Like Attracts Like? Revisiting Demographic Homophily in Entrepreneurship
Bibliographic record
Abstract
New high-tech ventures are an important source of job creation in the United States. However, access to job opportunities in high-tech entrepreneurship varies significantly across demographic groups. A well-established finding suggests that there is a strong tendency towards homogeneity both in the formation of entrepreneurial founding teams as well as the hiring of early employees. Prior work has emphasized the importance of founders’ influence over personnel selection processes in explaining the tendency towards homogeneity in start-ups’ workforces. However, disentangling the influence of personnel selection processes in producing workforce homogeneity from other possible mechanisms presents a significant challenge. Here, we propose that workforce homogeneity in start-ups may also result from workers’ tendency to self-sort into start-ups whose founders resemble them demographically. We use a unique dataset on the recruiting and hiring processes at a sample of high-tech start-ups to attribute between these different accounts. Our results suggest that the origins of demographic homogeneity between founders and the workers they hire lie in start-ups’ tendency to attract job candidates that resemble their founders, rather than their propensity to favor these candidates in personnel selection.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".