FINAL ECON

ExaminingSocioeconomicFactorsthatAffecta Country’sOlympicMedal Count
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Examining Socioeconomic Factors Affecting Countries’ Olympic Medal Count
Caleb Copeland and Kevin Kenney
Econ 350

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Abstract
In this paper, we attempt to examine potential socioeconomic factors that would explain
the unequal distribution of medals won by participating countries in the Olympics. We attempt to
identify a relationship between our dependent variable, total medals won, and our three
independent variables, a country’s GDP per capita, population size, and literacy rate. While our
results showed a positive coefficient for all three variables, the only statistically significant
correlation was in the relationship between total medals won and population size. We conclude
that the lack of significance in the results could be explained by the small sample size of 32
countries, which is only a fraction of the 203 countries that competed in the 2012 Olympic
Games.
Background
Since the founding of the modern Olympics, there has been an emphasis on the individual
competition among athletes, as opposed to the contest between countries. Although the spirit of
the Olympic Games has naturally valued participation over holistic success, results often rank
nations in accordance with the number of medals its athletes have accumulated. Medal counts by
country are widely reported by media outlets both during and after the Olympic Games.
Common Olympic knowledge and a quick overview of past results clearly indicate that medals

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are not spread out evenly among competing nations. At the 2004 Summer Olympics in Athens,
Greece, there were a total of 199 countries in participation1. Out of these 199 countries, 124 of
them did not win a single medal. On the other end of the spectrum, the 10 winningest countries
collected a total of 514 medals, which totaled to more than half of the available medals (Bian,
2005). This leads to an overt conclusion that the countries involved in the Olympic Games do not
have an equal capability to win medals. Thus, begging the question: What socioeconomic factors
give a country an advantage in acquiring Olympic medals?
This proposed question comes of importance when considering the large-scale impact the
Olympic Games have on the participating countries. The Olympics have long been considered a
prestigious event, where nations send their most qualified citizen-athletes to represent their
homeland during bouts of pure athletic competition. Therefore, the accomplishments of a
country’s athletes are typically regarded as a status of national prominence. Countries have the
ability to display their prowess on a worldwide scale, and thus, approach the Olympics Games
with the utmost seriousness. Furthermore, a nation that is successful in the Olympic Games will
attract attention from all across the globe. This publicity could provide a variety of economic
impacts, such as an increase in tourism and international trade. On the political side, success in
the Olympics could boost a country’s reputation and demand respect from other nations, as it
displays progress and capability. There are a multitude of benefits that stem from a country’s
Olympic accomplishments, and thus, it is certainly worthwhile to investigate the causes that
increase a country’s ability to win more medals than others.
1 It should be noted that although there are currently only 196 countries in the world, there are 206 National
Olympic Committees, representing nation-states and still disputed regions such as Kosovo.

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The uneven circulation of Olympic medals among participating countries might
potentially be explained by the varying strengths of countries in different sports i.e countries
with heavy annual snowfall will most likely be better in Winter Olympics sports such as cross
country skiing since they have access the necessary facilities. However, throughout the field of
economics, a wealth of research has been conducted to determine how a range of socioeconomic
factors may influence a country’s success in the Olympics. A 2010 research article in the
“International Journal of Sports Science and Engineering”, written by Yong Jiang, Tingting Ma,
and Zhe Huang, sought to determine the economic factors that influenced the medal count of 15
countries in the 2008 Beijing Summer Olympics. Their research concluded that the prominent
economic factors influencing a country’s Olympic results were the average annual GDP growth
rate over the three years prior to the Olympics (2005-2008), the average proportion of GDP
expended on health care between 2005-2008, and the education index of a country, which is
“measured by adult literacy rate (2/3 weight) and elementary school, high school, and college
comprehensive enrollment rate (1/3 weight)” (Jiang, Ma, & Huang, 2010). Additionally, they
researchers found Olympic results were related, with slightly lower correlation, to the variables
of GDP per capita growth rate from 2005-2008, total industrial production index, agricultural
production index per capita in the host year (2008), and population density (Jiang, Ma, & Huang,
2010). For the sake of accurate results, it should certainly be noted that this source only analyzed
the variables’ effects on a small sample of 15 countries, yet, this is promising evidence to support
the theory that economic variables can influence the success of a country at the Olympics.
Another article, published in 2014 in the “Journal of Statistics Education” and written by
Nancy Carter, Nathan Felton, and Neil Schwertman, investigated the effect of population size
and income levels on the medal count of all 203 nations in the 2012 London Summer Olympics.

