Insuring the 'Uninsurable': Catastrophe Bonds, Pandemics, and Risk Securitization
Bibliographic record
Abstract
In principle, governments could protect against the potential economic devastation of future pandemics by requiring businesses to insure against pandemic-related risks. In practice, though, insurers do not currently offer pandemic insurance. Although they may well be able to obtain sufficient actuarial data to set pandemic underwriting standards and rate tables, insurers are concerned that they lack sufficient capacity, as an industry, to cover those risks, which are likely to occur worldwide and to be highly correlated. Pandemics therefore are in the class of risks, like war, terrorism, and riots, that are deemed “uninsurable,” at least by private markets. This Article examines how risk securitization—a relatively recent and innovative private-sector alternative to government insurance, funded by the issuance of catastrophe (CAT) bonds—could be used to help insure pandemic-related risks. Risk securitization would utilize the “deep pockets” of the global capital markets, which have a far greater capacity than the global insurance markets, to absorb these risks. The Article also identifies and analyzes the novel legal and economic challenges that risk securitization would raise. Certain of these challenges parallel but are more complex than those arising in structuring traditional securitization transactions. Other challenges involve issues of first impression, including the extent to which risk securitization should be regulated as a form of reinsurance, the constitutionality of requiring that businesses purchase pandemic insurance, and the legality and relative prioritization of public-private risk sharing—such as Chubb’s recent government-risk-sharing proposal.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.006 |
| 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".