How innovating firms manage knowledge leakage: A natural experiment on the threat of worker departure
Bibliographic record
Abstract
Abstract Research Summary Knowledge protection strategies are crucial to innovating firms facing the risk of knowledge leakage. We examine the threat of worker departure as a key mechanism through which firms choose between patents and secrecy. We exploit a 1998 California court decision that ruled out‐of‐state noncompetes were not enforceable in California, thereby creating a loophole limiting non‐California firms in their enforcement of noncompetes against their workers. When facing a higher threat of worker departure, firms strategically increased patent filings, exchanging legal protection for public disclosure of the invention. These effects were magnified for large‐sized firms and for those in complex and fast‐growing industries. Further mechanism tests on the possession of trade secrets, inventor migration, saliency of the decision, and independent inventors support our theoretical account. Managerial Summary Innovating firms may use patents or secrecy, among other mechanisms, to protect their knowledge from leakage. How do firms make this important strategic choice? By using a natural experiment arising from a 1998 California court decision, we show the risk of worker departure can be a key driver. The decision significantly increased the risk of workers departing non‐California firms. Our findings show that, in response to the heightened risk, affected firms increasingly relied on patents, seeking legal protection although it meant public disclosure of the invention. The effects were greater for large‐sized firms and for those in complex and fast‐growing industries. We encourage managers to consider worker mobility and, more broadly, legal environments that govern labor market conditions when formulating knowledge protection strategies.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".