A Systemic Analysis of the Impact of the Pandemic on the Indian Tourism Economy
Bibliographic record
Abstract
The COVID-19 pandemic had a severe impact on the tourism industry across the world. Be it aviation or hospitality, transportation, tour operators or eateries, every activity related to tourism was adversely affected by the pandemic in an unprecedented manner. India saw the first severe impact during the first quarter of 2020–2021 when the tourism industry was severely affected, in terms of loss in tourism demand due to a significant fall in tourist arrivals. The industry saw gradual signs of recovery post-October 2020 but was hit again by the second wave during April–June 2021 and then by the third wave during November 2021–January 2022. Given the contribution that tourism makes to the entire economy in terms of income and employment generation, it is important to do a systemic estimation of the losses caused by the pandemic so that resilient policies are put in place to address the challenges at all levels and put the tourism sector back on the path it was traversing before the pandemic. This article presents the estimates of economic losses resulting from the changes experienced during the most critical period of the pandemic, that is, the first quarter of 2020–2021, which witnessed a complete lockdown, and the subsequent two quarters, wherein the sector started showing gradual recovery following various relaxations in economic activities and travel movements. The estimates are based on the methodology that draws from the framework laid out in the Tourism Satellite Account of India, which, in turn, is based on the methodological framework recommended by the United Nations World Tourism Organization. JEL Codes: L830, Z320, F620
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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 | 0.024 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".