One Cohort at a Time: A New Perspective on the Declining Gender Pay Gap
Bibliographic record
Abstract
This paper studies the interaction between the decrease in the gender pay gap and the stagnation in the careers of younger workers, analyzing data from the United States, Italy, Canada, and the United Kingdom.We propose a model of the labor market in which a larger supply of older workers can crowd out younger workers from top-paying positions.These negative career spillovers disproportionately affect the career trajectories of younger men because they are more likely than younger women to hold higher-paying jobs at baseline.The data strongly support this cohort-driven interpretation of the shrinking gender pay gap.The whole decline in the gap originates from (i) newer worker cohorts who enter the labor market with smaller-than-average gender pay gaps and (ii) older worker cohorts who exit with higher-than-average gender pay gaps.As predicted by the model, the gender pay convergence at labor-market entry stems from younger men's larger positional losses in the wage distribution.Younger men experience the largest positional losses within higher-paying firms, in which they become less represented over time at a faster rate than younger women.Finally, we document that labor-market exit is the sole contributor to the decline in the gender pay gap after the mid-1990s, which implies no full gender pay convergence for the foreseeable future.Consistent with our framework, we find evidence that most of the remaining gender pay gap at entry depends on predetermined educational choices.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".