The Trade Performance of Asian Economies During and Following the 2008 Financial Crisis
Bibliographic record
Abstract
This paper documents and compares the trade performance of the major Asian economies both during and following the 2008 financial crisis.We consider China, India, Thailand, Malaysia, South Korea, Japan, Singapore and Chinese Taiwan.We access separate country data files giving monthly trade performance for both the import and export sides throughout the crisis.We use these to compare the size, speed and acceleration of trade compression with the onset of the crisis, and the reverse effects on recovery.We do this in aggregate and by product and bilateral trading partner.The data reported show considerable diversity of country experience.Among manufacture exporters China has seen a major decline in trade with a slow recovery, whereas Korea experienced smaller initial impact but a quick rebound.Import impacts are mildest for India and commodity exporters including Malaysia.On the import side, the falls in world oil prices impact sharply on import values.We also compare trade impacts in the 2008 financial crisis with those in the 1930s and the Asian financial crisis.In the 1930s percentage impacts on trade in the first year were similar, but of much longer duration, reducing trade volumes in the US by nearly 80% by 1933, and placing Germany close to autarchy.In the 1998 Asian crisis trade impacts were much smaller since export markets in the OECD were not affected, but negative growth impacts on affected countries were greater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".