Determinants of the Economic Vulnerability of Businesses to Pandemics and Similar Events
Bibliographic record
Abstract
After providing a general overview of factors that make businesses economically vulnerable to pandemics (such as COVID-19), this article identifies specific elements that increase the vulnerability of businesses to pandemics. These specifics include the extent to which the demand for their production declines, how easy it is for them to reduce the costs of their production (cost escapability), the importance of disruptions or breaks in the supply chains of inputs utilized by businesses, and their ability to sustain their liquidity. Businesses that rely on personal contacts for sales are especially threatened, for example, those in the hospitality and tourism sector. However, others are also vulnerable for the reasons given. Nevertheless, some businesses do gain as a result of pandemics and similar events. Their economic gain adds to GDP. However, it could be more appropriate to regard their gains as a part of the cost of a pandemic rather than a benefit of it. The effect on the vulnerability of businesses if government policies designed to control pandemics is also considered. The main original contribution of this article is to show how the microeconomic theory of the firm can be adapted to conceptualize the vulnerability of individual businesses to pandemics, particularly COVID-19, while also noting the limitations of this approach.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".