IDEAS home Printed from https://ideas.repec.org/a/bpj/jqsprt/v6y2010i1n7.html

Negotiating Salaries through Quantile Regression

Author

Listed:
  • Wiseman Frederick

    (Northeastern University)

  • Chatterjee Sangit

    (Northeastern University)

Abstract

Estimating the salaries of professional athletes has received a substantial amount of attention both in the press and in academic journals. A statistical technique that can be used to obtain an estimate of a player's salary with a given set of performance characteristics is the classical least squares regression analysis. This technique does not work well, however, if the data upon which the model is based contain outliers or are not normally distributed. In this paper we focus our attention on the salaries of American League baseball players in 2007 and demonstrate the usefulness of an alternative estimation approach - that of quantile regression analysis. Our results indicate that ordinary least squares regression overestimates the salaries of poor players, and underestimates the salaries of star players. This, we believe, is a compelling reason to apply quantile regression in the prediction of baseball player salaries.

Suggested Citation

  • Wiseman Frederick & Chatterjee Sangit, 2010. "Negotiating Salaries through Quantile Regression," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(1), pages 1-15, January.
    • Handle: RePEc:bpj:jqsprt:v:6:y:2010:i:1:n:7
    DOI: 10.2202/1559-0410.1177
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1559-0410.1177
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1559-0410.1177?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jahn Hakes & Chad Turner, 2011. "Pay, productivity and aging in Major League Baseball," Journal of Productivity Analysis, Springer, vol. 35(1), pages 61-74, February.
    2. Jahn K. Hakes & Raymond D. Sauer, 2006. "An Economic Evaluation of the Moneyball Hypothesis," Journal of Economic Perspectives, American Economic Association, vol. 20(3), pages 173-186, Summer.
    3. Anthony C. Krautmann & Margaret Oppenheimer, 2002. "Contract Length and the Return to Performance in Major League Baseball," Journal of Sports Economics, , vol. 3(1), pages 6-17, February.
    4. Herbert F. Lewis & Thomas R. Sexton & Kathleen A. Lock, 2007. "Player Salaries, Organizational Efficiency, and Competitiveness in Major League Baseball," Journal of Sports Economics, , vol. 8(3), pages 266-294, June.
    5. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    6. Karl W. Einolf, 2004. "Is Winning Everything?," Journal of Sports Economics, , vol. 5(2), pages 127-151, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Terry, Ryan P. & McGee, Jeffrey E. & Kass, Malcolm J., 2018. "The not-so-free agent: Non-performance factors that contribute to free agent compensation premiums," Sport Management Review, Elsevier, vol. 21(2), pages 189-201.
    2. Rodney Fort & Young Hoon Lee & Taeyeon Oh, 2019. "Quantile Insights on Market Structure and Worker Salaries: The Case of Major League Baseball," Journal of Sports Economics, , vol. 20(8), pages 1066-1087, December.
    3. Holmes, Paul, 2011. "New evidence of salary discrimination in major league baseball," Labour Economics, Elsevier, vol. 18(3), pages 320-331, June.
    4. Reio Tanji, 2021. "Reference Dependence and Monetary Incentives: Evidence from Major League Baseball," Discussion Papers in Economics and Business 20-23, Osaka University, Graduate School of Economics.
    5. Boon L. Lee & Andrew C. Worthington, 2013. "A note on the ‘Linsanity’ of measuring the relative efficiency of National Basketball Association guards," Applied Economics, Taylor & Francis Journals, vol. 45(29), pages 4193-4202, October.
    6. Nikos Chatzistamoulou & Kounetas Kostas & Antonakis Theodor, 2022. "Salary Cap, Organizational Gap, and Catch-up in the Performance of NBA Teams: A Two-Stage DEA Model Under Heterogeneity," Journal of Sports Economics, , vol. 23(2), pages 123-155, February.
    7. An‐Pang Wang & Che‐Wei Chang & Juin‐Ming Tsai & Shiu‐Wan Hung, 2021. "A performance evaluation of Major League Baseball teams: An integrated social network and data envelopment analysis," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1421-1434, September.
    8. Louis J. Pantuosco, 2011. "Does It Pay to Be Unethical? The Case of Performance Enhancing Drugs in Mlb," The American Economist, Sage Publications, vol. 56(2), pages 58-68, November.
    9. Ryan Pinheiro & Stefan Szymanski, 2022. "All Runs Are Created Equal: Labor Market Efficiency in Major League Baseball," Journal of Sports Economics, , vol. 23(8), pages 1046-1075, December.
    10. Jahn K. Hakes & Chad Turner, 2008. "Long-Term Contracts in Major League Baseball," Working Papers 0831, International Association of Sports Economists;North American Association of Sports Economists.
    11. Chih-Hai Yang & Hsuan-Yu Lin & Chiang-Ping Chen, 2014. "Measuring the efficiency of NBA teams: additive efficiency decomposition in two-stage DEA," Annals of Operations Research, Springer, vol. 217(1), pages 565-589, June.
    12. Brian M. Mills, 2017. "Policy Changes In Major League Baseball: Improved Agent Behavior And Ancillary Productivity Outcomes," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 1104-1118, April.
    13. Lewis, Herbert F. & Lock, Kathleen A. & Sexton, Thomas R., 2009. "Organizational capability, efficiency, and effectiveness in Major League Baseball: 1901-2002," European Journal of Operational Research, Elsevier, vol. 197(2), pages 731-740, September.
    14. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    15. Simplice A. Asongu, 2013. "Fighting Corruption when Existing Corruption-Control Levels Count: What do Wealth-Effects Tell us in Africa?," Institutions and Economies (formerly known as International Journal of Institutions and Economies), Faculty of Economics and Administration, University of Malaya, vol. 5(3), pages 53-74, October.
    16. Tabuga, Aubrey D., 2007. "International Remittances and Household Expenditures: the Philippine Case," Discussion Papers DP 2007-18, Philippine Institute for Development Studies.
    17. Paul Hewson & Keming Yu, 2008. "Quantile regression for binary performance indicators," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 24(5), pages 401-418, September.
    18. Alfred Michael Dockery & Sherry Bawa, 2015. "The Impact of Children on Australian Couples’ Wealth Accumulation," The Economic Record, The Economic Society of Australia, vol. 91(S1), pages 139-150, June.
    19. Joachim Wagner, 2006. "Export Intensity and Plant Characteristics: What Can We Learn from Quantile Regression?," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 142(1), pages 195-203, April.
    20. Simplice Asongu & Nicholas M. Odhiambo, 2024. "The role of foreign aid in the nexus between capital flight and unemployment in sub-Saharan Africa," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 52(1), pages 1-15, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:bpj:jqsprt:v:6:y:2010:i:1:n:7. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyterbrill.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.