Opening Canada’s North: A Study of Trade Costs in the Territories
Bibliographic record
Abstract
Challenged by remote locations, small populations, rugged terrain and (at times) difficult climate conditions, Canada's territories rely heavily on imported goods to maintain their standards of living. At the same time, industries in the territories are highly reliant on access to export markets – especially the large and growing resource sectors of the region. But these trade flows face significant costs that improved infrastructure may help mitigate. A northern transportation corridor could help, and has recently gained prominence following recent reports and hearings by the Senate of Canada. The potential gains are large. This paper estimates trade costs in Canada's North. We find policy-relevant trade costs (those trade costs that policy changes may help lower) are substantial. The regulatory differences, time delays and lower infrastructure quality that inhibit trade add between 20 to 30 per cent to the cost of a delivered good for Yukon and Northwest Territories and over 60 per cent for Nunavut. Infrastructure may be a large cause of higher trade costs. We find that distance-related costs are 45 per cent higher per kilometre for trade with a territory than for trade between two provinces. The region’s economy, productivity, income and investment are significantly lower as a result. Using a detailed model of the Canadian economy, we find that lowering these barriers – such as through improving northern transportation infrastructure – could add up to $6.5 billion to Canada’s GDP, with most of that gain occurring in the territories. For the Yukon, Nunavut and Northwest Territories the gains equal about $40,000 per person, which is a 50 per cent increase in productivity. The Senate’s advocacy for reducing trade barriers is encouraging and the federal government broadly supports knocking down these barriers. It is time for all three levels of government to work together to create policies on, and funding for, improved infrastructure in Canada’s North and near-North.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".