Inequality and the Equalization Fund in Brazil: Redefining Strategies
Bibliographic record
Abstract
This article examines Brazilian states’ per capita income convergence from 1990 to 2020. A reduction in state inequalities was observed, attributed to the redistribution of resources through the State Participation Fund (FPE) and Constitutional Funds and the Municipal Participation Fund and transfers for health and education, benefiting federative entities with lower per capita incomes. However, a significant volume of resources aimed at income redistribution was characterized by overlapping actions, whose results were not evaluated in isolation. Moreover, while historically, less economically advantaged states strive to reach the national average, more economically advantaged states tend to maintain or enhance their relative positions. Barro’s analysis (2001) suggests that equalization funds, by benefiting entities with lower GDP per capita, discourage governors from promoting the state’s economic development, as this would imply a loss of FPE resources. This situation would be a government failure commonly pointed out by authors of the Public Choice School. In light of this, this study proposes a revision of the FPE distribution criteria, reducing the emphasis on the inverse of per capita income and including variables such as population, collection effort, and state size, aiming to promote a more effective and equitable convergence of per capita income in the Brazilian federation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".