Advertisement

Atlantic Economic Journal

, Volume 41, Issue 4, pp 405–411 | Cite as

A Note on Corruption and National Olympic Success

  • Christian PierdziochEmail author
  • Eike Emrich
Article

Abstract

Our research contributes to recent literature on the socioeconomic determinants of Olympic success at the national level. We find that less corruption increases the chances of national Olympic success.

Keywords

Olympic success Corruption 

JEL

L83 D73 

Notes

Acknowledgments

We thank the Bundesinstitut für Sportwissenschaft for financial support. We thank an anonymous reviewer for very helpful comments. The usual disclaimer applies.

References

  1. Bernard, A. B., & Busse, M. R. (2004). Who wins the Olympic Games: economic resources and medal total. The Review of Economics and Statistics, 86(1), 413–417.CrossRefGoogle Scholar
  2. Campbell, L. M., Mixon, F. G., & Sawyer, W. C. (2005). Property rights and Olympic success: an extension. Atlantic Economic Journal, 33(2), 243–244.CrossRefGoogle Scholar
  3. Emrich, E., Klein, M., Pitsch, W., & Pierdzioch, C. (2012). On the determinants of sporting success—a note on the Olympic Games. Economics Bulletin, 32(3), 1890–1901.Google Scholar
  4. Gärtner, M. (1989). Socialist countries’ sporting success before Perestroika - and after? International Review for the Sociology of Sport, 24(4), 283–297.CrossRefGoogle Scholar
  5. Heston, A., Summers, R., Aten, B. (2009). Penn world table version 6.3. Available at: http://pwt.econ.upenn.edu/php_site/pwt_index.php.
  6. Hoffmann, R., Lee, C. G., & Ramasamy, B. (2004). Olympic success and ASEAN countries: economic analysis and policy implications. Journal of Sports Economics, 5(3), 262–276.CrossRefGoogle Scholar
  7. Jackman, S. (2012). pscl: Classes and methods for R developed in the political science computational laboratory. Stanford University. Department of Political Science. R package version 1.04.4, http://pscl.stanford.edu/.
  8. Johnson, D. K. N., & Ali, A. (2004). A tale of two seasons: participation and medal counts at the summer and winter Olympic Games. Social Science Quarterly, 85(4), s974–s993.CrossRefGoogle Scholar
  9. Kiviaho, P., & Mäkelä, P. (1978). Olympic success: a sum of non-material and material factors. International Review for the Sociology of Sport, 13(2), 5–22.CrossRefGoogle Scholar
  10. Leeds, E. M., & Leeds, M. A. (2012). Gold, silver, and bronze: determining national success in men’s and women’s Summer Olympic events. Journal of Economics and Statistics, 232(3), 279–292.Google Scholar
  11. Lui, H.-K., & Suen, W. (2008). Men, money, and medals: an econometric analysis of the Olympic Games. Pacific Economic Review, 13(1), 1–16.CrossRefGoogle Scholar
  12. Maennig, W., Wellbrock, C. (2008). Sozioökonomische Schätzungen olympischer Medaillengewinne. Discussion Paper 20. Faculty of Economics and Social Sciences. University of Hamburg.Google Scholar
  13. Maennig, W., Wellbrock, C. (2012). London 2012 medal projection. Discussion Paper 45. Faculty of Economics and Social Sciences. University of Hamburg.Google Scholar
  14. Mullahy, J. (1986). Specification and testing of some modified count data models. Journal of Econometrics, 33(3), 341–365.CrossRefGoogle Scholar
  15. Novikov, A. D., & Maximenko, A. M. (1972). The influence of selected socio-economic factors on the level of sports achievements in the various countries: (using as an example the 18th Olympic Games in Tokyo). International Review for the Sociology of Sport, 7(1), 27–44.CrossRefGoogle Scholar
  16. Seppänen, P. (1988). A revisit to social and cultural preconditions of top level sport. International Review for the Sociology of Sport, 23(1), 3–14.CrossRefGoogle Scholar
  17. Shughart, W. F., & Tollison, R. D. (1993). Going for the gold: property rights and athletic effort in transitional economies. Kyklos, 46(2), 262–272.CrossRefGoogle Scholar
  18. R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org/.
  19. Zeileis, A., Kleiber, C., & Jackman, S. (2008). Regression models for count data in R. Journal of Statistical Software, 27(8), 1–25.Google Scholar

Copyright information

© International Atlantic Economic Society 2013

Authors and Affiliations

  1. 1.Department of EconomicsHelmut-Schmidt-UniversityHamburgGermany
  2. 2.Department of Sport ScienceEconomics and Sociology of Sport, Saarland UniversitySaarbrückenGermany

Personalised recommendations