IDEAS home Printed from https://ideas.repec.org/a/bla/scotjp/v68y2021i5p571-604.html
   My bibliography  Save this article

Productivity and efficiency at english football clubs: a random coefficient approach

Author

Listed:
  • Guohua Feng
  • Todd Jewell

Abstract

This paper analyzes productivity and efficiency of English professional football clubs from 1981–1982 to 2010–2011, using a random coefficient stochastic distance frontier (SDF) model. Our Bayes factor analysis indicates that this model is strongly favored over the commonly used fixed coefficient SDF model. Our empirical results show that clubs in our sample operate at different levels of technical efficiency and technical change. Our further analysis using ordered logistic regression suggests that technical efficiency is more important than technical change in predicting whether clubs in our sample are promoted or relegated.

Suggested Citation

  • Guohua Feng & Todd Jewell, 2021. "Productivity and efficiency at english football clubs: a random coefficient approach," Scottish Journal of Political Economy, Scottish Economic Society, vol. 68(5), pages 571-604, November.
    • Handle: RePEc:bla:scotjp:v:68:y:2021:i:5:p:571-604
    DOI: 10.1111/sjpe.12178
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/sjpe.12178
    Download Restriction: no
    File URL: https://libkey.io/10.1111/sjpe.12178?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
    ---><---

    References listed on IDEAS

    as
    1. Carlos Pestana Barros & Pedro Garcia-del-Barrio & Stephanie Leach, 2009. "Analysing the technical efficiency of the Spanish Football League First Division with a random frontier model," Applied Economics, Taylor & Francis Journals, vol. 41(25), pages 3239-3247.
    2. Simon Rottenberg, 1956. "The Baseball Players' Labor Market," Journal of Political Economy, University of Chicago Press, vol. 64(3), pages 242-242.
    3. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    4. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    5. Helmut M. Dietl & Martin Grossmann & Markus Lang, 2011. "Competitive Balance and Revenue Sharing in Sports Leagues With Utility-Maximizing Teams," Journal of Sports Economics, , vol. 12(3), pages 284-308, June.
    6. Koop G., 2002. "Comparing the Performance of Baseball Players: A Multiple-Output Approach," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 710-720, September.
    7. O'Donnell, Christopher J. & Coelli, Timothy J., 2005. "A Bayesian approach to imposing curvature on distance functions," Journal of Econometrics, Elsevier, vol. 126(2), pages 493-523, June.
    8. Feng, Guohua & Zhang, Xiaohui, 2012. "Productivity and efficiency at large and community banks in the US: A Bayesian true random effects stochastic distance frontier analysis," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1883-1895.
    9. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    10. Luis Orea, 2002. "Parametric Decomposition of a Generalized Malmquist Productivity Index," Journal of Productivity Analysis, Springer, vol. 18(1), pages 5-22, July.
    11. Shunsuke Managi, 2010. "Productivity measures and effects from subsidies and trade: an empirical analysis for Japan's forestry," Applied Economics, Taylor & Francis Journals, vol. 42(30), pages 3871-3883.
    12. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    13. Peter Dawson & Stephen Dobson & Bill Gerrard, 2000. "Estimating Coaching Efficiency in Professional Team Sports: Evidence from English Association Football," Scottish Journal of Political Economy, Scottish Economic Society, vol. 47(4), pages 399-421, September.
    14. Ho-chuan Huang, 2004. "Estimation of Technical Inefficiencies with Heterogeneous Technologies," Journal of Productivity Analysis, Springer, vol. 21(3), pages 277-296, May.
    15. Mundlak, Yair, 1996. "Production Function Estimation: Reviving the Primal," Econometrica, Econometric Society, vol. 64(2), pages 431-438, March.
    16. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
    17. Carlos Pestana Barros & Stephanie Leach, 2007. "Technical efficiency in the English Football Association Premier League with a stochastic cost frontier," Applied Economics Letters, Taylor & Francis Journals, vol. 14(10), pages 731-741.
    18. Scully, Gerald W, 1974. "Pay and Performance in Major League Baseball," American Economic Review, American Economic Association, vol. 64(6), pages 915-930, December.
    19. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    20. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    21. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    22. Cooper, W.W. & Ruiz, José L. & Sirvent, Inmaculada, 2009. "Selecting non-zero weights to evaluate effectiveness of basketball players with DEA," European Journal of Operational Research, Elsevier, vol. 195(2), pages 563-574, June.
    23. Pedro Garcia-del-Barrio & Stefan Szymanski, 2009. "Goal! Profit Maximization Versus Win Maximization in Soccer," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 34(1), pages 45-68, February.
    24. R. Todd Jewell, 2017. "Technical efficiency with multi-output, heterogeneous production: a latent class, distance function model of english football," Journal of Productivity Analysis, Springer, vol. 48(1), pages 37-50, August.
    25. Kumbhakar, Subal C., 2013. "Specification and estimation of multiple output technologies: A primal approach," European Journal of Operational Research, Elsevier, vol. 231(2), pages 465-473.
    26. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    27. Feng, Guohua & Zhang, Xiaohui, 2014. "Returns to scale at large banks in the US: A random coefficient stochastic frontier approach," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 135-145.
    28. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. David Boto‐García & Carlos Varela‐Quintana & Alvaro Muñiz, 2023. "Foreign players, team production, and technical efficiency: Evidence from European soccer," Bulletin of Economic Research, Wiley Blackwell, vol. 75(4), pages 1209-1241, October.