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A regression analysis of total medals vs. income levels and population size produced an adjusted
R-squared value of 25.51%, showing slight correlation between medal count and the independent
variables (Carter, Felton, & Schwertman, 2014). However, when the regression analysis of total
medals vs. income levels, population size, and the added variable of GDP was performed, the
adjusted R-squared value spiked to 70.25 %, showing strong correlation between total medal
count and these three variables (Carter, Felton, & Schwertman, 2014). This drastic change
created by the addition of one variable shows that GDP was the most influential economic factor
of their model. It’s not a surprise to see GDP having a large influence of total medal counts,
since countries that have a higher GDP are more developed and boast a population with higher
levels of per capita income. This extra per capita income would then translate into better access
to youth sports, more advanced facilities and training programs and above all give the citizen-
athletes a greater amount of leisure time to practice and hone their sporting skills. This source
was highly successful in providing evidence that economic factors can influence medal count,
especially when considering that the source used information from all 203 countries that
participated in the 2012 Summer Olympics in London.
There is proven regression evidence that a variety of socioeconomic factors could be used
to predict a country’s success at the Olympics. Therefore, to answer our proposed question of:
“What socioeconomic factors give a country an advantage in winning Olympic medals?”, we
will examine three independent variables to test through regression analysis. We hope to develop
a model that will help explain the uneven balance of medals won across participating countries at
the Olympics using these variables.

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Model
To investigate what factors would influence a country’s medal count at the Olympic
Games, we have developed a model that uses three independent variables. These variables are a
country’s GDP per capita, population size, and literacy rate at the year of the Olympics. We will
use a sample size of 33 countries and cross-sectional data from year 2012, in which the Summer
Olympics took place in London. We chose this sample size based on availability of data, but we
believe it is large enough to depict a feasible relationship between our independent variables and
dependent variables. The model we used is as follows:
E(Y) = B0 + B1 (GDP Per Capita) + B2 (Population Size) + B3 (Literacy Rate) + e
We chose these three independent variables because we believe they can each serve a
purpose in explaining the unequal balance of medals won by countries that participate in the
Olympic Games. They also are quite similar to the ones used in the previous scholarly work, so
we assume that they will produce homogenous results. The total medals (combination of gold,
silver, and bronze) won by the countries in our sample size represents our dependent variable,
shown in the equation as E(Y). We are running this specific multiple linear regression model to
determine the effect of the independent variables on the dependent variable. If an increase in the
independent variables leads to an increase in the dependent variable and a positive coefficient is
produced (or vice versa with a negative coefficient), we will have developed an explanation for
the uneven distribution of Olympic medals across competing countries in that socioeconomic
factors, such as the variables of GDP per capita, population, and literacy rate, can be a plausible
influence on and predictor of a country’s Olympic success. We expect our multiple linear
regression model to produce a positive coefficient between all of the independent variables and

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the dependent variable of total medals won, since as a country’s population becomes wealthier,
larger and better educated, it will be able to produce better athletes as well.
For our first independent variable, we chose GDP per capita. GDP per capita is gross
domestic product, the sum of gross value added from all producers within a country plus any
taxes on these products and minus any subsidies that are not included in the value of these
products, divided by the country’s midyear population. We saw this as a very basic, yet essential
measurement of a country’s economic status; how much income is each individual receiving
throughout a given year. We expect GDP per capita to have a positive coefficient in its
relationship with our dependent variable, total medals won. Not only did we notice this trend
throughout a multitude of research articles, but socioeconomic logic would lead one to support
this idea. GDP per capita shows economic progress for a country, and thus, the higher the GDP
per capita, the more financially stable its citizens and government are. This opens up
opportunities for a nation to focus on non-necessity activities, such as athletics. Due to the
country’s financial cushion, it will have both more time and monetary resources than less
economically stable countries to improve athletic progress. This can be done by building higher
quality sporting facilities, which would lead to better training for a nation’s athletes and also
increase general interest and participation among a country’s citizens, which would spur future
progress amongst the youth. Additionally, a country with better finances can afford to practice a
higher quality nutrition regiment, which would also increase the quality of their athletes’
performance. Lastly, economic stability allows citizens to progress athletically on a personal
level, at a faster rate due to the simple fact that the average person does not have to spend the
majority of his or her time working or searching for essential resources, as many people in less
wealthy countries must do to survive. Thus, the relationship between GDP per capita and

