What Makes Mega-Events Profitable? Determinants of Revenues and Costs of the Olympic Games and the Football World Cup

Abstract

Mega-events such as the Olympic Games and the FIFA World Cups pose major financial and management risks to the public sector. To better understand and anticipate these risks, this paper examines the determinants of revenues, costs, and profitability of these events. In a longitudinal analysis of 43 events between 1964 and 2018, our study found a positive correlation between a host country's GDP per capita with both the revenues and costs of these mega-events. While GDP is not correlated with profitability, greater economic freedom is, suggesting that countries with less government intervention, regardless of their wealth, produce better financial results.

Full text

WHAT MAKES MEGA-EVENTS PROFITABLE? DETERMINANTS OF REVENUES AND COSTS OF
THE OLYMPIC GAMES AND THE FOOTBALL WORLD CUP
Markus Lang, David Gogishvili, Martin Müller
19 April 2024

What Makes Mega-Events Profitable? Determinants of Revenues
and Costs of the Olympic Games and the Football World Cup
Markus Lang
Institute of Sport Sciences
University of Lausanne
markus.lang@unil.ch
David Gogishvili
Department of Geography and Sustainability
University of Lausanne
david.gogishvili@unil.ch
Martin Müller
Department of Geography and Sustainability
University of Lausanne
martin.muller@unil.ch
April 19, 2024

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What Makes Mega-Events Profitable? Determinants of Revenues and
Costs of the Olympic Games and the Football World Cup
Abstract
Mega-events such as the Olympic Games and the FIFA World Cups pose major financial and
management risks to the public sector. To better understand and anticipate these risks, this
paper examines the determinants of revenues, costs, and profitability of these events. In a
longitudinal analysis of 43 events between 1964 and 2018, our study found a positive
correlation between a host country's GDP per capita with both the revenues and costs of these
mega-events. While GDP is not correlated with profitability, greater economic freedom is,
suggesting that countries with less government intervention, regardless of their wealth, produce
better financial results.
Keywords: mega-events, economic impact, revenues, costs, regression, governance

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1. Introduction
Hosting major sporting events, also known as mega-events, like the Olympic Games and the
FIFA World Cup, has consistently attracted the attention of policymakers and scholars from
various disciplines. These events are often perceived as opportunities to generate significant
revenue and bring various economic benefits (Gillett and Tennent 2022; Tien, Lo, and Lin
2011). Tourists are expected to flock to host cities, new jobs are created, and global visibility
increases. The events also serve as platforms for local development, stimulating investment in
infrastructure and public services (Essex and Chalkley 2004), much of which are publicly
funded and expected to have a long-term positive impact. The planning and management of
these events often fall under the responsibility of public management, involving a complex
interplay of governmental bodies at different levels, private entities, and civic organizations
(Chappelet 2021; Müller 2015). Mega-events are among the riskiest undertakings of the public
sector in terms of delivering on promises, creating benefits, and containing costs (Jennings
2012; Priemus, Flyvbjerg, and van Wee 2008).
The role of public management is crucial (Gillett and Tennent 2017), as poor planning
and execution can lead to enormous upfront costs—such as building new facilities, upgrading
infrastructure, and organizational expenses—that risk burdening local budgets for years or even
decades (Flyvbjerg, Budzier, and Lunn 2021; but see Preuss and Weitzmann 2023). After the
events, host cities may face ongoing maintenance costs, unused facilities, and debt, creating
issues of public governance and fiscal responsibility (Müller 2015). This problem may raise
questions about the economic viability of hosting these events and highlights the need for
effective public management strategies to ensure that the anticipated economic and social
benefits are realised while mitigating risks and costs.
While the hosting of the Olympic Games and the Football World Cup is often regarded
as an opportunity for economic uplift (Szymanski 2010), it is crucial to consider the full

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spectrum of financial implications, both positive and negative. This complex interplay between
revenue generation and cost incurrence makes exploring the specific determinants that
influence these economic outcomes a vital area of research for public management. Current
research into the economic impact of these events is extensive but has not looked into key
drivers of revenue and costs. Too often, public administration lacks the data and the political
support to properly predict revenues and costs and undertake a comprehensive economic cost-
revenue calculation. This makes it challenging to evaluate mega-events as public policies and
to anticipate their economic costs, benefits, and risks relative to other public investment
projects.
This paper seeks to address this critical gap by conducting a comprehensive empirical
analysis of the determinants of revenue, expenses, and profitability in hosting mega-events.
Our study focuses on three of the most prominent mega-events in the sporting world, the
Olympic Summer Games, the Olympic Winter Games, and the FIFA Men’s Football World
Cup, in a longitudinal study covering the period from 1964 to 2018, including a total of 43
events.
Our findings reveal a positive relationship between a host country's GDP per capita and
both the revenue and costs of hosting these events. However, a higher GDP does not necessarily
equate to increased profitability. Additionally, our analysis indicates that countries with higher
levels of economic freedom tend to generate greater profitability from hosting mega-events.
Another intriguing observation is the association between a more democratic political system
in the host country and lower event revenues. These insights are valuable for a diverse range
of stakeholders, including event rights-holders, international federations, local organisers, and
public policymakers, aiding them in making more informed decisions for hosting and managing
future mega-events. Furthermore, this study contributes to public management literature by

