Economic Impact Analysis of the Olympics and Winter Olympics
Bibliographic record
Abstract
Although the first modem Olympics began in 1896 in Athens, it was not until the 1970``s that the Olympics took on its more economic form that we see today. As detailed by Brown and Massey (2001), both the 1972 Munich Olympics and 1976 Montreal Olympics suffered heavy losses, with Montreal losing what amounts to over one billion in US dollars. In 1984, however, the Los Angeles Olympics lessened public, government funding, and successfully focused more on investment from private firms reaching surpluses of almost half of a billion US dollars. Other such attempts were not as effective, such as Seoul in 1988, Barcelona in 1992, Sydney in 2000, and Athens in 2004, as there was more of a need for government funding. Our research sheds light on the economic impact to Olympic host countries by accounting for distinct economic characteristics, and overcoming some of the limitations of past research. Panel regression analysis using the two-way error component model, is used to consider time characteristics, like before and after hosting, and group characteristics, specific to the host countries analyzed. Even if there are correlations between group and time characteristics, we can receive the estimated coefficient about independent variables and show the significance that influences the economic effects of the host country. It is shown that the visible economic effects are more apparent in the short-term. Therefore, the study will examine the effects of the Olympics and Winter Olympics in terms of macroeconomic variables, such as GDP, change in growth rate, and change in the number of visiting tourists, and measure their effectiveness through panel regression analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".