Tourism and contribution to employment: global evidence
Bibliographic record
Abstract
Purpose This study examines how tourism contributes to employment. Design/methodology/approach Using various econometric techniques for panel data, the study estimates the contribution of tourism to employment in a sample of 148 economies from 2002 to 2017. The analysis is also carried out for three sub-samples according to income levels. Findings This study has three significant contributions: Firstly, it shows that investment and consumption in the tourism sector have positive benefits for employment. Furthermore, the improvement of institutional quality boosts these positive gains. Secondly, there is a U-inverted relationship between the income level and total contributions of tourism to employment. The development of the tourism industry would therefore follow the pattern suggested by the Kuznets curve hypothesis. Thirdly, the positive effects of tourism investment and consumption in tourism are evidenced in all three sub-samples. In contrast, the effects of institutions seem to be weaker in higher-income economies (implying that there is a larger space for low-income economies to use institutional reform to boost the development and contribution of tourism in their economies). Finally, institutional quality appears to enhance the contribution of tourism to employment. Originality/value The study highlights the importance of the tourism industry in enhancing employment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".