Optimal insurance design in the presence of government financial assistance
Why this work is in the frame
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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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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it