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February 2012

Gold, Silver, and Bronze:

Determining National Success in Men’s and Women’s Summer Olympic Events

Eva Marikova Leeds

Moravian College

Bethlehem, PA, USA

and

Michael A. Leeds

Temple University

Philadelphia, PA, USA

JEL Categories: L83, J16, J22

Key words: Olympic medals, gender differences

Earlier versions of this paper were presented at the 2009 Western Economic Association International

Pacific Rim Conference in Kyoto, Japan, and at the 2010 Western Economic Association International

Conference in Portland, Oregon. We are grateful to Brad Humphreys, Victor Matheson, and the

participants in both sessions for their helpful comments and suggestions. We also thank Melanie Leeds

for her valuable research assistance.

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I. Introduction

There is a large and growing literature on the determinants of national success in the Olympic Games.

One strand of this literature estimates how efficient countries are at producing Olympic medals, while the

other analyzes the underlying factors that determine success. Most papers use medal counts as their

measure of success, but some use the share of medals received. Some analyze total medals won, while

others separate out gold medals. All studies, however, share one characteristic: they do not distinguish

between the performances of men and women. By combining men’s and women’s performances, the

studies implicitly assume that the same factors affect both sexes and affect them in identical ways. This

paper differs from previous work by separately analyzing the determinants of success of male and female

athletes. Estimating the determinants of success separately yields more accurate results and provides

clearer insights into what drives athletic success. Knowing the unique factors that affect the performance

of men and women at the Olympic Games allows for better forecasting and policy analysis.

This study uses data on medals won at the four most recent Summer Olympic Games – Atlanta 1996,

Sidney 2000, Athens 2004, and Beijing 2008 – and data for participating nations to determine what forces

contribute to success on the Olympic stage. Some of our findings closely resemble the findings from

previous studies. For example, we find that large, wealthy nations are more successful than small, poor

nations in both men’s and women’s events. Other findings are new to the literature. For example, we

find that women (but not men) from Arab countries do worse than other women, while men (but not

women) from formerly Communist countries do better. We also find that the success by a nation’s

women at the Olympic Games closely parallels their success in the overall labor market. In general,

though, we find that most factors have very similar effects on the performance of men and women. These

include some variables – such as fertility rates – that one would appear to be uniquely related to the

performance of women.

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This paper contributes to the literature on Olympic success in two ways. In addition to analyzing the

performance of men and women separately, it is the first to use a negative binomial regression to estimate

the determinants of medal counts. Because the negative binomial is designed for count variables, it is

more appropriate than OLS, which some studies use. In the next section of this paper, we review the

literature on Olympic success. In section three, we present a medal production function and use it to

construct an empirical model, and we describe our data. In section four, we present the results of our

estimation. Section five concludes.

II. Review of Previous Estimates of Olympic Performance

A working paper by Johnson and Ali (2000) started the Olympic success literature by analyzing the total

participation and medal count in the Summer Olympic Games between 1952 and 1996. It uses many

explanatory variables: GDP per capita from each pre-Olympic year (expressed in PPP from Penn World

Tables), population, four dummies for the political regime, three characteristics of the former colonial

powers for nations previously colonized, two dummies (one for hosting the games and the other for

neighboring a host), and a time trend. It employs panel data and estimates the model using OLS with

country fixed effects. Seven variables, fewer than half the total, are statistically significant. An expanded

version of the paper (Johnson and Ali, 2004) looks at both Summer and Winter Olympic Games and

presents separate estimates for total and female participation as well as total and gold medal counts.

Hoffmann et al. (2002) present a much simpler model. Using a sample of all 76 countries that won

medals at the Sydney Olympic Games, they explain the total number of medals won by each nation by

running three separate OLS regressions. They do not distinguish between type of medal or the sex of the

recipient. Their main results appear in the last equation. Both GDP per capita and population enter as a

square root and support the hypothesis that larger populations provide a larger pool of talent and that

higher income can provide better infrastructure and more funding for sports. A socialist dummy accounts

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for the greater allocation of resources to athletics in centrally planned countries. Similarly, two dummies

for previous Olympic hosts account for the greater cultural affinity and support for sports in the host

nations. Finally, the Australian dummy captures a “home field” advantage. By including only those

countries that won medals, Hoffman et al. effectively truncate their sample. Their coefficients describe

the effect of explanatory variables only among the winners and do not provide unbiased estimates for all

countries.

