Optimal insurance design in the presence of government financial assistance
Bibliographic record
Abstract
This paper revisits the study of insurance demand in the context of potential government financial assistance, such as ex post disaster relief and ex ante premium subsidies. We impose the incentive-compatibility condition on the indemnity, and assume that the premium is determined by the actuarial-value-based premium principle. By applying Ohlin's lemma, we characterize the optimal forms of the indemnity function under independence between the relief event and the insurable loss. The optimal parameters of the indemnity function are derived, and both analytical and numerical comparative studies are conducted to demonstrate the effects of disaster relief and premium subsidies on the demand for insurance. Furthermore, we study two forms of dependence between the relief event and the insurable loss. First, we study one specific yet common loss-dependent relief probability case. Second, we study special cases of conditional insurable loss distributions using the hazard rate ordering. Also, we study the effect of premium subsidies on the insurance demand, and show that premium subsidies increase the demand for insurance under increasing absolute risk aversion. The results provide new insights into the study of natural hazard insurance demand in the presence of government interventions.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".