Apprenticeship as a stepping stone to beter jobs: Evidence from brazilian matched employer-employee data
Bibliographic record
Abstract
The objective of this paper is to evaluate the Brazilian Apprenticeship program (Lei do Aprendiz). This program is a youth-targeted ALMP that has been adopted at a large scale since 2000 in Brazil. The program concedes payroll subsidies to firms that hire and train young workers under special temporary contracts aiming to help them successfully complete the transition from school to work. We make use of a very rich longitudinal matched employee-employer dataset covering the universe of formally employed workers in Brazil, including apprentices. Our identification strategy exploits a discontinuity by age in the eligibility to enter the program in the early 2000’s, when 17 was the age limit to take part in the program. We examine the impacts on employability, wage growth and attachment to the formal labor market using other temporary workers as a control group. We find that the program increases the probability of employment in permanent jobs in 2-3- and 4-5-year horizons. We also find a positive impact on real wages that increases over time. These results hold when we isolate the effects of the training dimension of the program by using an alternative control group composed of subsidized temporary workers. We show evidence that the positive effects of the program are much larger for less-educated workers and for workers who had their first jobs in large firms. These results are robust to other choices of methods to address selection into the program based on unobservables.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".