The Importance of Skills for Innovation and Productivity
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.
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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 | 0.003 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".