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 tiraillees entre deux facons de faire de la R-D. D'un cote, elles preferent faire la R-D de maniere ouverte (en faisant faire de la R-D extramuros ou meme en vendant des services de R-D) afin de beneficier d'externalites de connaissance et de complementarites entre la R-D interne et la R-D externe. D'un autre cote, elles preconisent de faire la R-D en mode ferme (en faisant de la recherche intramuros et en se financant sur base de fonds propres ou de subventions) afin de minimiser les fuites de connaissance. Dans cette etude, nous examinons la dynamique des choix quant a la facon de faire de la recherche et l'effet de ces choix sur les rendements de celle-ci. Nous nous basons sur les donnees de l'enquete de Statistique Canada sur la recherche et developpement dans l'industrie canadienne (RDIC) pour la periode 1997-2006. La dimension panel de la base de donnees nous permet de controler pour l'heterogeneite individuelle inobservee dans l'estimation d'un modele Logit multinomial dyna (This abstract was borrowed from another version of this item.)
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".