This thesis studied the disparities between countries’ Olympic medal counts and had as its main research question:
Why are some countries more successful than others at winning medals at the Olympic Games from an economic growth perspective, and how does econometrics estimate the relationship between the Olympic medal distribution and its determinants suggested by the economic growth theory framework?
Using the Herfindahl-Hirschman Index to measure the level of competitiveness at the Olympic Games in the thesis’ chosen timeframe from 1996 to 2016, it was established that some countries do indeed win more medals than others, but that the competition is not dominated by any single country.
The role of technology in economic growth served as a theoretical framework to motivate the choice of regressors, representing the resources that a country has available and invests in Olympic technological progress. The proposed regressors were per capita GDP, population, team size, three host effects and tourism as a proxy for openness. Investment in sports was also suggested but could not be included in the main model due to limited data availability.
The tobit model often employed in the literature was argued to be misrepresenting the problem of modelling national Olympic success. A fixed effects regression model for panel data was chosen to estimate the relationship between medal count and the proposed regressors in consideration of preventing omitted variable bias arising from country-specific time-invariant factors.
Team size was found to be statistically significant for Olympic medals at the 5% level, with a predicted 6.3 medal increase for every 100 athlete increase in team size. Country effects were also significant for determining how many medals a country wins. Population and host effects were found to only impact medals through their effect on team size, and GDP per capita was not found to be statistically significant at all. Differences in the data used, the choice of model and the efforts to address omitted variable bias were given as possible reasons for the discrepancy between these findings and those in the literature.
A zero-inflated negative binomial model was also proposed as an alternative to the fixed effects model.
|Educations||MSc in Applied Economics and Finance, (Graduate Programme) Final Thesis|
|Number of pages||160|