Market disruption as a regime for athlete activism: An economic analysis of college football player protests

Highlights

•	Athletes’ engagement in activism generates considerable discussion from a variety of perspectives.
•	Limited understanding of the impacts that activism has on athletes and sports organizations exist in the literature.
•	The present study looks at activism by college football teams and its effect on attendance in the 2014 to 2016 seasons.
•	Results from regression analysis revealed that the athlete activism led to a long-run decline in attendance.

Abstract

In this study, the authors examine the market disruption of college football protests, under the belief the ability of market disruption can play a significant role for activism movements to challenge authority as well as expand opportunity for social change. Specifically considering game attendance as an important form of market demand, the authors employ regression modeling to analyze the relationship between game attendance and protests organized to struggle against racial injustices by football players at four Division I Football Bowl Subdivision programs. The results from the empirical models suggest that the voting demographics of the local market a team plays in have a significant relationship with attendance after a protest, with some schools experiencing declines in consumer interest. Results advance the current inquiry of athlete activism through an economic lens, which contributes to further discussions on the empowerment of athletes and their roles in social activism.

Keywords:

• Athlete activism
• Football attendance
• Economics
• Market disruption
• College sports

Acknowledgements

We would like to thank Drs. Craig Depken, E. Frank Stephenson, and Dennis Coates along with participants from the North American Association of Sports Economists sessions at the 2016 Southern Economic Association meetings for their comments on an earlier version of this paper. We also would like to acknowledge the helpful comments and suggestions from the two anonymous reviewers, the Associate Editor, and Editor. Any errors in this paper are our own.

Notes

1 These conferences, also known as the Power 5 conferences, are: Pac-12, Big12, Big Ten, ACC, and SEC.

2 We also calculated Variance Inflation Factors (VIF) to test for multicollinearity in the model. The results found high VIF scores (over 10) for the month variables. While the month variables had high VIF scores, their presence in the model is important in order to control for the passage of time on attendance. From this, we tested models with and without the month variables and returned similar results. Based on this, the models in this research included the month variables in order to control for the passage of time during the college football season. At the same time, models were also tested with the square term of the protest variables and returned extremely high VIF scores (over 400), and thus these square terms were removed from all models.

3 We also estimated additional models using the standard deviation of attendance for each university in each season. The reason to include these models was to test whether within season variation in attendance might affect the estimated results. As the variable did not significantly change the results of the models, we dropped the measures of within season attendance variation from the final models in this study. Furthermore, previous studies have noted the importance of uncertainty of outcome in understanding demand for college sport (Brown & Salaga, 2017). Based on this we tested models with using win differential and the betting odds for games. However, we excluded them from the model because they had high VIF scores when tested.

4 As the conference variables were dummy variables, the impact on attendance was calculated using the equation: [exp(x)-1] x 100%, where x is the coefficient of the conference variables.