Abstract
This paper describes the outcome of an artefactual field experiment of group discrimination using sports fans. The behavior of individuals whose identity is deeply tied to a larger group or popular institution is politically important, particularly when it comes to crafting public policy. Sports fans provide a unique opportunity to study individuals who openly identify their in-group and rival groups. The study identifies within-subject group-based discrimination by quantifying the difference in dictator game takes (out of a possible $10) between fans of an individual’s self-professed team and fans of an individual’s self-professed rival. Fifty-two sports fans each participated in nine separate power-to-take dictator games with group identification spanning three levels (NCAA Division III, NCAA Division I, and professional) of football fandom. The results suggest that individuals discriminate between in-group and out-group members. The average takings ratio with same-team fans is 0.657 while the average takings ratio with other-team fans is 0.848 and the difference of 0.190 is statistically different from zero. We discuss the results in the context of team and league governance focusing on fan interactions.
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Notes
While public press reports about this mitigation effort were initially positive, empirical analysis by Di Domizio and Caruso (2014) suggest that the fidelity cards did not have a noticeable impact on in-stadium attendance.
The authors ran a second study with 37 dictator-game “givers,” but the authors did not include sports identity in the second study.
The framing of giving and taking does not seem to matter if the recipient is a charity instead of another person (Grossman and Eckel 2015).
Because of the experiment design, the ultimatum game required Mills et al (2018) to use a small amount of deception. Instead of being matched with another real game player, the researchers matched participants (unbeknownst to the participants) with a virtual player whose responses were randomized.
We select these teams for geographical reasons. The experiment was run in La Crosse, Wisconsin, whose students are primarily from the region. The two Division III teams are chosen because of their long-standing rivalry. The two Football Bowl Series teams were chosen because Wisconsin is the flagship university of the University of Wisconsin system and is a member of the Big 10 conference; The Ohio State University, also a member of the Big 10 conference, was chosen because the two teams have played 84 times from 1913 through 2021 with Ohio State having a decisive edge in victories. However, the game has become a focus of Wisconsin fans in the past few decades. The three NFL teams are chosen because the Green Bay Packers are located in Wisconsin and have had long rivalries with the Minnesota Vikings but especially with the Chicago Bears.
There is evidence that women are more generous than men in a power-to-take game (Chowdhury et al. 2017), economics majors keep more of the money for themselves and have a more positive attitude towards greed (Wang et al. 2011), and that political preferences (such as support for government spending and redistribution) are correlated with generosity in the dictator game (Dawes et al. 2012).
A take frame is well suited for our study because it enhances the ‘rivalry’ aspect between the fans of different teams. In addition, unlike an experiment where the dictator and recipient are both present in the lab, we only had dictators in the lab. A potential concern for making every subject the dictator is that the subjects may believe that the experimenter may not transfer the amount to the recipient to save money. To mitigate this concern, we explicitly state in the instructions that “… Person B, who will be paid in cash, after the conclusion of the experiment.” Moreover, if subjects believed that Person B is hypothetical, then they would not have left any money for Person B, and always take $10 for themselves. Only 11 of the 52 subjects took $10 from both their most favored and most rival team. Note that taking $10 coincides with own-payoff maximizing behavior, so this figure captures the upper bound of distrust regarding non-payment to Person B.
Of the 52 subjects who participated, 23 first took from an in-group and then an out-group member while the remaining 29 first took from an out-group and then an in-group member.
We present an exploratory analysis of these additional decisions in the Results section.
With a sample size of 52 subjects and the critical value of 0.05 for alpha (α), the results have the requisite power (1- β) of 0.8 to detect a medium effect size (Cohen’s d > = 0.4) for a two-sided test of difference in means.
As a robustness check to compare our sample of participants to the general student body, we surveyed 74 random students (outside the lab, in-person, on-campus, between classes) and asked a single question related to their fanness of UWL. The results showed that our subject pool were significantly greater fans of UWL football than the sample of non-participants in the student body.
The difference-in-difference estimates are available upon request.
Note that the positive coefficient for Professional Fan—Division I Fan (A-B) is the result of a double negative. The difference between UW-Madison Fan and OSU Fan takes is -1.54 and the difference between Packers Fan and Vikings Fan takes is -1.23, for a difference of 0.41, with greater discrimination occuring between college fans.
