A history of U.S. tariffs: Quantifying strategic trade‑offs in tariff policy design
Bibliographic record
Abstract
U.S. tariff policy has historically balanced competing goals—revenue, protection, and reciprocity. Policy priorities have shifted over time in response to changing economic and political conditions. Using a calibrated general equilibrium model, we illustrate these trade-offs through the lens of tariff Laffer curves. A 70 % tariff maximizes U.S. revenue only in the absence of retaliation; this optimum falls to 30 % with reciprocal tariffs. A unilateral 25 % tariff delivers the largest domestic consumption gains through favorable terms-of-trade effects, though these gains vanish under retaliation. Simulations also show that multilateral retaliatory tariffs can partially offset losses for Mexico and Canada—unless escalation triggers broader trade conflict. The 2018–19 tariff war further illustrates how targeted tariffs distort relative prices and cross-border resource allocation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".