Testing the efficacy of the economic policy uncertainty index on tourism demand in USMCA: Theory and evidence
Bibliographic record
Abstract
The global economic outlook is more uncertain than ever before and sensitive to uncertainties related to a variety of economic policies decisions of all stakeholders and governments. These perceived uncertainties may be the culprit in shrinking the size of overall economic activity. Under increasing uncertainties, travel and vacation plans of consumers can be canceled or postponed. Therefore, policy-related economic uncertainties are expected to affect tourism demand beyond well-established economic and noneconomic factors. In this study, we explore the efficacy and the impact of the economic policy uncertainty (EPU) index in predicting the tourism demand on international tourist arrivals (a measure of tourism demand) to the United States from Mexico and Canada over the period of January 1996–September 2017. The findings of the study reveal that EPU is a significant predictor as increases in the EPU index lead to decreases in tourism demand to the United States. Canadian tourists seem to be more sensitive to EPUs. Increases in the EPU index cause them to reduce Canadians’ vacations to the United States proportionally more than the Mexicans. To enhance the explanatory power of current models, the uncertainty can be a theoretically significant construct thus needs to be included when calibrating demand models.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".