Do Training Programmes get the unemployed back to work?: A look at the Spanish experience ¤
Bibliographic record
Abstract
This work studies the e¤ect of some training courses for economic disadvantaged and unemployed people elaborated by Spanish National Institute of Employment (INEM) in terms of the consecution of a job. Two groups of Spanish unemployed people are compared between April of 2000 and February of 2001, one of them did training courses in the …rst quarter of 2000. Non-parametric techniques, parametric and semiparametric continuous time duration methods are used to analyze this relationship. The results suggest a higher positive e¤ect of some training courses for women than for men, specially in the case of those receiving some kind of economic help. Furthermore, young unemployed people and unemployed people with a reduced period of active labour demand have higher exit rates to a job. However, education and disabilities do not a¤ect signi…cantly the exit rate to a job. PRELIMINAR WORK (do not quote without permission) Acknowledgement: I would like to thank to César Alonso-Borrego and Juan José Dolado for advisoring of this work, and participants in seminar at CEMFI. Also thanks to Almudena Durán and Antonio Hernando (INEM) for giving to me the data bases used in this work. Financial support from research grant AP2000-0853 from the Spanish Ministry of Education is gratefully acknowledged. The usual disclaimer applies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".