Waging a Global Trade War Alone: The Cost of Blanket Tariffs on Friend and Foe
Bibliographic record
Abstract
We use an advanced model of the global economy to consider a set of scenarios consistent with the proposal to impose a minimum 60% tariff against Chinese imports and blanket minimum 10% tariff against all other US imports. The model’s structure, which includes imperfect competition in increasing-returns industries, is documented in Balistreri, Böhringer, and Rutherford (2024). The basis for the tariff rates is a proposal from former President Donald Trump (see Wolff 2024). We consider these scenarios with and without symmetric retaliation by our trade partners. Our central finding is that a global trade war between the United States and the rest of the world at these tariff rates would cost the US economy over $910 billion at a global efficiency loss of $360 billion. Thus, on net, US trade partners gain $550 billion. Canada is the only other country that loses from a US go-it-alone trade war because of its exceptionally close trade relationship with the United States.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".