Advertisement

Springer Nature is making Coronavirus research free. View research | View latest news | Sign up for updates

Grand Old (Tailgate) Party? Partisan Discrimination in Apolitical Settings

Abstract

Recent work in political science demonstrates that the American public is strongly divided on partisan lines. Levels of affective polarization are so great, it seems, that partisanship even shapes behavior in apolitical settings. However, this literature does not account for other salient identity dimensions on which people make decisions in apolitical settings, potentially stacking the deck in favor of partisanship. We address this limitation with a pair of experiments studying price discrimination among college football fans. We find that partisan discrimination exists, even when the decision context explicitly calls attention to another social identity. But, importantly, this appears to function mostly as in-group favoritism rather than out-group hostility.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    It could be that college football fandoms, like other social identities, have political content or are seen as connected with a political party either directly or through their connection to other social groups (e.g., class, region) that are perceived as comprising each political party (Ahler and Sood 2018). College football may thus not actually be orthogonal to politics. Even so, we find little evidence in our two studies that college football fandom and partisanship are closely related. In Study 1, while Democrats and Republicans are about equally likely to be Alabama fans as Auburn fans (χ2, p = 0.052), partisanship is weakly correlated with feeling thermometer ratings for fans from each school (Alabama = 0.092, Auburn = − 0.072), as well as identification as a school football fan (0.097). In Study 2, we find stronger but still low correlations between partisanship and school ratings (Boise State = 0.21; Nevada = 0.13), as well as team attachment (0.15). Measures are described in footnote 6.

  2. 2.

    While this is a hypothetical decision, individuals often behave similarly in hypothetical decision making as they do in real decision making. Especially when the potential payoff is small, or if the gamble is framed in terms of gains, rather than losses (as in our study), behavior in hypothetical and real economic decisions are indistinguishable (Kuhberger et al. 2002).

  3. 3.

    http://www.al.com/alabamafootball/index.ssf/2016/11/coaches_with_stints_at_alabama.html.

  4. 4.

    http://www.nationalfootballpost.com/cam-newton-jersey-taped-to-bear-bryant-statue/.

  5. 5.

    http://bleacherreport.com/articles/2592853-five-years-later-harvey-updyke-and-the-day-a-rivalry-went-too-far.

  6. 6.

    We appreciate that mturk users could have satisficed about their team fandom to gain access to the survey. However, we think that individuals with no team attachment would simply add some statistical noise to our study, especially given that they would be equally likely to be randomly assigned to any experimental group. We made a few efforts to measure team attachment. First, we scale together two questions, asked prior to treatment, to determine the strength of team attachment (Leach et al. 2008), “Being a [Alabama/Auburn] fan is an important part of how I see myself” and “Identifying with other [Alabama/Auburn] fans is central to who I am as an individual.” These questions had a sample mean of 0.62 (SD = 0.25) on a 0-1 scale, indicating some strength of identification. Further, we asked all respondents (again, pre-treatment) to rate both Alabama and Auburn on a feeling thermometer. On average, respondents rated their favorite team nearly 50 points higher (49.89, SD = 34.82) than the rival team. Only 6.65% of the sample rated the rival team higher, and another 3.03% rated them the same. While these are only self-reports, we think this indicates a relatively strong team attachment, on average, in our sample—one that is higher than the levels of team attachment for undergraduate students in Study 2.

  7. 7.

    Data collection for round 1 took 5 days to complete, while collection for Round 2 took 6 days.

  8. 8.

    It is possible that this occurred due to the surprising nature of the election. We predicted that partisanship would be salient in the week prior to the election, and that team attachment would be salient in the week prior to the Iron Bowl. However, given that Donald Trump won the election in a surprise (winning the Electoral College by a substantial margin, but losing the popular vote by a substantial margin), partisan loyalties may have remained heightened in the weeks after the election more than we would have expected. Indeed, Michelitch and Utych (2018) show that, as an election becomes more proximate, partisan attachment tends to increase, and this is true whether the election is approaching or retreating. All results presented in this paper are robust to including a control for the round of the survey, which is not depicted for brevity.

  9. 9.

    And perhaps more so in our study, as we specifically recruited fans of college football teams.

  10. 10.

    That is, Alabama fans were informed that James was an Auburn graduate and Auburn fans were informed that James was an Alabama graduate.

  11. 11.

    Additionally, treatments varied the price of James’s offer between $600 and $800 to address reactions to offer amounts seen as more or less credible. There were no moderating effects of the initial offer in any analyses.

  12. 12.

    This also reflects some evidence that partisanship may shape behavior only toward out-group members in the decision-making context (Michelitch 2015).

  13. 13.

    Pure independents were always coded as the treatment being out-partisan, since pure independents are unlikely to share a social identity with either partisan group.

  14. 14.

    Results are robust to a multinomial logit model with all choices left at their initial values. In column 1, the dependent variable is coded as 1 if the participant agrees to accept James’s offer, and 0 for any other response.

