The Graduate Wage and Earnings Premia and the Role of Non-Cognitive Skills
Bibliographic record
Abstract
Estimates of the graduate earnings premium typically do not allow for the effect of non-cognitive skills. Since such skills are unobservable in most datasets there is a concern that existing estimates of the graduate premium are contaminated by selection on such unobservables. We use data on a young cohort of individuals that allows us to control for the effects of non-cognitive skills. We find that the inclusion of non-cognitive skills, themselves jointly significantly positive, reduces the estimated graduate premia by an insignificant 1-2 percentage points from an average of 10-12%. Our second contribution is motivated by the greater reliance on administrative datasets in recent research that has focused on annual earnings rather than hourly wages and our results show that the graduate earnings differential is significantly greater than the wage differential. Since we use estimation methods that are NOT robust to selection on unobservables, we adopt Oster (2016) tests to show that it would take an implausible degree of selection on unobservables to drive our estimated wage and earnings returns to zero, and that a plausible upper bound to returns is around one-quarter to one-third below the OLS returns. We further find heterogeneous returns by broad major group and elite university, and we find large degree class differentials.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".