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deepening our understanding of the key factors influencing the financial dynamics of these
mega-events, helping to anticipate costs and revenues for these riskiest of major public projects.
2. Related Literature
2.1 Mega-projects and Public Management
Mega-events are popular as national and regional development policies in polities worldwide.
Among other things, this popularity has been linked to the rise of entrepreneurial strategies in
public administration, which saw cities and states compete to attract funding and public
attention that come with these events (Burbank, Andranovich, and Heying 2002; Swyngedouw,
Moulaert, and Rodriguez 2002). More recently, mega-events have also been employed as soft
power strategies, particularly by emerging nations such as China, Russia, Brazil, and Qatar,
aiming to position these countries on par with major Western countries (Wolfe 2020). Hosting
these events, however, constitutes a formidable challenge for public management.
Mega-events share some common elements with other mega-projects with significant public
sector involvement, such as dams, airports, railway and road infrastructure, and flagship
cultural buildings (Liu, Zhao, and Wang 2010). The coordination of a multitude of public,
private, and hybrid actors results in a complex governance system for decision-making (Gillett
and Tennent 2022; Pitsis et al. 2018). This frequently results in informational asymmetries
through a principal-agent situation in which public administrations as the principal have less
knowledge of the benefits and costs of a mega-event than the interest groups pursuing it,
therefore risking biased decision-making (Preuß, Andreff, and Weitzmann 2019). The regular
controversies surrounding these projects often require significant adaptations of the original
plan (Esposito, Terlizzi, and Crutzen 2022; Kundu, James, and Rigby 2023). In addition, the
extended planning and implementation period, often extending over more than ten years,
exposes mega-events to changing social, economic, and political circumstances. As a
consequence, public management of mega-events, as with other mega-projects, is subject to

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significant uncertainty and risk (Jennings 2012; Priemus, Flyvbjerg, and van Wee 2008;
Santamaria 2013), often resulting in cost overruns and benefit shortfalls (Flyvbjerg, Budzier,
and Lunn 2021; Catalão, Cruz, and Sarmento 2022).
Compared with other mega-projects, however, mega-events pose additional challenges for
public management. First, a strict and immovable timeline for delivery is set many years in
advance, which makes it impossible to reschedule commitments and trade off schedule
overruns against budget overruns (Flyvbjerg, Budzier, and Lunn 2021). Second, a rigid hosting
contract sets out the obligations of host countries and cities and makes the decision to host
irreversible. Third, the prominent role of international actors as event rights-owners (IOC,
FIFA), rule-setting organizations (international sports federations), sponsors, and contractors,
circumscribing decision-making of public management actors (Müller, Gogishvili, and Wolfe
2022b).
These factors compound the uncertainty surrounding the outcomes and costs of mega-event
hosting, making it hard for public administrators to anticipate, mitigate, and manage these risks.
For economic costs and benefits, public administration will be susceptible to estimates from
interest groups such as bid committees, which often present a biased picture, underestimating
costs and overestimating benefits (Chappelet 2019). A systematic overview of the revenues
and costs of past mega-events and their respective drivers is therefore of primary importance
for public administrators to make more informed decisions about the actual costs and benefits
of hosting.
2.2 Economic Impact of Mega-Events
A significant part of the challenge of mega-events for public management concerns their
economic aspect. Research has examined the economic impact of mega-events, assessing how
event-related expenditure affects the economy of a host region or country. Often quantified in
terms of changes in GDP, income, employment, and taxes, this area of research is extensive

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(see Scandizzo and Pierleoni 2018, for a comprehensive survey; Vierhaus 2019 overview of
international tourism effect). There is general agreement that initial projections of economic
benefits from events are usually overstated and that limited or even negative results do not
justify the financial commitment involved in hosting such events (Baade and Matheson 2016;
Flyvbjerg, Budzier, and Lunn 2021; Müller et al. 2021; Porter and Fletcher 2008; Zimbalist
2015). Thus, mega-events are known for overestimated revenues and underestimated costs
(Müller, Gogishvili, and Wolfe 2022b), a common feature among mega-projects (Flyvbjerg,
Bruzelius, and Rothengatter 2003).
Various methods have been used to assess the economic impact of mega-events, such
as multiplier analysis, input-output models, linkage models, econometric models, input-output
analysis, cost-benefit analysis, and computable-general-equilibrium analysis, and computable
general equilibrium models (Andersson, Armbrecht, and Lundberg 2008; Baade, Baumann,
and Matheson 2008; Lee and Taylor 2005; Preuss 2005; Taks et al. 2011). Jago et al. (2010),
Li and Jago (2013), and Jago (2010) present a literature review on the potential positive and
negative impacts of mega-events for host destinations and conclude that the economic
evaluation of mega-events has often fallen short of state-of-the-art assessment standards. Over
time, research has evolved from focusing on the narrow financial consequences of mega-events
to a broader examination of their wider economic impact. This evolution has moved from
simply quantifying direct impacts to a holistic assessment of the full economic impact on GDP,
wealth, and employment.
2.3 Financial Analysis of Mega-Events
The second research area focuses on the financial aspects, examining the revenues and
expenditures recorded by organizations involved in the events, such as the IOC, FIFA, and
local organizing committees. While there is abundant research on the financial aspects of mega-
events, most of these studies are case-specific, making it problematic to derive general

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conclusions. Longitudinal analysis comparing multiple events—such as the Summer
Olympics, Winter Olympics, and the World Cup—is relatively scarce. Fett (2020) offers one
of the first longitudinal and systematic studies of the Men's Football World Cup from 1950 to
2018, highlighting its evolution and detailing how these mega-events have become significant
economic, social, and political events. Fett shows a marked increase in organisational and
infrastructure costs. This escalation reflects a broader trend of increasing financial investment
from the organisers and returns for FIFA, which poses challenges for future hosts regarding
sustainability and economic impact. Graeff and Knijnik (2021) stand out as one of the few
studies to compare both the Olympic Games and the World Cup, though their sample is limited
to five World Cups (2006-2022) and three Summer Olympics (2008-2016). Their findings
suggest that expenses consistently surpass revenues, and public expenditure on these events is
also growing (Baade and Matheson 2016).
Several studies take a more specialised approach, examining specific aspects of
revenues and costs. For instance, Preuß et al. (2019) scrutinise the budgets of organizing
committees for both the Summer and Winter Olympics, concluding that revenues generally
offset expenditures. Lertwachara et al. (2021) argue that hosting major sporting events
(analysing the Olympic Games, the FIFA Men’s World Cup, the UEFA Men’s Championship,
and the Asian Games between 1960 and 2018) generally leads to an increase in foreign direct
investment (FDI) for the host country, with variations based on the specific event and country
characteristics. Flyvbjerg et al. (2021) provide the most systematic examination of cost
overruns, revealing that every Olympic Games has experienced cost overruns, which, on
average, are more extensive compared to other types of mega-projects. Overall, Müller et al.
(2022b) present the most comprehensive study to date. They systematically compare the major
costs and revenues of the Olympics and the World Cup by analysing time-series data covering

