Lakin_Thesis

Jeremy Lakin 1
Foul Play: The Developmental Impacts
of Hosting Mega-Sporting Events
By Jeremy Lakin
March 31, 2015

Jeremy Lakin 2
Abstract
This paper analyzes governments’ spending patterns on healthcare and education
in a three-year window surrounding a mega-sporting event. I draw on panel data spanning
214 countries from 1980 to 2013 during which 67 mega-sporting events occurred. I find
that hosting a mega-sporting event, either a global or regional game, affects how
governments allocate public funds. While past research has deemed these events as
potentially harmful to local governments, this paper finds that there are negative short-
term effects on education, and positive long-term effects on healthcare, especially in
developing countries.
1. Introduction
Since the first modern Summer Olympic Games in 1896, mega-sporting events
have grown tremendously in scale and frequency. There is an immeasurable sense of
national pride gained while watching your country’s athletes competing for and winning
gold medals. This sense of pride however is amplified when a country has the
opportunity to host the event as well. Countries fight as hard in bidding to host these
mega sports events (MSEs) as their athletes do competing in them, and are quick to boast
their achievements. After each MSE, host nations release figures on boosted employment,
event revenues, infrastructure improvements, and tourism spikes (Kuper and Szymanski
2014). Every event seems to be bigger than the last, and now more than ever developing
countries want their shot at the same successes claimed by the more developed host
nations. With the demand for MSEs exceeding their supply these global games, such as

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Olympics and FIFA World Cup, have been joined by regional games like the
Commonwealth Games, All Africa Games, Asian Games, and Pan Am Games.
The bidding process has always been questionable at best. The IOC and FIFA’s
attempts to even the playing field have only encouraged developing nations to take part in
the overly elaborate and quite expensive bidding process (Zimbalist 5, 127-8). This has
put even more scrutiny on host nations and exposed the games to more scandal. FIFA has
been long marred by stories of corruption and for its exorbitant standards. Reports have
come out that the games have led to displacement of poor people (Burke 2011), increases
in human trafficking (Barr 2011), and faulty or rushed construction (Zimbalist 91).
What’s worse is that despite all the positive claims delegations make to their constituents
to convince them the games will help, countries are lucky to break even let alone make a
profit (Baade and Matheson 2004).
Host nations spend up to four times more than their initial budgets, most of which
coming from public funding, and rarely complete all of their proposed projects meant to
improve the host cities’ infrastructure and tourism potential (Zimbalist 54-5). While
Baade and Matheson in particular have done comprehensive work explaining the
economics of these games, there is little work that speaks on what happens to the people
residing within the host countries once government funding is directed towards supplying
these massive MSEs. In this paper I will look specifically at spending on education and
healthcare as well as their effects on enrollment rates, immunization rates, access to
improved sanitation, and out-of-pocket expenditures as a percent of total private
healthcare spending. I will look at a three-year window spanning from the year prior to
the games to the year after the event. First I will look at whether host nations decrease

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their spending on education or health care, which could be in order to pay for these
games. I will then test to see whether a fall in spending causes a decrease in enrollment
levels, access to improved sanitation, access to immunizations, or out-of-pocket
healthcare expenditures.
2. Literature Review
There are a few key pieces of literature that shaped the course of my research. The
earliest came from an article by Dr. Frank Zarnowski called “A Look at Olympic Costs”
(Zarnowski 1993). In this paper, Zarnowski looks at spending on Summer Olympic
Games from the first one in Athens in 1896 up to his prediction for Atlanta in 1996. The
article was published in 1993, and thus the findings for Atlanta were preliminary figures
that he used to make estimates for the future of the games. Zarnowski found that while
revenues fluctuated from game to game, expenditures by host countries steadily
increased. He believed that this was not just due to inflation or infrastructure, but a
demonstration of importance placed upon hosting the games.
Two key figures in the field of sports economics, Victor Matheson and Robert
Baade, have written several articles on the economics of mega-sports events. In particular
their article, “Mega-Sporting Events in Developing Nations: Playing the Way to
Prosperity?” also looked at Olympic spending to determine what the impact of
fluctuations in cost and hosting mega-sporting events was, especially on developing
nations (Baade & Matheson 2004). This paper was influential to my thesis for several
reasons, but first and foremost because it broadened the scope beyond what most sports
economists study to look at “mega-sporting events” rather than a single series of games.

