Effects of COVID-19 on Variations of Taxpayers in Tourism-Reliant Regions: The Case of the Mexican Caribbean
Bibliographic record
Abstract
Given the tourism industry’s risk and vulnerability to pandemics and the need to better understand the impacts on tourism destinations, this research assesses the effect of the COVID-19 outbreak on the variation of taxpayer units in the Mexican Caribbean region, which includes some of the major sun-and-sand beach destinations in Latin America. Using monthly data of registered taxpayer entities at the state and national levels as the analysis variable, probability distributions and definite integrals are employed to determine variations of the year following the lockdown, compared with previous years’ data. Results indicate that despite the government’s measures to restrict businesses’ operations and a reduction in tourism activities, registered taxpayers at the regional level did not decrease for most of 2020. Further, as business activities and tourism recovered, taxpayer units increased at the end of 2020 and beginning of 2021. Surprisingly, such a pattern was not observed at the national level, which yielded no statistically significant variations. A discussion of factors influencing the resilience of the tourism region in the study (e.g., outbound markets’ geographic proximity, absence of travel restrictions, closure of competing destinations) and implications for public finances are presented.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".