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1964 to 2018. They find that more than 80% of these events did not generate sufficient revenues
to cover all incurred costs.
In our study, we extend the current body of research, particularly building on the work
and dataset by Müller et al. (2022a; 2022b). In response to their suggestion for applying
regression analyses, we aim to identify key predictors that offer a more nuanced understanding
of the factors influencing revenues, costs, and the overall financial outcome of mega-events.
3. Methods
3.1 Data Collection
Our study focuses on three of the most prominent mega-events in the sporting world: the
Olympic Summer Games, the Olympic Winter Games, and the FIFA Men’s Football World
Cup. Our analysis covers the time frame from 1964 to 2018 to ensure a comprehensive
understanding. This longitudinal approach allows for examining trends, fluctuations, and
potential shifts in the determinants of revenue and costs over time. It also enables us to account
for various economic, social, and technological changes that may have impacted the financial
dynamics of hosting these events.
Our data collection involved a review of publicly available sources such as official
reports from organizing committees, IOC, FIFA, and host governments. In cases where these
primary sources did not provide conclusive data, we supplemented our research with
information from audit reports, media sources, and academic literature, as detailed in (Müller
et al. 2022). We undertook a two-step currency normalization process to facilitate meaningful
comparisons across different events and periods. First, we converted all monetary values to US
dollars (USD) based on the World Bank's national currency unit values. Subsequently, we
adjusted these USD figures for inflation to the base year 2018 using the World Bank Consumer
Price Index. This method, similar to that adopted by Turner et al. (2019) and Essex and
Chalkley (2004), results in values expressed in USD2018. This adjustment allows for

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comparing monetary values in real terms, effectively accounting for inflationary changes over
time.
For revenues associated with the IOC’s global sponsorship program, which are reported
per Olympic quadrennium rather than per individual Olympic Games, we applied a 2:1 revenue
split between the Summer and Winter Games. This allocation was based on the relative scale
and viewership of these events. The complete dataset, including the methodologies and
findings, has been published on Harvard Dataverse (Müller, Gogishvili, and Wolfe 2022a).
3.2 Sample
In our study, we selected the Summer Olympic Games, the Winter Olympic Games, and the
FIFA World Cup for analysis, as they represent some of the largest and most significant events
globally. This selection facilitates a comparative study between different types of events: the
World Cup, a single-sport event hosted in multiple locations, and the Olympic Games,
encompassing multiple sports but held in a single core location. Such a comparison provides
valuable insights into the varying dynamics of these event types.
Our dataset spans from 1964 to 2018, encompassing 14 Summer Olympic Games, 15
Winter Olympic Games, and 14 World Cups, totalling 43 events. Our analysis starts in 1964
for two main reasons. First, the early 1960s began a significant expansion in these events,
marked by technological advancements like live satellite transmission and increased urban
development activities (Essex and Chalkley 1998; Müller et al. 2023). Second, the availability
and reliability of data on revenue and cost streams improved significantly post-1964. Including
earlier data would lead to a higher incidence of missing values, affecting the robustness of our
analysis. Thus, 1964 presents an optimal balance between historical comprehensiveness and
data quality.

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3.3 Measures
3.3.1 Dependent variables
Our regression analysis focuses on revenues, costs, and return on investment (ROI) as the
primary dependent variables. It is essential to account for the time component in our regression
model since we deal with time-dependent data, as revenues and costs of mega-events have
exhibited a growth trend over the years (Müller, Gogishvili, and Wolfe 2022a). Therefore, we
added the Year as a control variable.
The ROI is calculated by dividing an event's net profit (the difference between revenues
and costs) by its total costs, measuring its financial efficiency. For revenue, we specifically
consider three major components: revenue from broadcasting rights, sponsorship (domestic
and international), and ticket sales. According to prior research, notably Baade and Matheson
(2016), these three sources constitute over 90% of recent Olympic Games and World Cup
revenues. This high proportion emphasises their significance and justifies their use as a robust
approximation of an event’s total revenue. Such an approach ensures that our analysis is
grounded in these large-scale sporting events' most impactful financial aspects.
In defining the cost parameters for our analysis, we align with the methodology of
Flyvbjerg et al. (2021) and focus on two primary types of costs: operational expenses and sports
venue capital costs. This approach is consistent with similar studies by Preuß et al. (2019) and
others, and it allows for a clear identification of direct, event-specific expenditures.
In our analysis, we exclude indirect event-related costs such as hotel capacity
expansion, public transport, airport extensions, and power supply improvements, mainly due
to the difficulty in accurately attributing these costs to the event itself and the potential
ambiguity highlighted by Baade and Matheson (2016) and Kassens-Noor (2012) about whether
such expenses would have occurred without the event. Costs for Olympic villages, media
centers, and similar facilities are also omitted, as Preuß et al. (2019) note that they are not