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The article is essentially two parts, the first of which attempts to explain the huge
disparity between ex ante and ex post figures on job creation and revenue for hosting a
mega-sports event, and the second makes the case why a developing nation in particular
should not host such an event.
While much Baade and Matheson’s works are anecdotal, pertaining to certain
instances within games, several overarching themes have played a part in my research as
well. The primary idea is that while countries fight to host mega-sporting events because
they believe it will bring a huge economic windfall, the vast majority of the time there is
little to no economic impact. The authors explain how substitution spending and
crowding out account for a large portion of revenues, and that the supposed economic
“multiplier” of the economic windfall is reduced by the income that is spent elsewhere,
such as by non-locals working at the events who spend their revenues back home. The
case against developing nations attempting to reap the non-existent benefits industrialized
host-nations boast of is really founded on the argument that hosting any kind of mega-
sporting event is expensive. While developing nations might spend less than
industrialized ones, countries are still spending large amounts of money on sports and
infrastructure spending that citizens would rather see put towards healthcare and
education.
A particularly poignant quote from the article was about Nigeria’s expenditures to
build a new soccer stadium. It cost more than the government’s expenditures on either
healthcare or education. This is neither a new or discontinued issue as we saw with the
2014 World Cup in Rio Di Janeiro. Citizens have become more vocal for their distrust in
government spending priorities. In fact several other articles have used anecdotal

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evidence to prove just how much developing nations suffer from hosting mega-sporting
events. In an article by Vinyak Uppal about the potential infrastructural benefits from
hosting the 2010 Commonwealth Games, discusses the potential for developing nations
to either grow from infrastructural improvement, or buckle under the weight of
government spending. Uppal presents both Baade and Matheson’s reasons why
developing nations shouldn’t host event, with several counterpoints promoting the
potential for developing nations to grow.
However later reports such as an article from The Guardian by Jason Burke
discusses one of the many failures of India to regulate the spending of the games to
improve Delhi. In his article he relays one tragic account of government-hired contractors
destroying an elementary school for poor children for infrastructural improvements for
the games (Burke 2010). In another recent event, the 2010 World Cup in South Africa,
police report that the increase in tourism, what Uppal cites as a potential benefit of
hosting a mega-sporting event, as the main cause behind a spike in human trafficking in
areas around the new soccer arenas (Barr & Noren 2011). It’s difficult to account for
what exactly has gone so wrong in several past mega-sporting events, because the
difference of whether or not a nation is developing or industrialized does not account for
rising costs of games, or even the game’s revenue.
Matheson goes further in depth on the topic in a later paper focusing on large-
scale sporting events in the United States, such as the Super Bowl, NCAA Final Four,
and the World Series (Matheson 2006). He often refers back to the previous paper he co-
authored, particularly in comparing spending patterns and ex ante reports for these large-
scale American sports events with mega-sporting events such as the World Cup in Japan

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and South Korea and the Los Angeles Olympics. He refers to several psychological
factors that could be responsible for the growth, such as a positive sports results leading
to a happier labor force and increased productivity. He also suggests a few reasons why
certain games might succeed over others such as use of prior sports infrastructure, the
scale of the game (regional vs. global), spending on non-sports infrastructure and whether
or not it’s likely to be in use after the games, and complimentary spending on security,
sanitation, transportation. However even these recommendations fail to account for the
economic or developmental failures of countries hosting regional events such as the 1995
All Africa Games or the 2010 Commonwealth Games.
In the second edition of Soccernomics by Simon Kuper and Stefan Szymanski,
they delve further into the individual contributions of Baade and Matheson. Most of their
chapter on “Happiness” reiterates their skepticism of the economic windfall host
countries hope for by hosting MSEs. Kuper and Szymanski also provide more
contemporary context since this second edition is printed in early 2014, the authors had
access to more ex post data, a larger pool of events to analyze, had the 2014 World Cup
as a primary focus on economics in developing nations as opposed to South Korea
(Kuper and Szymanski 2014). This chapter also helped establish the theoretical
mechanisms behind both their research and my own by suggesting “happiness” as a
driving factor in hosting an MSE or tournament (as this book is ultimately geared towards
soccer). Szymanski, with the help of a different co-author, found a correlation between
hosting a tournament and increased happiness, especially amongst older men and less-
educated people. Kuper and Szymanski pose the question why politicians don’t just say
they want to host an MSE to increase happiness to which they respond, “that politicians

