R&D Spillovers in Canadian Industry: Results from a New Micro Database
Bibliographic record
Abstract
Business investment in research and development (R&D) makes a key contribution to rising living standards. Firms undertaking the R&D can reduce production costs and introduce new products that provide benefits to consumers that are not fully captured in selling prices. Further, it is very difficult for R&D-performing firms to prevent some of the knowledge created from leaking out or spilling over to other firms. Since firms do not take these positive spillover benefits into consideration when making investment decisions, most governments subsidize business investment in R&D with the expectation that economic performance will improve as a result. Our study confirms the existence of substantial spillover benefits from R&D performed in Canada, so government support for R&D is justified. However, we do not find any empirical evidence to support the current policy of subsidizing R&D at a higher rate when it is performed by small firms than when it is performed by large firms. We also find much lower private rate of return on R&D performed by small firms than by large firms. Subsidies appear to be playing a key role in this result
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.029 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".