The structural deficit of the Olympics and the World Cup: Comparing costs against revenues over time
Bibliographic record
Abstract
The Olympic Games and the Football World Cups are among the most expensive projects in the world. While available theoretical explanations suggest that the revenues of mega-events are overestimated and the costs underestimated, there is no comprehensive empirical study on whether costs exceed revenues. Based on a custom-built database from public sources, this article compares the revenues and costs of the Olympic Games and World Cups between 1964 and 2018 ( N = 43), together totalling close to USD 70 billion in revenues and more than USD 120 billion in costs. It finds that costs exceeded revenues in most cases: more than four out of five Olympics and World Cups ran a deficit. The average return-on-investment for an event was negative (– 38%), with mean costs of USD 2.8 billion exceeding mean revenues of USD 1.7 billion per event. The 1976 Summer Olympics in Montréal, the 2014 Winter Olympics in Sochi and the 2002 World Cup in Japan/South Korea recorded the highest absolute deficits. The Summer Olympics 1984 in Los Angeles, the Winter Olympics 2010 in Vancouver and the 2018 World Cup in Russia are among the few events that posted a surplus. The article concludes that the Olympic Games and the Football World Cup suffer from a structural deficit and could not exist without external subsidies. This finding urges a re-evaluation of these events as loss-making ventures that lack financial sustainability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".