The Effect of US Dollar-weakened Adjustment on Sino-South Korea Bilateral Product Trade
Bibliographic record
Abstract
What effect does a weakening US dollar(USD) against RMB have on Sino-South Korea bilateral product trade during 2002-2013? This paper uses statistical comparison and GMM estimation to show that firstly, along with a weakening US dollar, we find no obvious evidence of China's export deflection to South Korea(SK), whereas SK obviously increases the share of product export to China. Along with a weakening US dollar, there are higher product trade complementarities and lower product trade competitiveness between China and South Korea. Secondly, following a weakening US dollar, real depreciation of RMB/Won exchange rate of goods is bad for China's product export to SK, and does not bring about the increase of China's real import from SK. In the meantime, real depreciation of RMB/USD exchange rate is harmful to China' s export to the US, In addition, it does not bring about the export of China's deflection to SK, but reduces China's import from SK. The rise of RMB/Won real exchange rate volatility can bring about significant and adverse impacts on Sino-SK bilateral import and export, whereas economic growth in the two countries can enhance significantly Sino-SK bilateral trade. Therefore, some policy suggestions are that it is very important to establish and deepen Sino-SK Free Trade Area to expand trade markets. The two central banks increase the currency swap scale, and create new financial instruments to reduce exchange rate risks, which promotes Sino-SK bilateral trade to the utmost.
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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 | 0.003 |
| 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 | 0.000 | 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".