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directly essential and vary widely. Additionally, we exclude bidding costs (De Nooij 2014),
which are typically small compared to other expenses and complex to document accurately, as
well as in-kind costs and opportunity costs due to their variable accounting standards and
challenging quantification.
By excluding these indirect and in-kind costs, our cost estimation leans towards being
conservative. Consequently, we are likely underestimating the actual costs associated with
hosting mega-events, which, in turn, may lead to an overestimation of any potential
profitability. Finally, it is essential to note that revenues and costs often accrue to different
organizations: while IOC and FIFA hold a significant share of the overall revenue, most major
cost items are borne by the hosts. Our profitability calculations, therefore, refer to the
hypothetical profitability of the event overall (as though all major costs and revenues accrued
to the same organization) and not to the profitability of a specific organization.
3.3.2 Independent variables
Our study employs several independent variables to characterise countries along multiple
dimensions, including wealth, economic freedom, and political systems. We use the host
country’s Gross Domestic Product (GDP) per capita to capture a country's wealth.
We utilise the Economic Freedom of the World (EFW) Index by the Fraser Institute to
measure economic freedom in a country.
1
This index, available from 1950, is a comprehensive
evaluation of economic freedom across nations, encompassing several vital components such
as the size of government, legal systems, property rights, access to sound money, freedom to
trade internationally, and the regulation of credit, labour, and business. These components are
further broken down into subcategories and scored numerically, with the total score ranging
1
The EFW index is widely used in the academic literature. For a literature review, see Hall and Lawson (2014).
A comprehensive overview of the methodology employed to compute the EFW index is available here:
https://www.fraserinstitute.org/sites/default/files/uploaded/2022/economic-freedom-of-the-world-2022-
appendix.pdf

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from 0 to 10. A higher score on the EFW Index indicates greater economic freedom,
characterised by institutions and policies that support voluntary exchange, protect property
rights, and limit government interference in economic decisions.
We use the Polity Score available through the Center for Systemic Peace to assess a
country's political system.
2
This score, available for most countries from 1800, rates the level
of democracy and autocracy in a country's political system. It ranges from – 10 to + 10, where
– 10 denotes a fully autocratic regime, + 10 is a fully democratic system, and 0 is a balance
between autocracy and democracy. The Polity Score considers factors like the competitiveness
of political participation, the presence of checks and balances, the extent of political rights and
civil liberties, and the transparency of the decision-making process. This comprehensive
scoring system provides a detailed and nuanced view of a country's political landscape.
3.3.3 Control variables
In our analysis, recognizing the distinct nature of the events under study, we incorporate a
dummy variable to differentiate between the types of events. This variable is coded as follows:
0 for the Summer Olympic Games, 1 for the Winter Olympic Games, and 2 for the FIFA World
Cup. This approach allows us to control for the inherent differences between these events, each
with its unique characteristics and economic implications.
Additionally, we include 'Year' as another control variable in our model. The inclusion
of the year is crucial as it accounts for temporal factors that might influence the financial
aspects of these events, which could include changes in global economic conditions,
advancements in technology, evolving sports marketing trends, and other time-related variables
that could impact revenues, costs, and overall profitability.
2
The Polity Score is also widely used in academic literature, see, e.g., Jaggers and Gurr (1995). A detailed
explanation of the methods used to calculate the Polity score is available here:
https://www.systemicpeace.org/inscr/p5manualv2018.pdf

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Table 1 in our study presents the descriptive statistics for the variables under
consideration. In this table, we display the unadjusted values for revenues and costs.
[Insert Table 1 about here]
The data clearly illustrates the substantial variation in revenues and costs across
different events and over time. This variation indicates the diverse nature of the events in our
sample, encompassing various scales, geographies, and operational complexities.
3.4 Data Analysis
All statistical analyses were performed using the Stata 18.0 statistical analysis software
package. We used Ordinary Least Square (OLS) linear regression with robust standard errors
to determine the factors influencing the revenues, costs, and profitability of the Olympics and
the World Cup. First, we examined the factors affecting the three primary revenue streams
(broadcasting rights, sponsorships, and ticket sales) and the two significant cost drivers
(organizational expenses and sports venue capital costs). To address the non-normal
distribution of the data and to increase the validity of our findings, we opted to use the
logarithmic form of revenues and costs, as well as some of the explanatory variables, such as
GDP. Second, we turned to a sub-sample that included only the Summer and Winter Olympic
Games. This analysis aimed to assess whether the patterns and relationships observed in the
whole sample persist specifically within the context of the Olympics. We check for
multicollinearity by using the variance inflation factors on each variable. The results suggest
that multicollinearity is not an issue within the different models we are estimating, as the
highest value for any variable is 3.92, well within the threshold value of 10.
4. Results
4.1 Full Sample
In the first step, we examine the determinants of ROI, total revenues, and total costs. The
regression results are displayed as Models 1-3 in Table 2.

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[Insert Table 2 about here]
The regression analysis reveals a positive and significant relationship between a host
country’s GDP per capita and the associated revenues and costs of hosting the Olympic Games
and the FIFA World Cup. Specifically, a 1% increase in GDP corresponds to a 0.32% rise in
adjusted total revenues, indicating that wealthier nations tend to generate higher revenues. In
terms of costs, a 1% increase in GDP is associated with a 0.56% increase in total costs,
reflecting that more affluent countries often incur greater expenses, likely due to higher
standards in infrastructure and services as well as higher labour costs. However, the impact of
GDP on ROI is statistically insignificant.
3
This result implies that a higher GDP per capita does
not automatically increase these events’ profitability. The lack of a significant correlation
between GDP and ROI suggests that while wealthier countries can afford to host more
elaborate and extensive events, the financial returns on these investments do not necessarily
scale in proportion to their economic size. This finding highlights a critical aspect of event
hosting: the ability of the host country and international bodies such as the IOC and FIFA to
successfully manage and balance the economic aspects of such large-scale events to achieve a
favourable ROI.
4
Regarding the Polity score, the analysis reveals a subtle yet intriguing relationship with
total revenue. Specifically, the negative but marginally significant relationship (-0.0286, *)
suggests that a more democratic political structure, denoted by a higher Polity score, is weakly
associated with slightly lower revenues from hosting the Olympic Games and the World Cup.
3
In our analysis, GDP per capita emerges as a significant predictor for both log total revenues and log total costs
associated with hosting mega-events, indicating its substantial impact on the financial dynamics of such events.
However, the lack of a significant relationship between GDP per capita and ROI suggests a differential
sensitivity of revenues and costs to economic scale, where their proportional increase does not necessarily
translate into enhanced efficiency or profitability as measured by ROI.
4
The relationship between GDP and ROI might be non-linear or influenced by interaction effects with other
variables. Such complexities may not be captured in a standard linear regression model. For instance, the impact
of GDP on ROI could depend on factors like the type of event, economic freedom, or political environment.

