License to Broker: How Mobility Eliminates Gender Gaps in Network Advantage
Bibliographic record
Abstract
Brokerage in intra-organizational networks is critical to performance, but women exhibit less brokerage in their social networks and receive lower performance returns to the brokerage they exhibit than men do. We uncover a condition under which the gender gaps in network advantage are entirely negated: mobility. When women move between units of the organization, they increase their brokerage more than mobile men do. Further, such mobility eliminates the gender gap in returns to brokerage. Using a rich dataset including the personnel records, monthly performance, and email communications of thousands of employees in a large financial institution, we find support for our arguments by comparing the networks and objective performance of those who changed jobs with matched non-movers prior to and following each job change. In probing why this might be the case, we find that women movers are more likely to maintain communication ties to colleagues from their previous roles and that these persistent ties give them a discernible and gender-role-congruent explanation for connecting otherwise disconnected units and benefiting from network brokerage. Our results illuminate important mechanisms by which social network dynamics and mobility affect gender inequality and performance in organizations.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".