  15. 15.

    This variable was smoothed to allow any minimum price (including one of $500,000) to take a maximum value of $3000, or three times the initial asking price. A total of 21 respondents volunteered a minimum price of greater than $3000. Results are robust to setting a threshold of $1000 or $5000 for smoothing.

  16. 16.

    Full regression models of these analyses, and all analyses presented only graphically in text, are available in the Online Appendix.

  17. 17.

    On a five-point scale, from Strongly Agree to Strongly Disagree, allowing for a neutral midpoint.

  18. 18.

    Agreement with these statements is also measured on a five-point scale from Strong Agree to Strongly Disagree.

  19. 19.

    Cohen’s d values near 0.20 denote small but meaningful effects (Cohen 1992).

  20. 20.

    Differences between the co-partisan treatment and the control are insignificant (F = 0.17, p > 0.10). Moreover, standardized effect sizes (Cohen’s d) are near 0.13.

  21. 21.

    Full results for these models are available in Appendix B.

  22. 22.

    Indeed, per Michelitch and Utych (2018), early November in a non-election year seems to be the time when partisan loyalties are generally least intense.

  23. 23.

    Additionally, since we were worried that James Anderson from North Carolina may give off a racial, rather than purely partisan, cue, we have changed the name to a more obvious white name, Jake Stewart from Montana.

  24. 24.

    Individuals requesting more than the original $200 amount had their minimum offer price set at $200 in this analysis.

  25. 25.

    Cronbach’s α = 0.85 for the 3 social distance questions, and only 0.47 for the 2 trust questions. Each scale is recoded to run 0-1.

  26. 26.

    Indeed, team attachment is weaker in our Boise State sample than among Alabama and Auburn fans. We modify the two team identity attachment items for the present context: “Being a Boise State fan is an important part of how I see myself” and “Identifying with other Boise State fans is central to who I am as an individual.” Responses were recorded on a 4-point Likert scale, ranging from strongly disagree to strongly agree, with no midpoint response, and recoded from 0 to 1, with 1 indicating strong identification. The mean value was 0.36 (SD = 0.32), demonstrating an overall low attachment. By comparison, similar questions in Study 1 indicated a substantially stronger attachment to teams from Alabama and Auburn fans, with a mean of 0.62 (SD = 0.25).

References

  1. Abramowitz, A. I., & Webster, S. (2016). The rise of negative partisanship and the nationalization of U.S. elections in the 21st century. Electoral Studies, 41, 12–22.

  2. Acharya, A., Blackwell, M., & Sen, M. (2018). Deep roots: How slavery still shapes southern politics. Princeton: Princeton University Press.

  3. Ahler, D. J., & Sood, G. (2018). The parties in our heads: Misperceptions about party composition and their consequences. The Journal of Politics, 80(3), 964–981.

  4. Allport, G. W. (1979). The nature of prejudice. New York: Perseus Books Publishing.

  5. Azari, J., & Hetherington, M. J. (2016). Back to the future? What the politics of the late nineteenth century can tell us about the 2016 election. The Annals of the American Academy of Political and Social Science, 667(1), 92–109.

  6. Berinsky, A. J., Huber, G. A., & Lenz, G. S. (2012). Evaluating online labor markets for experimental research: amazon. Com’s Mechanical Turk. Political Analysis, 20(3), 351–368.

  7. Bogardus, E. S. (1947). Measurement of personal–group relations. Sociometry, 10(4), 306–311.

  8. Brewer, M. B. (1999). The psychology of prejudice: ingroup love and outgroup hate? Journal of Social Issues, 55(3), 429–444.

  9. Campbell, A., Converse, P. E., Miller, W. E., & Stokes, D. E. (1960). The American Voter. New York: Wiley.

  10. Chandra, K. (2012). Constructivist theories of ethnic politics. New York: Oxford University Press.

  11. Cialdini, R. B., et al. (1976). Basking in reflected glory: Three (football) field studies. Journal of Personality and Social Psychology, 34(3), 366–375.

  12. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.

  13. Collins, A. M., & Loftus, E. F. (1975). A spreading-activation theory of semantic processing. Psychological Review, 82(6), 407–428.

  14. Davis, N. T. (2018). Perceptions of elites and (asymmetric) sorting. Research & Politics. https://doi.org/10.1177/205316801877709.

  15. Eberhardt, J. L., & Goff, P. A. (2004). Seeing race. In C. S. Crandall & M. Schaller (Eds.), Social psychology of prejudice historical and contemporary issues (pp. 219–236). Lawrence: Lewinian Press.

  16. Ellemers, N., Spears, R., & Doosje, B. (2002). Self and social identity. Annual Review of Psychology, 53(1), 161–186.

  17. Hewstone, M., Islam, M. R., & Judd, C. M. (1993). Models of crossed categorization and intergroup relations. Journal of Personality and Social Psychology, 64(5), 779–793.

