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College football attendance: a panel study of the Football Bowl Subdivision

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  • Gregory A. Falls
  • Paul A. Natke

Abstract

Panel data with an instrumented real ticket price are used to estimate a regular season game-day attendance and per cent of capacity regression equations. Better team performance, whether short term (season wins), intermediate term (bowls games in last 10 years) or long term (lifetime winning percentage), higher undergraduate enrolment, traditional rivalries and video coverage increase per cent of capacity used. Poor weather (more rain or cloud cover), higher travel costs and larger local population decrease it. Fan interest wanes as a season progresses, but this is offset as a team wins more games. Games played near a National Football League stadium, those with conference opponents, non-FBS opponents and non-BCS opponents have lower stadium utilization. The substantive results of the analysis do not change when attendance is used as the dependent variable rather than per cent of capacity.

Suggested Citation

  • Gregory A. Falls & Paul A. Natke, 2014. "College football attendance: a panel study of the Football Bowl Subdivision," Applied Economics, Taylor & Francis Journals, vol. 46(10), pages 1093-1107, April.
    • Handle: RePEc:taf:applec:v:46:y:2014:i:10:p:1093-1107
    DOI: 10.1080/00036846.2013.866208
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    References listed on IDEAS

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    Cited by:

    1. Nicholas Masafumi Watanabe & George B Cunningham, 2020. "The impact of race relations on NFL attendance: An econometric analysis," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-21, January.
    2. Gregory A. Falls & Paul A. Natke & Linlan Xiao, 2022. "College football attendance in the long run: The Football Championship Subdivision," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(6), pages 2172-2183, September.
    3. Paul A. Natke & Elizabeth A. Thomas, 2019. "Does a marching band impact college Football game attendance? A panel study of Division II," Applied Economics Letters, Taylor & Francis Journals, vol. 26(16), pages 1354-1357, September.
    4. Jeremy K. Nguyen & Adam Karg & Abbas Valadkhani & Heath McDonald, 2022. "Predicting individual event attendance with machine learning: a ‘step-forward’ approach," Applied Economics, Taylor & Francis Journals, vol. 54(27), pages 3138-3153, June.
    5. Watanabe, Nicholas M. & Yan, Grace & Soebbing, Brian P., 2019. "Market disruption as a regime for athlete activism: An economic analysis of college football player protests," Sport Management Review, Elsevier, vol. 22(5), pages 600-612.
    6. Jason A. Winfree, 2020. "Rivalries, Bowl Eligibility, and Scheduling Effects in College Football," Journal of Sports Economics, , vol. 21(5), pages 477-492, June.
    7. Gregory A. Falls & Paul A. Natke, 2016. "College Football Attendance: A Panel Study of the Football Championship Subdivision," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 37(8), pages 530-540, December.
    8. Siriklao Sangkhaphan & Yang Shu, 2020. "Impact of seasonal rainfall on economic growth in Thailand," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 10(2), pages 1-2.

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