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have still barely discovered the language of happiness. They talk about money,” (Kuper
and Szymanski 292).
Recently Andrew Zimbalist published a comprehensive report on the economics
of the Olympics and World Cup. Zimbalist analyzes both the long- and short-term
economic effects of the games, and gives detailed analyses of the Barcelona, Sochi,
London, and Rio games. Having consulted with Baade and Matheson in the past, he
combines anecdotal knowledge with past economic studies to dispel the myth that hosting
the Olympics or World Cup will promote a country or city’s economic growth. Claiming
Barcelona and Los Angeles to be the only truly successful games economically, he is
very critical of government spending on the games. Those two games were heavily
funded by private partners, while every other game has required “significant financial
contribution form the public sector…financed in one of three ways: (1) by cutting back
on other public services, (2) by raising taxes, or (3) by government borrowing”
(Zimbalist 48).
What is certain is that countries are fighting harder than ever to host these games,
despite protests from citizens to focus on spending money on public services like
education, health care, and non-sports infrastructure (Crellin & Gupta, Zimbalist 94-100).
While several of these past articles refer to spending in these sectors anecdotally, there is
yet to be a study that conclusively determine how large of an effect, if any, hosting a
mega-sporting event has on education and healthcare spending. Furthermore there is
much greater attention given to global games than regional ones. In this study global
games consist of the Summer Olympics, Winter Olympics, FIFA World Cup, and FIFA
Women’s World Cup, while the pool of regional games will consist of the

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Commonwealth Games, the Asian Games, the Pan American Games, and the All Africa
Games.
Again, while there are a few sources concerning overall spending on the
Olympics and World Cup, most analysis from the games come from FIFA ex ante
reports, local news coverage, and national budget reports (if the host nation is
industrialized and transparent enough to release them). This paper also proposes a more
definitive timeline in which we might see changes in spending. These games are usually
awarded to host nations seven years in advanced. By accounting for planning time, and
the occasional host nation switch, I can narrow in on a defined spending timeline.
3. Mechanisms
Before I can hypothesize on why a host nation might divert funding to pay for an
MSE, it must first be explained why they would host one at all. As previously stated
Kuper and Szymanski have compiled data on the correlation between hosting an event
and happiness. They sympathize that politician’s work is difficult because “you try to get
money to build, say, roads, but other politicians stop you. Even when you have the
money…people pop up to object…being a politician is an endless, tedious struggle with
your enemies. But it isn’t when you want to host a sports tournament,” (Kuper and
Szymanski 293-4). While politicians claim they want to host for expected economic
benefits (Baade and Matheson, Kuper and Szymanski, Zimbalist), the only incentive
backed by data is happiness. This is an especially pertinent factor when considering that
in the history of the eight events this study focuses on, the incumbent party hosting the
event is re-elected 70% of the time.

