Analysing International Trade Patterns: Comparative Advantage for the World’s Major Economies
Bibliographic record
Abstract
Using disaggregate product data classified by the Harmonized System code, this paper computes the revealed comparative advantage (RCA) for seven major economies that, when combined, contributed more than 80% of global manufacturing exports in 1996-97 and 2006-07. Results show that in the last decade, Canada, the US, and Japan have lost their share of global exports, while China has increased its share three-fold. These losses occurred mainly for low-tech products for the US, but medium and high-tech (MHT) products for Canada and Japan. However, MHT products comprise the highest share of Japanese exports (70%) compared to Canada (which has the lowest share, approximately half of Japan’s). Canada is the only economy whose contribution to global MHT exports is lower than that of global total exports. Japan also has the highest share of RCA-based MHT exports of other East Asian countries (OEACs) and the US. China has the highest share of non-RCA- based MHT exports. Finally, the trade patterns for OEACs and Mexico did not change greatly in any dimension in the last decade. However, products with RCA have changed substantially in all economies, with the highest in Mexico.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".