Pobreza e impactos heterogéneos de las políticas activas de empleo juvenil : el caso de PROJOVEN en el Perú
Bibliographic record
Abstract
Analiza la relación entre la pobreza de los hogares y los impactos de políticas activas \nde promoción del empleo en el Perú. En particular, analizamos el Programa de Capacitación Laboral Juvenil PROJOVEN, que, desde 1996, ha beneficiado directamente a cerca de 50.000 jóvenes pobres. La situación de pobreza de los beneficiarios de PROJOVEN es aproximada con un índice basado en 21 activos de los hogares. Tres resultados principales emergen. Primero, las desigualdades demográficas y socioeconómicas encontradas entre los beneficiarios y la población elegible se deben principalmente a decisiones individuales de los jóvenes antes que a decisiones administrativas del operador del programa. Segundo, se observa alta heterogeneidad en la distribución de impactos por cuantiles de ingresos y fuertes diferencias en la distribución de impactos entre varones y mujeres. Tercero, la heterogeneidad de los impactos no se explica por el nivel de pobreza de los beneficiarios. Los más pobres \nentre los pobres se benefician igual del programa que los menos pobres. Es la entidad que proporciona los servicios de capacitación (calidad) la que explica la heterogeneidad de los impactos.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".