Bibliographic record
Abstract
The tariff duties already enforced or threatened by the Trump administration are likely to increase costs and prices in the US economy, but by how much? To address this question, we identify and quantify three channels: direct taxation, cost increase linked to taxes on intermediate inputs, and altered pricing strategy resulting from strategic complementarities across firms. Evidence from three recent episodes of additional tariff protection show that our framework provides sensible assessments of ensuing price increases, which usually materialize gradually and do not reach their maximum level for at least four months. We reckon that the additional duties enforced up to December 2018 should increase inflation in the US by 0.25% to 0.38%. Should all US imports from China be hit with a 25% tariff, the total inflationary impact would range between 0.66% and 0.99%. Levying 25% additional duties on imports of autos and auto parts would more or less double down this effect, by adding 0.67% to 1.03% to inflation if all providers are targeted, and 0.47% to 0.73% if Canada and Mexico are excluded. These estimates show that the additional duties considered by the Trump administration, if applied extensively, might push up consumer prices by more than one percentage point. This is far from negligible from the point of view of both consumers’ purchasing power and financial stability, thus potentially seriously limiting the administration’s room for maneuver. The contrast with China is stark; there, the inflationary impact of retaliatory measures is small, and more than counterbalanced by the wide-ranging tariff cuts enforced over the last year.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".