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

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.

Keywords

Olympic efficiency stochastic frontier analysis diversification inequality mobility

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References

Abdulai

Tietje

(2007). Estimating technical efficiency under unobserved heterogeneity with stochastic frontier models: Application to northern German dairy farms. European Review of Agricultural Economics, 34(3), 393–416. https://doi.org/10.1093/erae/jbm023

Aigner

Lovell

C. A. K.

Schmidt

(1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37. https://doi.org/10.1016/0304-4076(77)90052-5

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. In Rodriguez

Késenne

Koning

(Eds.), The economics of competitive sports (pp. 185–215). Edward Elgar. https://doi.org/10.4337/9781783474769.00018

Ball

D. W.

(1972). Olympic games competition: Structural correlates of national success. International Journal of Comparative Sociology, 13(3-4), 186–200. https://doi.org/10.1177/002071527201300302

Battese

G. E.

Coelli

T. J.

(1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332. https://doi.org/10.1007/BF01205442

Bernard

A. B.

Busse

M. R.

(2004). Who wins the Olympic games: Economic resources and medal totals. Review of Economics and Statistics, 86(1), 413–417. https://doi.org/10.1162/003465304774201824

Caudill

S. B.

Ford

J. M.

(1993). Biases in frontier estimation due to heteroscedasticity. Economics Letters, 41(1), 17–20. https://doi.org/10.1016/0165-1765(93)90104-K

Caudill

S. B.

Ford

J. M.

Gropper

D. M.

(1995). Frontier estimation and firm-specific inefficiency measures in the presence of heteroscedasticity. Journal of Business & Economic Statistics, 13(1), 105–111. https://doi.org/10.1080/07350015.1995.10524583

Churilov

Flitman

(2006). Towards fair ranking of olympics achievements: The case of Sydney 2000. Computers & Operations Research, 33(7), 2057–2082. https://doi.org/10.1016/j.cor.2004.09.027

10.

Coelli

T. J.

Prasada Rao

D. S.

O’Donnell

C. J.

Battese

G. E.

(2005). An Introduction to Efficiency and Productivity Analysis. Springer.

11.

Collier

Johnson

A. L.

Ruggiero

(2011). Measuring technical efficiency in sports. Journal of Sports Economics, 12(6), 579–598. https://doi.org/10.1177/1527002510391582

12.

Csurilla

Fertő

(2022). How long does a medal win last? Survival analysis of the duration of Olympic success. Applied Economics, 54(43), 5006–5020. https://doi.org/10.1080/00036846.2022.2039370

13.

Csurilla

Fertő

(2023). The less obvious effect of hosting the Olympics on sporting performance. Scientific Reports, 13(1), 819. https://doi.org/10.1038/s41598-022-27259-8

14.

Csurilla

Fertő

(2024). How to win the first Olympic medal? And the second? Social Science Quarterly, 105(5), 1544–1564. https://doi.org/10.1111/ssqu.13436

15.

Csurilla

Gyimesi

Kendelényi-Gulyás

Sterbenz

(2021). Where is victory most certain? The level of luck-based noise factor in summer Olympic sports. Acta Oeconomica, 71(3), 369–386. https://doi.org/10.1556/032.2021.00018

16.

De Bosscher

Shibli

van Bottenburg

De Knop

Truyens

(2010). Developing a method for comparing the elite sport systems and policies of nations: A mixed research methods approach. Journal of Sport Management, 24(5), 567–600. https://doi.org/10.1123/jsm.24.5.567

17.

De Bosscher

Shibli

Weber

A. C.

(2019). Is prioritisation of funding in elite sport effective? An analysis of the investment strategies in 16 countries. European Sport Management Quarterly, 19(2), 221–243. https://doi.org/10.1080/16184742.2018.1505926

18.

de Cássio Rodrigues

Alberto Gonçalves

Silveira Gontijo

(2019). A two-stage DEA model to evaluate the efficiency of countries at the Rio 2016 Olympic games. Economics Bulletin, 39(2), 1538–1545.

19.

del Corral

Gomez-Gonzalez

Sánchez-Santos

J. M.

(2017). A country-level efficiency analysis of the 2016 summer Olympic games in Rio: A complete picture. International Journal of Sport Finance, 12(3), 265–293. https://doi.org/10.1177/155862351701200306

20.

Dollar

Kraay

(2002). Growth is good for the poor. Journal of Economic Growth, 7(3), 195–225. https://doi.org/10.1023/A:1020139631000

21.

Forrest

McHale

I. G.

Sanz

Tena

J. D.

(2017). An analysis of country medal shares in individual sports at the Olympics. European Sport Management Quarterly, 17(2), 117–131. https://doi.org/10.1080/16184742.2016.1248463

22.

Forrest

Sanz

Tena

J. D.

(2010). Forecasting national team medal totals at the summer Olympic games. International Journal of Forecasting, 26(3), 576–588. https://doi.org/10.1016/j.ijforecast.2009.12.007

23.

Garcia-del-Barrio

Gomez-Gonzalez

Sánchez-Santos

J. M.

(2020). Popularity and visibility appraisals for computing Olympic medal rankings. Social Science Quarterly, 101(5), 2137–2157. https://doi.org/10.1111/ssqu.12835

24.

Globan

Rewilak

(2024). A new Index to rank nations at the summer olympics. Managing Sport and Leisure, 30(5), 881–899. https://doi.org/10.1080/23750472.2024.2312100

25.

Gracenote. (n.d.). Podium. Retrieved 27 August 2021, from https://sportsdemo1.gracenote.com/documentation/podium

26.

Greene

(2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269–303. https://doi.org/10.1016/j.jeconom.2004.05.003

27.

