A decision framework for optimal crop reinsurance selection
Bibliographic record
Abstract
Purpose – In the USA, private insurance companies serve as an integral part of the delivery and risk sharing of the federal crop insurance program. Governed by the Standard Reinsurance Agreement (SRA), private crop insurance companies must designate an eligible crop insurance contract to the assigned risk, developmental, or commercial funds. While the SRA restricts the private sector delivery system in a number of ways, the assignment of contracts to crop insurance funds, however, is left solely to the discretion of individual crop insurance companies. Thus, as to the companies' profitability viewpoint, the optimal selection of the crop insurance funds is the most important task. Therefore, the purpose of this paper is to provide a decision framework for crop insurance companies to make optimal decisions regarding the purchases of crop reinsurance. This information and framework may also be useful for crop insurance firms in China when considering crop reinsurance decisions. Design/methodology/approach – The paper studied three commonly used parametric loss distributions and presented a general guideline to choose the most profitable fund within the company's risk bearing level. Findings – The paper finds many important features in the commonly used loss distributions, which are useful to maximize the company's underwriting returns. Originality/value – The paper provides a general decision framework for optimally ceding risks to reinsurance. While this paper focused on agricultural insurance decisions by firms, the concept could be applied to general reinsurance decisions.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".