Tcha and Pershin (2003) examine why some countries have consistent success in specific sports. To

identify each country’s specialty, Tcha and Pershin define a nation’s “revealed comparative advantage”

(RCA) as the ratio of two other ratios. The numerator is the ratio of country i’s medals in sport j to the

total number of medals it wins. The denominator is the ratio of the number of medals awarded in sport j

to the total number of medals awarded overall in the specific Olympic Games. For example, Ethiopia,

which wins virtually all of its medals in distance runs, has an RCA in track and field. Tcha and Pershin

estimate the determinants of 66 countries’ revealed comparative advantage by regressing the RCAs for six

broad categories of sports on a set of factors that reflect a country’s factor endowments. Because the

RCAs in the sample are bounded below by 0, they use a tobit analysis on data spanning the 1988 and

1996 Summer Games.

Bernard and Busse (2004) raise the econometric bar for studies of the Olympics. They use panel data to

explain the share of medals each country won between 1960 and 1996. They use year dummies in a

random effects tobit model for over 150 countries. Their basic results indicate that doubling of GDP per

capita in an average country increases the medal share by about 1.5%. Alternative specifications indicate

that centrally planned countries have medal shares approximately 6% higher than other countries and that

having hosted the games raises the medal share by about 2%. The model generates surprisingly good

predictions for the medal count in Sydney. The major problem with Bernard and Busse is the model

specification: their latent variable is never negative.

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Another branch of the literature estimates how efficiently countries generate Olympic medals. One

approach applies data envelopment analysis (DEA), a procedure based on linear programming, to

estimate a country’s relative efficiency. In their study of all Summer Olympics since 1992, Lozano et al.

(2002) weight the number gold, silver, and bronze medals to capture the differing values that countries

place on winning these medals. Zhang et al. (2009) go one step further and impose lexicographic

preferences that consider first gold medals, then silver, then bronze in a DEA analysis of the 2004 Athens

Games. Finally, Wu et al. (2010) constrain all predicted values of the number of medals to be integers in

their DEA analysis of the 2008 Beijing Games. All three studies are extremely parsimonious. They use

only two explanatory variables: population and GDP per capita. In addition to omitting other potential

explanatory variables, the studies are restricted to countries that have won medals at the relevant Games.

These studies are largely technical exercises. They rank countries in terms of efficiency but do not

present any behavioral or policy insights.

Rathke and Woitek (2008) take a different approach, stochastic frontier analysis, to estimate how

efficiently a nation produces its share of medals. They, too, assume that all the resources available to a

country’s Olympic athletes are captured by two variables, in this case population and GDP (not GDP per

capita). Unlike the DEA studies, this one incorporates quadratic terms of these variables as well as

additional variables – whether a country was part of the Soviet Union, had a planned economy, or hosted

a previous or later Olympics - to capture how efficiently countries marshal their resources. Rathke and

Woitek present separate efficiency estimates for men and women, but their underlying equations are

based on overall, not gender-specific, performance. For example, they find that, for Summer Olympics

extending from the 1952 Helsinki Games to the 2004 Athens Games, the United States was more efficient

than the Soviet Union in swimming but less efficient in gymnastics.

The efficiency papers implicitly specify a medal production function with three arguments: capital (as

measured by GDP or GDP per capita), labor (as measured by population), and total factor productivity.

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In the DEA analyses, total factor productivity is a black box. Rathke and Woitek (2008) explicitly model

total factor productivity as a function of the country’s economic system and attitudes.

III. Empirical Model and Data

All of the studies presented in Section II analyze overall Olympic performance. There is reason to believe,

however, that each nation has differing levels of success in women’s sports and men’s sports. Table I

lists the four countries that won the most total medals and gold medals in men’s and women’s events at

the 2008 Summer Olympics. We exclude all events – mixed doubles badminton, mixed individual and

team equestrian events, and tornado multi-hull sailing – in which men and women compete together. The

table also shows the number of medals each of these countries won in men’s and women’s events. The

number of total medals is more than three times the number of gold medals for two reasons. Multiple

medals were issued in the case of ties, and some sports, such as judo, awarded two bronze medals.

