Tourism Development and Italian Economic Growth: The Weight of the Regional Economies
Bibliographic record
Abstract
This research aims to study the relationship between economic growth and the increase in the tourism sector in Italy. Unlike most of the literature, we use the value added in the main economic sectors involved in tourism activity as a proxy for tourism development. The use of the tourism value added allows us to analyze the effect of both international and domestic tourism on per capita GDP growth. The main working hypothesis we tested is whether the relationship between GDP growth and the expansion of the tourism sector is in any way influenced by the geographic area referenced and/or the time period considered. Accordingly, we conducted our analysis at both the national and subnational (cluster) levels, splitting the original sample into two equal subperiods (1997–2008 and 2009–2019). The panel VAR analysis shows that for the country as a whole, tourism growth depends on the past value of the economic growth rate, especially for the subperiod 2009–2019. The cluster analysis clarifies that these outcomes are strongly determined by the cluster that covers the wealthiest Italian regions.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 0.000 |
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".