Job Mobility and Wage Trajectories for Men and Women in the United States
Bibliographic record
Abstract
Young American workers typically change employers many times in the course of establishing their careers. This article examines the consequences of this mobility for wage inequalities between and among men and women. Using multilevel modeling and data from the 1979 to 2002 waves of the National Longitudinal Survey of Youth 1979 (NLSY79), I disentangle the various ways in which mobility shapes the trajectories of wage growth. Findings caution against accepting the adequacy of prevalent economic models of mobility—models that tend to isolate individual workers'moves from broader patterns of work history and that treat mobility as a decontextualized individual choice. Although workers who frequently switch employers generally end up earning less than their more-stable counterparts, the type, timing, and relative level of changes strongly affect the ultimate wage differential. Differences in the degree of men's and women's labor-force attachment and family circumstances are also influential. Workers who are less attached to the labor force benefit less from changing employers, and women who are married or have children also tend to experience less-favorable mobility-wage outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".