A quantitative theory of the gender gap in wages
Bibliographic record
Abstract
This paper measures how much of the gender wage gap over the life cycle is due to the fact that working hours are lower for women than for men. We build a quantitative theory of fertility, labor supply, and human capital accumulation decisions to measure gender differences in human capital investments over the life cycle. We assume that there are no gender differences in the human capital technology and calibrate this technology using wage-age profiles of men. The calibration of females assumes that children reduce the hours of work of mothers and that there is an exogenous gendergap in hours of work. We find that our theory accounts for all of the increase in the gender wage gap over the life cycle in the NLSY79 data. The impact of children on the labor supply of females accounts for 56% and 45% of the increase in the gender wage gap over the life cycle among non-college and college individuals. We also find that children play an important role in understanding the variation of the gender wage gap across recent cohorts of women and the slower wage growth faced by black women relative to non-black women in the U.S. economy.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".