Catastrophe risk sharing and public-private partnerships : From natural disasters to terrorism
Bibliographic record
Abstract
Recent extreme events showed how insurers, deprived of reinsurance capacity at an affordable price, could decide to stop covering for specific extreme events and rapidly let people and firms uncovered. Developing public-private partnerships could constitute one of the most appealing ways to solve the problem of financing the consequences of those extremes events by taking advantage of strengths of both sectors. Catastrophic risks present, however, very specific characteristics which really challenge any traditional economic approach to analyse those issues. So as of today little has been done in the economic literature to reassess the role of public and private sectors with respect to making available protection to victims as well as better understanding how those risks are effectively shared between all partners in those partnerships. This paper aims to provide a partial answer by analysing policy issues related to risk sharing between insurers and a dedicated state-backed governmental reinsurer, who are part of a national partnership of insurance against extreme events. The insurance is mandatory with premium policy that is decided by Treasury. We show that a government can modulate its premium policy levied against insured to make the private insurers in the country participating in the partnership instead of leaving the market and then to adopt two different strategies: (1) to behave as a simple financial intermediary between insured and the public reinsurer so as the former supports the largest portion of the risks or (2) to conserve the largest part of risks to benefit from government incentives. National schemes for covering against natural hazards and those developed post 9/11 for emerging terrorism provide illustrations.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".