Do Male-Female Wage Differentials Reflect Differences in the Return to Skill? Cross-City Evidence From 1980-2000
Bibliographic record
Abstract
Over the 1980s and 1990s the wage differentials between men and women (with similar observable characteristics) declined significantly.At the same time, the returns to education increased.It has been suggested that these two trends may reflect a common change in the relative price of a skill which is more abundant in both women and more educated workers.In this paper we explore the relevance of this hypothesis by examining the cross-city co-movement in both male-female wage differentials and returns to education over the 1980-2000 period.In parallel to the aggregate pattern, we find that male-female wage differentials at the city levels moved in opposite direction to the changes in the return to education.We also find this relationship to be particularly strong when we isolate data variation which most likely reflects the effect of technological change on relative prices.We take considerable care of controlling for potential selection issues which could bias our interpretation.Overall, our cross-city estimates suggest that most of the aggregate reduction in the male-female wage differential observed over the 1980-2000 period was likely due to a change in the relative price of skill that both females and educated workers have in greater abundance.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".