Asymmetric Analysis of Tourism and Economic Growth in South Asian Countries: Lessons for Policymakers towards Mitigating the Adverse Effects of Covid-19
Bibliographic record
Abstract
Since the first quarter of 2020, due to the spread of the Covid-19 pandemic, which is continuing unabated with the periodical emergence of new variants, international tourism has become one of the most affected sources of external earnings for developing countries. For the South Asian countries, the crisis was predicted to result in a 42% to 60% drop in tourist arrivals in 2020 and 2021. Tourism has been providing a great impetus to the growth of the informal sector supported by information and communication technology and the participation of women, both full-time and part-time in several small and mini-enterprises. This panel study employing a nonlinear econometric methodology confirms the existence of an asymmetric association between tourism and economic growth for six South Asian countries for the period 1995 to 2018 for which data series are complete and officially available. While the positive partial-sum decomposition of tourism increased economic growth, the negative-sum decomposition of tourism had a much greater adverse effect on economic growth. There are some relevant conclusions with policy implications in the context of continuing uncertainties.Copyright© 2022 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".