Casual inspection shows that there is considerable overlap between the countries that dominate men’s and

women’s sports. Using the data set we describe below, we find the simple correlation of the total medals

won by a nation’s men and women to be about 0.9. There are, however, some notable differences. The

four top medal winners in the women’s events account for a higher percentage of total medals and gold

medals (41.67 and 47.62 percent) than do the top four medal winners in the men’s sports (32.12 and 40.74

percent). The Herfindahl-Hirschman Index (HHI) generalizes these findings. It shows that both overall

medals and gold medals are more broadly distributed for men’s sports. For men, the HHI for total medals

is 0.0406, while the HHI for gold medals is 0.0598. For women, the corresponding figures are 0.0601

and 0.0848. These findings suggest that, despite the high correlation between the Olympic performances

of a nation’s men and women, treating men and women separately sheds additional light on the

determinants of national success in Olympic competition.

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Like much of the literature we estimate a medal count. Like Bernard and Busse (2004) and the efficiency

analyses, we assume that countries produce medals with a production function that includes capital, labor,

and total factor productivity according to a given, time-invariant technology:

Mijt = f(Kjt, Ljt, Ajt) (1)

where Mijt is the number of medals won by sex i (i=men, women) from country j during Olympic year t.

Consistent with previous studies, we model Kjt and Ljt as real GDP per capita (GDPC) and population

(POP), and we expect both variables to have a strong, positive impact on Olympic success for both men

and women. Ajt is total factor productivity. We model Ajt as a function of three sets of variables. The first

set indicates a nation’s ability to marshal its resources in support of specific goals. The second set

represents its desire to use its resources to promote success in international sports, and the third set

reflects the relative importance that a nation places on the success of its women athletes.

We capture a nation’s ability to direct resources by including the relative size of each economy’s

government sector (GPCT). As the government controls more of an economy’s resources, the

government’s ability to support sports also rises. Thus, countries whose government sector comprises a

larger the share of the economy should have greater Olympic success. The openness of an economy

(OPEN) has an ambiguous effect. On the one hand, it increases a nation’s productive efficiency, which

should improve Olympic performance. On the other hand, it could reduce the central government’s

control over where resources flow, thereby reducing Olympic success.

Governments might be willing to underwrite their athletes more for some Olympic Games than for others.

In recent years, several host countries have made particular efforts to see their athletes succeed, as was the

case for China’s “Project 119,” named for the number of gold medals China sought to capture at the 2008

Summer Games, and Canada’s “Own the Podium” program for the 2010 Winter Games. The “home field

advantage” can come through less formal channels as well. Less travel, larger numbers of supportive

fans, and greater familiarity with the facilities could all contribute to greater than normal success. We

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therefore include a dummy variable to capture whether a country was host of the current Olympic Games

(CHOST). We also expect countries with more mature Olympic Committees to be better at lobbying for

and making use of resources in support of their athletes. We measure the maturity of a country’s

programs with a dummy variable that indicates whether a country had participated in any Summer

Olympic Games up to and including the 1920 Antwerp Games (D1920).

We also accounted for the fact that some countries have long used sports as a propaganda tool for their

political regimes. For example, Mandell (1971) and Maraniss (2008) show how politics affected the 1936

Berlin Games and the 1960 Rome Games, while Ungerleider (2001) shows the lengths to which the East

German regime went to promote Olympic success. The former Soviet Union and its satellites in the

Warsaw Pact countries were among the most avid users of athletics as propaganda. Although our sample

comes after the dissolution of the old Communist bloc in Central and Eastern Europe, it was important to

control for possible lingering effects of the countries’ athletic institutions. We did this with two sets of

dummy variables. The first (EXSSR) indicates whether a country had been a member of the old Soviet

Union.1 The second (EXCOM) denotes countries that had been part of the Communist bloc but had not

been part of the Soviet Union itself. Because the Baltic Countries (Estonia, Lithuania, and Latvia) had a

history of independence prior to their incorporation in the Soviet Union after the Second World War, we

counted them in this second category.2 It is particularly important to include these variables for women,

as these countries promoted women’s sports. They so dominated women’s sports that the US once

advocated keeping separate medal counts, fearful that combining men and women would tip the overall

count in favor of the Soviet Union and grant it a propaganda victory. (Maraniss, 2008: 8-9.) Finally, we

include a dummy variable (NOWCOM) to indicate whether a country currently is Communist.3

1 These countries consisted of Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia,

Tajikistan, Turkmenistan, Ukraine, and Uzbekistan. 2 The other countries were Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Hungary,

Montenegro, Poland, Romania, Serbia (and Serbia and Montenegro), Slovenia, Slovakia. Although East Germany

was a member of the Communist bloc, we did not include Germany in this group. 3 This consisted of China, Cuba, the Democratic Republic of Korea, Laos, and Vietnam.

