Recruiting for Ideas: How Firms Exploit the Prior Inventions of New Hires
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
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 new recruits' prior inventions? Our estimates suggest they do, quite significantly in fact, by approximately 219% 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, with much of the diffusion to others being limited to the recruit's immediate collaborative network. 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' premove patents. Specifically, we employ a difference-in-differences approach to compare premove versus postmove citation rates for the recruits' prior patents and 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. This paper was accepted by Kamalini Ramdas, entrepreneurship and innovation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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