Analysis of the Brazilian Program of Subsidies for Rural Insurance Premium: Evolution from 2005 to 2014
Bibliographic record
Abstract
Rural insurance is inserted in the field of agricultural policies to mitigate risks that farmers face. It was an innovation for the Brazilian government from the implementation standpoint, despite the existence of similar programs in other countries. The purpose of this paper is to assess the recent evolution of the Brazilian Rural Insurance Premium Subsidy Program (PSR) and its main variables: amount insured area, policies, average area, benefiting producers, total premiums involved and total subsidy. The study examined in detail the PSR representation by region and farming. In order to evaluate the results of this program on agricultural policy, an exploratory and descriptive analysis was performed with the objective of studying the evolution of the Brazilian rural insurance in the context of PSR, using the information available in the Ministry of Agriculture, Livestock and Supply (MAPA) about the program. The information and data were collected between July and August 2015. The study was based on data collected from 2005 to 2013 with some general data of 2014 program included in the study. Even though the focus of the analysis was on the most recent years, 2009-2013. Data analysis revealed that the increased supply and demand for rural insurance is in the South and in the agricultural modalities for grains and fruits, with growth potential in other sectors and other regions in the country. PSR, as public policy, was responsible for the expansion of the rural insurance market in Brazil, encouraging and providing the access of producers to agricultural insurance by subsidizing the premium fee. Although this expansion has been slow and gradual, Brazil had in 2013 about 13.8% of the agricultural area with rural insurance coverage. This reveals the need for expanding the program to popularize this important risk mitigation tool.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".