The Facts About Referrals: Toward an Understanding of Employee Referral Networks
Bibliographic record
Abstract
Using unique personnel data from nine large firms in three industries, we document five consistent facts about hiring through employee referral networks. First, referred applicants have similar skill characteristics to non-referred applicants, both observable-to-the-firm (e.g., schooling) and unobservable-to-the-firm (e.g., cognitive and non-cognitive ability), but are more likely to be hired, more likely to accept job offers, and have higher pre-job assessment scores. Second, referred workers have similar skill characteristics to non-referred workers. Third, referred workers are less likely to quit and are more productive, but only on rare high-impact performance metrics; on most standard non-rare performance metrics, referred and non-referred workers perform similarly. Fourth, referred workers have slightly higher wages, but yield substantially higher profits per worker. Fifth, workers who make referrals have higher productivity than others, are less likely to quit after making a referral, and refer those like themselves on particular productivity metrics. Differences between referred and non-referred workers tend to be larger at low-tenure levels; for young, Black, and Hispanic workers; and in strong labor markets. No leading class of theories can alone account for all or most of these results, leading us to suggest several theoretical extensions.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".