Doing R&D In A Closed Or Open Mode: Dynamics And Impacts On Productivity
Bibliographic record
Abstract
On the one hand, firms prefer to perform R&D in an open mode (letting R&D be performed extramurally or even selling their R&D services) to benefit from knowledge spillovers and complementarities between internal and external R&D. On the other hand, they may also like to perform R&D in a closed mode (funding and executing their R&D intramurally) to minimize outgoing externalities. We examine the dynamic process by which firms change the way of doing R&D and how these strategic choices of doing R&D affect their productivity growth. This study is based on the Statistics Canada Research and Development in Canadian Industry survey (RDCI), which collects data on R&D performed in the business sector in Canada. The paper is based on data for the period 1997 to 2006. The panel dimension of the data allows to control for unobserved characteristics of R&D performers by estimating a multinomial Logit model with unobserved heterogeneities using maximum simulated likelihood (MSL) method. Les firmes sont tiraillées entre deux façons de faire de la R-D. D'un côté, elles préfèrent faire la R-D de manière ouverte (en faisant faire de la R-D extramuros ou même en vendant des services de R-D) afin de bénéficier d'externalités de connaissance et de complémentarités entre la R-D interne et la R-D externe. D'un autre côté, elles préconisent de faire la R-D en mode fermé (en faisant de la recherche intramuros et en se finançant sur base de fonds propres ou de subventions) afin de minimiser les fuites de connaissance. Dans cette étude, nous examinons la dynamique des choix quant à la façon de faire de la recherche et l'effet de ces choix sur les rendements de celle-ci. Nous nous basons sur les données de l'enquête de Statistique Canada sur la recherche et développement dans l'industrie canadienne (RDIC) pour la période 1997-2006. La dimension panel de la base de données nous permet de contrôler pour l'hétérogénéité individuelle inobservée dans l'estimation d'un modèle Logit multinomial dynamique à partir de la méthode du maximum de vraisemblance simulé.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".