Products and Provinces; A Disaggregated Panel Analysis of Canada’s Manufacturing Exports
Bibliographic record
Abstract
The waning of the commodity boom places renewed emphasis on manufacturing as an engine for Canadian growth. However, Canadian manufacturing exports have been relatively stagnant since 2000. While the exchange rate depreciation over the past two years has energized export growth, the response has not been as strong as would have been expected given the size of the depreciation. More fundamental issues appear to be impeding the growth of the Canadian manufacturing sector. This study analyzes the structural factors behind export competitiveness by using unique Canadian data on exports, which are disaggregated both by province and by product. Matching exports to similarly disaggregated data on R&D, the capital stock and other supply-side variables, we find that these variables significantly affect export growth, beyond the impact of the exchange rate. In particular, investment in R&D, capital infrastructure and vocational training improves innovation and production capacity. These results are robust to a factor-augmented approach that controls for multicollinearity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".