Hadri

(1999). Estimation of a doubly heteroscedastic stochastic frontier cost function. Journal of Business & Economic Statistics, 17(3), 359–363. https://doi.org/10.1080/07350015.1999.10524824

28.

Hsiao

(2014). Analysis of Panel Data (3rd edn). Cambridge University Press.

29.

Jenkins

S. P.

Van Kerm

(2006). Trends in income inequality, pro-poor income growth, and income mobility. Oxford Economic Papers, 58(3), 531–548. https://doi.org/10.1093/oep/gpl014

30.

Johnson

D. K. N.

Ali

(2004). A tale of two seasons: Participation and medal counts at the summer and winter Olympic games. Social Science Quarterly, 85(4), 974–993. https://doi.org/10.1111/j.0038-4941.2004.00254.x

31.

Klöcker

J. A.

Daumann

(2025). Evaluation of Global South’s efficiency at the Summer Olympics. PLOS ONE, 20(1), e0315054. https://doi.org/10.1371/journal.pone.0315054

32.

Kodde

D. A.

Palm

F. C.

(1986). Wald criteria for jointly testing equality and inequality restrictions. Econometrica, 54(5), 1243–1248. JSTOR. https://doi.org/10.2307/1912331

33.

Kumbhakar

S. C.

Wang

H.-J.

Horncastle

A. P.

(2015). A Practitioner’s Guide to Stochastic Frontier Analysis Using Stata. Cambridge University Press.

34.

Lei

Xie

Liang

(2015). Measuring olympics achievements based on a parallel DEA approach. Annals of Operations Research, 226(1), 379–396. https://doi.org/10.1007/s10479-014-1708-1

35.

Lei

Dai

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, 243(3), 964–973. https://doi.org/10.1016/j.ejor.2014.12.032

36.

Lins

M. P. E.

Gomes

E. G.

Soares de Mello

J. C. C. B.

Soares de Mello

A. J. R.

(2003). Olympic ranking based on a zero sum gains DEA model. European Journal of Operational Research, 148(2), 312–322. https://doi.org/10.1016/S0377-2217(02)00687-2

37.

Lozano

Villa

Guerrero

Cortés

(2002). Measuring the performance of nations at the summer olympics using data envelopment analysis. Journal of the Operational Research Society, 53(5), 501–511. https://doi.org/10.1057/palgrave.jors.2601327

38.

Meeusen

van Den Broeck

(1977). Efficiency estimation from Cobb-Douglas production functions with composed error. International Economic Review, 18(2), 435–444. https://doi.org/10.2307/2525757

39.

Moosa

I. A.

Smith

(2004). Economic development indicators as determinants of medal winning at the Sydney Olympics: An extreme bounds analysis. Australian Economic Papers, 43(3), 288–301. https://doi.org/10.1111/j.1467-8454.2004.00231.x

40.

Noland

Stahler

(2017). An old boys club No more: Pluralism in participation and performance at the Olympic Games. Journal of Sports Economics, 18(5), 506–536. https://doi.org/10.1177/1527002515588138

41.

Otamendi

Doncel

L. M.

(2014). Medal shares in winter Olympic Games by sport: Socioeconomic analysis after Vancouver 2010: Medal shares in winter Olympic games after Vancouver 2010. Social Science Quarterly, 95(2), 598–614. https://doi.org/10.1111/ssqu.12055

42.

Potts

T. B.

(2014). Governance, corruption and Olympic success. Applied Economics, 46(31), 3882–3891. https://doi.org/10.1080/00036846.2014.948672

43.

Rathke

Woitek

(2008). Economics and the Summer Olympics: An efficiency analysis. Journal of Sports Economics, 9(5), 520–537. https://doi.org/10.1177/1527002507313743

44.

Rewilak

(2021). The (non) determinants of Olympic success. Journal of Sports Economics, 22(5), 546–570. https://doi.org/10.1177/1527002521992833

45.

Sauer

Frohberg

Hockmann

(2006). Stochastic efficiency measurement: The curse of theoretical consistency. Journal of Applied Economics, 9(1), 139–165. https://doi.org/10.1080/15140326.2006.12040642

46.

Scelles

Andreff

Bonnal

Andreff

Favard

(2020). Forecasting national medal totals at the summer Olympic games reconsidered. Social Science Quarterly, 101(2), 697–711. https://doi.org/10.1111/ssqu.12782

47.

Singleton

Reade

J. J.

Rewilak

Schreyer

(2024). How big is home advantage at the Olympic games? In Research handbook on major sporting events (pp. 88–103). Edward Elgar Publishing. https://doi.org/10.4337/9781800885653.00014

48.

Tcha

Pershin

(2003). Reconsidering performance at the Summer Olympics and revealed comparative advantage. Journal of Sports Economics, 4(3), 216–239. https://doi.org/10.1177/1527002503251636

49.

Trivedi

Zimmer

(2014). Success at the Summer Olympics: How much do economic factors explain? Econometrics, 2(4), 169–202. https://doi.org/10.3390/econometrics2040169

50.

Weber

A. C.

De Bosscher

Kempf

(2018). Positioning in Olympic winter sports: Analysing national prioritisation of funding and success in eight nations. European Sport Management Quarterly, 18(1), 8–24. https://doi.org/10.1080/16184742.2017.1336782

51.

Liang

Yang

(2009). Achievement and benchmarking of countries at the Summer Olympics using cross efficiency evaluation method. European Journal of Operational Research, 197(2), 722–730. https://doi.org/10.1016/j.ejor.2008.06.030

52.

Liang

N. A.

(2010). Cross-Efficiency evaluation approach to Olympic ranking and benchmarking: The case of Beijing 2008. International Journal of Applied Management Science, 2(1), 76. https://doi.org/10.1504/IJAMS.2010.029798