Are China's Exports Crowding Out or Being Crowded Out? Evidence from Japan's Imports
Bibliographic record
Abstract
Abstract Previous studies have investigated whether Chinese exports have crowded out those from other countries. However, what has yet to be considered is the evidence based on different quality varieties. Using the most detailed Harmonized System 9‐digit product‐level data, the present paper provides evidence of crowding‐out and crowded‐out effects across different product quality segments and across manufacturing sectors by quality segments. The empirical evidence presented in this paper shows that the crowding‐out effects of Chinese exports have been greatest at the lower end of the quality spectrum but less significant at the higher quality spectrum. Moreover, since 2007, China's own exports of lower quality manufactured goods have been increasingly crowded out. The key policy implication is that China's export path is in line with that taken by other Asian economies in previous decades; the crowded‐out effect could achieve win–win outcomes for countries involved; and lower income countries would do well to be open to receive those relocated low value‐added industries from China. However, the relocation policy in China is best implemented gradually as climbing up the product quality ladder takes time.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".