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Does higher sport supply lead to higher sport demand? A city level analysis


  • Sandrine Poupaux
  • Christoph Breuer


This paper explores the decision to participate in sports activities and the subsequent frequency of participation using data from a big German city, Munich, representative sample of individuals in 2008. Individual and socio-economic variables characterizing the individuals were collected. A new type of variable, which has not been included in the existing econometric studies yet, is introduced: the availability of sport infrastructures, including their geographical localization and the type of infrastructure. If building sport infrastructures can be seen as an investment and as a consequence a cost for the city, sport infrastructures can also be considered as a factor influencing positively the sport demand. However, the localization of such an infrastructure can be seen as a time and income constraint for the sport participant if the distance from the home is too important. Traditional non linear econometric analysis, logit and poisson models, as well as two-level nonlinear hierarchical models are used to examine the empirical evidence provided by the data collected by survey including 11.715 persons. The results suggest that social and individual characteristics are of paramount importance in determining sports participation and sports frequency, as shown in the 2 recent econometric studies based on UK and US data (Downward, 2007 and Humpreys and Ruseski, 2007). In our study related to the city Munich, we can see that the impact of the variable age is non linear, that the gender is highly significant in explaining the differences of sport participation and the impact of the level of school attendance on sport practice are significantly explanatory. A very interesting result is the explanatory power of the variable ethnicity, or nationality of the person and we take a particular attention on it. The regression coefficients related to different nationalities differs among sport disciplines. These differences could be explained no only by sociological reasons, but also by economic reasons, among other things. The economic variables, taking alone, particularly the monthly income of the person interweaved, have a lower impact on both the decision to practice sport and the frequency of the sport activity. The most innovative result of our econometric study is to state, through an analysis of each kind of sport infrastructures, that the sport practice supply in an acceptable distance from the individual home has a significant and positive impact on both the decision of practicing a sport and on the frequency of this activity. These results, related to the city Munich, open an alternative way of considering the urban sport demand. Such a study could allow predicting the outcomes of political decisions in the domain of sport for all at the city level, the econometrical models using there being able to predict on how many percent the sport participation would increase if a new sport infrastructure would be built.

Suggested Citation

  • Sandrine Poupaux & Christoph Breuer, 2009. "Does higher sport supply lead to higher sport demand? A city level analysis," Working Papers 0905, International Association of Sports Economists;North American Association of Sports Economists.
  • Handle: RePEc:spe:wpaper:0905

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    References listed on IDEAS

    1. Jeffery Borland, 2003. "Demand for Sport," Oxford Review of Economic Policy, Oxford University Press, vol. 19(4), pages 478-502, Winter.
    2. Paul Downward & Joseph Riordan, 2007. "Social Interactions And The Demand For Sport: An Economic Analysis," Contemporary Economic Policy, Western Economic Association International, vol. 25(4), pages 518-537, October.
    3. Paul Downward, 2004. "On leisure demand: a Post Keynesian critique of neoclassical theory," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 26(3), pages 371-394.
    4. Henry Braun & Douglas Jones & Donald Rubin & Dorothy Thayer, 1983. "Empirical bayes estimation of coefficients in the general linear model from data of deficient rank," Psychometrika, Springer;The Psychometric Society, vol. 48(2), pages 171-181, June.
    5. Paul Downward, 2007. "Exploring the Economic Choice to Participate in Sport: Results from the 2002 General Household Survey," International Review of Applied Economics, Taylor & Francis Journals, vol. 21(5), pages 633-653.
    6. Joseph J. Seneca & Paul Davidson & F. Gerard Adams, 1968. "An Analysis of Recreational Use of the TVA Lakes," Land Economics, University of Wisconsin Press, vol. 44(4), pages 529-534.
    7. Humphreys, Brad & Ruseski, Jane, 2009. "The Economics of Participation and Time Spent in Physical Activity," Working Papers 2009-9, University of Alberta, Department of Economics.
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    Cited by:

    1. Yong Kang Cheah & Andrew K. G. Tan, 2014. "Determinants Of Leisure-Time Physical Activity: Evidence From Malaysia," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 59(02), pages 1-22.

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    JEL classification:

    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism

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