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Olympic success should have a positive coefficient, as a country with higher economic stability
can support the time, resources, and interest to progress athletically.
We chose a country’s overall population size as our second independent variable. We saw
population size as a factor that would certainly have easily accessible data for all the countries in
our sample size. More importantly, we believe a larger population would improve Olympic
success, thus producing a positive coefficient. The thought process behind this assumption is
based off the simple principle that the greater a country’s population is, the larger the pool of
capable athletes and there will be a greater variety of athletic events and programs that can be
explored within that country. For example, a country with a smaller population may only send a
handful of athletes to the Olympics in a narrower spectrum of events. However, if a country has
a greater population, there is a larger athlete pool with the potential of superior athletic traits,
which would produce a higher number of Olympic-caliber athletes. If a country has a larger
number of athletes, then there is also much higher likelihood that it will be able to compete in
many, if not all the Olympic events offered. A country with, for example, 10 citizens of
Olympic-caliber athleticism would most likely not develop 10 long distance runners.
Realistically, the 10 athletes would spread across a variety of events, such as different track and
field roles, or possibly just different sports in general. This theory is also supported in the fact
that only athletes/teams who qualify for a particular event can compete in the Olympics. Due to
the immense number of athletes from other countries attempting to qualify for a particular sport,
especially the more common ones, it would be in a country’s best interest to spread their athletes
across a wide variety of events to increase their likelihood of winning medals. Therefore, we
believe population size to be a strong indicator of Olympic success and expect a positive
coefficient between population size and total medals.

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Lastly, our third independent variable is a country’s literacy rate. Similar to the other
variables used, we noticed a common trend in previous regression articles that showed a higher
literacy rate related to more medals won by that country. Literacy rate is measured as a
percentage of a country’s population aged 15 years old or older that can both read and write a
short, simple statement about their everyday life with understanding. On a primary level, literacy
rate is indicative of a country’s educational progress. As more citizens are provided with
educational opportunities, more become capable of reading and writing, which increase the
literacy rate. However, one must consider how a country achieves educational progress. As with
many other national characteristics, educational attainment is achieved through economic
progress. A country that has greater financial stability is capable of allocating its monetary
resources to build schools, pay teacher salaries, and improve other factors, such as public
transportation, that will allow its citizens to take advantage of educational opportunities. When a
quality education system is in place and backed by adequate finances, the number of citizens able
to pursue an education will increase with time. This is explained previously in our analysis, as
economic stability allows people to pursue self-interests, rather than life’s necessities.
Therefore, a higher literacy rate is related with greater economic progress. And as described with
GDP per capita, economic stability allows citizens the opportunity to explore non-necessity
activities, such as athletics. In conclusion, literacy rate is a prominent indication of economic
status, which in turn, will promote athletic development. We expect our third variable, literacy
rate, to have a positive coefficient in its relationship with total Olympic medals won as well.
We hope that by implementing our three variables into the multiple linear regression
model, we will be able to show that they have a positive influence on a country’s success at the
Olympic Games, as measured by total medals won.