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Nations that support their Olympic athletes need not support their female athletes. To capture a country’s

attitude toward its women athletes, we include four variables that capture a country’s support for

achievement by women in general and by extension its women athletes. The first variable is the fertility

rate of a country (FERT), as measured by the average number of live births per woman in a given

country. We hypothesize that, as the fertility rate falls in a country, the overall status of women, and their

likelihood of Olympic success, increases.

Next, we include the year in which women in a given country attained the right to vote (SUFF). We

expect that countries that granted suffrage earlier have evolved political and social institutions that are

more supportive of women. One manifestation of this support is greater success of the nation’s women in

international athletic competitions.

We also include the ratio of the labor force participation rate for women to the labor force participation

rate for men (LFPRRAT). In the United States, female labor force participation and the participation of

women in interscholastic and intercollegiate athletics have risen together. If this is generally the case,

then women athletes from countries with a higher value of LFPRRAT should enjoy greater success.

Finally, we include a regional dummy variable that denotes whether a country is in the Arab World

(ARAB). Women’s empowerment is a particular problem in the Arab world. The United Nation’s Arab

Human Development Reports (see in particular United Nations, 2005) have all cited the lack of women’s

empowerment as one of the three developmental deficits facing the Arab world today. The lack of

empowerment in general is likely to lead to failure in the athletic world. Moreover, Arab states have been

particularly hostile to women’s participation in athletics. Saudi Arabia, for example, does not merely

prohibit women from participating in the Olympics. (Along with Qatar and Brunei, it sends only men’s

teams.) It also discourages women from all forms of exercise. “Physical activity of any kind is forbidden

in Saudi Arabia’s state-run schools for girls.” (Zoepf, 2010:A1)

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We had hoped to include the percentage representation of women in a nation’s parliament. However,

these data became available only in the late 1990s, which would have meant deleting the Atlanta Games,

and have been relatively spotty. Hence, we chose not to include this variable.

The arguments of our estimating equation are thus

yijt = f(GDPCjt, POPjt, GPCTjt, OPENjt, CHOSTjt, D1920j, EXSSRJ, EXCOMj, NOWCOMj, FERTjt,

SUFFj, LFPRRATjt, ARABj, jt) (2)

Our sample consists of an unbalanced panel of all countries that participated in any of the 1996 through

2008 Olympic Games. We chose these four Olympic Games because they took place during a period of

relative political stability. The break-up of the Soviet Union in 1991 led to the emergence of many new

nations, all of which participated in the 1996 Atlanta Games. (At the 1992 Barcelona Games, the

countries that had belonged to the former Soviet Union appeared as the Unified Team.) Two factors

cause the panel to be unbalanced. First, while this period did not see a large expansion in the number of

countries, a few countries, such as Timor L’Este and Macedonia, came into existence during this period.

Second, data are not available for all countries in all years.

Since the dependent variables are count variables, either a Poisson or a negative binomial regression is

appropriate. The negative binomial distribution, however, is more flexible than the Poisson because it

does not restrict the mean and variance to be identical. (See Wooldridge, 2002.)4 A goodness of fit test

(not shown here) confirmed that the negative binomial was more appropriate than the Poisson because of

excess variation of the dependent variable. Because we include time-invariant variables that identify

nations, we opt for random effects rather than fixed effects estimation.5

4 Results for a Poisson regression – not shown here – were very similar to the negative binomial results. 5 We had hoped to perform a Hausman test for whether a fixed effect model would be superior. However, this test

could not be performed because so many of the countries had zeroes for all values of the dependent variable.

