Innovation and Appropriability: Revisiting the Role of Intellectual Property
Bibliographic record
Abstract
It is more than 25 years since the authors of the Yale and Carnegie surveys studied how firms seek to protect the rents from innovation.In this paper, we revisit that question using a nationally representative sample of firms over the period 2008-2015, with the goal of updating and extending a set of stylized facts that has been influential for our understanding of the economics of innovation.There are five main findings.First, while patenting firms are relatively uncommon in the economy, they account for an overwhelming share of R&D spending.Second, firms consider utility patents less important on average than other forms of IP protection, like trade secrets, trademarks, and copyrights.Third, industry differences explain a great deal of the level of firms' engagement with IP, with high-tech firms on average being more active on all forms of IP.Fourth, we find no significant differences in the use of IP strategies across firms at different points of their life cycle.Lastly, unlike age, firms of different size appear to manage IP significantly differently.On average, larger firms tend to engage much more extensively in the protection of IP, and this pattern cannot be easily explained by differences in the type of R&D or innovation produced by a firm.We also discuss the implications of these findings for innovation research and policy.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 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".