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Record W2612885150

Bringing Home the Gold? A Review of the Economic Impact of Hosting Mega-Events

2016· review· en· W2612885150 on OpenAlexaboutno aff
Douglas Barrios, Stuart H. Russell, Matthew Andrews

Bibliographic record

VenueDigital Access to Scholarship at Harvard (DASH) (Harvard University) · 2016
Typereview
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsChinaPopulationTourismBusinessEconomic impact analysisDownloadEconomyState (computer science)PoliticsEconomicsGeographyPolitical scienceDemography
DOInot available

Abstract

fetched live from OpenAlex

There is perhaps no larger sports policy decision than the decision to host or bid to host a mega-event like the FIFA World Cup or the Summer Olympics. Hosts and bidders usually justify their decisions by touting their potential impact. Many organizers and promoters either fund or widely disseminate ex-ante studies that tend to highlight the positive effects of the event. For instance, the consultancy firm Ernst & Young produced a 2010 report prior to the 2014 World Cup in Brazil that painted an optimistic picture of the event’s potential legacy. It estimated that an additional R$ 142.39 billion (4.91% of 2010 GDP) would flow through the Brazilian economy over the 2010-2014 period, generating 3.63 million jobs per year, R$ 63.48 billion (2.17% of 2010 GDP) of income for the population and additional tax collection of R$ 18.13 billion (0.62% of 2010 GDP) for the local, state and federal governments. Ernst & Young estimated that during the same period 2.98 million additional visitors would travel to Brazil, increasing the international tourist inflow up to 79%. Such results, if true, would clearly attractive for governments considering a bid, but these expected impacts don’t always materialize. Moreover, hosting mega-events requires significant investments - and the cost of these investments is rising. Zimbalist notes emerging economies like China, Brazil, and South Africa have increasingly perceived "mega-events as a sort of coming-out party signaling that [they are] now a modernized economy, ready to make [their] presence felt in world trade and politics" (Zimbalist 2015). Their intentions may be noble, but the intention of using mega-events as a "coming-out party" means developing countries hoping to host them need to make massive investments. They are confronted by significant obstacles in that they lack sufficient stadiums, accommodations, transportation systems, and other sports-related infrastructure. As a result, each of the mega-events hosted by emerging economies has been exorbitantly expensive. The 2014 World Cup cost Brazil between USD 15 billion and USD 20 billion, while Beijing reportedly spent USD 40 billion prior to the 2008 Summer Olympic (Zimbalist 2015). Additionally, as the debt-ridden 1976 Summer Olympics in Montreal demonstrates, expensive mega-events are not limited to emerging economies alone. Flyvbjerg and Stewart have even shown that every Olympics since 1960 has gone over budget (Flyvbjerg and Stewart 2012). Such incredible figures, in terms of both costs and benefits, beget the question: are mega-events worth it? Which type of reports should governments focus their attention on? What economic consequences should a government reasonably expect? With such high stakes, policymakers need to choose wisely. We attempt to answer these questions and aid the decisions of policymakers by providing a concise review of the rich academic literature on mega-events. For the purposes of this paper, we mainly focus on the Summer Olympic Games and the FIFA World Cup as mega-events. However, we also leverage information regarding events like the Winter Olympic Games, the UEFA football championships, and the Commonwealth Games. These events are organized on a smaller scale than the previous two, but they might provide some insights on how to best understand mega-events. We focus on claims surrounding the direct or indirect mechanisms that facilitate the impact that ex-ante studies predict. We provide a review of these claims and their validity according to the existing literature. Section 1 focuses on the argument that mega-events lead to increased economic activity in the host economy. Specifically, we evaluate whether or not mega-events leads to access to previously inaccessible funds and increased investments. These investments could theoretically come from supranational organizations, private stakeholders, or public stakeholders. We also consider whether or not these new expenditures and investments have the multiplicative effect that many ex-ante studies assume they have. We finally investigate if the economic activity surrounding mega-events leads to increased revenues and tax collection for host governments. Overall, the existing academic literature suggests that any increased economic activity resulting from the event is routinely dwarfed by additional public budgetary commitments. Moreover, the arguments regarding multiplicative effects and increased revenues also tend to be exaggerated. Section 2 shifts the focus to the potential impact of mega-events on a specific industry: tourism. We explore the effect of mega-events on the number of tourists visiting the host region and their spending habits. We explore this channel both for analyses specific to a single mega-event and for cross-country evaluations incorporating many events. Next, we consider the impact of a mega-event on a region’s brand and image in the international community with the idea of testing if hosting the competition will impact future tourism. Finally, we consider if mega-events lead to increases in the capacity of a city or country to welcome future tourists as a result of improved airport infrastructure, accommodations, and/or transportation systems. As was true in Section 1, the academic literature suggests that the claims of many ex-ante studies are misleading. Our review finds that there is some evidence for increases in tourist arrivals to certain events, but those increases are far smaller than what is generally predicted beforehand. These effects are also usually dependent on factors, such as the timing of the competition, that are specific to the host region and the event itself. Section 3 briefly discusses other potential qualitative and social impacts of mega-events such as international business relations, crime reduction, and the "feel-good effect." In the penultimate section, Section 4, we discuss how these conclusions should impact the decision-making of policymakers. Finally, in a short conclusion, we summarize the findings of our review.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.013
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.063
GPT teacher head0.351
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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