Building economic resilience to pandemic risk in Switzerland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper examines the scope for pandemic insurance in Switzerland, addressing the residual revenue losses faced by firms despite comprehensive fiscal and monetary policies during COVID‐19. While these policies provided critical support, they failed to fully mitigate revenue declines from government‐imposed business interruptions. We highlight how pandemic insurance could reduce firms' exposure to revenue shocks and lessen reliance on costly interventions. Drawing insights from the Swiss Elemental Pool, a successful framework of risk‐pooling for natural catastrophes, we explore its applicability to pandemic risks. Given the systemic nature of pandemics, we argue that intertemporal risk‐sharing, capital accumulation, and risk transfer to financial markets can support a viable public–private partnership (PPP) for pandemic insurance. While conceptually promising, such a PPP requires further empirical evaluation of costs, benefits, and policy interactions. A well‐designed framework could enhance resilience to future pandemics and reduce the economic burden of ex‐post interventions.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it