Living Up to Expectations: How Job Training Made Women Better Off and Men Worse Off
Bibliographic record
Abstract
We study the interaction between job and soft skills training on expectations and labor market outcomes in the context of a youth training program in the Dominican Republic.Program applicants were randomly assigned to one of 3 modalities: a full treatment consisting of hard and soft skills training plus an internship, a partial treatment consisting of soft skills training plus an internship, or a control group.We find strong and lasting effects of the program on personal skills acquisition and expectations, but these results are markedly different for young men and young women.Shortly after completing the program, both male and female participants report increased expectations for improved employment and livelihoods.This result is reversed for male participants in the long run, a result that can be attributed to the program's negative short-run effects on labor market outcomes for males.While these effects seem to dissipate in the long run, employed men are substantially more likely to be searching for another job.On the other hand, women experience improved labor market outcomes in the short run and exhibit substantially higher levels of personal skills in the long run.These results translate into women being more optimistic, having higher self-esteem and lower fertility in the long run.Our results suggest that job-training programs of this type can be transformative -for women, life skills mattered and made a difference, but they can also have a downside if, like in this case for men, training creates expectations that are not met.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".