Revealing the Concealed Effect of Top Earnings on the Gender Gap in the Economic Value of Higher Education in the United States, 1980–2017
Bibliographic record
Abstract
The expansion of women's educational attainment may seem to be a promising path toward achieving economic equality between men and women, given the consistent rise in the economic value of higher education. Using yearly data from 1980 to 2017, we provide an updated and comprehensive examination of the gender gap in education premiums, showing that it is not as promising as it could and should be. Women receive lower rewards to their higher education across the entire wage distribution, and this gender gap increases at the very top education premiums-the top quarter and, even more so, the top decile. Moreover, insufficient theoretical and methodological attention to this top premium effect has left gender inequality concealed in the extensive empirical studies on the topic. Specifically, when we artificially censor the top at the 80th wage percentile, the gender gaps in education premium reverse. Lastly, the growth in earnings inequality in the United States, which is greatly affected by the expansion of top earnings, is associated with the growing gender gap in education premiums over time. We discuss the meaning and implications of this structural disadvantage at a time when women's educational advantage keeps growing and higher education remains the most important factor for economic attainment.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".