The 2025 Trade War: Dynamic Impacts Across U.S. States and the Global Economy
Bibliographic record
Abstract
We use a dynamic trade and reallocation model with downward nominal wage rigidities to quantitatively assess the economic consequences of the recent increase in the U.S. tariffs on imports from Mexico, Canada, and China, as well as the “reciprocal” tariff changes announced on “Liberation Day” and retaliatory measures by other countries. Higher tariffs trigger an expansion in U.S. manufacturing employment, but this comes at the expense of declines in service and agricultural employment, with overall employment declining as lower real wages reduce labor-force participation. For the United States as a whole, real income falls around 1% by 2028, the last year we assume the high tariffs are in effect. Importantly, our analysis disaggregates the U.S. into its 50 states, while incorporating cross-state redistribution of the tariff-generated fiscal revenue, allowing us to analyze which states gain or lose more from the shock. Around half of the states lose, with some states experiencing real income declines of more than 3%. Turning to cross-country results, some close U.S. trading partners—like Canada, Mexico, China, and Ireland—suffer the largest real income losses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".