Trade War and Peace: U.S.-China Trade and Tariff Risk from 2015–2050
Bibliographic record
Abstract
We use the dynamics of U.S. imports across goods in the period around the U.S.-China trade war with a model of exporter dynamics to estimate the dynamic path of the probability of transiting between Normal Trade Relations and a trade war state.We find (i) there was no increase in the likelihood of a trade war before 2018; (ii) the trade war was initially expected to end quickly, but its expected duration grew substantially after 2020; and (iii) the trade war reduced the likelihood that China would face Non-Normal Trade Relations tariffs in the future.Our findings imply that the expected mean future U.S. tariff on China rose more under President Biden than under President Trump.We also show that the trade response to the trade war is similar to the response to the 1980 liberalization that initially granted China access to U.S. markets at NTR terms and was expected to be quickly reversed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".