Modeling Exit and Entry of Farmers in a Crop Insurance Program
Bibliographic record
Abstract
This paper examines the factors influencing farmer participation in crop insurance schemes, but unlike previous studies that focus on total demand, participation is disaggregated into entrants and those exiting. Modeling entry and exit decisions separately illustrates that the effect of a given variable is often muted by aggregation. In addition, the approach in this paper distinguishes between price and yield variables rather than total returns and is consequently able to demonstrate that price variables are particularly important for farmers considering enrolling in crop insurance, while yield variables and other risk management opportunities are more important for farmers who have been in the program but are deciding to exit. The result suggests that moral hazard is reduced significantly by calculating the coverage yield level for an individual producer on the basis of a moving average of past yields for that farmer. While yield and its variance are particularly influential in the participation decision for farmers currently enrolled, its significant impact on the insurance decision for all farmers highlights the importance of crop insurance as a potential adaptation strategy to weather events.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".