The asymmetric effects of medical tourism and information technology on economic growth: evidence from panel quantile regression
Bibliographic record
Abstract
Medical tourism is a sub-segment of tourism that is lucrative for recipient countries. Estimated at US$11.56 billion in 2022, the market value of medical tourism is projected to reach US$53.51 billion in 2028. Given the importance of that industry, this study attempts to contribute to the literature on medical tourism, information and telecommunication technology (ICT) and economic growth. Unlike previous related studies, we explore the asymmetric effects of medical tourism and ICT on economic growth using panel quantile regression, using a balanced 2013–2021 panel sample across 48 countries. To enhance robustness and reliability, our growth model accommodates various control variables (e.g. capital, population growth, energy consumption). We found that although medical tourism and ICT contribute significantly to economic growth, this effect tends to be asymmetric. Moreover, the effect of ICT is greater in low- and middle-income countries, whereas the effect of medical tourism is greater in upper-middle-income countries.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".