Modelling Tourism Demand in Macau: A Panel Analysis
Bibliographic record
Abstract
This study aims to examine the determinants affecting tourism demand in Macau.Macau is highly dependent on the tourism and gambling industry, and its economic activities are almost fully controlled by these industries.To ensure the sustainable growth of the tourism industry in Macau, it is crucial to identify the factors influencing its tourism demand.A panel regression covering quarterly data of ten major tourist-generating countries from 2010Q1 to 2019Q4 has been employed in this study.The selected determinants include income level of origin countries, transportation cost, and exchange rate.The empirical results from the fully modified ordinary least square (FMOLS) and dynamic ordinary least square (DOLS) models clearly showed that the income levels in origin countries and exchange rate significantly affect tourism demand in Macau.Income level is positively related to tourism demand while the exchange rate adversely affects tourism demand in Macau.Transportation cost, interestingly, is a statistically insignificant determinant for the Macau tourism demand.This is because majority of the tourists visiting Macau are from neighbouring countries such as China and Hong Kong, and thus the travel cost is not their main consideration when deciding to travel.The empirical findings of this study validated the linkage among income level, exchange rate, and transportation cost on tourism demand in Macau.This study is useful for the government and key industry players to design strategic tourism plans for sustainable growth of the tourism industry in Macau.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".