Structural change and global trade flows: Does an emerging giant matter?
Bibliographic record
Abstract
Abstract In this paper, we develop a novel trade‐accounting framework that is based on a multi‐country, multi‐industry model of trade. The framework links observed changes in wages, sectoral employment shares, total labor force, and bilateral trade costs to changes in bilateral trade values at the sector level. In our application, we quantify the changes in trade patterns from 1995 to 2010 among 15 advanced and emerging market economies attributable to structural change in China, focusing on three manifestations of trade creation and destruction: China’s replacement of manufactured final goods exports to advanced economies at the expense of other economies; an expansion of China’s imports of manufactured final goods and commodities; and an expansion of China’s imports of parts and components that are then processed and exported as manufactured final goods to the advanced economies. Our main findings are: (a) scale effects have more than compensated for the loss of competitiveness due to higher wages in China; (b) China’s wage growth has been an economically more significant determinant of trade creation and destruction than its reallocation of labor across sectors, and (c) structural change in China has shifted other countries toward more commodity‐intensive production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".