Does tourism reduce the shadow economy? An international evidence
Bibliographic record
Abstract
This paper investigates one of the positive contributions of tourism to the economy through the lens of its influences on the shadow economy. Specifically, our study analyzes the effects of five indicators of tourism consumption (including domestic tourism spending, international travel and tourism consumption, business tourism spending, leisure tourism spending, and outbound tourism spending on the percentage of shadow economy to GDP) in 129 economies between 1996 and 2015. We find interesting results that contribute to the existing literature about tourism economics. Firstly , the development of the inbound tourism industry reduces the shadow economy significantly, while outbound tourism causes higher underground economic activities. Secondly , the influence of tourism on the shadow economy is significant in both the short-run and long run with a stronger effect in the long run. Thirdly , the effect of tourism on the shadow economy is more significant in the 42 High-Income Economies and 54 Low and Lower-middle Income Economies, while it is less obvious in the 33 Upper-Middle Income Economies. These findings have been checked by a battery of robustness checks ensuring their statistical consistency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.009 | 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".