Economic Impact Analysis versus Cost Benefit Analysis: The Case of a Medium-Sized Sport Event
Bibliographic record
Abstract
This paper empirically illustrates the difference between a standard economic impact analysis (EIA) and a cost-benefit analysis (CBA). The EIA was conducted using an existing (input-output) I-O model (STEAM). The benefit side of the CBA included non-local visitor spending, the revenue of the local organizing committee (LOC), the consumer surplus, and public good value of the sport event for the local residents. The cost side of the CBA was estimated based on the opportunity costs related to the construction of the stadium (including labor costs and the cost of borrowing), imports, and ticket sales to locals. The EIA indicated that the 2005 Pan-American Junior Athletic Championships generated a net increase in economic activity in the city of $5.6 million. The CBA showed a negative net benefit of $2.4 million. Both methods presented challenges and limitations, but CBA has the distinct advantage that it identifies the net benefits associated with hosting a sport event.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".