Do Women Choose Different Jobs from Men? Mechanisms of Application Segregation in the Market for Managerial Workers
Bibliographic record
Abstract
This paper examines differences in the jobs for which men and women apply in order to better understand gender segregation in managerial jobs. We develop and test an integrative theory of why women might apply to different jobs than men. We note that constraints based on gender role socialization may affect three determinants of job applications: how individuals evaluate the rewards provided by different jobs, whether they identify with those jobs, and whether they believe that their applications will be successful. We then develop hypotheses about the role of each of these decision factors in mediating gender differences in job applications. We test these hypotheses using the first direct comparison of how similarly qualified men and women apply to jobs, based on data on the job searches of MBA students. Our findings indicate that women are less likely than men to apply to finance and consulting jobs and are more likely to apply to general management positions. These differences are partly explained by women’s preference for jobs with better anticipated work–life balance, their lower identification with stereotypically masculine jobs, and their lower expectations of job offer success in such stereotypically masculine jobs. We find no evidence that women are less likely to receive job offers in any of the fields studied. These results point to some of the ways in which gender differences can become entrenched through the long-term expectations and assumptions that job candidates carry with them into the application process.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".