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More autocratic states might have more means to coerce additional revenues, such as
sponsoring from state-owned businesses, as was typical for the mega-events in China and
Russia. These sponsorships are frequently seen as a way of projecting soft power (Chadwick,
Widdop, and Burton 2022).
Moreover, the finding that EFW has a significant positive impact (0.264, **) on the
ROI for hosting these mega-events is particularly revealing. The analysis suggests that
countries with higher economic freedom generally experience better financial returns from
these events.
The coefficients for the Winter Olympics and World Cup dummies suggest that these
events, while globally significant, tend to be smaller in scale in terms of revenue generation
and costs compared to the Summer Olympics. The Year variable highlights a clear trend of
increasing revenue and expenses over time, reflecting the growth trend of hosting large-scale
international sporting events (Müller et al. 2023), with implications for future planning and
budgeting.
In the next step, we split the total revenue into three components: broadcast,
sponsorship, and ticketing revenues, and examine their determinants (see Models 4-6 in Table
2). Similarly, we split total costs into operational and venue costs (see Models 7 and 8 in Table
2).
The strong and positive association of GDP with all revenue streams underscores the
pivotal role of a host country's economic size in generating revenue across all three categories.
The significant coefficients for broadcast, sponsorship, and ticketing revenues indicate that
wealthier nations, with their larger economies, are better positioned to capitalise on these
revenue sources. A higher GDP often aligns with enhanced broadcasting infrastructure and
larger media markets, suggesting wealthier nations possess more advanced media sectors and
a more comprehensive international presence. This, in turn, renders them more appealing to

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global broadcasters. The positive correlation with sponsorship revenue reflects the greater
market exposure and consumer spending power in larger economies. Sponsors are often drawn
to events in wealthier countries due to the enhanced visibility and potential for greater
investment returns. Though weaker, the relationship between GDP and ticketing revenue is still
significant. This finding could be attributed to the greater spending capacity of residents in
wealthier countries and these countries’ ability to attract international visitors who can afford
higher ticket prices.
In the final step, we examine the determinants of different types of costs. The results
show that GDP does not have a statistically significant effect on operational expenses or venue
costs, suggesting that a country's economic size does not consistently predict these specific
types of event costs. That GDP shows a significant correlation with total costs but not with its
sub-components can be attributed to several factors: First, when costs are aggregated into a
single total, the individual variances and unique factors influencing each cost component can
be overshadowed. This aggregation can sometimes reveal trends that are not apparent when
examining components individually. Second, there could be interaction effects between GDP
and other variables that influence Total Costs but are not as pronounced when looking at
operational and venue costs separately. Third, the relationship between GDP and expenses
might be non-linear or have threshold effects. This means that GDP's impact on costs could
change at different GDP levels, which might be more detectable in the aggregate (total costs)
than in the individual components.
Polity shows no significant impact on both types of costs, indicating that the level of
democracy or autocracy in a host country does not markedly influence operational expenses or
venue construction and preparation. However, EFW shows a mixed influence: it's positively
associated with the operating costs (though not significantly) and negatively associated with
venue costs, which is significant at the 10% level. This result suggests that venue costs might

17
be managed more efficiently in countries with higher economic freedom, potentially due to
more market-oriented approaches and less regulatory burden. In contrast, the impact on
operational costs is less clear.
4.2 Sub-sample –Olympics only
Now, we turn to our sub-sample, which exclusively includes the Summer and Winter Olympic
Games, to assess whether the patterns and relationships observed in the entire sample persist
specifically within the context of the Olympics. Models 1-3 in Table 3 explore the determinants
influencing the Olympic Games' total revenues, costs, and profitability.
[Insert Table 3 about here]
In our analysis specific to the Olympics, many patterns seen in the entire sample are
maintained. However, a key difference emerges in the relationship between GDP and total
costs. Unlike in the whole sample, in the Olympics-only sample, the correlation between a
country's economic size (GDP) and the total costs of hosting the event is not statistically
significant. This finding indicates a unique aspect of the Olympics, where the economic size
of the host country does not consistently predict the total costs associated with the event.
The determinants of revenues for the different revenue streams (Models 4-6) and cost
types (Models 7 and 8) for the Olympics-only sample are shown in Table 3 above. In our
analysis specific to the Olympic Games, GDP significantly influences broadcast revenues
alone, exhibiting a positive and notable impact. However, this effect of GDP is not observed
in the case of sponsorship and ticketing revenues, where its influence is no longer statistically
significant. The absence of a significant correlation in these areas indicates that factors other
than the host country's economic size might drive sponsorship and ticketing revenues during
the Olympics. Factors such as stadium capacities, location, the strength of local sports culture,
and the potential disruption from single events like protests or virus outbreaks (such as the Zika