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Data
The data for this experiment was collected from a variety of quality sources which provided us
with a reliable data set of 32 countries who participated in the 2012 Olympic Games in London.
The 32 countries were chosen since they were the countries with the top 32 total medal counts
when the 2012 Games came to a close. To measure the dependent variable, total medal count, we
used data from the International Olympic Committee’s website, Olympics.org. The databases for
every Olympics’ total medal count can be found here under “the results” tab within the Olympic
Games section. This allowed us to measure the total number of medals earned and in what place
each competing country finished when the Games ended.
Figure 1
Figure 1 represents a histogram of the data we obtained for the total medal counts for all
32 countries. As one can clearly see, the histogram is incredibly skewed to the right. This skew
to the right indicates that the vast majority of countries earn very little medals or no medals at all
and only a handful of countries dominate the competition. Which indicates that there must exist a
0
5
10
15
20
Frequencey
Total Medal Bin Range
Total Medal Counts 2012 Summer Olympics

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set of factors that allow only a relative few (10 or so out of 203) of the participating countries to
fall on the extreme right tail of the distribution. The purpose of our study is to hopefully identify
these factors that affect a country’s ability to win a large number of medals, as the independent
variables we have chosen: GDP per Capita, population size and literacy rate.
As for the independent variable’s data, we used the World Bank’s extensive economic
and social databases. For our first variable of GDP per Capita, we utilized the data set titled GDP
per capita (Current US $) in the World Bank’s economic indicators section. This gave us the
international data needed to measure each participating country’s GDP per Capita, high or small.
We used the World Bank’s databases again in order to obtain the population size, at the time of
the Olympics (2012), for the 32 countries included in our study. The data set was simply named
Population, total and was found under the “Climate Change” group of economic indicators.
Census data from every country is reported to the World Bank in order to create this data set.
Finally, the World Bank was also able to provide us with each participating countries’ literacy
rate (pretty impressive database). We specifically choose to use the data under the Literacy rate,
adult total (% of people ages 15 and above) heading because we believed this to be the all-
encompassing data set of the numerous other literacy rate measures, such as total female literacy
rate, etc.
We saw our three independent variables, GDP per capita, population size, and literacy
rate, as viable socioeconomic factors that could potentially explain the unequal distribution of
medals across countries in the Olympics throughout history. This skewedness is seen on a much
smaller scale in Figure 1, in which the countries in our sample certainly show an uneven spread
of medals across the 32 countries. We proceeded to run the regression tests to determine if our
variables could be considered significant influences on Olympic success.

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Results
After running our multiple regression test on our data set, we were able to find only one
significant correlation between our chosen independent variables and the amount of total medals
won, which was population size (X2). GDP per capita, just barely missed out on becoming
statistically significant, but literacy rate was quite far away from being significant.
To begin, we examined the coefficients of our model. All three of our coefficients carried
the sign that we had expected them to carry, which was positive. The coefficient for GDP per
capita was positive at .000365041, but extremely low. We believe the reason why the coefficient
is so small is because GDP per capita can increase by hundreds or even thousands of dollars a
year, which means a $1 increase will do little to increase total medal count. For population size,
the coefficient was again, as predicted, positive and extremely small at 6.952E-08. Very tiny
coefficient, but again, population size can fluctuate by even more so than GDP per capita,
sometimes in the hundreds of thousands. Finally, the literacy rate coefficient was again positive
as predicted, but this time it wasn’t extremely small at .298700618. While this might seem
relatively large, one must remember that it is quite difficult and time consuming for a country to
increase its literacy rate by 1% in order to gain the predicted .298700618 total medals.
We tested the effects of the independent variables on total medal counts in the Olympics
by running t-tests for each variable. The results of our hypothesis tests showed that only one of
our independent variables were significant, that being population size. The t* test statistic for
population size came out to be 4.9257 which was well above our tcv of 2.048, giving us the
necessary requirements to reject the null hypothesis of B2 being 0. We also used a p-value
hypothesis test on each of the variables and the results were similar. Population size was the only

