Tourism Development and Economic Growth: A Comparative Study for the G-6 Leaders
Bibliographic record
Abstract
Purpose: The paper investigates the relationship between tourism development and economic growth for the six richest countries globally for the period 1995-2017 by estimating a simultaneous system equations model. The purpose of this paper is to examine the long-run relationship between these variables by the use of the two-stage least squared methodology. Design/Methodology/Approach: A structural system equation model is estimated for the G-6 leader countries and then we apply a Monte Carlo simulation method, in order to find out the predictive ability of the equation model. Findings: The results of this study indicated that there is a positive relationship between tourism development and economic growth taking into account the negative effect of interest rates and the positive effect of investments, trade openness, and consumption on economic growth. Practical Implications: The group of six leader countries is a group consisting of Canada, France, Germany, Italy, United Kingdom, and USA regarded as the most industrialized countries in the world. Originality/Value: The study offers an in-depth insight into econometric modelling of economic growth.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".