Stronger together: International tourists “spillover” into close countries
Bibliographic record
Abstract
This paper explores the spillover effect of spatial proximity on international tourism in all 195 countries using data from the World Bank. We use a spatial proximity measure to calculate the number of neighbors that each country has and how the neighboring nations’ international tourist arrivals “unintentionally” affect each country’s international tourism. We define spatial proximity using both the conventional contiguity measure and the minimum-distance measure (MDM) of proximity: the two closest points between countries on their outer boundaries. By constructing spatial lag models (SLM) and spatial error models (SEM), we capture the spillover effects between neighbors. Our findings suggest that a country’s international tourism flows over the period of 1995–2019 are strongly influenced by international tourist arrivals to the nation’s neighboring countries; ranging from 8.1% to 45.8%, depending on the model used. Particularly, the spillover effect was more prominent for the period from 2015–2019, as compared to 1995–1999, implying increasing dependence among neighboring countries in international tourism, which directly contrasts the common assumption that technology is making geographic distance less relevant. This paper provides several important implications for both scholars and practitioners, although further study is required to determine the effects of historical interactions and spatial relations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".