The Impact of China’s Retaliatory Tariffs on U.S. Agricultural Exports
Bibliographic record
Abstract
This paper examines the impact of China’s retaliatory tariffs on U.S. agricultural exports during the 2018 trade war. I first quantify the impact of China’s retaliatory tariffs on U.S. agricultural exports to China. Then I select four major agricultural destinations besides China as case studies to analyze the trade diversion effects. I merge the U.S. monthly agricultural export data with China’s retaliatory tariff data and then build a two-way fixed effects regression model. The results suggest a significant reduction in U.S. monthly agricultural exports to China as well as pronounced trade diversions to Canada and Mexico. For a 10% increase in China’s retaliatory tariffs, the average monthly agricultural exports to China decreased by 3.23%, and the exports to Canada and Mexico increased by 2% and 4.15%, respectively. Besides, I also find that the U.S. expanded both volumes and numbers of agricultural products exporting to Canada and Mexico as China increased retaliatory tariffs.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".