Gains from Trade for Canada’s North: The Case of a Northern Infrastructure Corridor
Bibliographic record
Abstract
In the 19th century, building the Canadian Pacific Railroad was a crucial element for successful and profitable trade between populated and developing portions of southern Canada. In the 21st century, that crucial element could be a northern corridor that eases east-west trade through Canada’s North. Two recent Senate reports, supported by federal Transportation Minister Marc Garneau, concluded such a project could “unlock significant economic opportunities for our Country.” We explore this possibility. Canada’s North relies heavily on imports for many important goods and services, especially manufactured products, agricultural goods, and professional and scientific services. While imports account for 28 per cent of spending for Canadian provinces, that figure stands at 35 per cent for the Yukon, 39 per cent for the Northwest Territories and 40 per cent for Nunavut. Exports are also critical, especially for the Northern resource sector, which exports 80 per cent of production compared to 66 per cent for the provinces. But due to its vast geography, sparse population, challenging weather and poor trade infrastructure, the costs of trading are large; the gains liberalization equally so. In this paper, we summarize recent analysis that quantified the magnitude and consequences of trade costs with and between Canada’s territories. We find that while distance matters, it matters much more for the territories. Specifically, we find the per-mile trade costs are 45 per cent larger for the territories than the provinces, suggesting lower quality infrastructure is an important cause. The gains from lowering such costs are large. We find the combined annual GDP of Nunavut, Yukon and the Northwest Territories could increase by nearly $4.7 billion – a massive increase of roughly 50%. A northern corridor providing better trade infrastructure would benefit provinces and territories alike. And while the initial outlay for northern infrastructure including multiple transportation modes would be significant, the long-term gains in GDP may justify such costs.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".