Working for Jessica or Michael? Implications of gender stereotypes for job application intentions at technology startups
Bibliographic record
Abstract
Abstract Research Summary We examine a critical yet underexplored aspect of human resource management in nascent technology ventures: employee recruitment. Applying theories of gender stereotyping, we contend that female‐led technology startups face greater obstacles in attracting job applicants than their male counterparts. Evidence from a randomized online experiment conducted in 2020/2021 with 777 US job seekers substantiates this barrier, indicating that the disparities are partly rooted in gender‐stereotypical perceptions of female technology entrepreneurs as less competent, agentic, and warm, which contribute to less favorable assessments of their ventures' economic potential and employee empowerment potential. Startups with gender‐diverse leadership teams appear to overcome these biases. Confirmatory evidence comes from a 2024 replication study with 455 US job seekers, underscoring the need to address gender biases in the technological ecosystem. Managerial Summary In the competitive landscape of technology startups, attracting talent is key. Our study reveals that startups with female leaders face gender biases during recruitment, with job candidates perceiving female technology entrepreneurs as less competent, agentic, and warm—and their startup ventures as less likely to have what it takes to grow and to empower employees. Analysis from a randomized online experiment involving 777 US job seekers in 2020/2021 and a follow‐up study with 455 US job seekers in 2024 confirm such biases. Crucially, a gender‐balanced leadership team significantly counters such biases, enhancing the venture's appeal to potential hires. These insights highlight the need for technology startups to promote gender diversity within their leadership to dismantle stereotypes and attract a broader talent pool.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".