Gender-Targeted Job Ads in the Recruitment Process: Evidence from China
Bibliographic record
Abstract
We document how explicit employer requests for applicants of a particular gender enter the recruitment process on a Chinese job board. We find that 95 percent of callbacks to gendered jobs are of the requested gender; worker self-selection (compliance with employers' requests) and employer callback decisions from applicant pools (enforcement) both contribute to this association, with compliance playing the larger role. Explicit gender requests account for over half of the gender segregation and gender wage gap observed on the board.Ad-level regressions with job title and firm fixed effects suggest that employers' explicit gender requests have causal effects on the gender mix of applications received, especially when the employer's likely gender preference is hard to infer from other contents of the ad. Application-level regressions with job title and worker fixed effects show that both men and women experience a callback penalty when applying to a gender-mismatched job; this penalty is significantly greater for women (44 percent) than men (26 percent).
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".