A quantitative theory of the gender gap in wages
Bibliographic record
Abstract
Using panel data from the National Longitudinal Survey of Youth (NLSY), we document that gender differences in wages almost double during the first 20 years of labor market experience and that there are substantial gender differences in employment and hours of work during the life cycle. A large portion of gender differences in labor market attachment can be traced to the impact of children on the labor supply of women. We develop a quantitative life-cycle model of fertility, labor supply, and human capital accumulation decisions. We use this model to assess the role of fertility on gender differences in labor supply and wages over the life cycle. In our model, fertility lowers the lifetime intensity of market activity, reducing the incentives for human capital accumulation and wage growth over the life cycle of females relative to males. We calibrate the model to panel data of men and to fertility and child related labor market histories of women. We find that fertility accounts for most of the gender differences in labor supply and wages during the life cycle documented in the NLSY data.
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How this classification was reachedexpand
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".