O Ritmo de Queda na Desigualdade no Brasil é Adequado? Evidências do Contexto Histórico e Internacional
Bibliographic record
Abstract
Este texto utiliza duas abordagens para responder se o ritmo de queda da desigualdade no Brasil está adequado ou não. A primeira é comparar o ritmo de queda no coeficiente de Gini no Brasil com a queda no mesmo indicador em alguns países hoje pertencentes à Organização para a Cooperação e Desenvolvimento Econômico (OCDE) - Espanha, Estados Unidos, França, Noruega, Países Baixos, Reino Unido e Suécia - , enquanto os mesmos construíam seus estados de bem-estar social durante o século passado. A segunda é calcular por quanto tempo o Brasil deverá manter o mesmo ritmo de queda para alcançar os níveis de desigualdade hoje observados em alguns países da OCDE que podem servir como referência: o Canadá, os Estados Unidos e o México. Os dados indicam que o ritmo de queda da desigualdade no Brasil de 0, 7 ponto de Gini ao ano é superior ao ritmo que todos os países analisados seguiram enquanto construíam seus estados de bem-estar social, salvo a Espanha, cujo ritmo foi um pouco superior (0, 9 ponto ao ano). Por seu turno, as distâncias que nos separam dos países-referência escolhidos são seis anos para o México, 12 para os Estados Unidos, e 24 anos para o Canadá. A conclusão geral do estudo é que o ritmo de queda na desigualdade é adequado, mas que o desafio será manter este ritmo por várias décadas para alcançar o nível de desigualdade, por exemplo, do Canadá. The following study uses two approaches to judge whether inequality in Brazil is falling fast enough. The first is to compare the variation of the Gini coefficient in Brazil with what was observed in several countries that today belong to the Organisation for Economic Co-operation and Development (OECD) - France, Netherlands, Norway, Spain, Sweden, United Kingdom, and United States - while they built their social welfare systems during the last century. The second approach is to calculate for how long Brazil must keep up the fall in the Gini coefficient to attain the same levels of inequality of three OECD countries that can be used as a reference: Canada, Mexico, and the United States. The data indicate that the Gini coefficient in Brazil is falling 0.7 point per year and that this is superior to the rhythm of all the OECD countries analyzed while they built their welfare systems but Spain, whose Gini fell 0.9 point per year during the 1950s. The time needed to attain various benchmarks in inequality are: six years to Mexico, twelve to the United States and 24 to Canadian inequality levels. The general conclusion is that the speed with which inequality is falling is adequate, but the challenge will be to keep inequality falling at the same rate for another two or three decades.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".