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Kuper and Szymanski affirm that legislative control not only affects who bids for
games, but how international sporting bodies choose host countries as well. They state
that the IOC chose Japan for the 2020 Olympics because they could better appropriate the
funds, while FIFA chose Qatar and Russia because they expect fewer protests in less
democratic regimes (287-8). So while countries might bid because it builds political
cohesion, their level of democracy, or lack there of, affects who is chosen to host MSEs.
While both democratic and autocratic host nations are able to mobilize funding towards
an MSE, neither has been historically successful in mobilizing private donations to pay
for MSEs. Without private funding, countries must cut back on providing public services
(Zimbalist 48). Healthcare and education are likely to get cut because they are least
directly related to a country’s ability to host an MSE unlike infrastructure, income, or tax
policies.
Ironically, Zimbalist credits a country’s ability to not use public funds as an
indicator of possible success. He claims that funding from private sources added to the
success of Barcelona and Los Angeles (17, 73-4). However countries exaggerate their
levels of private funding to secure bids. As previously stated by Zimbalist, MSE’s have
required major support from public funds, and even what appears to be private funds
“may end up as public funding because some loans from the state bank won’t be repaid
and additional budgetary appropriations are slated to subsidize private losses on certain
investments,” (81). Simply put, rich investors are promised some level of return on
investments, even if that return comes out of the public coffers. The unanswered question
is where the money would have otherwise gone.

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Even when the projects these re-appropriated funds go towards are meant to
benefit the public, there is a lack of vision or follow-through. Railways are built between
airports and rich neighborhoods that already have sufficient infrastructure, projects are
halfway finished by the time of the games, and huge stadiums are left in small towns
without the ability to fill them to the same capacity as during the games (92-6). The focus
is always on infrastructure, mostly to distract from where the funding is coming from.
The problem for hosts is that the people have noticed. The number of bids for games is
decreasing, even with the IOC and FIFAs attempts to give bids to developing countries.
There has not been as heavy of a stigma on hosting the Olympics since 1984 following
the protests, massacre, and financial disaster of the 1968, 1972, and 1976 games
respectively (1). Now that the short-term results are more publicized, it is past time we
examine the long-term effects.
Knowing that host countries use public funds to pay for MSE’s there are three
paths these diversions can take (48,81). These funds could be spent to an exorbitant
degree from which the host nation will not recover. A decrease in spending on healthcare
and education will cause deep negative effects on enrollment and my healthcare
indicators. A second option begins the same way. A host nation diverts funding from
public services to pay for the MSE causing negative developmental effects. However, this
decrease was temporary, and the country would normal spending after the games,
resulting in an upswing in developmental indicators. Finally there is the long-term option
that most countries benefit from even if they expected short-term and long-term benefits.
In this model the infrastructure improvements, even if they are not built in time for the
games, benefit the host nation in the long run.

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4. Data
4.1 Game Selection
The games I’ve chosen to study are the Sumer Olympics, Winter Olympics, FIFA
World Cup, FIFA Women’s World Cup, the Commonwealth Games, the Pan American
Games, the All Africa Games, and the Asian Games. The Olympics and World Cup are
the largest sports events in the world. While smaller global events are growing in size,
I’ve specifically chosen those four regional games because there is a more diverse pool of
host countries, more events, and greater global exposure. Due to availability of data my
thesis focuses on games that occur between 1980 and 2014.
4.2 Dependent Variables
My main education-spending variable is expenditure per student as a percent of
GDP per capita. The World Bank has this data disaggregated as spending per student on
primary, secondary, and tertiary education (Table 1). Expenditure per student is, “the
total public expenditure per student in [primary, secondary, or tertiary] education as a
percentage of GDP per capita. Public expenditure (current and capital) includes
government spending on educational institutions (both public and private), education
administration as well as subsidies for private entities (students/households and other
privates entities)” (World Bank).

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The average expenditure per student is 16.141, 22.059, and 75.964 of GDP per capita for
primary, secondary, and tertiary education respectively. All three of these indicators
assume enrollment. Thus for those who are enrolled in tertiary education, total private
and public expenditures per student average 75.964% of someone’s GDP per capita.
My secondary education regressions involve the effect of spending patterns on the
gross percentage of enrollment (Table 2). This percentage is “the total enrollment in
secondary education, regardless of age, expressed as a percentage of the population of
official secondary education age. GER can exceed 100% due to the inclusion of over-
aged and under-aged students because of early or late school entrance and grade
repetition,” (World Bank). I used gross enrollment rather than net enrollment due to a
lack of available data for net tertiary enrollment. Due to the ambiguity associated with
gross enrollment, an increase could be caused by either an increase in enrollment, or an
increase in students repeating grades.