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outbreak's negative impact on ticket sales in Brazil) can all significantly influence ticket sales
(Kassam 2016; Marques 2014; Müller et al. 2023).
The role of Polity in the Olympics-specific context reveals a nuanced pattern. Its
significant negative impact on both broadcast (-0.0900, ***) and sponsorship revenues (-
0.0891, ***) suggests that democratic governance structures, which often come with more
stringent regulations and a focus on equitable practices, might constrain certain revenue-
generating activities. This result could reflect how democratic norms and practices shape
broadcast rights negotiations and sponsorships, possibly prioritizing broader societal
considerations over maximum revenue generation. However, the lack of a significant impact
on ticketing revenue in the Olympics context contrasts with the wider sample, implying that
the host country's political structure might influence ticket pricing and sales to a lesser degree
in the Olympics.
EFW, intriguingly, not only maintains its significant and positive influence on ticketing
revenue (0.568, ***) but also emerges as a significant factor for sponsorship revenue (0.550,
**). This expanded impact in the Olympics context highlights the role of economic freedom in
facilitating efficient, market-driven strategies not only in ticket sales but also in attracting and
managing sponsorships. The increased significance of EFW in these areas suggests that in the
Olympics, economic freedom might enable a more conducive environment for commercial
activities, offering more flexibility and opportunity for revenue generation in these domains.
In the specific context of the Olympic Games, Table 5 above reveals distinct patterns
regarding the determinants of operational and venue costs. These findings contrast with the
broader sample. In the Olympics-only sample, GDP shows a significant association with
operational costs (0.627, *), indicating a direct correlation between the economic size of the
host country and the organizational expenses incurred for the Olympics. This significant

19
relationship likely stems from elevated expenditures for labour and various inputs integral to
the event's organization.
Regarding Polity, its significant negative association with operational costs (-0.0886,
***) in the Olympics context presents a notable shift from the broader sample. This finding
suggests that more democratically governed countries, characterized by transparent and
accountable decision-making processes, might be more adept at managing operational costs
efficiently. Specifically, a one-point increase in the Polity score is associated with an
approximate 8.48% reduction in operational costs for hosting the Olympic Games.
5
The
emphasis on democratic principles could lead to more informed and cost-effective approaches
to organizing such large-scale events.
EFW does not significantly impact the operational or venue costs in the Olympics-
specific analysis. This lack of significant influence contrasts with the broader sample, where
EFW was statistically significant and negative for venue-related costs. This variation suggests
that the unique, standardized requirements of the Olympic Games might diminish the role
economic freedom typically plays in cost management in other mega-events. The standardized
nature of the Olympics could mean that the efficiencies normally gained through economic
freedom are less applicable or evident in this particular context.
5. Discussion and Conclusion
For the first time, this study has investigated the determinants of costs and revenues of the
Olympic Games and the World Cup, shifting from dominant descriptive analyses to a more
5
In a log-linear regression model, the coefficient can be interpreted as the expected change in the logarithm of
the dependent variable (in this case, operational costs) for a one-unit change in the independent variable (Polity
score). To convert this change into a percentage, we use the formula (eβ-1)x100%, where β is the coefficient.
Substituting β = -0.0886 yields an approximate 8.48% reduction in operational costs for a one-point increase in
the Polity score.

20
inferential approach. Doing so offers valuable perspectives on the financial dynamics of these
two largest mega-events.
Our results indicate a positive correlation between a mega-event host country's GDP
per capita and the associated revenue and costs of hosting mega-events. However, this
increased capacity for generating revenue and incurring costs in wealthier nations does not
directly translate to enhanced profitability (ROI). It suggests that while wealthier countries can
organise larger events, the return on investment from these events is not necessarily
proportional to the size of the country's economy. GDP might have a proportionately different
impact on revenues and costs. If the rate at which costs increase with GDP is greater than the
rate at which revenues increase, this could lead to a negligible or non-significant effect on ROI.
In other words, wealthier countries generate more revenue and incur proportionally higher
costs, which could offset any positive impact on ROI.
Interestingly, our data reveals that the overall political regime type (i.e., the level of
democracy and autocracy within a country's political system), particularly a more democratic
political structure, is loosely associated with slightly lower revenues from hosting mega-events.
This finding may hint at various underlying dynamics. For instance, democratic nations, often
characterized by higher transparency, accountability, and public scrutiny (Grix 2013), might
prioritize sustainable and responsible event management over maximizing revenue. This
approach could manifest in more prudent financial planning, stricter regulatory compliances,
and possibly less aggressive commercialization strategies, which, while ethically sound, might
not yield the highest possible financial returns (Preuss 2004).
Additionally, democratic countries might allocate resources with a broader range of
public interests in mind, balancing the needs and concerns of different stakeholders. This
approach could include investing in legacy projects with long-term community benefits that do
not necessarily translate into immediate revenue generation. In contrast, less democratic

21
nations might have fewer checks and balances, allowing for more aggressive revenue-
maximizing strategies, albeit possibly at the expense of broader social or ethical considerations
(Maennig and Zimbalist 2012). Additionally, less democratic governments can often coerce
state-owned companies into funding the event, for example through sponsorship, thus resulting
in higher sponsorship revenues (Chadwick, Widdop, and Burton 2022).
Furthermore, the bidding and hosting processes in democratic countries are often
subject to extensive public debate and democratic decision-making, which can lead to more
cautious and balanced event budgets, which could result in more conservative financial
projections and potentially less risk-taking in revenue generation strategies (Baade and
Matheson 2002; Booth 2011; Rowe 2012). It is also possible that in democratic settings, public
opposition and environmental concerns might lead to more restrained and sustainable event
scopes, potentially limiting revenue opportunities (Makarychev and Yatsyk 2016). In sum,
while democratic governance structures bring about certain efficiencies and ethical standards
in managing large-scale events, these same structures might also impose constraints and
considerations that can modestly temper revenue maximization.
Moreover, host countries with higher levels of economic freedom tend to generate
greater profitability from mega-events. This outcome likely arises from several critical facets
associated with economic freedom. First, economic freedom often correlates with more
efficient resource allocation and utilization. In environments where market mechanisms are
less hindered by government intervention, resources, including labour, capital, and materials,
can be deployed more effectively (Hall and Lawson 2014). This efficiency is crucial in the
context of large-scale events, where the optimal allocation of resources can significantly impact
the overall cost-effectiveness and profitability.
Second, countries with higher economic freedom typically boast a more business-
friendly environment, translating into easier processes for setting up and operating businesses,

