The Comparison of Major APEC Members' Value-added Trade Competitiveness in Global Value Chain
Bibliographic record
Abstract
In global value chains(GVC),it is difficult for traditional trade statistical methods to accurately reflect the degree of benefit from the participation in international division of labor.Based on an accounting framework put forward by KPWW,this paper breaks up APEC members' gross exports from the perspective of value-added trade and compares the value-added trade competitiveness among nine major APEC economies.The results show that according to value-added trade statistics,the trade position and trade imbalance of all APEC members do not basically change while the contribution of export to their economic growth is declining.All APEC members' value-added exports are less than their gross exports based on traditional trade statistical methods,and the proportion of value-added exports in Korea,Mexico,China and Canada are relatively low,indicating that trade benefit from global value chain is overestimated owing to traditional trade statistical methods.The decomposition of double counted terms shows that Russia is at the upstream raw material supply link in global value chain;the United States is at the upstream or downstream links in global value chain;Australia is at the upstream or middle links;China,South Korea,Mexico,Indonesia and Canada are at the middle processing and manufacturing links;although Japan is also at the middle link in global value chain,it exports core components and can gain more benefits from its upstream and downstream trade.Value-added trade statistics not only can really reflect the position of a country in international division of labor,but also better reflects the distribution of trade interests and amends the distortions of trade balance.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".