IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/18196.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    Note: ED LS PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w18196.pdf
    Download Restriction: no

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. David Card & Gordon B. Dahl, 2011. "Family Violence and Football: The Effect of Unexpected Emotional Cues on Violent Behavior," The Quarterly Journal of Economics, Oxford University Press, vol. 126(1), pages 103-143.
    10. 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.
    11. Devin G. Pope & Jaren C. Pope, 2009. "The Impact of College Sports Success on the Quantity and Quality of Student Applications," Southern Economic Journal, Southern Economic Association, vol. 75(3), pages 750-780, January.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Avery, Christopher & Cadman, Brian & Cassar, Gavin, 2016. "Academics vs. Athletics: Career Concerns for NCAA Division I Coaches," Working Paper Series 16-013, Harvard University, John F. Kennedy School of Government.
    3. 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.
    4. 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.
    5. 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.
    6. Jason M. Lindo & Peter M. Siminski & Isaac D. Swensen, 2015. "College Party Culture and Sexual Assault," NBER Working Papers 21828, National Bureau of Economic Research, Inc.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:18196. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (). General contact details of provider: http://edirc.repec.org/data/nberrus.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.