Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires
Bibliographic record
Abstract
When firms recruit inventors, they acquire not only the use of their skills but also enhanced access to their stock of ideas.But do hiring firms actually increase their use of the new recruits' prior inventions?Our estimates suggest they do, quite significantly in fact, by approximately 202% on average.However, this does not necessarily reflect widespread "learning-by-hiring."In fact, we estimate that a recruit's exploitation of her own prior ideas accounts for almost half of the above effect.Furthermore, although one might expect the recruit's role to diminish rapidly as her tacit knowledge diffuses across her new firm, our estimates indicate that her importance is surprisingly persistent over time.We base these findings on an empirical strategy that exploits the variation over time in hiring firms' citations to the recruits' pre-move patents.Specifically, we employ a difference-in-differences approach to compare pre-move versus post-move citation rates for the recruits' prior patents and the corresponding matched-pair control patents.Our methodology has three benefits compared to previous studies that also examine the link between labor mobility and knowledge flow: 1) it does not suffer from the upward bias inherent in the conventional cross-sectional comparison, 2) it generates results that are robust to a more stringently matched control sample, and 3) it enables a temporal examination of knowledge flow patterns.
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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 | 0.022 |
| 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 | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".