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 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 | 0.000 | 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 | 0.000 |
| 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 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".