Two Sides of the Same Coin: U.S. "Residual" Inequality and the Gender Gap
Bibliographic record
Abstract
In this paper we show that the two major developments experienced by the US labor market - rising inequality and narrowing of the male-female wage gap - can be explained by a common source: the increase in price of cognitive skills and the decrease in price of motor skills. We obtain the price of a multidimensional vector of skills by combining a hedonic price framework with data on the skill requirements of jobs from the Dictionary of Occupational Titles (DOT) and workers’ wages from the CPS. We find that in the 1968-1990 period the returns to cognitive skills increased 4-fold and the returns to motor skills declined by 30%. Given that the top of the wage distribution of college and high school graduates is relatively well endowed with cognitive skills, these changes in skill prices explain up to 40% of the rise in inequality among college graduates and about 20% among high school graduates. In a similar way, because women were in occupations intensive in cognitive skills while men were in motor-intensive occupations, these skill price changes explain over 80% of the observed narrowing of the male-female wage gap.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.023 | 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".