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variable that showed statistical significant with a p-value of .00003397789, which is well above
the 95% confidence p-value level of .05. It is important to note that GDP per capita was just
barely under the cut off for being statistically significant and we believe had we collected just a
few more data points that GDP per capita could have been significant at the 95% level.
After considering the variables individually, we investigated our model as a whole. The
adjusted coefficient of determination or R2 ended up being .451471591, suggesting a moderately
strong relationship between our variables and total medal count. In general, the close the value is
to 1, the better the model is at explaining the variability in the data. A value of 0 indicates that
the model has no effect on the dependent variable. So our value of .451471591, being nearly in
the middle of the two, implies that our variables have a moderate effect on determining a
country’s total medal count. In order to confirm this, we ran an overall fit test of the model using
an F* statistic and the same results were found. Our F* of 9.50494 was well above the Fcv of
2.946, further proving the validity of our model.
Finally, we checked our model for any potential underlying problems by testing for
multicollinearity, heteroskedsticity, and autocorrelation. The only problem that seemed to appear
in our model was autocorrelation; two of the variables were found to be positively 1st order
autocorrelated and one was inconclusive. However, we believe these results should be nullified
since we used a cross-sectional data set and autocorrelation only really appears in time-series
data. We used a Pearson correlation test to test multicollinearity and our highest value of .39827
is low enough not to be considered problematic. Our Goldfeld-Quant test revealed there to be no
significant heteroscedasticity in the model and our Durbin-Watson test was thrown off by the
nature of our cross-sectional data.

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Conclusion
In conclusion, the multiple regression output exhibited, that for the set of countries that
we used, the factors of GDP per capita, population size and overall literacy rate have a
moderately significant impact on the total medals a country wins in an Olympics. Since one of
our variables was significant at the .01 level and another one was just barely above the .05
confidence level.
These results were pretty much in line with what we were expecting, since we believed
these variables would indicate a country’s base ability to produce Olympic-caliber athletes. We
were however surprised at the strength of the correlation, because we initially believed the
variables to have a very strong and significant relationship with total medal counts. One possible
reason for this was the size of our data set. We only chose to use 32 countries from the 203 that
participated in the 2012 Olympics, even though there were quite a few more, because we thought
any number over 30 would be sufficient and since a majority of competing nations do not win
any medals at all. Had we used the entire data set for the 2012 Olympics, we firmly believe that
our coefficients for all three variables would increase further and we would predict the GDP per
capita, that just barely missed out on being statistically significant, would become significant had
we added the rest of the participating countries. We also used a cross-sectional data set rather
than a time-series one and that could have affected our data, since there may have been an
underlying variable, present at the 2012 Olympics, which possible skewed our results.
Despite these possible anomalies in the data, we firmly believe that through the model we
constructed and tested, a country’s performance in the Olympics can be predicted by key
socioeconomic factors including GDP per capita, population size and adult literacy rates.

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Works Cited
Bian, X. (2005). Predicting Olympic Medal Counts: The Effects of Economic Development on
Olympic Performance. The Park Place Economist, 8, 37-44. Retrieved December 2,
2015, from https://www.iwu.edu/economics/PPE13/bian.pdf
Jiang, Y., Ma, T., & Huang, Z. (2010). The Economic Factors Analysis in Olympic Game.
International Journal of Sports Science and Engineering, 04(03), 181-187. Retrieved
December 3, 2015, from
http://www.worldacademicunion.com/journal/SSCI/SSCIvol04no03paper07.pdf
Carter, N., Felton, N., & Schwertman, N. (2014). A Classroom Investigation of the Effect of
Population Size and Income on Success in the London 2012 Olympics. Journal of
Statistics Education, 22(2), 1-20. Retrieved December 3, 2015, from
http://www.amstat.org/publications/jse/v22n2/carter.pdf
Trivedi, P., & Zimmer, D. (2013). Success at the Summer Olympics: How Much Do Economic
Factors Explain? Econometrics, 169-202. Retrieved December 2, 2015, from
http://fbe.unimelb.edu.au/?a=796777
Andreff, M., & Andreff, W. (2011). Economic Prediction of Medal Wins at the 2014 Winter
Olympics. North American Association of Sports Economics, 1-28. Retrieved December
1, 2015, from http://college.holycross.edu/RePEc/spe/Andreff_WinterPredictions.pdf
Watkins, A. (2014, May 1). Olympic Caliber Countries: How Macroeconomic Factors and
Previous Performance Impacted the 2012 London Olympic Games. Retrieved December
2, 2015, from
http://trace.tennessee.edu/cgi/viewcontent.cgi?article=2773&context=utk_chanhonoproj

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