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My main healthcare-spending variable is healthcare spending per capita. The
World Bank calculates this as “the sum of public and private health expenditures as a
ratio of total population. It covers the provision of health services (preventive and
curative), family planning activities, nutrition activities, and emergency aid designated
for health but does not include provision of water and sanitation”. My other health
variables of interest are the percent of population with access to improved sanitation,
percent of children 12-23 months old that have received immunizations, and out-of-
pocket expenditures as a percent of total private expenditures on health (Table 3). My
data for these three also come from the World Bank and have the most variance. Except
for healthcare spending per capita, my dependent variables are expressed in percent
change.
4.3 Independent Variables
The most important independent variables are my indicators of my three-year
window. I use a variable for the year before, during, and after an MSE to examine how
the spending patterns change over the three-year period. I also use year and region fixed

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effects defined by the World Bank’s regional designation. These regions are East Asia &
The Pacific, Europe & Central Asia, Latin America & the Caribbean, Middle East &
North Africa, North America, South Asia, and Sub-Saharan Africa. My other controls are
the log of population (from the World Bank), polity (from the polity2 data set), state
fragility index (from the Center for systemic Peace), and log of total government
expenditure (from the World Bank).
5. Research Design
In order to host MSEs, countries must draw from public funds that would
normally go to providing services. Countries can either do so by directly reallocating
funds, or by reallocating funds that would have been used to increase the budget. The
former would result in a decrease in spending during the three-year window of the games,
while the latter would result in either no change, or an increase in spending. Thus these
hypotheses are:
H1: Hosting an MSE will cause a decrease in spending on healthcare and education.
H2: Hosting an MSE will not decrease spending on healthcare and education.
If my first hypothesis is correct, I expect my indicators to reflect one of the three
potential patterns I previously proposed:
H3: Reallocating public funds away from healthcare and education will have deep,
harmful effects on their respective indicators.
H4: Reallocating public funds away from healthcare and education will have a short-term
negative effect.

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H5: Reallocating public funds away from healthcare and education will have long-term
positive effects because of infrastructure improvements.
In my study I’m looking to determine what the relationship is between hosting an
MSE and government spending on education and healthcare. My spending per capita
variables are my dependent variables for my first regressions and my primary equations
are:
Equation 1: expenditure per student on primary education (% of GDPPC) = a + year
before event + year of event + year after event +log (total government expenditure)c,y +
state fragility indexc,y + polityc,y + log (population)c,y + region fixed effects + year fixed
effect + error
Equation 2: expenditure per student on secondary education (% of GDPPC) = a + year
before event + year of event + year after event +log(total government expenditure)c,y +
state fragility indexc,y + polityc,y + log(population)c,y + region fixed effects + year fixed
effect + error
Equation 3: expenditure per student on tertiary education (% of GDPPC) = a + year
before event + year of event + year after event +log(total government expenditure)c,y +
state fragility indexc,y + polityc,y + log(population)c,y + region fixed effects + year fixed
effect + error
Equation 4: healthcare spending per capita= a + year before event + year of event + year
after event +log(total government expenditure)c,y + state fragility indexc,y + polityc,y +
log(population)c,y + region fixed effects + year fixed effect + error
My secondary equations have the same controls as the first four equations, but the
dependent variables are the development indicators I have focused on:

