IDEAS home Printed from https://ideas.repec.org/a/sae/jospec/v27y2026i6p682-712.html

Efficiency in the Olympic Games: The Role of Mobility and Inequality

Author

Listed:
  • Gergely Csurilla
  • Imre FertÅ‘
  • Lajos Baráth

Abstract

This paper presents the analysis of Olympic efficiency by introducing a weighted performance score that values both medals and near-podium finishes and by applying a random-parameters stochastic frontier model with heteroscedastic inefficiency to the Summer Games, 1996–2020. Unlike earlier studies, we allow production elasticities to vary across countries and examine the role of sports diversification in shaping efficiency. The results highlight the importance of broad participation across disciplines, showing that diversification not only raises potential output but also reduces inefficiency. Distributional analysis further reveals modest declines in inequality and meaningful opportunities for upward mobility, even as top performers persist. The findings suggest that efficient Olympic nations combine scale, diversification, and organizational quality, offering policy lessons beyond resource levels alone.

Suggested Citation

  • Gergely Csurilla & Imre FertÅ‘ & Lajos Baráth, 2026. "Efficiency in the Olympic Games: The Role of Mobility and Inequality," Journal of Sports Economics, , vol. 27(6), pages 682-712, August.
    • Handle: RePEc:sae:jospec:v:27:y:2026:i:6:p:682-712
    DOI: 10.1177/15270025261450150
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/15270025261450150
    Download Restriction: no
    File URL: https://libkey.io/10.1177/15270025261450150?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. Pravin K. Trivedi & David M. Zimmer, 2014. "Success at the Summer Olympics: How Much Do Economic Factors Explain?," Econometrics, MDPI, vol. 2(4), pages 1-34, December.
    2. Jie Wu & Liang Liang, 2010. "Cross-efficiency evaluation approach to Olympic ranking and benchmarking: the case of Beijing 2008," International Journal of Applied Management Science, Inderscience Enterprises Ltd, vol. 2(1), pages 76-92.
    3. Wu, Jie & Liang, Liang & Yang, Feng, 2009. "Achievement and benchmarking of countries at the Summer Olympics using cross efficiency evaluation method," European Journal of Operational Research, Elsevier, vol. 197(2), pages 722-730, September.
    4. Alexandre de Cássio Rodrigues & Carlos Alberto Gonçalves & Tiago Silveira Gontijo, 2019. "A two-stage DEA model to evaluate the efficiency of countries at the Rio 2016 Olympic Games," Economics Bulletin, AccessEcon, vol. 39(2), pages 1538-1545.
    5. Caudill, Steven B. & Ford, Jon M., 1993. "Biases in frontier estimation due to heteroscedasticity," Economics Letters, Elsevier, vol. 41(1), pages 17-20.
    6. Li, Yongjun & Lei, Xiyang & Dai, Qianzhi & Liang, Liang, 2015. "Performance evaluation of participating nations at the 2012 London Summer Olympics by a two-stage data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 243(3), pages 964-973.
    7. Xiyang Lei & Yongjun Li & Qiwei Xie & Liang Liang, 2015. "Measuring Olympics achievements based on a parallel DEA approach," Annals of Operations Research, Springer, vol. 226(1), pages 379-396, March.
    8. Johannes Sauer & Klaus Frohberg & Heinrich Hockmann, 2006. "Stochastic Efficiency Measurement: The Curse of Theoretical Consistency," Journal of Applied Economics, Taylor & Francis Journals, vol. 9(1), pages 139-165, May.
    9. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, March.
    10. Nicolas Scelles & Wladimir Andreff & Liliane Bonnal & Madeleine Andreff & Pascal Favard, 2020. "Forecasting National Medal Totals at the Summer Olympic Games Reconsidered," Social Science Quarterly, Southwestern Social Science Association, vol. 101(2), pages 697-711, March.
    11. Kumbhakar,Subal C. & Wang,Hung-Jen & Horncastle,Alan P., 2015. "A Practitioner's Guide to Stochastic Frontier Analysis Using Stata," Cambridge Books, Cambridge University Press, number 9781107029514.
    12. S Lozano & G Villa & F Guerrero & P Cortés, 2002. "Measuring the performance of nations at the Summer Olympics using data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(5), pages 501-511, May.
    13. Stephen P. Jenkins & Philippe Van Kerm, 2006. "Trends in income inequality, pro-poor income growth, and income mobility," Oxford Economic Papers, Oxford University Press, vol. 58(3), pages 531-548, July.
