Evaluating the cumulative impact of the US–China trade war along global value chains
Bibliographic record
Abstract
Abstract The US–China trade war has been a key aspect of empirical review in recent times. Using the OECD Inter‐Country Input–Output Model, this study proposes an improved incomplete tariff pass‐through measurement method of cumulative tariff costs incurred across GVCs. Such an approach provides a more accurate picture of the impact of the US–China trade war on not only themselves but also third‐party countries. Our study found that five rounds of tit‐for‐tat tariff escalation has resulted in an indirect tariff burden of around 23 billion US dollars (USD) in total, of which 67% was caused by the US’s tariffs on Chinese imports. Moreover, perhaps unsurprisingly, the United States and China have suffered most economically, and in addition to direct tariff costs, they have to bear the indirect tariff burden of approximately 10 and 6.5 billion USD, respectively. This was followed by the EU, Canada and Mexico, which incurred indirect tariff costs of around 700 million to 1.7 billion USD. In addition, the burden on third‐party countries is expected to rise by 30%–70%, if we consider the hypothesis of complete tariff pass‐through.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".