Are Soft Skills Enough? Experimental Evidence on Skill Complementarity for College Graduates
Bibliographic record
Abstract
The authors study how complementarities in skill may affect the returns to vocational training using a randomized controlled trial in Cairo, Egypt. Participants, who were college-educated, were given either a four-week training in soft skills (e.g., grooming, time management), technical skills (e.g., Microsoft programs, English language), or a mix of the two (half of each). Findings show large differences in outcomes between the three treatments. The technical and mixed treatments do best in the short term, raising first-job income by about 15%, relative to the soft skill treatment. In the longer term, the mixed-skill treatment significantly outperforms the other two treatments, giving participants 20–27% higher income. The high returns for this group may come from increased access to jobs that require speaking English, which may be at higher-quality employers. Overall, the results suggest that curriculum details play an important role in the outcomes of vocational training programs and that leveraging skill complementarity can yield tangible benefits.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.013 | 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".