Resilience of Regional Trade Agreements: Evidence from United State of America Tariff Treatment of USMCA and Japan in the Post-Crisis Era
Bibliographic record
Abstract
This study examines the resilience of regional trade agreements through an analysis of U.S. tariff treatment under the United States–Mexico–Canada Agreement (USMCA) and the U.S.–Japan Trade Agreement, using data obtained from the 2025 U.S. Tariff Database. The study adopts a quantitative research design using secondary data collected by extracting product-level tariff schedules reported under the Harmonized System (HS-8) classification. The dependent variable, tariff resilience, is proxied by the extent and stability of preferential tariff relief across tariff lines, while the independent variables include MFN tariff rate, USMCA preferential rate, and Japan preferential rate, representing multilateral, regional, and bilateral treatment respectively. Using the Kaplan–Meier survival simulation; this study evaluated the persistence and depth of preferential treatment over time, with findings showing a clear hierarchy in tariff treatment. MFN rates are consistently the highest, Japan’s tariffs provide partial but significant relief, and USMCA tariffs are almost entirely eliminated. Agricultural products, processed foods, and manufactured goods benefit most from complete tariff removal under USMCA, while Japan’s concessions, although meaningful, leave some residual exposure. These findings further illustrates that USMCA offers a 100% survival rate for covered tariff lines, reflecting its strength as a resilience mechanism, whereas MFN treatment gradually declines in coverage; highlighting the strategic importance of regional trade agreements in stabilizing trade flows during crises. The study concludes that deep regional integration enhances resilience, while multilateral frameworks require revitalization to complement regional arrangements.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".