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Since we hypothesize that income and population have diminishing marginal effects, we use the natural

logarithm of both population and real GDP per capita. Both measures come from the Penn World Tables

(Heston, 2009). Their measure of income (rgdpch) adjusts GDP for purchasing power.

Our measures of the government’s control of resources and of an economy’s openness also come from the

Penn World Tables. To measure the government’s control of resources, we use the percentage of the

economy’s GDP consisting of government expenditure (kg). Openness is the percentage of GDP made up

by the sum of imports and exports (openc).

Our measure of fertility comes from the United Nation’s Human Development Report web page

(http://hdr.undp.org/en/statistics/). In some cases, a nation’s fertility data were missing for only one year.

For many of these nations, the three remaining values were identical. In that case, we assumed that the

missing value was the same as the others. When there was more than one missing value or when the

remaining values were not identical, we left the cell(s) blank.

We took our measure of women’s suffrage from the website Onewomen: Women’s Suffrage

(http://www.onlinewomeninpolitics.org/suffr_chrono.htm). At first, we were concerned that newly

independent countries could be mistakenly characterized as socially conservative if women and men were

unable to vote until independence. That was generally not the case, as colonies often voted and

enfranchised women before becoming independent states. For example, Cameroon and Djibouti granted

women the right to vote in 1946. Of the nations that participated in the Olympics, Saudi Arabia has yet to

grant women the vote, and Brunei has no suffrage for men or women. Rather than delete Saudi Arabia,

we assigned it the value 2012, which is six years after the next-last country (the United Arab Emirates).

Because Brunei has no voting at all, we left it blank. Some countries did not report the date of

enfranchisement. Of these, only Macau is in our sample. Our data for men’s and women’s labor force

participation rates come from the World Bank (http://data.worldbank.org/indicator). We designated a

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country as Arab if it was a member of the Arab League. All time-series are reported for the year before

the Olympic games. Table II contains means and standard deviations of all the independent variables.

IV. Results

The estimates of the random effects, negative binomial regressions appear in Tables III and IV. The first

column of each table shows the determinants of the overall medal count, the second column shows the

determinants of gold medals, and the third column shows the determinants of silver and bronze medals.

The overall quality of the regressions is very good, as the large values of the Wald χ2 attest. We focus on

the last two columns of Tables III and IV to contrast the determinants of gold medals with the

determinants of silver and bronze medals as well as the determinants of women’s and men’s

performances.

In Tables V and VI we convert the coefficients in Tables III and IV into incident rate ratios (IRRs),

excluding the IRRs for variables that are never statistically significant. Converting the negative binomial

coefficients to IRRs allows us to interpret the impact of each variable more intuitively. The incident rate

ratio shows how the dependent variable, which can be interpreted as a rate (such as 2 gold medals per

Olympic Games) changes when a given independent variable increases by one unit. Specifically, it is a

fraction with the new rate in the numerator and the original rate in the denominator. Thus, if a variable

has an IRR of 1.0, then increasing its value by one unit has no impact on the dependent variable. An IRR

of 1.1 means that the independent variable increases the rate by 10 percent, while a value of 0.9 means

that it reduces the rate by 10 percent. (See UCLA, 2011, for a discussion of the conversion of negative

binomial coefficients to IRRs.)

Consistent with all previous studies of the Olympics, real GDP per capita and population have a strong,

positive impact on Olympic performance at all levels for both men and women. The coefficients imply

that men and women from a country in which the natural logarithm of real GDP per capita is one unit

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higher than in an otherwise identical country will win gold medals at 1.7 times the rate of men and

women from the poorer country. Evaluating these variables at their means tells us that, all else equal,

men and women from a country with real GDP per capita of about $6,000 (ln (6000)=8.7, roughly the

level in Azerbaijan’s) will win a little more than half as many gold medals as men and women from an

otherwise identical country with real GDP per capita of about $16,400 (ln(16400)=9.7, roughly that of

Slovakia). The IRRs are slightly higher for silver and bronze medals.

An increase of one unit in the natural logarithm of population (in thousands) roughly doubles the

incidence of gold medals and of silver and bronze medals won by women. Again evaluating at the means,

women from a country with 14.8 million people (= ln(16.5), roughly the size of Ecuador) win about twice

as many gold medals and silver and bronze medals as the women from an otherwise identical country

with about 5.4 million people (= ln(15.5), again like Slovakia). The impact for men is slightly smaller.

