Bibliographic record
Abstract
Abstract. This paper analyses R&D cooperation with asymmetric spillovers. It is shown that the change in R&D by a firm following cooperation is proportional to the gap between the spillover rate transmitted by that firm and a critical level of spillovers. In consequence, cooperation increases total R&D investments when the average of firms’ spillover rates is sufficiently high. Whereas with symmetric spillovers cooperation is always beneficial to firms, with asymmetric spillovers only a very limited range of spillovers makes cooperation beneficial to both firms. Asymmetries also create a potential conflict between maximizing total welfare and maximizing effective cost reduction. JEL classification: L13, O33 Coopération en R&D avec effets de retombée asymétriques. Ce mémoire analyse l’effet de la coopération en R&D quand il y a des asymétries dans les effets externes technologiques. On montre que le changement dans la R&D d’une entreprise à la suite de la coopération est proportionnel à l’écart entre l’externalité transmise par cette entreprise et une valeur critique de l’externalité. En conséquence, la coopération augmente les investissements totaux en R&D quand le taux de retombée moyen est suffisamment élevé. Alors que la coopération est toujours bénéfique lorsque les externalités sont symétriques, les asymétries rendent la coopération non bénéfique pour au moins l’une des entreprises pour un vaste éventail de paramètres. Les asymétries peuvent aussi créer un conflit potentiel entre la maximisation du bien‐être total et la maximisation des réduction de coûts effectifs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".