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 recent U.S. tariff increases 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 lead to a rise in U.S. manufacturing employment, but this comes at the cost of declines in service and agricultural jobs.Overall employment falls, as lower real wages reduce labor-force participation.Real income in the U.S. declines by about 1% by 2028, the final year we assume the tariffs remain in effect.A key feature of our analysis is the disaggregation of the U.S. into its 50 states, incorporating cross-state redistribution of tariff revenue.This allows us to identify which states gain or lose more from the policy shock.Around half of the states experience real income losses, with some seeing declines greater than 3%.At the international level, close U.S. trading partners-including Canada, Mexico, China, and Ireland-suffer the largest real income losses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".