We had expected government control of resources to improve Olympic performance, but we found no

significant effect for any type of medal and either sex. Apparently, greater government control either

does not mean that more funds flow to Olympic sports or the greater expenditure does not translate into

more medals.

Greater openness in a nation’s economy reduced all medals, though the impact is very small. For

example, a 10 percentage point increase in our measure of openness reduces the gold medals won by

women by only 0.06. Such a decline could occur if openness reduces a nation’s control over its

resources, but our finding that control has no effect suggests that other forces are at work. The negative

impact of openness could reflect freer flow of talent in and out of a country. While athletes cannot easily

change their citizenship and compete for another country, they can easily pursue professional

opportunities elsewhere, thereby jeopardizing their “amateur” status.

Our results generally support the idea of a “home field advantage,” though hosting the current Olympic

Games has a bigger impact on gold medal performances than on other medals. It increases the rate of

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gold medals won by a nation’s women by 63 percent but has no impact on the number of silver and

bronze medals. For men, the rate of gold medals rises by 58 percent, and the rate of silver and bronze

medals rises by 31 percent.

Our measure of the maturity of a country’s programs, the dummy variable that indicates whether a

country had participated in any Summer Olympic Games up to and including the 1920 Antwerp Games,

had no impact on either men’s or women’s performance. In regressions not show here, we included two

other variables that might have been related to a nation’s desire to fund athletics. A dummy variable

indicating whether a country had hosted an Olympic Game since the Second World War closely

resembled a variable that had been significant in previous studies. It had no impact in our regressions.

We also included the number of times a country had reached candidate status for hosting Games between

1996 and 2008. We had thought that this might capture a country’s support of its Olympic athletes better

than a variable that focuses solely on the selected hosts. This, too, had no discernible effect.

Being from a former Soviet republic, a formerly Communist country, or a country that still claims to be

Communist has no impact on women’s performance, but the first two variables have a large impact on the

performance of men. The rate of gold medals for men from a former Soviet republic is 2.4 times that for

men from otherwise identical countries, while the rate of silver and bronze medals is 4.4 times as great.

The rate of gold medals for men from a formerly Communist country (that is not a former Soviet

republic) is 2.8 times that of men from an otherwise identical country, while the rate of silver and bronze

medals is 3.3 times as great. The remnants of the old Communist sports structures thus continue to affect

the performance of men, but no longer have an impact on women.

As expected, higher fertility rates and later adoption of women’s suffrage both lead to fewer medals for

women athletes, though later suffrage affects only gold medals. To our surprise, the first two of these

variables also lead to fewer medals for men. An increase of 1 in the number of children born to the

average woman in a country reduces gold medals and silver and bronze medals won by that nation’s

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women by about 30 percent. A similar decrease occurs for the gold medals for that nation’s men. The

silver and bronze medals won by men falls by about 19 percent.

A one-year delay in women’s suffrage reduces the rate of gold medals by about 2.4 percent and of silver

and bronze medals by about 1.4 percent for a nation’s women. The impact on men is about 1.4 percent

for both types of medals. Again, the differences by sex and by medal type are not statistically significant.

The unexpected results for men might be due to the relatively high correlation between the fertility and

suffrage and the natural logarithm of GDP. All else equal, higher fertility rates might also create greater

financial strain for households. This, in turn, reduces the ability to support the Olympic aspirations of

men and women.

Unlike the first two variables, the ratio of the labor force participation rates has no impact on men, but it

increases the rate of silver and bronze medals by a factor of almost 3.4 and of gold medals by a factor of

almost 8.0 for women. Thus, women from countries in which they play a more active role outside the

home have greater Olympic success. Being from an Arab country also did not significantly affect men,

but it reduced the number of silver or bronze medals won by women by about 70 percent.

While there is often great variation in the point estimates across both gender and type of medal, we

generally cannot be confident that the true coefficients differ. None of the estimates for gold medals

differ significantly from the estimates for silver and bronze medals for either men or women. Moreover,

only one set of coefficients – those for the impact of being a former Soviet republic on silver and bronze

medalists – differ significantly for men and women at the 95% level.

