Domestic patent rights, access to technologies and the structure of exports
Bibliographic record
Abstract
Abstract Recent years have seen major reforms in patent laws around the world. We study the effects of variations over time and across countries in the strength of domestic patent rights (PRs) on exports in high‐R&D goods. Adopting a generalized factor‐proportions framework, we interact industry research intensity with national PRs. Countries with stronger PRs have significantly greater exports in research‐intensive sectors. These effects are positive in emerging and developing economies but smaller than in developed economies. The sensitivity of high‐R&D exports to PRs rises with inward flows of patent applications, FDI employment and intra‐firm trade with multinational firms. Résumé Droits de brevet domestiques, accès aux technologies, et structure des exportations . Au cours des années récentes, on a vu des réformes majeures du droit des brevets autour du monde. On étudie les effets de variations (dans le temps et entre pays) dans la robustesse des droits de brevet domestiques (DBs) sur les exportations des biens à forte intensité de R&D. À l’aide d’un cadre de référence généralisé axé sur les proportions de facteurs, on étudie les interactions entre l’intensité en recherche des industries et les DBs nationaux. Les pays dotés de DBs plus robustes ont des exportations plus grandes de manière significative dans les secteurs à forte intensité de recherche. Ces effets sont positifs dans les pays en émergence et en voie de développement, mais plus faibles dans les pays développés. La sensibilité des exportations de biens à forte intensité de R&D aux régimes de DBs s’accroît à proportion que s’accroissent les flux d’applications de brevets en provenance de l’extérieur, l’emploi attaché aux flux d’investissements direct de l’étranger, et le volume du commerce intra‐firme avec les firmes plurinationales.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".