Are there Asymmetric Causal Relationships between Tourism and Economic Growth in a Panel of G-7 Countries?
Bibliographic record
Abstract
The aim of this paper is to analyse the relationship between tourism activity and economic performance for G-7 countries. This paper attempts to answer two questions. Firstly, it will look at whether economic growth causes increased tourism activity for Italy, Canada, Japan, France, the UK, the US and Germany. Secondly and most importantly, the paper will address the question of whether increased tourism activity causes economic growth for these countries. That is, the authors aim to examine whether the tourism led hypothesis is valid for any of these countries. This is done by applying the asymmetric panel causality test suggested by Hatemi-J (2011) to these countries for the period 1995-2012. This approach is an attempt to find out which tourism shocks, negative or positive, have a greater impact on economic performance and which of the GDP shocks have a greater impact on tourism activity for each country. The results show that there is a causal relationship between tourism activity and economic growth, with GDP actively causing tourism activity for Canada, Germany, France, Italy and Japan. In this case, Canada and Germany are the only two countries where a symmetric causal relationship is found. More importantly, the results further show that tourism activity causes GDP growth for Germany, France, Italy and US. Germany, France, and the US, however, are the only three countries where a symmetric causal relationship is found. Further, one could conclude that the TLGH is not valid for G-7 countries given that positive tourism activity shocks do not lead to positive economic output shocks for any of the countries.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".