Global Trade Creation, Trade Diversion, and Economic Impacts from Changing Global Transport Costs
Bibliographic record
Abstract
Despite the complex political and economic trade environment, the generalized costs of transportation still play an important role in determining trade patterns. This paper analyzes how global trade patterns would simultaneously change as a result of new global transportation costs and for which countries the new patterns of global trade would be beneficial or detrimental. A random utility-based multiregion input–output model helps quantify the size of the impacts, identify the countries and sectors that would gain or lose the most, and explain the change in trade patterns that would create these impacts. Results suggest that Canada is most susceptible to negative economic impacts caused by decreases in global transportation costs, mainly because of its important trade relationship with the United States. Canada’s economy benefits from its proximity to the United States, but as transportation costs decrease, this proximity becomes less relevant as the United States increasingly trades with more distant countries. The United States suffers the same susceptibility to negative economic impacts with decreases in global transportation costs, but to a larger absolute (smaller relative) extent. However, global transportation cost reductions are especially beneficial to Japan, China, and South Korea, which absorb much of the trade diverted away from the United States and Canada.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".