IDEAS home Printed from https://ideas.repec.org/a/sae/jospec/v21y2020i4p363-390.html
   My bibliography  Save this article

NCAA Expenditure and Efficiency: Analyzing Generated and Allocated Revenue in the Football Bowl Subdivision

Author

Listed:
  • R. Todd Jewell

Abstract

Using a stochastic production function approach and a dynamic panel data estimator, this study creates estimates of time-varying efficiency in the production of generated revenues for NCAA Division I football bowl subdivision athletic programs. These efficiency estimates are then compared to the use of allocated revenues—fees from students and direct payments from the university budget—by college athletic departments. While all schools that are less efficient in the production of generated revenue are shown to use allocated revenue more intensively, a major finding is power-conference schools that are less efficient in their use of expenditure inputs tend to rely more heavily on allocated revenue in the form of student fees to support the activities of the program.

Suggested Citation

  • R. Todd Jewell, 2020. "NCAA Expenditure and Efficiency: Analyzing Generated and Allocated Revenue in the Football Bowl Subdivision," Journal of Sports Economics, , vol. 21(4), pages 363-390, May.
    • Handle: RePEc:sae:jospec:v:21:y:2020:i:4:p:363-390
    DOI: 10.1177/1527002520906530
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/1527002520906530
    Download Restriction: no
    File URL: https://libkey.io/10.1177/1527002520906530?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. Jeffery Borland, 2003. "Demand for Sport," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 19(4), pages 478-502, Winter.
    2. 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.
    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. David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March.
    5. Willis A. Jones, 2013. "Exploring the Relationship Between Intercollegiate Athletic Expenditures and Team On-Field Success Among NCAA Division I Institutions," Journal of Sports Economics, , vol. 14(6), pages 584-605, December.
    6. 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.
    7. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    8. Young Hoon Lee & David Berri, 2008. "A Re‐Examination Of Production Functions And Efficiency Estimates For The National Basketball Association," Scottish Journal of Political Economy, Scottish Economic Society, vol. 55(1), pages 51-66, February.
    9. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    10. 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.
    11. Gary J. Colbert & E. Woodrow Eckard, 2015. "Do Colleges Get What They Pay For? Evidence on Football Coach Pay and Team Performance," Journal of Sports Economics, , vol. 16(4), pages 335-352, May.
    12. Michael S. Rimler & Seongho Song & David T. Yi, 2010. "Estimating Production Efficiency in Men’s NCAA College Basketball: A Bayesian Approach," Journal of Sports Economics, , vol. 11(3), pages 287-315, June.
    13. Michael L. Anderson, 2017. "The Benefits of College Athletic Success: An Application of the Propensity Score Design," The Review of Economics and Statistics, MIT Press, vol. 99(1), pages 119-134, March.
    14. Fiona Carmichael & Giambattista Rossi & Denis Thomas, 2017. "Production, Efficiency, and Corruption in Italian Serie A Football," Journal of Sports Economics, , vol. 18(1), pages 34-57, January.
    15. Bernd Frick & Young Lee, 2011. "Temporal variations in technical efficiency: evidence from German soccer," Journal of Productivity Analysis, Springer, vol. 35(1), pages 15-24, February.
    16. Jody W. Lipford & Jerry K. Slice, 2017. "Cost spreading in college athletic spending in the United States: estimates and implications," Education Economics, Taylor & Francis Journals, vol. 25(4), pages 379-393, July.
    17. Adam Hoffer & Brad R. Humphreys & Donald J. Lacombe & Jane E. Ruseski, 2015. "Trends in NCAA Athletic Spending," Journal of Sports Economics, , vol. 16(6), pages 576-596, August.
    18. Trevor Collier & Andrew L. Johnson & John Ruggiero, 2011. "Measuring Technical Efficiency in Sports," Journal of Sports Economics, , vol. 12(6), pages 579-598, December.
    19. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    20. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    21. 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)

