Friends like this: The impact of the US–China trade war on global value chains
Bibliographic record
Abstract
Abstract This paper considers the indirect impact the recent tariff increases between the United States and China can have on third countries through links in global supply chains. We combine data from input–output relationships, imports and tariffs, to calculate the impact of the tariff increases by both the United States and China on cumulative tariffs paid by third countries. We show that the tariff hikes increase cumulative tariffs for other countries and thus hurt trade partners further downstream in global supply chains. We also show that this is particularly important for tariff increases on Chinese imports in the United States. These are likely to be used as intermediates in production in the United States, which are then re‐exported to third countries. The most heavily hit third countries are the closest trade partners, namely the EU, Canada and Mexico. We estimate that the tariffs impose an additional burden of around 500 million to 1 billion US dollars on these countries. China's tariffs on US imports have less of an effect.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".