What Is Restraining Non-Energy Export Growth? Staff Analytical Note 2018-25 (English)
Bibliographic record
Abstract
This note summarizes the key findings from Bank of Canada staff analytical work examining the reasons for the recent weakness in Canadian non-energy exports. Canada steadily lost market share in US non-energy imports between 2002 and 2017, mostly reflecting continued and broad-based competitiveness losses. In addition to this evidence from the demand side, industry analysis points to supply constraints that are limiting export growth, such as physical capacity and shortages of skilled labour. Transportation bottlenecks, environmental and regulatory changes, and the inability to source raw materials also appear to be limiting export growth in some industries. Evidence suggests supply-side capacity constraints at the industry level as well. These constraints mainly reflect a decline in the factors of production, such as labour input and capital stock, which are likely related to the ongoing competitiveness losses. Simulations using the Bank of Canada’s ToTEM model suggest that demand factors such as competitiveness issues explain most of the recent weakness in non-commodity exports. The simulations also indicate that a monetary policy reaction is required, independent of whether the weakness is driven by demand or supply factors. Staff expect broad-based competitiveness losses and structural supply factors to continue to restrain the growth of non-energy exports over the projection horizon, which extends until 2020.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".