Signaling Specific Skills and the Labor Market of College Graduates
Bibliographic record
Abstract
We study how signaling skills that are specific to college majors affect labor market outcomes of college graduates. We rely on census-like data and a regression discontinuity design to study the impacts of a well-known award given to top performers on a mandatory nationwide exam in Colombia. The award allows students to signal their high level of specific skills when searching for a job. These students earn 7 to 12 percent more than otherwise identical students lacking the signal. This positive return persists five years after graduation. The signal mostly benefits workers who graduate from low-reputation colleges, and allows workers to find jobs in more productive firms and in sectors that better use their skills. We rule out that the positive earnings returns are explained by human capital. The signal favors mostly less advantaged groups, implying that reducing information frictions about students skills could potentially shrink earnings gaps. Our results imply that information policies like those that formally certify skills can improve the efficiency in talent allocation of the economy and, at the same time, level the playing field.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".