Public Policy and Growth in Canada: An Applied Endogeneous Growth Model with Human and Knowledge Capital Accumulation
Bibliographic record
Abstract
We analytically investigate and assess the interactions between knowledge driven growth, acquisition of human capital, and the role of strategic public policy for the Canadian economy within the context of a general equilibrium model. For the purpose of characterization of a fully advanced economy, sources of economic growth are endogenized through the simultaneous interaction of two activities: technological innovations and human capital formation. Both of these activities have cross spill-over effects onto each other. With the aid of the modeling framework utilized in this paper, we aim to contribute to the intense discussion on “Canada’s Innovation/R&D puzzle” from a macroeconomic perspective, with a focus on the role of strategic public polices. To this end, we investigate alternative public policies aimed at fostering the development of human capital (investment in education, learning, basic research) and those at enhancing total factor productivity through investments in innovation; and study the impact of these public subsidization policies on patterns of growth, investment and capital accumulation, social welfare, and burden on government budget. Our results suggest that the re-allocation effects triggered by public subsidization policies on higher education and, alternatively on Industry/business R&D might be contributing to the current discussion that Canadian economy is falling short of it’s potential in (business) technological innovation and R&D production.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".