Reject and Resubmit: A Formal Analysis of Gender Differences in Reapplication and Their Contribution to Women’s Presence in Talent Pipelines
Bibliographic record
Abstract
A common explanation for women’s underrepresentation in many economic contexts is that women exit talent pipelines at higher rates than men. Recent empirical findings reveal that, in male-dominated selection contexts, women are less likely than men to reapply after being rejected for an opportunity. We examine the conditions under which this gender difference contributes to women’s underrepresentation in talent pipelines over time. We formally model and analyze the population dynamics of a generic selection context, which we then ground using three distinct empirical settings. We show that gender differences in reapplication are an important mechanism of gender segregation in some selection contexts but negligible in others. The extent to which gender differences in reapplication contribute to women’s underrepresentation is driven in part by the rejection rate. Higher rejection rates increase the stock of rejected applicants, which in turn enables gender differences in reapplication to disproportionally reduce women’s representation. The results demonstrate that interactions between individuals’ choices on the supply side and screeners’ behavior on the demand side may have consequences for gender inequality, even if we were able to fully eliminate demand-side biases. We discuss the theoretical and policy implications of our research for understanding women’s underrepresentation in talent pipelines. We also interrogate the effectiveness of common interventions focused on encouraging women to apply for opportunities in male-dominated domains. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1635 .
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.047 | 0.002 |
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".