Assessing effects of federal crop insurance supply on acreage and yield of specialty crops
Bibliographic record
Abstract
Abstract Crop insurance may affect harvested acreage and yield by influencing producers’ behavior such as land allocation and input use. Although specialty crops are a major source of farm income, especially on the U.S. west coast, they have not received as much attention as field crops in previous empirical studies. This paper assesses the effect of moral hazard and adverse selection associated with the federal crop insurance program (FCIP) on the acreage and yield of major specialty crops in California. An econometric method that expands the switching regression model is developed to assess the effect. Results suggest that federal crop insurance can change specialty crop growers’ production responses to climate and soil conditions. The moral hazard effect tends to increase the acreage and yield of the specialty crops, whereas the adverse selection effect tends to have the opposite effect. The overall effect of the FCIP on acreage and yield of specialty crops is found to be moderate.
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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".