Factors that affect the demand of tourism in Mexico: competitive analysis
Bibliographic record
Abstract
Purpose Several studies have been made that analyze factors that affect the demand of tourism from several optics. This paper aims to study the factors that determine the demand for tourism in Mexico, through an econometric analysis, by using the Johansen cointegration model (1991) to determine the long-term elasticity between the demand of tourists and the wealth related to its main markets (the USA and Canada) and the relative prices in Mexico and its two main competitors (the Dominican Republic and Costa Rica). Design/methodology/approach The authors used econometric analysis using Johansen’s cointegration model (1991), using as a dependent variable the demand of tourists from the main countries of origin (the USA and Canada), taking as data the number of tourists by air in the period 1980-2015, according to information from the SIIMT. The independent variables are the relative wealth of the country of origin of the tourists (wealth of the tourist in Mexico concerning the wealth in their country of origin) and the relative prices of the destination country with respect to the country of competition. The source for per capita income and the consumer price index is the World Bank. Findings The results obtained in this document show that in the long-term the price is a factor of impact in the purchase decision of both markets analyzed. Presenting an elastic demand to the price, which implies that the market is sensitive to the variations of the price of tourist services, opting for the destination that offers better prices, with a higher sensitivity to the price when compared with Costa Rica. Coinciding with previous studies carried out in other tourist destinations, such as in the work of Patsouratis et al. (2005). Originality/value The main contribution of this work is to determine the long-term relationship, through a cointegration analysis of Johansen (1991). A methodology that has not been used to perform a competitive analysis between countries. Additionally, the present work uses variables different from those considered in previous works; the dependent variable is the demand of tourists from the main countries of origin (the USA and Canada) and as dependent variables the relative wealth of the country of origin of the tourists (Wealth of the tourist in Mexico with respect to wealth in their country of origin) and the relative prices of the destination country with respect to the country of competition.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".