Innovation and Establishments' Productivity in Canada: Results from the 2005 Survey of Innovation
Bibliographic record
Abstract
Research teams from 18 OECD countries used the methodology introduced by Crepon-Dugay and\nMairesse (CDM) to analyze the impact of innovation on labour productivity using firm data from\nnational innovation and administrative surveys. To ensure international comparability, the OECD ‘core’\nCDM model did not include variables for which data were missing in some countries. In spite of this\nshortcoming, the results are broadly in line with theoretical hypotheses and previous studies and show\na surprising degree of similarity between countries. This paper builds on the Canadian application of\nthe ‘core’ model used for the OECD project. It uses to the full extent all information available on\nmanufacturing establishments from the Canadian Survey of innovation 2005 linked with the Annual\nSurvey of Manufactures and Logging (ASML).\nThe estimated econometric model controls for selection bias, simultaneity, size of firm and industry\neffects. The main findings suggest that (1) export outside of the US market, size of the firm and use of\ndirect or indirect government support are factors increasing the probability to innovate and having\npositive innovation sales. (2) Exports (both to the US and outside of the US market), cooperation with\nother firms and organizations, and high share of the firms’ revenue coming from sales to its most\nimportant client are all factors correlated with higher innovation expenditures per employees.\nMoreover, firms with a higher market share at the beginning of the period are spending more on\ninnovation by the end of the period. (3) Firms with higher innovation expenditures per employee\ngenerate more innovation sales per employee. Other factors increasing innovation sales are human\nand physical capital and introduction of process innovations. (4) Finally, the firms generating more\ninnovation sales per employees achieve higher labour productivity, even when the size of firms, the\nintensity of human and physical capital and labour productivity at the beginning are taken into account.\nThe results add valuable further information to and are in line with the simpler model applied to 18\nother OECD countries. The paper concludes with discussion of policy implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".