    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. R. Todd Jewell, 2017. "Technical efficiency with multi-output, heterogeneous production: a latent class, distance function model of english football," Journal of Productivity Analysis, Springer, vol. 48(1), pages 37-50, August.
    2. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
    3. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    4. Feng, Guohua & Zhang, Xiaohui, 2014. "Returns to scale at large banks in the US: A random coefficient stochastic frontier approach," Journal of Banking & Finance, Elsevier, vol. 39(C), pages 135-145.
    5. Oum, Tae H. & Yan, Jia & Yu, Chunyan, 2008. "Ownership forms matter for airport efficiency: A stochastic frontier investigation of worldwide airports," Journal of Urban Economics, Elsevier, vol. 64(2), pages 422-435, September.
    6. Yan, Jia & Sun, Xinyu & Liu, John J., 2009. "Assessing container operator efficiency with heterogeneous and time-varying production frontiers," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 172-185, January.
    7. Carlos Pestana Barros & Nicolas Peypoch & Scott Tainsky, 2014. "Cost efficiency of French soccer league teams," Applied Economics, Taylor & Francis Journals, vol. 46(8), pages 781-789, March.
    8. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    9. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2022. "Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach," Empirical Economics, Springer, vol. 62(3), pages 1345-1363, March.
    10. Jerzy Marzec & Andrzej Pisulewski, 2021. "Measurement of technical efficiency in the case of heterogeneity of technologies used between firms - Based on evidence from Polish crop farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 152-161.
    11. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    12. Carlos Pestana Barros & Gaël Bertrand & Laurent Botti & Scott Tainsky, 2014. "Cost efficiency of French rugby clubs," Applied Economics, Taylor & Francis Journals, vol. 46(23), pages 2721-2732, August.
    13. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    14. Peter Zweifel & Philippe K. Widmer, 2023. "Accounting for heterogeneity in the measurement of hospital performance," Applied Economics, Taylor & Francis Journals, vol. 55(57), pages 6701-6716, December.
    15. Feng, Guohua & Serletis, Apostolos, 2010. "Efficiency, technical change, and returns to scale in large US banks: Panel data evidence from an output distance function satisfying theoretical regularity," Journal of Banking & Finance, Elsevier, vol. 34(1), pages 127-138, January.
    16. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    17. Antonio Arbelo & Marta Arbelo-Pérez & Pilar Pérez-Gómez, 2022. "Are SMEs less efficient? A Bayesian approach to addressing heterogeneity across firms," Small Business Economics, Springer, vol. 58(4), pages 1915-1929, April.
    18. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    19. Francisco González-Gómez & Andrés J. Picazo-Tadeo, 2010. "Can We Be Satisfied With Our Football Team? Evidence From Spanish Professional Football," Journal of Sports Economics, , vol. 11(4), pages 418-442, August.
    20. Guohua Feng & Chuan Wang & Apostolos Serletis, 2018. "Shadow prices of $$\hbox {CO}_{2}$$ CO 2 emissions at US electric utilities: a random-coefficient, random-directional-vector directional output distance function approach," Empirical Economics, Springer, vol. 54(1), pages 231-258, February.

    More about this item

    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:bla:scotjp:v:68:y:2021:i:5:p:571-604. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/sesssea.html .

    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.