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Equation 5: gross primary enrollment = a + year before event + year of event + year after
event +log(total government expenditure)c,y + state fragility indexc,y + polityc,y +
Equation 6: gross secondary enrollment = a + year before event + year of event + year
after event +log(total government expenditure)c,y + state fragility indexc,y + polityc,y +
Equation 7: gross tertiary enrollment = a + year before event + year of event + year after
event +log(total government expenditure)c,y + state fragility indexc,y + polityc,y +
Equation 8: access to improved sanitation (% of population) = a + year before event +
year of event + year after event +log(total government expenditure)c,y + state fragility
indexc,y + polityc,y + log(population)c,y + region fixed effects + year fixed effect + error
Equation 9: % of 12-23 month olds immunized = a + year before event + year of event +
year after event +log(total government expenditure)c,y + state fragility indexc,y + polityc,y
+ log(population)c,y + region fixed effects + year fixed effect + error
Equation 10: out-of-pocket expenditures for healthcare = a + year before event + year of
event + year after event +log(total government expenditure)c,y + state fragility indexc,y +
polityc,y + log(population)c,y + region fixed effects + year fixed effect + error
6. Results
Table 1 contains the results of my education regressions (Equation 1, 2, 3, 5, 6,
and 7). According to Equation 1 in Column 1, there is a statistically significant decrease
in spending per student on primary education of 3.553 percent of GDPPC the year of the

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games, and another decrease of 2.907 percent the year after the games, which might be
significant. Average spending per student is about 16 percent of GDPPC, meaning the
games correlate with an almost 25% decrease in primary education spending the year of
an MSE. Spending per student on secondary education (Column 3) also decreases
significantly by 4.272 percent the year before the games, and 2.198 percent the year of
the games. There is a statistically significant increase the year after the games as well,
suggesting an overcompensation of spending.
Tertiary spending per student (Column 5) has the largest decrease the year prior to
the games. As previously stated, for students that are enrolled in tertiary education,
average spending per student is 75% of GDPPC. The decrease experienced the year
before the games decreases this spending by more than half. There is a significant
increase the year of the games, but that doesn’t makeup for the losses the year before
them. Thus in every category of education spending per capita there is a significant
decrease prior to or during the games. This does not change whether or not I control for
log of total government spending. I find evidence of reallocation away from education
spending prior to and during the games. For secondary education spending this decrease
is offset by spending after the games, but it is not for either primary or tertiary spending.
This is consistent with my first hypothesis, that host nations reduce funding to education
spending to pay for hosting an MSE.
Despite hosting an MSE having a negative impact on education spending, this
does not correlate with a significant drop in gross enrollment for primary or secondary
education. While this could be due to students repeating grades, it is much more likely
that students continue to go to school despite the reduced funds. There is however a

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decrease in gross tertiary enrollment the years prior to and during an MSE, followed by a
rebound in gross enrollment the year after the games consistent with an increase in
tertiary spending. This supports H4: a reduction in spending on education, results in a
short-term decrease in gross enrollment.

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Table 5 analyzes the effects of hosting on healthcare spending and three
secondary indicators: percent of the population with access to improved sanitation,
immunizations, and the out-of-pocket expenditures as a percent of total private healthcare
spending. Whether or not I control for government spending, there is a positive trend the
years before and during the games, followed by a decrease in spending per capita the year
after (Table 5 Columns 1 and 2) however this is decrease is not significant. The
secondary indicators do not follow these spending patterns. For example access to
improved sanitation (Columns 3 and 4) and immunizations (columns 5 and 6) increase,
and the percent of out-of-pocket spending (Columns 7 and 8) decrease the year before the
games. Despite an increase in healthcare spending per capita the year of the games, there
are statistically insignificant decreases in access to sanitation and immunizations. The
next year both indicators increase again. This could be explained by a crowding factor. If
foreigners fill the hospitals built or improved for the games, the locals lose their access to
doctors and health services. Furthermore, doctors could have reduced availability if they
are working at the games. On the other hand there is a consistent decrease in the percent
of private healthcare expenditures as out-of-pocket payments each of the three years.
Individually these results validate conflicting hypotheses. My significant
increases in healthcare spending per capita support my second hypothesis, that there is no
decrease in healthcare spending when a country hosts an MSE. However my results for
access to sanitation and immunizations support my fourth hypothesis of short-term
negative effects, which does not make sense if there isn’t a decrease in spending.

