Impact evaluation of the Brazilian non-contributory pension program BPC (Benefício de Prestações Continuada) on family welfare
Bibliographic record
Abstract
This study evaluates the effect of the Benefício de Prestação Continuada (BPC) program on family welfare.The program is targeted to poor disabled and elderly people providing monthly stipends equal to one monthly minimum wage.The establishment of an age at which the person becomes eligible for the benefit created a discontinuity in the probability of being treated which is explored for identification.We developed a procedure to decompose the stipends from social programs using the PNAD dataset, thereby identifying which programs the person participates.Therefore, from 2001 to 2008 we estimate the effect of the BPC on variables such as household composition, labor force participation, weekly worked hoursfor the elder and co-residents -besides child labor and school attendance.We found that the program have significant effects on child labor reduction, on labor force participation for members between 30 and 49 years-old, and on the number of members between 30 and 59 years-old.It was also observed an expected labor force participation reduction for the elderly, but no effects on school attendance of children.No significant effects on worked hours were found.The complexity of the findings highlights the need of studying the heterogeneity of social cash transfers, where many latent aspects are yet to be uncovered.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".