A Quantitative Analysis of the US–China Trade Tension
Bibliographic record
Abstract
Abstract How would an escalation of trade tensions between the world's two largest economies reshape global trade patterns and welfare? This study quantified the global effects of potential tariff increases under the second Trump administration using a quantitative general equilibrium model that captured input–output trade linkages. A simulation of a 30 percent increase in US tariffs on Chinese imports indicated that China's exports to the US would fall by 59.1 percent for intermediate goods and 52.7 percent for final goods, with significant diversion toward Mexico and Canada. At the same time, US imports would shift toward Association of Southeast Asian Nations countries, South Korea, and a few other economies. Certain third countries would experience modest welfare improvements but broader tariff escalation scenarios would result in welfare losses for all economies. These findings underscore the critical importance of maintaining open trade polices and stable international trade relations for global economic welfare. Dialogue and cooperation between the US and China are essential to navigate trade complexities and foster a more resilient and prosperous global economy.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".