Trade, transport costs and trade imbalances: An empirical examination of international markets and backhauls
Bibliographic record
Abstract
Abstract The US trade deficit has been growing for over 25 years and has been accompanied by enlarging freight rate differentials. While traditional models of trade have ignored these gaps assuming symmetry across all bilateral trade costs, the specific linkages between trade imbalances and international transportation costs have remained unexplored. Given the current trade policies, the implications arising from the endogenous adjustment of bilateral transport costs to policy‐induced changes in the US trade deficit are of particular importance. To break new ground on this issue, we develop and estimate a model of international trade and transportation that accounts for the effects of persistent trade imbalances. The theoretical results are supported by our empirical analysis and indicate that bilateral transport costs adjust to a country's trade imbalance. The implication is that a unilateral import policy, for example, will cause spillover effects into the bilaterally integrated export market. To illustrate, we use our empirical results to simulate the anticipated spillover effect from the Chinese ban on waste imports. We find that China's ban and the projected 1.5% rise in the US trade deficit will lead to not only a 0.77% reduction of transport costs charged on US exports to China but also a 0.34% increase in transport costs on US imports from China.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".