The Political Economy of Japanese Foreign Aid: The Role of Yen Loans in China's Economic Growth and Openness
Bibliographic record
Abstract
Over the past quarter of a century, China's economic growth, its transition from a socialist to a market-based economy and the integration of the Chinese economy into the global economic system have all progressed significantly. On the other hand, during the same period Japan has been the single largest source of foreign aid to China of all donor nations and international aid organizations, by providing more than half of China's total bilateral aid receipt. This article looks at the role of Japanese foreign aid in China's economic growth and increasing openness, and explains Japan's grand strategy in implementing its aid policy to China. My analysis suggests that there is a positive, albeit indirect, link between Japanese development fund, which is widely known as yen loans, and the growth and increasing openness of the Chinese economy. The evidence also indicates that the link between Japan's provision of yen loans to China and Japan's immediate corporate gains is surprisingly weak. Indeed, Japan has benefited indirectly because yen loans have contributed to the economic growth and openness of China, which in turn make it a better economic partner and more responsible regional neighbour for Japan. In the end, China's economic development, the incorporation of the Chinese economy into the global economic framework and China's transition to a market economy are in Japan's national economic as well as political interests.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".