Factors Affecting Farmers’ Crop Insurance Participation in China
Bibliographic record
Abstract
China's latest crop insurance program, launched in 2007, provides an excellent opportunity to explore the factors affecting farmers’ crop insurance purchase decisions, particularly decision making when crop insurance was first introduced into rural communities. This study surveyed all households in Kuangjiaqiao Village, Changde, Hunan Province, China over a four‐year period, from 2007 to 2010. Using basic regression models for cross‐sectional analysis and advanced models to consider lag effects, this study identifies the dominant factors influencing farmers’ crop insurance decisions. Results indicate farmers developed a dynamic adaptive process toward the new crop insurance. Farmers initially made relatively arbitrary decisions that were significantly influenced by community insistence or pressure to conform. Then, farmers gradually established more rational decision‐making mechanisms in which yield volatility, education, and engagement experience became statistically significant. The focus on the initial stages of the crop insurance program from this study helps improve our understanding of the demands within this rapidly growing market in China.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".