The impact of the US safeguard measures on Northeast Asian producers: General equilibrium assessments
Bibliographic record
Abstract
In March 2002, the United States imposed tariff-rate quotas (TRQs) of about 30 percent on most imported steel above set quotas. This measure is expected to reduce steel exports from East Asian countries, particularly from Japan and Korea. At the same time, the U.S. action is likely to harm its automobile, metal products, and other related industries by raising the cost of intermediate input. Using a dynamic multi-country computable general equilibrium (CGE) model, we evaluate the effects of U.S. steel protection on the economic welfare, steel trade, and sectoral output and unit cost of the United States and its trading partners, with particular attention to those of Japan, China, Korea, and Taiwan, over the period 2002-2005. The results indicate that although the U.S. welfare increases slightly in 2002-2003, it declines in 2004-2005 mainly because the price of steel increases in the U.S. market. U.S. steel imports from East Asian countries and the EU decline by 2.5-2.8 percent, whereas those from Canada and Mexico increase by 2.2 percent, thus largely offsetting the fall in the total U.S. steel imports. The protection causes output contraction in the steel-consuming industries in the United States and output expansion in those industries in Japan, Korea, and Taiwan, but these effects are extremely small. These results suggest that the impact of the U.S. imposition of TRQs is minimal.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".