Public policy and R&D when research joint ventures are costly
Bibliographic record
Abstract
In this paper we examine the role of policy when forming a R&D joint venture is costly. Contrary to previous studies, we document an active role for public policy, since the interests of firms are not necessarily aligned with societal interests. The nature of policy, however, depends on the joint venture cost. If it is relatively low, then policy may call for subsidizing the joint venture to encourage collaboration. If forming a joint venture is very costly, however, then there are cases where social welfare is improved if policy encourages R&D competition with no joint venture. JEL Classification: D43, L13 Politique publique et R&D quand les alliances stratégiques en recherche sont coûteuses. Ce mémoire examine le rôle de la politique publique quand la mise en place d'une alliance stratégique en recherche est coûteuse. Contrairement à ce qu'ont suggéré des études antérieures, les auteurs montrent qu'il y a un rôle positif pour la politique publique, à proportion que les intérêts des entreprises ne sont pas nécessairement alignés sur les intérêts de la société. La nature de la politique dépend cependant du coût de l'alliance stratégique. Si le coût est faible, alors une politique de subvention pour encourager la collaboration peut s'imposer. Si le coût est élevé, alors il existe des cas où une politique de concurrence dans le R&D sans alliance stratégique peut mieux servir le mieux être collectif.
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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 | 0.015 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.003 |
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".