Public-Private Programs for Covering Extreme Events: The Impact of Information Distribution on Risk-Sharing
Bibliographic record
Abstract
Recent extreme events have significantly raised the question of the role of public and private sectors in providing adequate financial protection to victims. Developing publicprivate insurance programs could constitute one of the most appealing ways to solve the problem of financing the consequences of those large-scale catastrophes. However, catastrophic risks present very specific characteristics which challenge any traditional economic approaches to analyzing them. Further, the government may have better information about the risk than insurers (e.g., national security). Currently, little has been done in the economic literature to better understand how this assumption impacts on how risks are shared between all stakeholders in such partnerships.This paper analyzes policy issues related to risk/information sharing between insurers and a dedicated State-backed governmental reinsurer, who are part of a national partnership program. The government develops a mandatory coverage against catastrophic risks and decides the level of premiums levied against the insureds. Using a game-theoretical approach, we show that a government can act to induce private insurers in the country to participate in the partnership instead of leaving the market. By modulating its premium policy, the government can also led them to adopt two different strategies: (1) behave as a simple financial intermediary between the insured and the public reinsurer so that the latter supports the largest portion of the risks or (2) conserve the largest part of risks to benefit from market conditions created by the government seeking to avoid its intervention ex post to bail out the public reinsurer. The paper also discusses the impacts of government information-sharing strategies on the game equilibrium. Illustrations are provided for natural hazards and terrorism risk.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".