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In order to account for this I have disaggregated my healthcare spending data into
three categories of country development: developing nations, BRICS and recently
industrialized nations, and developed countries. In the least developed countries, where
the fewest MSEs have occurred, there is a very different set of results. In Table 3 Column
1 we see that healthcare spending decreases the year before the games, increases the year
they occur, and decrease again the year after. However the other three indicators show
improvements the year prior to the games. The year of the games healthcare spending
increases, but access to sanitation decreases. However the year after the games yields the
most statistically significant results. Access to sanitation and immunizations increases
(Columns 2 and 3), the percent of out-of-pocket payments as a percent of health
expenditures decreases, and healthcare spending per capita decreases but it is not
statistically significant. My regional fixed effects do not deviate from the general results
except in magnitude of increases in public spending. However they also reveal that
relative to East Asia, health copays significantly increase. Since average copay decreases,
and yet the other regions reveal increased copays, it’s possible that East Asia’s decreases
are weighing down a general trend towards more expensive healthcare bills. Overall these
insignificant decreases and the general trends support my fifth hypothesis, that there are
long-term positive effects in developing countries.

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In BRICS and recently industrialized countries, Table 4 has inconclusive results
for what happens to each indicator the year of the games. The general trend though
indicates that healthcare spending per capita increases (Column 1), access to sanitation
increases (Column 2), access to immunizations increases (Column 3), but that the size of
the co-pay also increases (Column 4). This is the first case in which it does so. There are
also interesting differences with my regional fixed effects. In South Asia, where the
majority of Asian Games and more Commonwealth Games have occurred, there is a
decrease in healthcare spending per capita in Column 1. This corresponds with a
significant decrease in immunizations in Column 3, and an increase in average copay in
the region in Column 4. While there might be increases in healthcare spending per capita
for the other two regions controlled for in this sample, they also exhibit decreases in

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access to sanitation (Column 2), and immunizations for Europe and Central Asia, which
is really Russia (Column 3). In general BRICS and recently developed countries have
mixed, but not significant effects. This is not to say that then they are equipped to host
MSE’s, but more that they must be very careful with spending since there results are
erratic.
Finally there is a significant decrease in healthcare spending per capita the year
after the games in developed countries (Table 5, Column 1). This is especially true in
MENA and Latin America where spending decreases by more than $2000 per capita. In
Column 2 access to improved sanitation significantly increases by 45% as hotels and
stadiums are constructed all around the host city, and then there is a decrease the year
after once people stop using the facilities built specifically for the games. This is
especially true in Latin America and North America. This makes sense in Latin America

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given it is the source of most of the tales of displacement for games, however it also
makes sense in North America given that the majority of infrastructure built for games is
not accessible to certain populations living in the cities. People there are less likely to
repurpose facilities as anything other than sports related infrastructure and thus get less
return from their investments. Immunizations in Column 3 do not follow the general
trend of healthcare spending. They increase the year before the games, decrease the year
of, and increase the year after. Here regional fixed effects all indicate an increase in
immunizations. Finally out-of-pocket expenses go down the year before the games in
Column 4, and then increase the next two years. Again the regional fixed effects yield
mixed results. Europe, Central Asia, and Latin America indicate an increase in copay,
which matches their spending patterns. Thus developed countries in those two regions are
less likely to subsidize healthcare in general. Overall there are the most significant
negative results, constituent with the fourth hypothesis of negative short-term effects.

Jeremy Lakin 27
7. Conclusions
I have found significant evidence of reallocation of spending away from
education prior to the games. While spending per student after the games offsets the
decrease in secondary education, it does not offset primary or tertiary education spending
cuts. This corresponds with a short-term decrease in enrollment rates. While developing
countries have mixed results, there are general long-term improvements in healthcare
spending per capita, access to sanitation and immunizations, and out-of-pocket healthcare
expenditures. BRICS and recently industrialized countries also have mixed results, but in
general there are no significant effects on healthcare. Finally developed countries also
exhibit short-term negative impacts. This makes sense considering developed countries
have more complex infrastructure, and thus any change in funding offsets the balance
necessary to maintain it. Developing countries on the other end of the spectrum have such
little infrastructure that construction improves the average quality of life. BRICS and
recently industrialized countries must then balance these effects and be particularly
careful in their spending choices.
8. Recommendations
My recommendations based on my findings are based on a need for greater
scrutiny of these games. More and more people are coming forward against their
countries hosting these games because we now have a greater understanding of how
empty the promises are of Olympic-sized revenue. First and foremost there needs to be a
complete overhaul of the bidding process. There is no justifiable reason that cities