V. Conclusion

Our study extends previous work on Olympic success in two ways. We separately analyze the

determinants of success by men and women, and we separate gold medal performance from silver and

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bronze medal performance. Previous studies have either used a nation’s total medal count or compared

gold medals with total medals won.

Separating the sample into medals won by men and women has econometric implications. When using

total medals won (or a variant, such as the share of total medals) as the dependent variable, one implicitly

assumes that the explanatory variables have the same impact for both men and women even when some

variables could have different impacts. In addition, separating the sample led us to re-examine the

determinants of Olympic performance. To study the performance of women, we included several

explanatory variables –fertility rates, dates of suffrage, labor force participation rates, and an indicator for

Arab nations – that were not used in previous studies. The significant impact of these variables shows

that previous results could suffer from omitted variable bias.

Several of our results have important policy implications. Our finding that greater participation in the

labor force leads to greater Olympic success suggests that countries can generate greater Olympic success

by breaking down barriers that keep women out of the labor force. Our results for fertility and labor force

participation show that policies that promote the participation of women in a nation’s economy and that

reduce the number of children a woman has will also improve the athletic performance of women and – in

the case of the latter variable – of men as well.

Perhaps our most surprising result is that some variables that one might expect to affect only the

performance of women, suffrage and fertility, affect men as well. This last result implies that countries

can improve the Olympic performance of both men and women by tackling some issues thought to pertain

solely to women. The general importance of “women’s issues” extends beyond the athletic arena, as our

findings reinforce such broader studies as the Arab Human Development Report.

On a global level our findings point out several factors that can lead to a more equal distribution of

Olympic medals among nations. Given the impact of income on medal counts, we can project that, as the

incomes of developing countries catch up with those of developed countries, so will their Olympic

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performance. In addition, our results for suffrage suggest that policies that promote the participation of

women in the political sphere will improve the athletic performance of both men and women. As better

data on the political representation of women become available, we will be able to make stronger

statements in this regard.

Table I: Distribution of Medals at the Beijing Games

Men Women

Medal Finish Total Medals Gold Medals Total Medals Gold Medals

First USA (53) China (24) China (55) China (26)

Second China (42) USA (20) USA (54) USA (16)

Third Russia (41) Russia (12) Russia (32) Russia (10)

Fourth France (32) UK (10) Australia (24) Australia (8)

Number of

countries winning a

medal

79 48 60 33

Total Medals 523 162 396 126

% won by top four

countries

32.12% 40.74% 41.67% 47.62%

HHI 0.0406 0.0598 0.0601 0.0848

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Table II: Means of the Explanatory Variables

Variable Mean Value

Real GDP per capita 11,224

(12,392)

Population 32,900,000

(122,655,000)

Index of government control of resources 19.88

(11.56)

Openness index for the economy 90.43

(50.87)

Host of current Olympics 0.0053

(0.0724)

Participated in 1920 Olympics or Earlier 0.17

(0.38)

Former Soviet Republic 0.06

(0.24)

Currently Communist Country 0.03

(0.16)

Fertility 3.24

(1.74)

Year Women’s Suffrage Attained 1947.42

(20.98)

Women’s LFPR 51.33

(15.50)

Men’s LFPR 76.56

(8.30)

Arab nation 0.11

(0.31)

Standard deviations in parentheses

18

Table III: Determinants of Medals for Women Athletes

Variable Total Medals Gold Medals Silver & Bronze Medals

Natural Logarithm of Real GDP per Capita

.613*** (4.01)

.524*** (2.57)

.706*** (4.10)

Natural Logarithm of Population

.741*** (7.54)

.688*** (6.42)

.700*** (7.57)

Degree of Government Control of Resources

.0182 (1.30)

.0252 (1.27)

.0138 (0.89)

Degree of Openness of the Economy

-.00696*** (-2.86)

-.00634** (-1.97)

-.00788*** (-3.03)

Host of Current Olympic Games

.206* (1.78)

.489*** (2.73)

.0527 (0.35)

Participated in 1920 Olympic Games

-.0857 (-0.22)

-.249 (-0.64)

-.101 (-0.29)

Former Soviet Republic -.298 (-0.53)

-.583 (-0.94)

.00291 (0.01)

Former Soviet Satellite .361 (0.77)

.229 (0.44)

.394 (0.88)

