Tourism and Economic Growth in South Asian Countries: Asymmetric Analysis and Lessons for Mitigating the Adverse Effects of Covid-19 Pandemic
Bibliographic record
Abstract
Since the first Quarter of 2020, due to the spread of the Covid-19 pandemic, which is still continuing unabated with the periodical emergence of new variants, international tourism has become one of the most adversely affected sources of external earnings of developing countries. The World Travel and Tourism Council has predicted that border closures, as part of travel bans imposed by all affected countries to contain spread of the pandemic combined with the shattered travelers’ confidence, would lead to a loss of 100 million jobs on a global scale in 2021 and 2022 along with an expected fall in world tourism by 60 to 80 percent. For the South Asian countries, the crisis would result in a 42 to 60 percent drop in tourist arrivals in 2020 and 2021. Tourism has also been providing a great impetus to the growth of informal sector supported by information and communication technology with participation of women, both full time and part time, in significant number of 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. While a given size of positive partial sum decomposition of tourism increased growth, the negative partial sum decomposition of tourism of the same size resulted in a greater adverse effect on economic growth. There are some lessons of policy implications which are drawn from the study in the context of continuing uncertainties.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".