Assessing the effects of premium subsidies on crop insurance demand: An analysis for grain production in Southern Brazil
Bibliographic record
Abstract
Following the well-succeed experience of developed countries such as Canada and the United States, Brazil implemented the Crop Insurance Program (PSR) in 2005 seeking to provide subsidies for the purchase of crop insurance policies by Brazilian farmers. Despite the importance of this public policy, there is no empirical investigation about the effects of premium subsidies on the quantity demanded for crop insurance in Brazil. This paper aimed to fill this gap through the investigation of the three grains – corn, soybeans and wheat – that are most cultivated in southern Brazil, the region where PSR is most developed. A fixed effects model was applied to an unbalanced panel data of municipalities of southern Brazil considering the years between 2006 and 2015. Three measures of crop insurance demand were considered: level of total premiums, level of total premiums per hectare and level of total liability per hectare. Results was in line with previous literature, suggesting the existence of a positive, although inelastic, effect of the subsidy level on the demand for crop insurance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".