Hosting Annual International Sporting Events and Tourism: Formula 1, Golf or Tennis (CEIBS Working Paper, No. 013/2020/ECO)
Bibliographic record
Abstract
Hosting sports events to attract international tourists is a common policy practised by many host governments. Hosting mega-sports events like the Olympics are said to leave a legacy that could impact the attractiveness of a country/city in the long term. However, the opportunity to host these mega-events are limited and expensive. This study considers the economic impact of hosting annual international sporting events, specifically the extent to which Formula-1, ATP Tennis, and PGA Golf can attract international tourists. Using monthly data from 1985 – 2018 and we show that the effect differs from one sport to another within a country, and the same sport across countries. Hosting the Formula-1 is most effective for Canada but has no significant impact in the UK. ATP tennis has a significant impact on all three countries but may not be the star event. Policy-makers must consider carefully the sport that gives the best bang-for-the-buck.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
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; both teacher heads agree on what is shown here.
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".