Cross-program Differences in Returns to Education and the Gender Earnings Gap
Bibliographic record
Abstract
University programs differ in their gender earnings gaps, the difference between the subsequent earnings of the program’s male and female enrollees. A program could have a positive gender earnings gap because the program attracts higher-ability men than women (a selection effect), or because the program increases the earnings of male enrollees more than female enrollees (a causal effect). To understand the source of cross-program differences in gender earnings gaps, we exploit a discontinuity built into the Danish national university admissions system, which provides a quasi-random assignment of similar applicants to different programs. Enrolling in a program with a $1 larger gender earnings gap, holding average earnings constant, does not affect male earnings but reduces female earnings by $0.45. This effect is small as women enter the labor market but increase over time, and cannot be explained by channels related to marriage or childbirth. Our results show that programs that appear worse for women – in the sense of having economically significant gender earnings gaps – are worse for women because they reduce female earnings more than programs with smaller gaps.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".