Global Value Chains in the Current Trade Slowdown
Bibliographic record
Abstract
Real growth in global trade has decelerated significantly since its sharp recovery in 2010. Year-on-year growth in global real trade1 decelerated from 13.3 percent at the end of the first quarter of 2010, to 9.9, 3.1, and 0.5 percent at the end of the first quarters of 2011, 2012, and 2013, respectively, while picking back up to 3.9 percent in the year leading up to the fourth quarter of 2013.2 This aggregate deceleration in global trade includes absolute declines in real trade for many product categories and regions. In the wake of the Great Trade Collapse of 2008–9, understanding of the behavior of trade in slowdowns has improved. Among the many explanations offered for the Great Trade Collapse, including explanations related to uncertainty, trade financing, and new protectionist measures by governments, there has been a significant focus on whether the emergence of global value chains (GVCs) in international trade, and their behavior, are a contributing factor in trade slowdowns.3 Relationship between GVCs and Trade Decelerations GVCs involve trade in goods that have multiple production stages that take place in many different countries (that is, “production fragmentation ” or “slicing up the value chain”) and in which multiple imports and exports of intermediate goods are necessary to produce a final good, which may also be exported. Since the emergence of the North American GVC in automobiles in the 1960s and the East Asian electronics GVC in the 1970s, the role of GVCs in international trade has become more important and has attracted increasing at-tention. There are several potential reasons why GVC trade may behave differently. First, there is a crude statistical argument for a relation-ship between GVC trade and slowdowns, arising from the fact that there are more trade flows in GVC trade than in non-GVC trade. If an exported good is produced entirely within the exporting country, and there is a reduction in demand, then one trade flow disappears. But if the exported good is produced with an imported input, there are two trade flows (the import of the input and the export of the final good), and
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".