A public micro pension programme in Brazil: Heterogeneity among states\n and setting up of benefit age adjustment
Bibliographic record
Abstract
Brazil is the 5th largest country in the world, despite of having a ``High\nHuman Development'' it is the 9th most unequal country. The existing Brazilian\nmicro pension programme is one of the safety nets for poor people. To become\neligible for this benefit, each person must have an income that is less than a\nquarter of the Brazilian minimum monthly wage and be either over 65 or\nconsidered disabled. That minimum income corresponds to approximately $2$\ndollars per day. This paper analyses quantitatively some aspects of this\nprogramme in the Public Pension System of Brazil. We look for the impact of\nsome particular economic variables on the number of people receiving the\nbenefit, and seek if that impact significantly differs among the 27 Brazilian\nFederal Units. We search for heterogeneity. We perform regression and spatial\ncluster analysis for detection of geographical grouping. We use a database that\nincludes the entire population that receives the benefit. Afterwards, we\ncalculate the amount that the system spends with the beneficiaries, estimate\nvalues \\textit{per capita} and the weight of each UF, searching for\nheterogeneity reflected on the amount spent \\textit{per capita}. In this latter\ncalculation we use a more comprehensive database, by individual, that includes\nall people that started receiving a benefit under the programme in the period\nfrom 2nd of January 2018 to 6th of April 2018. We compute the expected\ndiscounted benefit and confirm a high heterogeneity among UF's as well as\ngender. We propose achieving a more equitable system by introducing `age\nadjusting factors' to change the benefit age.\n
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".