Modelling International Tourism Demand for the Caribbean
Bibliographic record
Abstract
This paper provides models of international tourism demand for four destination countries (the Bahamas, Barbados, Jamaica and St Lucia) in the Caribbean region, where visitor arrivals are mainly from Canada, Germany, the UK and the USA. The authors use a less restrictive econometric technique – the autoregressive distributed lag (ARDL) bounds test – to test for cointegration among the variables in the tourism demand equations. The empirical results from the bounds test indicate the existence of a unique long-run relationship between tourist arrivals, per capita real income, tourism prices and transport costs. The estimated results show that changes in tourists' income, tourism prices, travel costs, the 9/11 2001 terrorist attacks on the USA and the 2003 US–Iraq War significantly affect tourists' travel decisions. The results also show that tourism demand for Caribbean destinations is highly income elastic in both the short run and the long run, but relatively price inelastic in both periods. The price inelasticities suggest that if policy makers and/or tourism stakeholders increase the prices of the tourism products and services, this would not decrease the number of tourist arrivals proportionally but would increase total tourism receipts or revenues. Overall, the CUSUM and CUSUMSQ stability tests reveal that the parameters of the international tourism demand models are stable and, as such, they can be used for policy analysis and forecasting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".