1 Role of Skills and Government Support in Stimulating Innovation By
Bibliographic record
Abstract
Rapid progress in skill-biased technologies has increased the demand for skilled workers in all countries. Lack of skilled workers could become a serious impediment to innovation. In this study, we first examine the importance of skills and government support for innovation using firm-level data from Statistics Canada’s Survey of Innovation 1999. We then investigate the role of differences in skills in explaining the differences in productivity levels among Canadian manufacturing industries. After controlling for other factors, we find that firms ’ practices of hiring new graduates from universities and hiring experienced employees have positive and significant impacts on innovation outcomes, and that they are equally important for both product and process innovation. In addition, after controlling for industries characteristics, inter-industry differences in labour productivity levels among Canadian two-digit manufacturing industries are positively related to differences in capital intensity, R&D intensity and skills intensity, proxied by two variables: the proportion of employees with 1-3 years post-secondary education; and the percentage of employees with a university degree and more. ∗ We would like to thank Frances Anderson, Brian Nemes, Guy Sabourin, and Susan Schaan of Statistics Canada for their support and facilitation of our access to the Linked 1999 Survey of Innovation database. The views expressed in this paper are of the authors only and do not reflect in any way those of either Industry Canada or the Government of Canada. We are also grateful to Renée St-Jacques for her support and comments. 2
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".