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Equal Strength or Dominant Teams: Policy Analysis of NFL

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  • Biner, Burhan

Abstract

In North America, professional sports leagues operate mostly as cartels. They employ certain policies such as revenue sharing, salary caps to ensure that teams get high revenues and players get high wages. There are two major hypotheses regarding the talent distribution among the teams that would maximize the total revenues, dominant teams rule and equal strength team rule. This paper examines the revenue structure of National Football League and proposes policy recommendations regarding talent distribution among the teams. By using a unique, rich data set on game day stadium attendance and TV ratings I am able to measure the total demand as a function of involved teams’ talent levels. Reduced form regression results indicates that TV viewers are more interested in close games, on the other hand stadium attendees are more interested in home teams’ dominance. In order to identify the true effects of possible policy experiments, I estimate the parameters of the demand for TV as functions of team talent , fixed team and market variables by using partial linear model described as in Yatchew (1998) which uses non-parametric and difference-based estimators. I then estimate the demand for stadium attendance using random coefficients model by using normative priors for the 32 cities that hosts the teams. Estimated demand for TV ratings and stadium attendance corroborates the findings of reduced form regressions, stadium demand and TV demand working against each other. We therefore propose a “somewhat” equal strength team policy where big market teams has a slight advantage over the others. Total revenues of the league is maximized under such a policy.

Suggested Citation

  • Biner, Burhan, 2009. "Equal Strength or Dominant Teams: Policy Analysis of NFL," MPRA Paper 17920, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:17920
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    File URL: https://mpra.ub.uni-muenchen.de/17920/1/MPRA_paper_17920.pdf
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    References listed on IDEAS

    as
    1. Scott E. Atkinson & Linda R. Stanley & John Tschirhart, 1988. "Revenue Sharing as an Incentive in an Agency Problem: An example from the National Football League," RAND Journal of Economics, The RAND Corporation, vol. 19(1), pages 27-43, Spring.
    2. Kesenne, Stefan, 2000. "The Impact of Salary Caps in Professional Team Sports," Scottish Journal of Political Economy, Scottish Economic Society, vol. 47(4), pages 422-430, September.
    3. Andrew Welki & Thomas Zlatoper, 1999. "U.S. professional football game-day attendance," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 27(3), pages 285-298, September.
    4. Yatchew, A., 1997. "An elementary estimator of the partial linear model," Economics Letters, Elsevier, vol. 57(2), pages 135-143, December.
    5. Yatchew,Adonis, 2003. "Semiparametric Regression for the Applied Econometrician," Cambridge Books, Cambridge University Press, number 9780521812832, April.
    6. Rodney Fort & James Quirk, 1995. "Cross-subsidization, Incentives, and Outcomes in Professional Team Sports Leagues," Journal of Economic Literature, American Economic Association, vol. 33(3), pages 1265-1299, September.
    7. El-Hodiri, Mohamed & Quirk, James, 1971. "An Economic Model of a Professional Sports League," Journal of Political Economy, University of Chicago Press, vol. 79(6), pages 1302-1319, Nov.-Dec..
    8. Stefan Szymanski, 2003. "The Economic Design of Sporting Contests," Journal of Economic Literature, American Economic Association, vol. 41(4), pages 1137-1187, December.
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    Cited by:

    1. Biner, Burhan, 2013. "Is parity good? Externalities in professional sports," Economic Modelling, Elsevier, vol. 30(C), pages 715-720.

    More about this item

    Keywords

    Perfect Competition; Dominant Team; Cartels;

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • L52 - Industrial Organization - - Regulation and Industrial Policy - - - Industrial Policy; Sectoral Planning Methods
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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