The Facts About Referrals: Toward an Understanding of Employee Referral Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it