Current Communist Country

.598 (0.77)

.246 (0.32)

.738 (1.04)

Fertility Rate -.375*** (-3.21)

-.351** (-2.27)

-.391*** (-3.11)

Date of Women’s Suffrage

-.0214** (-2.35)

-.0247** (-2.52)

-.0139 (-1.60)

Ratio of Women’s LFPR to Men’s LFPR

.830 (1.45)

2.074** (2.24)

1.210* (1.84)

Arab Country -1.018 (-1.55)

-.542 (-0.62)

-1.185* (-1.69)

Constant 31.967* (1.79)

37.952* (1.93)

16.743 (0.97)

Wald 195.86 149.30 197.56

Number of Observations 696 696 696 t-statistics in parentheses

*Significant at 10% level

**Significant at 5% level

***Significant at 1% level

19

Table IV: Determinants of Medals for Men Athletes

Variable Total medals Gold Medals Silver & Bronze Medals

Natural Logarithm of Real GDP per Capita

.535*** (4.78)

.538*** (3.34)

.599*** (4.88)

Natural Logarithm of Population

.635*** (8.74)

.655*** (7.01)

.574*** (8.27)

Degree of Government Control of Resources

.0129 (1.52)

.0195 (1.33)

.0116 (1.21)

Degree of Openness of the Economy

-.00982*** (-4.98)

-.0120*** (-3.82)

-.00976*** (-4.56)

Host of Current Olympic Games

.333*** (3.23)

.456*** (2.74)

.273* (1.98)

Participated in 1920 Olympic Games

.416 (1.42)

.447 (1.51)

.325 (1.26)

Former Soviet Republic 1.281*** (3.00)

.860* (1.89)

1.482*** (3.93)

Former Soviet Satellite 1.206*** (3.17)

1.031** (2.49)

1.193*** (3.43)

Current Communist Country

.781 (1.28)

.854 (1.39)

.856 (1.58)

Fertility Rate -.234*** (-2.81)

-.346*** (-2.77)

-.208** (-2.32)

Date of Women’s Suffrage

-.0134* (-1.88)

-.0142* (-1.88)

-.0147** (-2.29)

Ratio of Women’s LFPR to Men’s LFPR

-.431 (-1.11)

.0578 (0.09)

-.112 (-0.23)

Arab Country -.623 (-1.45)

-.453 (-.77)

-.389 (-0.94)

Constant 21.496 (1.62)

19.180 (1.30)

21.0192* (1.68)

Wald 271.46 206.51 285.46

Number of Observations 696 696 696 t-statistics in parentheses

*Significant at 10% level

**Significant at 5% level

***Significant at 1% level

20

Table V: Incident Rate Ratios for Women Athletes

Variable Total medals Gold Medals Silver & Bronze Medals

Ln(Real GDP per Capita) 1.846*** 1.689*** 2.025***

Ln(Population) 2.098*** 1.990*** 2.013***

Openness of the Economy 0.993*** 0.994** 0.992***

Former Soviet Republic 0.742 0.558 1.003

Formerly Communist 1.434 1.257 1.483

Current Host 1.229* 1.630*** 1.054

Fertility Rate 0.687*** 0.704** 0.676***

Date of Women’s Suffrage 0.979** 0.976** 0.986

LFPR Ratio 2.292 7.960** 3.355*

Arab Country 0.361 0.581 0.306*

*Significant at 10% level

**Significant at 5% level

***Significant at 1% level

Table VI: Incident Rate Ratios for Men Athletes

Variable Total medals Gold Medals Silver & Bronze Medals

Ln(Real GDP per Capita) 1.708*** 1.712*** 1.821***

Ln(Population) 1.887*** 1.926*** 1.776***

Openness of the Economy 0.990*** 0.988*** 0.990***

Former Soviet Republic 3.868*** 2.362* 4.401***

Formerly Communist 3.425*** 2.803** 3.298***

Current Host 1.398*** 1.577*** 1.314**

Fertility Rate 0.796*** 0.707*** 0.812**

Date of Women’s Suffrage 0.987* 0.986* 0.985**

LFPR Ratio 0.636 1.060 0.894

Arab Country 0.534 0.635 0.677

*Significant at 10% level

**Significant at 5% level

***Significant at 1% level