Insuring Eggs in Baskets: Should the Government Insure Individual Risks?
Bibliographic record
Abstract
The vast majority of crop and revenue insurance policies sold in North America are single‐crop policies that insure against low yields or low revenues for each crop grown on a particular farm. This practice of insuring one crop at a time runs counter to the traditional risk management practice of diversifying across several enterprises to avoid putting all of one's eggs in a single basket. This paper examines the construction of a whole‐farm crop revenue insurance program to include livestock price risk. The results show that at coverage levels of 95% or lower, the fair insurance premiums for this product on a well‐diversified Iowa farm are far lower than the fair premiums for the corn crop alone on the same farm. The calculation of premium rates for the whole‐farm insurance product is derived from a method for imposing correlations first proposed by Iman and Conover in 1982. The potential income transfer from crop insurance is also examined. We find that the income transfer due to the subsidization of single‐commodity policies is greater than the total premium for whole‐farm policies. La grande majorité de polices d'assurances de récolte et de revenu agricole vendues en Amérique du nord sont des polices de récolte individuelle qui assurent contre des rendements faibles ou bas revenu pour chaque récolte cultivée par une ferme particulière. Cette pratique de n'assurer une récolte à la fois fonctionne à l'opposé de la pratique traditionnelle de gestion des risques par la diversification à travers plusieurs entreprises pour éviter de mettre tous ses oeufs dans un seul panier. Cet article examine la construction d'un programme d'assurance agricole pour l'ensemble des récoltes produites par une ferme y compris le bétail évalue le risque. Les résultats montrent qu'aux niveaux d'assurance plus petits ou égaux à 95 pourcents, les primes d'assurance justes pour ce type de police pour une ferme bien diversifiée de l'Iowa sont bien inférieures aux primes justes pour une police de simple couvrant le maïs et ce pour la même ferme. Le calcul des taux de prime pour une police couvrant l'ensemble des récoltes est dérivé d'une méthode pour imposer des corrélations originalement proposée par Iman et Conover en 1982. Le transfert potentiel de revenu à partir de l'assurance de récolte agricole est également examiné. Nous trouvons que le transfert de revenu dûà la subvention des polices pour récolte singulière est plus grand que la prime entière pour une police couvrant l'ensemble des récoltes.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".