    14. Hadri, Kaddour, 1999. "Estimation of a Doubly Heteroscedastic Stochastic Frontier Cost Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 359-363, July.
    15. Imad A. Moosa & Lee Smith, 2004. "Economic Development Indicators as Determinants of Medal Winning at the Sydney Olympics: An Extreme Bounds Analysis," Australian Economic Papers, Wiley Blackwell, vol. 43(3), pages 288-301, September.
    16. Awudu Abdulai & Hendrik Tietje, 2007. "Estimating technical efficiency under unobserved heterogeneity with stochastic frontier models: application to northern German dairy farms," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 34(3), pages 393-416, September.
    17. Kodde, David A & Palm, Franz C, 1986. "Wald Criteria for Jointly Testing Equality and Inequality Restriction s," Econometrica, Econometric Society, vol. 54(5), pages 1243-1248, September.
    18. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    19. Pedro Garcia‐del‐Barrio & Carlos Gomez‐Gonzalez & José Manuel Sánchez‐Santos, 2020. "Popularity and Visibility Appraisals for Computing Olympic Medal Rankings," Social Science Quarterly, Southwestern Social Science Association, vol. 101(5), pages 2137-2157, September.
    20. Wladimir Andreff & Madeleine Andreff, 2015. "Economic prediction of sport performances from the Beijing Olympics to the 2010 FIFA World Cup in South Africa: the notion of surprising sporting outcomes," Post-Print halshs-01244495, HAL.
    21. Dollar, David & Kraay, Aart, 2002. "Growth Is Good for the Poor," Journal of Economic Growth, Springer, vol. 7(3), pages 195-225, September.
    22. T. Potts, 2014. "Governance, corruption and Olympic success," Applied Economics, Taylor & Francis Journals, vol. 46(31), pages 3882-3891, November.
    23. Daniel K. N. Johnson & Ayfer Ali, 2004. "A Tale of Two Seasons: Participation and Medal Counts at the Summer and Winter Olympic Games," Social Science Quarterly, Southwestern Social Science Association, vol. 85(4), pages 974-993, December.
    24. Gergely Csurilla & Imre Fertő, 2022. "How long does a medal win last? Survival analysis of the duration of Olympic success," Applied Economics, Taylor & Francis Journals, vol. 54(43), pages 5006-5020, September.
    25. Wladimir Andreff & Madeleine Andreff, 2015. "Economic prediction of sport performances from the Beijing Olympics to the 2010 FIFA World Cup in South Africa: the notion of surprising sporting outcomes," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01244495, HAL.
    26. Trevor Collier & Andrew L. Johnson & John Ruggiero, 2011. "Measuring Technical Efficiency in Sports," Journal of Sports Economics, , vol. 12(6), pages 579-598, December.
    27. Andrew B. Bernard & Meghan R. Busse, 2004. "Who Wins the Olympic Games: Economic Resources and Medal Totals," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 413-417, February.
    28. Forrest, David & Sanz, Ismael & Tena, J.D., 2010. "Forecasting national team medal totals at the Summer Olympic Games," International Journal of Forecasting, Elsevier, vol. 26(3), pages 576-588, July.
    29. Johannes Sauer & Klaus Frohberg & Henrich Hockmann, 2006. "Stochastic efficiency measurement: The curse of theoretical consistency," Journal of Applied Economics, Universidad del CEMA, vol. 9, pages 139-166, May.
    30. 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.
    31. Lins, Marcos P. Estellita & Gomes, Eliane G. & Soares de Mello, Joao Carlos C. B. & Soares de Mello, Adelino Jose R., 2003. "Olympic ranking based on a zero sum gains DEA model," European Journal of Operational Research, Elsevier, vol. 148(2), pages 312-322, July.
    32. 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.
    33. 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.
    34. Nicolas Scelles & Wladimir Andreff & Liliane Bonnal & Madeleine Andreff & Pascal Favard, 2020. "Forecasting National Medal Totals at the Summer Olympic Games Reconsidered," Post-Print hal-04083639, HAL.
    35. 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.
    36. Johan Rewilak, 2021. "The (non) determinants of Olympic success," Journal of Sports Economics, , vol. 22(5), pages 546-570, June.
    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. Pedro Garcia‐del‐Barrio & Carlos Gomez‐Gonzalez & José Manuel Sánchez‐Santos, 2020. "Popularity and Visibility Appraisals for Computing Olympic Medal Rankings," Social Science Quarterly, Southwestern Social Science Association, vol. 101(5), pages 2137-2157, September.
