Whose Jobs Are These? The Impact of the Proportion of Female Managers on the Number of New Management Jobs Filled by Women versus Men
Bibliographic record
Abstract
In this paper, we examine the relationship between an organization’s proportion of female managers and the number of new management jobs initially filled by women versus men. We draw on theories of job differentiation, job change, and organizational demography to develop theory and predictions about this relationship and whether the relationship differs for jobs filled by female and male managers. Using data on a sample of New York City advertising agencies over a 13-year period, we find that the number of newly created jobs first filled by women increases with an agency’s proportion of female managers. In contrast, the effect of the proportion of female managers on the number of new management jobs filled by men is positive initially but plateaus and turns negative. In showing these influences on job creation, we highlight the dynamic and socially influenced nature of jobs themselves: new jobs are created regularly in firms and not merely as a response to technical and administrative imperatives. The results also point to another job-related process that differs between women and men and that could potentially aggravate, mitigate, or alleviate inequality: the creation of jobs. Thus this research contributes to literatures on demography, the organization of work, and inequality.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".