22
including those involved in event hosting, like hospitality, tourism, and retail (Coyne and
Moberg 2015). Such an environment can foster a vibrant ecosystem of service providers and
vendors, contributing to a more prosperous and profitable event.
Additionally, higher economic freedom often comes with less bureaucratic red tape,
which can expedite various processes related to event hosting, from construction and
infrastructure development to the procurement of goods and services (Rauch and Evans 2000).
This streamlined approach can reduce delays and cost overruns, positively impacting ROI.
Interestingly, the lack of a significant correlation between EFW and total revenue or
total costs suggests that while economic freedom enhances the efficiency of hosting events, it
does not necessarily scale up revenue or drive down costs directly. This finding could imply
that the benefits of economic freedom are more about 'doing things better' rather than 'doing
more things.' In other words, countries with higher economic freedom might not necessarily
host larger events or generate higher gross revenues, but they manage their events in a way that
yields better returns on the costs incurred. This insight highlights the importance of not just the
size or grandeur of an event but also how effectively and efficiently it is managed. It
underscores that economic freedom, perhaps by fostering more efficient operations and a
conducive business environment, can be crucial in enhancing major international sporting
events' financial viability and success.
When focusing solely on the Olympics, we find that most of the relationships observed
in the whole sample hold, with a notable exception being the relationship between GDP and
total costs. The correlation between GDP and total costs loses statistical significance in the
Olympics-only sample. This deviation suggests that a country's economic size does not
necessarily correlate with higher total costs in the context of the Olympics. Unlike the broader
sample, where wealthier countries tend to incur higher costs, this pattern does not clearly
manifest in the Olympics-specific analysis. This result could imply that with their more

23
standardized format and requirements (Essex and Chalkley 1998), the Olympics might have a
more uniform cost structure across different host countries, regardless of their economic size.
Such a finding underscores the unique nature of the Olympics, where factors other than GDP
might play a more pivotal role in influencing the total costs of hosting the event.
The study contributes to the public management literature by alleviating the lack of
longitudinal studies (Wond and Macaulay 2011) and enhancing our understanding of the
crucial factors that drive revenues and costs of mega-events over time. As mega-events are
beset by significant uncertainty regarding their financial implications, leading to excessive risk-
taking by the public sector (Flyvbjerg, Budzier, and Lunn 2021; Jennings 2012), our regression
models and results provide much-needed information to understand the drivers of financial
outcomes better and reduce the associated uncertainty. As such, it is the first paper, to our
knowledge, to build a model to predict the revenues and costs of these two largest mega-events
and lay the basis for further exploration of these questions with statistical or interpretive
methods.
5.1 Practical Implications
Stakeholders, ranging from event rights-holders and international federations to local
organizers and public management policymakers, can benefit from these findings to make well-
informed decisions in planning and executing future mega-events. Rightsholders such as the
IOC and FIFA, who have the power to attribute these events, may consider awarding these
events to countries that demonstrate higher political freedoms, which is likely to reduce
deficits. For public administrators, our model allows for the prediction of revenues and costs
with relatively high probability, therefore decreasing the significant uncertainty surrounding
the financial aspects of these events and preventing the need to rely on biased interest group
studies. It should be noted, however, that venue costs are less well predictable, as they likely
depend much on the condition of venues in different host locations, which is not captured in

24
our variables. Finally, our research also shows the various revenues and costs associated with
the Summer Olympics, the Winter Olympics, and the World Cup, therefore allowing public
administrators to make a more informed choice about which event to host in line with their
funding capacities. This knowledge is vital for authorities contemplating whether to bid for
such events, as it enables a more accurate cost-benefit analysis. At the same time, public
management needs to recognize that even the most diligent preparation and most efficient
management may not be sufficient to turn mega-events into profitable ventures, given that the
cards are stacked in favour of rights-owning organizations (Baade and Matheson 2016; Müller,
Gogishvili, and Wolfe 2022b).
5.2 Limitations and Suggestions for Future Research
Our study, while comprehensive, needs to acknowledge certain limitations in its research
design. First, our analysis includes major cost and revenue items. Still, it is not exhaustive, as
events generate other costs, although it is not always possible to establish a direct link between
them or access this information. Hence, our financial estimations should be viewed as
approximations based on the data available. Second, the issue of how to account for
investments in sports venues poses a challenge. These costs can be amortized over an extended
period, suggesting they should not be entirely attributed to the event that necessitated them.
However, as Alm et al. (2016) note, venues built for mega-events often become ongoing
financial liabilities for host cities, indicating that the actual costs might exceed initial
construction expenses. Third, our focus is on direct revenues and costs. We recognise that
including indirect revenues and expenses could yield different outcomes, but such measures
are beyond the scope of this analysis. Finally, our study faces limitations regarding the range
of predictors and sample size. The constraints in data availability restrict the breadth of
variables we can include in our analysis. Additionally, the relatively small sample size limits
our study's statistical power, potentially affecting our findings' robustness and generalizability.

25
While our regression models exhibit high R2 values, indicating a strong explanatory
power within our dataset, the relatively small number of observations necessitates caution in
interpreting these results. The potential for overfitting and the limited generalizability of these
findings underscore the need for further validation with more extensive and diverse datasets to
assert the robustness of these relationships confidently.
Our study opens multiple avenues for future research. First, delving into non-linear and
interaction effects could prove fruitful, given that these were beyond the scope of our current
analysis. Moreover, a deeper exploration of indirect costs and benefits is essential despite the
difficulties in their precise measurement. There is also a significant need to examine the non-
economic costs and benefits further, especially since they are often cited as justifications for
hosting large-scale events. It would be valuable to delve deeper into these additional factors to
develop a more comprehensive model for predicting and understanding the financial
complexities of hosting the Olympic Games. Last, expanding our study to include a broader
spectrum of events is advisable because the exceptional scale of the Olympic Games and FIFA
World Cup may position them as outliers rather than standard benchmarks. This could lead to
identifying other aspects that significantly impact the costs of hosting mega-events that have
not yet been explored.

