Evaluating Tourism's Economic Effects: Comparison of Different Approaches
Bibliographic record
Abstract
There are several models available to evaluate the economic impact of tourism. All are different from each other in terms of nature, structure, result driven, demand of the data and complexity. Most of the time it is not sure that model is appropriate for the situation where is it been applied. Numerous practices including ‘Multiplier Analysis’ and ‘Input–Output Analysis’ are still frequently used for estimation of economic impacts of tourism in change of traveller's expenditure. All the existing techniques have serious limitations, and therefore, alternative techniques have been proposed to address the existing problems. Amongst these models are ‘Computable General Equilibrium (CGE) model’ and ‘Money Generation Model (MGM)’ that are comprehensively used in Australia, the United Kingdom, the United States and Canada to estimate economic impacts of changes and policies, across many sectors. Within the tourism industry, CGE technique has not been used broadly, resulting in poor estimation of economic impacts of tourism. Considering it, this paper will support the arguments of CGE and MGM modelling as the favoured practises in analysing the economic impacts of tourism and will discuss its prospective for the future research in this area.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".