References
Arif I, Hoffer A, Stansel D, Lacombe D (2020) Economic freedom and migration: a metro area-level analysis. South Econ J 87(1):170–190
Arif I, Hoffer A, Humphreys B, Style M (2022) New sports facilities do not drive migration between US cities. In press, Economics of governance
Ashby NJ (2007) Economic freedom and migration flows between US states. South Econ J 73(3):677–697
Balliet D, Wu J, De Dreu CKW (2014) In-group favoritism in cooperation: a meta-analysis. Psychol Bull 140(6):1556
Bardsley N (2008) Dictator game giving: altruism or artefact? Exp Econ 11(2):122–133
Baxter J, Carlsson-Wall M, Chua WF, Kraus K (2019) Accounting for the cost of sports-related violence. Account Audit Account J 32(7):1956–1981
Ben-Ner A, McCall B, Stephane M, Wang H (2009) Identity and in-group/out-group differentiation in work and giving behaviors: experimental evidence. J Econ Behav Organ 72(1):153–170
Berry WD, Fording RC, Hanson RL (2003) Reassessing the “race to the bottom” in state welfare policy. J Politics 65(2):327–349
Billings S, Depken C II (2012) Sport events and criminal activity: A spatial analysis. In: Jewell R (ed) Violence and aggression in sporting contests: economics, history and policy. Springer, New York, pp 175–187
Cart J (2011) Numerous fights marked Raiders-49ers game before shooting. Los Angeles Times. latimesblogs.latimes.com/lanow/2011/08/numerous-fights-marked-raiders-49ers-game-before-shooting.html. Accessed May 2021
Chowdhury SM, Jeon JY, Saha B (2017) Gender differences in the giving and taking variants of the dictator game. South Econ J 84(2):474–483
Dalakas V, Melancon J (2012) Fan identification, schadenfreude toward hated rivals, and the mediating effects of Importance of Winning Index (IWIN). J Serv Mark 26:51–59
Dawes CT et al (2012) Generosity and political preferences. IFN Working Paper. Research Institute of Industrial Economics (IFN), Stockholm https://doi.org/10.2139/ssrn.2179522
Di Domizio M, Caruso R (2014) Hooliganism and demand for football in Italy: attendance and counter violence policy evaluation. Ger Econ Rev 16(2):123–137
Engel C (2011) Dictator games: a meta study. Exp Econ 14(4):583–610
Fischbacher U (2007) Z-tree: Zurich toolbox for ready-made economic experiments. Exp Econ 10(2):171–178
Grossman PJ, Eckel CC (2015) Giving versus taking for a cause. Econ Lett 132:28–30
Heere B, James J (2007) Stepping outside the lines: developing a multi-dimensional team identity scale based on social identity theory. Sport Manag Review 10(1):65–91
Hoffer AJ (2016) Special-interest spillovers and tobacco taxation. Contemp Econ Policy 34(1):146–157
Hoffer A, Humphreys BR, Lacombe DJ, Ruseski JE (2015) Trends in NCAA athletic spending: arms race or rising tide? J Sports Econ 16(6):576–596
Hoffer A, Humphreys BR, Ruseski JE (2019) State cigarette taxes and health expenditures: evidence from dynamic spatial lag panel models. Pap Reg Sci 98(2):925–950
Hoffer AJ, Lacombe DJ (2017) Excise tax setting in a dynamic space-time framework. Public Financ Rev 45(5):628–646
Hoffer AJ, Pincin JA (2015) The effects of conference realignment on NCAA athletic departments. Appl Econ Lett 22(15):1209–1223
Hoffer A, Pincin JA (2016) The effects of revenue changes on NCAA athletic departments’ expenditures. J Sport Soc Issues 40(1):82–102
Humphreys, B. 2015 The future of behavioral economics in sports economics research. Keynote Presentation at the European Sports Economics Association Conference, Zurich, Switzerland
Jabbar, N. Alcohol will be sold at Champions League and Europa League matches from next season. Sportbible.com (Jun 26 2018), Available at www.sportbible.com/football/news-alcohol-will-be-sold-at-champions-and-europa-league-matches-20180626, last Accessed May 2021.
Kanbur, R., & Keen, M. (1993). Jeux sans frontieres: Tax competition and tax coordination when countries differ in size. Am Econ Rev 877–892
Lane T (2016) Discrimination in the laboratory: a meta-analysis of economics experiments. Eur Econ Rev 90:375–402
Lenor S, Lenten L, McKenzie J (2016) Rivalry effects and unbalanced schedule optimisation in the Australian football league. Rev Ind Organ 49:43–69
List JA (2007) On the interpretation of giving in dictator games. J Political Econ 115(3):482–493
Menaker B, McGranahan D, Sheptak R (2019) Game day alters crime pattern in the vicinity of sport venues in cleveland, OH”. J Sport Saf Secur 1:1
Mills B, Tainsky S, Green B, Leopkey B (2018) The ultimatum game in the college football rivalry context. J Sport Manag 32(1):11–23
Paul R, Weinbach A (2013) Determinants of dynamic pricing premiums in major league baseball. Sport Mark Q 22:152–165
Sanford K, Scott F (2016) Assessing the intensity of sports rivalries using data from secondary market transactions. J Sports Econ 17:159–174
Schimmel KS (2006) Deep play: sports mega-events and urban social conditions in the USA. The Sociol Rev 54(2):160–174
Szymanski S, Winfree J (2018) On the optimal realignment of a contest: the case of college football. Econ Inq 56(1):483–496
Taylor, P. The Hillsborough stadium disaster. Inquiry by the Rt Hon Lord Justice Taylor (1989).
Tiebout CM (1956) A pure theory of local expenditures. J Polit Econ 64(5):416–424
Wang Long, Malhotra Deepak, Keith Murnighan J (2011) Economics education and greed. Acad Manag Learn Educ 10(4):643–660
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Funding for the experiment came from a Menard Family Initiative at the University of Wisconsin – La Crosse (UWL) research grant.
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Depken, C.A., Hoffer, A.J. & Kidwai, A.H. An artefactual field experiment of group discrimination between sports fans. Econ Gov 23, 411–432 (2022). https://doi.org/10.1007/s10101-022-00278-x
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DOI: https://doi.org/10.1007/s10101-022-00278-x