (Occupancy Level).

Figure A13 
Effect of Ghost Matches per 100,000 Inhabitants.

Figure A13

Effect of Ghost Matches per 100,000 Inhabitants.

Figure A14 
Effect of Matches and Visitors per 100,000 Inhabitants (Top Leagues).

Figure A14

Effect of Matches and Visitors per 100,000 Inhabitants (Top Leagues).

Figure A15 
Effect of Matches and Visitors per 100,000 Inhabitants on Deaths.

Figure A15

Effect of Matches and Visitors per 100,000 Inhabitants on Deaths.

Figure A16 
Effect of Matches and Visitors per 100,000 Inhabitants on COVID-19 Cases (Extended Observation Period).

Figure A16

Effect of Matches and Visitors per 100,000 Inhabitants on COVID-19 Cases (Extended Observation Period).

Figure A17 
Mobility Change on Match Days (Daily Event Study).

Figure A17

Mobility Change on Match Days (Daily Event Study).

Figure A18 
Effect of Matches per 100,000 Inhabitants (Different Regional Inference Approach I).

Figure A18

Effect of Matches per 100,000 Inhabitants (Different Regional Inference Approach I).

Figure A19 
Effect of Matches per 100,000 Inhabitants (Different Clusters for Standard Errors).

Figure A19

Effect of Matches per 100,000 Inhabitants (Different Clusters for Standard Errors).

Figure A20 
Effect of Matches per 100,000 Inhabitants (4 Week Post-Treatment Effect Window).

Figure A20

Effect of Matches per 100,000 Inhabitants (4 Week Post-Treatment Effect Window).

Figure A21 
Effect of Matches and Visitors per 100,000 Inhabitants – Controlling for NPIs.

Figure A21

Effect of Matches and Visitors per 100,000 Inhabitants – Controlling for NPIs.

Figure A22 
Effect of Matches per 100,000 Inhabitants (Travel Robustness Checks).

Figure A22

Effect of Matches per 100,000 Inhabitants (Travel Robustness Checks).

Figure A23 
Effect of Matches per 100,000 Inhabitants (Absorbing Dummy Approach).

Figure A23

Effect of Matches per 100,000 Inhabitants (Absorbing Dummy Approach).

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Published Online: 2022-04-29

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