    2. Efecan, Volkan & Temiz, İzzettin, 2023. "Assessing the technical efficiency of container ports based on a non-monotonic inefficiency effects model," Utilities Policy, Elsevier, vol. 81(C).
    3. Ali M. Oumer & Amin Mugera & Michael Burton & Atakelty Hailu, 2022. "Technical efficiency and firm heterogeneity in stochastic frontier models: application to smallholder maize farms in Ethiopia," Journal of Productivity Analysis, Springer, vol. 57(2), pages 213-241, April.
    4. Ajayi, Victor & Weyman-Jones, Tom, 2021. "State-level electricity generation efficiency: Do restructuring and regulatory institutions matter in the US?," Energy Economics, Elsevier, vol. 104(C).
    5. Sabrina Auci & Laura Castellucci & Manuela Coromaldi, 2021. "How does public spending affect technical efficiency? Some evidence from 15 European countries," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 108-130, January.
    6. Deng, Yaguo & Wiper, Michael Peter & Veiga, Helena, 2024. "A Bayesian semi-parametric approach to stochastic frontier models with inefficiency heterogeneity," DES - Working Papers. Statistics and Econometrics. WS 43837, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Cristian Barra & Nazzareno Ruggiero, 2022. "How do dimensions of institutional quality improve Italian regional innovation system efficiency? The Knowledge production function using SFA," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 591-642, April.
    8. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    9. Prakash, Navendu & Singh, Shveta & Sharma, Seema, 2025. "Technological catch-up, nonmonotonicity, and convergence: Parametric evidence from the BRICS and European banking systems," Economic Systems, Elsevier, vol. 49(1).
    10. Schlembach, Christoph & Schmidt, Sascha L. & Schreyer, Dominik & Wunderlich, Linus, 2022. "Forecasting the Olympic medal distribution – A socioeconomic machine learning model," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    11. Luigi Brighi & Paolo Silvestri, 2019. "Inefficiency in Childcare Production: Evidence from Italian Microdata," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 5(1), pages 103-133, March.
    12. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    13. Salem Gheit, 2022. "A Stochastic Frontier Analysis of the Human Capital Effects on the Manufacturing Industries’ Technical Efficiency in the United States," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 8(3), pages 215-238, July.
    14. Marin, Giovanni & Palma, Alessandro, "undated". "Technology Invention and Diffusion in Residential Energy Consumption. A Stochastic Frontier Approach," Energy: Resources and Markets 230687, Fondazione Eni Enrico Mattei (FEEM).
    15. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    16. Saldias, Rodrigo & von Cramon-Taubadel, Stephan, 2012. "Access to credit and the determinants of technical inefficiency among specialized small farmers in Chile," DARE Discussion Papers 1211, Georg-August University of Göttingen, Department of Agricultural Economics and Rural Development (DARE).
    17. Narangerel Ganbold & Shah Fahad & Hua Li & Tumendemberel Gungaa, 2022. "An evaluation of subsidy policy impacts, transient and persistent technical efficiency: A case of Mongolia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9223-9242, July.
    18. Alexander Rathke & Ulrich Woitek, 2008. "Economics and the Summer Olympics," Journal of Sports Economics, , vol. 9(5), pages 520-537, October.
    19. Ather Hassan Dar & Somesh Kumar Mathur & Sila Mishra, 2021. "The Efficiency of Indian Banks: A DEA, Malmquist and SFA Analysis with Bad Output," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(4), pages 653-701, December.
    20. Vanesa D’Elia & Gustavo Ferro, 2019. "Empirical Efficiency Measurement in Higher Education: An Overview," CEMA Working Papers: Serie Documentos de Trabajo. 708, Universidad del CEMA.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • O57 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Comparative Studies of Countries
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

    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:sae:jospec:v:27:y:2026:i:6:p:682-712. 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.