The role of trade costs in global production networks: evidence from China's processing trade regime
Bibliographic record
Abstract
In a seminal contribution, Yi (2003) has shown that vertically specialized trade should be more sensitive to changes in trade costs than regular trade. Yet empirical evidenceof this remains remarkably scant. This paper uses data from China's processing trade regime to analyze the role of trade costs on trade within global production networks (GPNs). Under this regime, firms are granted duty exemptions on imported inputs as long as they are used solely for export purposes. As a result, the data provide information on trade between three sequential nodes of a global supply chain: the location of input production, the location of processing (in China) and the location of further consumption. This makes it possible to examine the role of both trade costs related to the import of inputs (upstream trade costs) and trade costs related to the export of final goods (downstream trade costs) on intra-GPN trade. The authors show that intra-GPN trade differs from regular trade in that it not only depends on downstream trade costs, but also on upstream trade costs and the interaction of both. Moreover, intra-GPN trade is more sensitive to oil price movements and business cycle movements than regular trade. Finally, the paper analyzes three channels through which intra-GPN trade have amplified the trade collapse during the recent Global Recession.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".