Trucking Across the Border: The Relative Cost of Cross-border and Domestic Trucking, 2004 to 2009
Bibliographic record
Abstract
Despite the elimination of tariff barriers between Canada and the United States, the volume of trade between the two countries has been less than would be expected if there were no impediments. While considerable work has been done to gauge the degree of integration between the Canadian and U.S. economies through trade, relatively little analysis has parsed out the underlying costs for cross-border trade. The costs of crossing the border can be divided into formal tariff barriers, non-tariff barriers, and the cost of the transport system itself. This paper focuses on the latter by estimating the cost of shipping goods by truck between Canada and the U.S. during the 2004-to-2009 period. The analysis assesses the degree to which costs to ship goods by truck to and from the U.S. exceed those within Canada by measuring the additional costs on a level and an ad valorem basis. The latter provides an estimate of the tariff equivalent transportation cost that applies to cross-border trade. These costs are further broken down into fixed and variable (line-haul) costs. Higher fixed costs are consistent with border delays and border compliance costs which are passed on to the consumers of trucking services. Higher line-haul costs may result from difficulties obtaining backhauls for a portion of the trip home. Such difficulties may stem from trade imbalances and regulations that restrict the ability of Canadian-based carriers to transport goods between two points in the United States.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".