The Impact of the PSR Rural Insurance Program on the Agricultural Productivity in the Matopiba Region of Brazil
Bibliographic record
Abstract
The present work aims to analyze the impact of a government subsidy program of rural insurance in Brazil, (called the Programa de Subvenção ao Prêmio de Seguro Rural - PSR), on the productivity of insured producers in the MATOPIBA region of the country, which encompasses four Brazilian states, Maranhão, Tocantins, Piauí and Bahia, between the years 2008 to 2019. For this, municipalities were selected that had at least one insured producer throughout the analyzed period. The variables used were the number of producers, the number of insurance policies, the planted area, the productivity obtained and the insured financial amount of the producers. The methodological procedure was based on Auto-regressive Vectors (VAR) for panel data. The results showed a concentration, of all the variables used in the research, in the state of Bahia, mainly in the municipalities of Formosa do Rio Preto and São Desidério, whose main economic activity is soy production. It was also found that the impulse response functions on productivity obtained through a shock in the other variables, except the planted area variable, the others showed positive initial (short-term) responses until the second year. The average time for responses to smooth over time occurs from the sixth year onwards.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".