Jeremy Lakin 28
bidding for the Olympics should pay a $650,000 application fee (Zimbalist 5). While
FIFA had the right idea about inclusivity by trying to assign Cups based on a rotating
continents system, there needs to be more pragmatism (130). The United States has not
hosted the Olympics since 1996. The Atlanta Games were moderately successful, and the
Los Angeles Games in 1984 was one of the most successful MSEs ever. The United
States and other highly developed nations have the infrastructure to support these games,
and the organizing committees should let them. Should the organizing committees not
accept this, or in the spirit of the games, continue to seek developing countries as hosts,
there needs to be a massive lowering of standards. FIFA requires a host for the Men and
Women’s World Cup to have “eight modern stadiums of at least 40,000 capacity, one of
which must have 60,000 for the opening match, and another with 80,000 capacity, for the
final contest” (91). This is simply unsustainable. Furthermore countries must not bend to
the whims of the governing bodies. Brazil not only obeyed FIFA, but also built twelve
stadiums for the games, most of which are now white elephants, un-fillable for the local
teams now residing in them and too expensive to operate. There is much to be gained
from hosting an MSE, even if the gains are not financial. In order to achieve these gains,
there must be much more pragmatic planning, or else ordinary citizens will curry the
burden of bad decision making.

Jeremy Lakin 29
References
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Nov. 2014
http://www.thefreelibrary.com/All+Africa+Games%3a+a+financial+fiasco.-
a017869449
Baade, Robert A., and Victor A. Matheson. "Mega-Sporting Events in Developing
Nations: Playing the Way to Prosperity?" COLLEGE OF THE HOLY CROSS,
DEPARTMENT OF ECONOMICS FACULTY RESEARCH SERIES WORKING
PAPER NO. 04-04 (2004). Web. 1 Sept. 2014.
<http://college.holycross.edu/RePEc/hcx/Matheson_Prosperity.pdf>.
Barr, John, and Nicole Noren. "Concerns Raised about Illegal Sex Trade." ESPN. ESPN
Internet Ventures, 7 Jan. 2011. Web. 1 Nov. 2014.
Burke, Jason. "'Shining India' Makes Its Poor Pay Price of Hosting Commonwealth
Games." The Guardian 11 July 2010. Web. 1 Sept. 2014.
<http://www.theguardian.com/world/2010/jul/11/slum-school-india-
commonwealth-games>.
Gupta, Girish, and Olivia Crellin. "Brazil Protests Run Gamut from Health Care to World
Cup." USA Today 23 June 2013. Web. 1 Oct. 2014.
<http://www.usatoday.com/story/news/world/2013/06/23/brazil-
protests/2449079/>.
Kuper, Simon, and Stefan Szymanski. "Happiness." Soccernomics. 2nd ed. New York:
Nation, 2014. Print.

Jeremy Lakin 30
Matheson, Victor A. "Mega-Events: The Effect of the World’s Biggest Sporting Events
on Local, Regional, and National Economies." COLLEGE OF THE HOLY
CROSS, DEPARTMENT OF ECONOMICS FACULTY RESEARCH SERIES
PAPER NO. 06-10* (2006). Web. 1 Sept. 2014.
<http://college.holycross.edu/RePEc/hcx/Matheson_MegaEvents.pdf>.
Uppal, Vinyak. "The Impact of the Commonwealth Games 2010 on Urban Development
of Delhi." Theoretical and Empirical Researches in Urban Management 1.10
(2009). Web. 1 Oct. 2014. <http://www.um.ase.ro/no10/2.pdf>.
Zarnowski, Frank. "A Look at Olympic Costs." International Journal of Olympic History
1.2 (1993). Print.
Zimbalist, Andrew. Circus Maximus: The Economic Gamble Behind Hosting the
Olympics and the World Cup. Washington: Brookings Institution, 2015. Print.

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