General Equilibrium Trade Modelling with Canada-US Transportation Costs
Bibliographic record
Abstract
Transportation costs are an important topic in international trade, but seldom have researches paid attention to general equilibrium trade modelling with transportation costs and explored their relevant effects.This paper uses different numerical general equilibrium trade model structures to simulate the impacts of transportation costs on both welfare and trade for a Canada-US country pair case.We compare two groups of model structure, Armington assumption models and homogeneous goods models.Within these two groups of models, we also compare balanced trade structures to trade imbalance structures, and production function transportation costs to iceberg transportation costs.Armington goods models generate absolute welfare gains from transportation cost elimination than homogeneous goods models.Welfare gains under balanced trade structures are larger in production function transportation cost scenarios, but are larger in iceberg transportation cost scenario under trade imbalance structures.Canada's welfare gains with iceberg transportation cost are significantly larger than gains with production function transportation cost.On trade effects, homogeneous goods models generate more export and import gains, balanced trade structures have more trade variations, and iceberg transportation cost generate more trade effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".