Does Gender Matter for Academic Promotion? Evidence from a Randomized Natural Experiment
Bibliographic record
Abstract
Several countries have recently introduced gender quotas in hiring and promotion committees at universities. This paper studies whether these policies increase the presence of women in top academic positions. The identification strategy exploits the random assignment mechanism in place between 2002 and 2006 in all academic disciplines in Spain to select the members of promotion committees. We find that a larger proportion of female evaluators increases the chances of success of female applicants to full professor positions. The magnitude of the effect is large: each additional woman on a committee composed of seven members increases the number of women promoted to full professor by 14%. Conversely, when committee members decide on promotions to associate professor positions, we do not observe any significant interaction between the gender of evaluators and the gender of candidates. If anything, in this case a larger share of female evaluators is associated with fewer successful female applicants. The evidence is consistent with the existence of ambivalent sexism.
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How this classification was reachedexpand
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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".