Women Managers and the Gender Wage Gap: Workgroup Gender Composition Matters
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Women's representation in managerial positions is a common metric for gender equity in organizations. Whether women managers improve gender equity among their subordinates is, however, less clear. Drawing on rich longitudinal personnel data from a large Korean food company, we provide new insight into this question by focusing attention on key micro-contexts for interaction and relational politics within organizations: workgroups. Building on social-psychological theories about in-group preference and value threats, we theorize that workgroup gender composition conditions the relationship between supervisor gender and gender earnings differentials. Results from regression models with workgroup fixed effects confirm this insight. Women supervisors are associated with smaller gender earnings gaps in workgroups when they are male-dominated. This relationship is stronger for less-advantaged workers, with supervisor gender and workgroup gender composition mattering more for “sticky floors” than “glass ceilings.”
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it