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The Benefits of College Athletic Success: An Application of the Propensity Score Design with Instrumental Variables

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  • Michael L. Anderson

Abstract

Spending on big-time college athletics is often justified on the grounds that athletic success attracts students and raises donations. Testing this claim has proven difficult because success is not randomly assigned. We exploit data on bookmaker spreads to estimate the probability of winning each game for college football teams. We then condition on these probabilities using a propensity score design to estimate the effects of winning on donations, applications, and enrollment. The resulting estimates represent causal effects under the assumption that, conditional on bookmaker spreads, winning is uncorrelated with potential outcomes. Two complications arise in our design. First, team wins evolve dynamically throughout the season. Second, winning a game early in the season reveals that a team is better than anticipated and thus increases expected season wins by more than one-for-one. We address these complications by combining an instrumental variables-type estimator with the propensity score design. We find that winning reduces acceptance rates and increases donations, applications, academic reputation, in-state enrollment, and incoming SAT scores.

Suggested Citation

  • Michael L. Anderson, 2012. "The Benefits of College Athletic Success: An Application of the Propensity Score Design with Instrumental Variables," NBER Working Papers 18196, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18196
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    1. Yoav Benjamini & Abba M. Krieger & Daniel Yekutieli, 2006. "Adaptive linear step-up procedures that control the false discovery rate," Biometrika, Biometrika Trust, vol. 93(3), pages 491-507, September.
    2. Meer, Jonathan & Rosen, Harvey S., 2009. "The impact of athletic performance on alumni giving: An analysis of microdata," Economics of Education Review, Elsevier, vol. 28(3), pages 287-294, June.
    3. Devin G. Pope & Jaren C. Pope, 2009. "The Impact of College Sports Success on the Quantity and Quality of Student Applications," Southern Economic Journal, John Wiley & Sons, vol. 75(3), pages 750-780, January.
    4. Tucker, Irvin B., 2004. "A reexamination of the effect of big-time football and basketball success on graduation rates and alumni giving rates," Economics of Education Review, Elsevier, vol. 23(6), pages 655-661, December.
    5. Joshua D. Angrist & Guido M. Kuersteiner, 2011. "Causal Effects of Monetary Shocks: Semiparametric Conditional Independence Tests with a Multinomial Propensity Score," The Review of Economics and Statistics, MIT Press, vol. 93(3), pages 725-747, August.
    6. David Card & Gordon B. Dahl, 2011. "Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(1), pages 103-143.
    7. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    8. Murphy, Robert G. & Trandel, Gregory A., 1994. "The relation between a university's football record and the size of its applicant pool," Economics of Education Review, Elsevier, vol. 13(3), pages 265-270, September.
    9. Paul W. Grimes & George A. Chressanthis, 1994. "Alumni Contributions to Academics," American Journal of Economics and Sociology, Wiley Blackwell, vol. 53(1), pages 27-40, January.
    10. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    11. LaLonde, Robert J, 1986. "Evaluating the Econometric Evaluations of Training Programs with Experimental Data," American Economic Review, American Economic Association, vol. 76(4), pages 604-620, September.
    12. Sarah E. Turner & Lauren A. Meserve & William G. Bowen, 2001. "Winning and Giving: Football Results and Alumni Giving at Selective Private Colleges and Universities," Social Science Quarterly, Southwestern Social Science Association, vol. 82(4), pages 812-826, December.
    13. Steven D. Levitt, 2004. "Why are gambling markets organised so differently from financial markets?," Economic Journal, Royal Economic Society, vol. 114(495), pages 223-246, April.
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    Cited by:

    1. Liu, Zhuping & Duan, Jason A & Mahajan, Vijay, 2020. "Dynamics and peer effects of brand revenue in college sports," International Journal of Research in Marketing, Elsevier, vol. 37(4), pages 756-771.
    2. Tabakovic, Haris & Wollmann, Thomas G., 2019. "The impact of money on science: Evidence from unexpected NCAA football outcomes," Journal of Public Economics, Elsevier, vol. 178(C).
    3. Allen R. Sanderson & John J. Siegfried, 2015. "The Case for Paying College Athletes," Journal of Economic Perspectives, American Economic Association, vol. 29(1), pages 115-138, Winter.
    4. John Fizel & Charles Brown, 2014. "Assessing the Determinants of NCAA Football Violations," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 42(3), pages 277-290, September.
    5. Allen R. Sanderson & John J. Siegfried, 2018. "The National Collegiate Athletic Association Cartel: Why it Exists, How it Works, and What it Does," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 52(2), pages 185-209, March.
    6. Christopher Avery & Brian Cadman & Gavin Cassar, 2016. "Academics vs. Athletics: Career Concerns for NCAA Division I Coaches," NBER Working Papers 22120, National Bureau of Economic Research, Inc.
    7. Jason M. Lindo & Peter Siminski & Isaac D. Swensen, 2018. "College Party Culture and Sexual Assault," American Economic Journal: Applied Economics, American Economic Association, vol. 10(1), pages 236-265, January.
    8. Niebler, Sarah & Urban, Carly, 2017. "Does negative advertising affect giving behavior? Evidence from campaign contributions," Journal of Public Economics, Elsevier, vol. 146(C), pages 15-26.
    9. Jonathan Willner, 2019. "Private Universities and NCAA D-III Athletics as a General Recruiting Tool," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(3), pages 293-307, August.
    10. Rhodes, M. Taylor, 2013. "Pigskin, Tailgating and Pollution: Estimating the Environmental Impacts of Sporting Events," UNCG Economics Working Papers 13-19, University of North Carolina at Greensboro, Department of Economics.
    11. Laura Beaudin, 2018. "Examining the Relationship Between Athletic Program Expenditure and Athletic Program Success Among NCAA Division I Institutions," Journal of Sports Economics, , vol. 19(7), pages 1016-1045, October.
    12. Adam G. Walker, 2015. "Division I Intercollegiate Athletics Success and the Financial Impact on Universities," SAGE Open, , vol. 5(4), pages 21582440156, October.
    13. Jerome Segura & Jonathan Willner, 2018. "The Game Is Good at the Top," Journal of Sports Economics, , vol. 19(5), pages 645-676, June.

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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • I20 - Health, Education, and Welfare - - Education - - - General
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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