    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. Young Hoon Lee, 2009. "Frontier Models and their Application to the Sports Industry," Working Papers 0903, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy), revised 2009.
    2. Aditi Bhattacharyya, 2012. "Adjustment of inputs and measurement of technical efficiency: A dynamic panel data analysis of the Egyptian manufacturing sectors," Empirical Economics, Springer, vol. 42(3), pages 863-880, June.
    3. Rosen Azad Chowdhury & Dilshad Jahan & Tapas Mishra & Mamata Parhi, 2023. "A Quality Dimension? A Re-appraisal of Financial Development and Economic Growth Nexus in a Quality-Quantity Setting," Working Papers 2023-02, Swansea University, School of Management.
    4. K. Ravirajan & K.R. Shanmugam, 2021. "Efficiency of commercial banks in India after global financial crises," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(628), A), pages 65-82, Autumn.
    5. MAIMOUNA DIAKITE & Jean-François BRUN, 2016. "Tax Potential and Tax Effort: An Empirical Estimation for Non-Resource Tax Revenue and VAT’s Revenue," EcoMod2016 9537, EcoMod.
    6. Farsi, Mehdi & Filippini, Massimo, 2009. "An analysis of cost efficiency in Swiss multi-utilities," Energy Economics, Elsevier, vol. 31(2), pages 306-315, March.
    7. Richard Adjei Dwumfour & Eric Fosu Oteng-Abayie & Emmanuel Kwasi Mensah, 2022. "Bank efficiency and the bank lending channel: new evidence," Empirical Economics, Springer, vol. 63(3), pages 1489-1542, September.
    8. Sickles, Robin C. & Song, Wonho & Zelenyuk, Valentin, 2018. "Econometric Analysis of Productivity: Theory and Implementation in R," Working Papers 18-008, Rice University, Department of Economics.
    9. Mastromarco Camilla & Laura Serlenga & Yongcheol Shin, 2013. "Globalisation and technological convergence in the EU," Journal of Productivity Analysis, Springer, vol. 40(1), pages 15-29, August.
    10. Massimo Del Gatto & Adriana Di Liberto & Carmelo Petraglia, 2011. "Measuring Productivity," Journal of Economic Surveys, Wiley Blackwell, vol. 25(5), pages 952-1008, December.
    11. William Horrace & Seth Richards-Shubik & Ian Wright, 2015. "Expected efficiency ranks from parametric stochastic frontier models," Empirical Economics, Springer, vol. 48(2), pages 829-848, March.
    12. Marvin A. Titus & Adriana Vamosiu & Shannon Hayes Buenaflor & Casey Maliszewski Lukszo, 2021. "Persistent Cost Efficiency at Public Community Colleges in the US: A Stochastic Frontier Analysis," Research in Higher Education, Springer;Association for Institutional Research, vol. 62(8), pages 1168-1197, December.
    13. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    14. Julio Peña & Julio Aguirre & René Cerca D'amico, 2004. "Pesca demersal en Chile: eficiencia técnica y escalas de operación," Revista de Analisis Economico – Economic Analysis Review, Universidad Alberto Hurtado/School of Economics and Business, vol. 19(1), pages 119-160, June.
    15. Gralka, Sabine, 2018. "Stochastic frontier analysis in higher education: A systematic review," CEPIE Working Papers 05/18, Technische Universität Dresden, Center of Public and International Economics (CEPIE).
    16. William Greene, 2004. "Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 959-980, October.
    17. Simwaka, Kisu, 2012. "Maximum likelihood estimation of a stochastic frontier model with residual covariance," MPRA Paper 39726, University Library of Munich, Germany.
    18. Martini, Gianmaria & Scotti, Davide & Viola, Domenico & Vittadini, Giorgio, 2020. "Persistent and temporary inefficiency in airport cost function: An application to Italy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 999-1019.
    19. Kim, Jung Woo & Lee, Jeong Yeon & Kim, Jae Yong & Lee, Hoe Kyung, 2006. "Sources of productive efficiency: International comparison of iron and steel firms," Resources Policy, Elsevier, vol. 31(4), pages 239-246, December.
    20. Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.

    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:sae:jospec:v:21:y:2020:i:4:p:363-390. 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: SAGE Publications (email available below). General contact details of provider: .

    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.