  18. Iyengar, S., & Krupenkin, M. (2018). The strengthening of partisan affect. Political Psychology, 39(4), 201–218.

  19. Iyengar, S., Sood, G., & Lelkes, Y. (2012). Affect, not ideology: A social identity perspective on polarization. Public Opinion Quarterly, 76(3), 405–431.

  20. Iyengar, S., & Westwood, S. J. (2015). Fear and loathing across party lines: New evidence on group polarization. American Journal of Political Science, 59(3), 690–707.

  21. Kuhberger, A., Schulte-Mecklenbeck, M., & Perner, J. (2002). Framing decisions: Hypothetical and real. Organizational Behavior and Human Decision Processes, 89(2), 1162–1175.

  22. Leach, C. W., et al. (2008). Group-level self-definition and self-investment: A hierarchical (multicomponent) model of in-group identification. Journal of Personality and Social Psychology, 95(1), 144–165.

  23. Lelkes, Y., & Westwood, S. J. (2017). The limits of partisan prejudice. The Journal of Politics, 79(2), 485–501.

  24. Mason, L. (2013). The rise of uncivil agreement: Issue versus behavioral polarization in the American electorate. American Behavioral Scientist, 57(1), 140–159.

  25. Mason, L. (2015). ‘I disrespectfully agree’: The differential effects of partisan sorting on social and issue polarization. American Journal of Political Science, 59(1), 128–145.

  26. Mason, L. (2016). A cross-cutting calm. Public Opinion Quarterly, 80(S1), 351–377.

  27. Mason, L. (2018). Uncivil agreement. Chicago: University of Chicago Press.

  28. McConnell, C., Margalit, Y., Malhotra, N., & Levendusky, M. (2018). The economic consequences of partisanship in a polarized era. American Journal of Political Science, 62(1), 5–18.

  29. Michelitch, K. (2015). Does electoral competition exacerbate interethnic or interpartisan economic discrimination? Evidence from a field experiment in market price bargaining. American Political Science Review, 109(01), 43–61.

  30. Michelitch, K., & Utych, S. (2018). Electoral cycle fluctuations in partisanship: Global evidence from eighty-six countries. The Journal of Politics, 80(2), 412–427.

  31. Mills, B. M., Scott Tainsky, B., Green, C., & Leopkey, B. (2018). The ultimatum game in the college football rivalry context. Journal of Sport Management, 32(1), 11–23.

  32. Mullen, B., Migdal, M. J., & Hewstone, M. (2001). Crossed categorization versus simple categorization and intergroup evaluations: A meta-analysis. European Journal of Social Psychology, 31(6), 721–736.

  33. Nicholson, S. P. (2012). Polarizing Cues. American Journal of Political Science, 56(1), 52–66.

  34. Pew Research Center. (2016). partisanship and political animosity in 2016. http://www.people-press.org/2016/06/22/partisanship-and-political-animosity-in-2016/.

  35. Riach, P. A., & Rich, J. (2002). Field experiments of discrimination in the market place. The Economic Journal, 112(483), F480–F518.

  36. Roccas, S., & Brewer, M. B. (2002). Social identity complexity. Personality and Social Psychology Review, 6(2), 88–106.

  37. Smidt, C. D. (2017). Polarization and the decline of the American floating voter. American Journal of Political Science, 61(2), 365–381.

  38. Taber, C. S., & Young, E. (2013). Political information processing. In L. Huddy, D. O. Sears, & J. S. Levy (Eds.), The Oxford handbook of political psychology (pp. 525–558). New York: Oxford University Press.

  39. Tajfel, H., & Turner, J. (1979). An integrative theory of intergroup conflict. In W. Austin & S. Worchel (Eds.), The social psychology of intergroup relations. Brooks/Cole: Monterey, CA.

  40. Tyler, B. David, & Cobbs, J. (2016). All rivals are not equal: Clarifying misrepresentations and discerning three core properties of rivalry. Journal of Sport Management, 31(1), 1–35.

  41. Wann, D. L. (2006). Understanding the positive social psychological benefits of sport team identification: The team identification-social psychological health model. Group Dynamics: Theory, Research and Practice, 10(4), 272–296.

  42. Westfall, J., Van Boven, L., Chambers, J. R., & Judd, C. M. (2015). Perceiving political polarization in the United States. Perspectives on Psychological Science, 10(2), 145–158.

Download references

Author information

Correspondence to Andrew M. Engelhardt.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Additional information

We thank Cindy Kam, the anonymous reviewers, and participants at the 2017 Midwest Political Science Association Meeting for helpful feedback and comments. Replication data and code are available at: https://doi.org/10.7910/DVN/8AJYXZ.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 20 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Engelhardt, A.M., Utych, S.M. Grand Old (Tailgate) Party? Partisan Discrimination in Apolitical Settings. Polit Behav (2018). https://doi.org/10.1007/s11109-018-09519-4

Download citation

Keywords

  • Polarization
  • Partisanship
  • Social identity theory
  • Experiments