REMOVING THE “VEIL OF IGNORANCE”: NONLINEARITIES IN EDUCATION EFFECTS ON GENDER WAGE INEQUALITIES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A large literature studies the mean gender wage gap in developing countries and finds mixed evidence about the role of education policies in closing gender earnings inequalities. We contribute to this literature by exploring two types of nonlinearities in wage earning regressions: (1) nonlinearities on the effects of education on expected earnings along the distribution of education endowments; and (2) heterogeneities on the contributions of education to the gender wage gap at different quantiles of the wage distribution. Our analyses provide new insights on how these nonlinear effects can be used to set up better targeted gender and development policies. ( JEL I26, C14)
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it