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Success at the Summer Olympics: How Much Do Economic Factors Explain?

Author

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  • Pravin K. Trivedi

    (School of Economics, University of Queensland, St. Lucia, QLD 4072, Australia
    Department of Economics, Indiana University, Bloomington, IN 47408, USA)

  • David M. Zimmer

    (Department of Economics, Western Kentucky University, Bowling Green, KY 42101, USA)

Abstract

Many econometric analyses have attempted to model medal winnings as dependent on per capita GDP and population size. This approach ignores the size and composition of the team of athletes, especially the role of female participation and the role of sports culture, and also provides an inadequate explanation of the variability between the outcomes of countries with similar features. This paper proposes a model that offers two substantive advancements, both of which shed light on previously hidden aspects of Olympic success. First, we propose a selection model that treats the process of fielding any winner and the subsequent level of total winnings as two separate, but related, processes. Second, our model takes a more structural angle, in that we view GDP and population size as inputs into the “production” of athletes. After that production process, those athletes then compete to win medals. We use country-level panel data for the seven Summer Olympiads from 1988 to 2012. The size and composition of the country’s Olympic team are shown to be highly significant factors, as is also the past performance, which generates a persistence effect.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jecnmx:v:2:y:2014:i:4:p:169-202:d:43197
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    References listed on IDEAS

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    2. 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.
    3. 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).
    4. Kufenko, Vadim & Geloso, Vincent, 2021. "Who are the champions? Inequality, economic freedom and the Olympics," Journal of Institutional Economics, Cambridge University Press, vol. 17(3), pages 411-427, June.
    5. Mabliny Thuany & Thayse Natacha Gomes & Lee Hill & Thomas Rosemann & Beat Knechtle & Marcos B. Almeida, 2021. "Running Performance Variability among Runners from Different Brazilian States: A Multilevel Approach," IJERPH, MDPI, vol. 18(7), pages 1-12, April.

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