Research on Corn Income Insurance Pricing under the “Insurance + Futures” Model
Bibliographic record
Abstract
Global climate change has increased systemic risks for grain-producing households. It has led to fluctuations in corn yields and prices, which threaten the stability of farmers’ incomes. China has developed an innovative agricultural risk management mechanism through the ‘insurance plus futures’ model, which transfers farmers’ income risks to the futures market. However, existing agricultural income insurance programs face major challenges, including the absence of standardized yield and price data and inefficiencies in risk pricing. This paper develops an income-insurance pricing model that integrates Copula functions with Asian options. The model relaxes the traditional assumption of risk independence and mitigates the impact of short-term price volatility on insurance payouts. Empirical analysis based on the 2024 corn “insurance plus futures” project in Da’an City, Jilin Province, shows that the proposed model strengthens agricultural risk protection, optimizes premium rate design, and improves product sustainability. The findings offer methodological innovation for improving China’s policy-based agricultural insurance system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".