Insuring the 'Uninsurable': Catastrophe Bonds, Pandemics, and Risk Securitization
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 it