26
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30
TABLES
Table 1: Descriptive Statistics
Variable
Obs
Mean
Std. Dev.
Min
Max
ROI
37
-0.38
0.52
-0.95
1.93
Total Revenue ($M)
38
1,791
1,720
32
5,840
Total Costs ($M)
42
2,976
3,119
70
15,084
Ticketing Revenue ($M)
42
213
237
6
1,081
Broadcast Revenue ($M)
43
856
918
7
3,127
Sponsor Revenue ($M)
38
595
644
1
2,093
Cost of Venues ($M)
42
1,826
2482
8
12,616
Cost of Organization ($M)
43
1,125
990
24
3,277
GDP per capita
41
29,898
17,830
4,408
59,685
EFW
41
7.12
1.07
3.61
8.7
Polity
43
6.7
5.99
-9
10
Event Dummy
43
1
.82
0
2
Year
43
1991
16
1964
2018

31
Table 2: Regression Results
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
ROI
Log Total
Revenues
Log Total
Costs
Log
Broadcast
Revenues
Log
Sponsorship
Revenues
Log
Ticketing
Revenues
Log
Operational
Costs
Log Venue
Costs
Log GDP per capita
0.00994
0.320***
0.564**
0.450**
0.581**
0.270*
-0.0675
0.437
(0.127)
(0.102)
(0.248)
(0.202)
(0.246)
(0.148)
(0.224)
(0.338)
Polity
-0.0314
-0.0286*
-0.0331
-0.0319
-0.0108
-0.0387*
0.00370
0.00206
(0.0190)
(0.0144)
(0.0313)
(0.0220)
(0.0434)
(0.0204)
(0.0322)
(0.0412)
EFW
0.264**
0.103
-0.257
0.00305
-0.0806
0.308**
0.263
-0.609*
(0.104)
(0.0907)
(0.155)
(0.127)
(0.250)
(0.116)
(0.173)
(0.312)
Winter Olympics
-0.301
-0.976***
-1.136***
-0.997***
-0.886***
-1.456***
-0.813***
-1.370***
(0.276)
(0.182)
(0.334)
(0.231)
(0.308)
(0.146)
(0.267)
(0.492)
World Cup
-0.190
-0.840***
-1.032***
-1.300***
-0.683**
-0.273
-1.546***
-1.103*
(0.207)
(0.160)
(0.299)
(0.237)
(0.269)
(0.166)
(0.262)
(0.584)
Year
0.00825
0.0873***
0.0651***
0.0989***
0.116***
0.0494***
0.0701***
0.0694***
(0.00496)
(0.00429)
(0.00822)
(0.00576)
(0.00880)
(0.00358)
(0.00689)
(0.0125)
Constant
-18.47*
-156.6***
-111.4***
-181.0***
-217.1***
-83.73***
-120.0***
-117.0***
(9.057)
(8.378)
(15.44)
(11.06)
(17.82)
(6.605)
(13.22)
(22.77)
Observations
35
36
40
41
36
40
41
40
R-squared
0.286
0.945
0.778
0.922
0.910
0.918
0.859
0.554
Notes: The unit of observation is an event. Coefficients are estimated by OLS regression models. Robust standard errors are in parentheses. For models
1-3, the dependent variables are ROI, total revenues and total costs. For models 4-6, the dependent variables are the three revenues streams (broadcast,
sponsorship and ticketing) and for models 7 and 8, the dependent variables are the two cost types (operational and venue costs).
*** p<0.01, ** p<0.05, * p<0.1

32
Table 3: Regression Results – Olympics only
Model 1
Model 2
Model 3
Model 4
Model 5
Model 6
Model 7
Model 8
ROI
Log Total
Revenues
Log Total
Costs
Log
Broadcast
Revenues
Log
Sponsorship
Revenues
Log
Ticketing
Revenues
Log
Operational
Costs
Log Venue
Costs
Log GDP per capita
0.102
0.457**
0.483
1.049***
0.110
0.0514
0.627*
0.0531
(0.279)
(0.209)
(0.404)
(0.277)
(0.295)
(0.162)
(0.315)
(0.601)
Polity
-0.0399
-0.0650***
-0.0555
-0.0900***
-0.0891***
-0.0121
-0.0886***
0.00177
(0.0418)
(0.0159)
(0.0375)
(0.0212)
(0.0269)
(0.0220)
(0.0297)
(0.0550)
EFW
0.326*
0.190
-0.0995
-0.149
0.550**
0.568***
-0.0431
-0.108
(0.164)
(0.169)
(0.226)
(0.218)
(0.194)
(0.0996)
(0.228)
(0.322)
Winter Olympics
-0.355
-1.014***
-1.071***
-1.131***
-0.695**
-1.442***
-0.879***
-1.280**
(0.314)
(0.208)
(0.367)
(0.226)
(0.272)
(0.143)
(0.248)
(0.548)
Year
0.00580
0.0800***
0.0558***
0.0826***
0.108***
0.0491***
0.0527***
0.0615***
(0.00936)
(0.00663)
(0.0111)
(0.00573)
(0.00904)
(0.00551)
(0.00547)
(0.0180)
Constant
-14.89
-143.9***
-92.97***
-153.0***
-199.9***
-83.13***
-89.16***
-101.2***
(16.64)
(12.88)
(19.99)
(10.91)
(17.60)
(10.18)
(10.82)
(31.60)
Observations
23
23
27
27
23
26
27
27
R-squared
0.297
0.946
0.751
0.939
0.937
0.951
0.839
0.591
Notes: The unit of observation is an event. Coefficients are estimated by OLS regression models. Robust standard errors are in parentheses. For models
1-3, the dependent variables are ROI, total revenues and total costs. For models 4-6, the dependent variables are the three revenues streams (broadcast,
sponsorship and ticketing) and for models 7 and 8, the dependent variables are the two cost types (operational and venue costs).
*** p<0.01, ** p<0.05, * p<0.1