Intra-Industry Trade, Global Value Chains, and the Political Economy of Selective Trade Protection
Bibliographic record
Abstract
Abstract Recent trade wars have confronted the trade policy literature with a major puzzle. How can we explain protectionist tendencies in the context of global economic integration? In this article, I aim to provide an answer to the question why, and under which conditions, internationally oriented companies are in favor of trade restrictions. More specially, I argue that intra-industry trade (IIT) and global value chains (GVCs) give rise to internally conflicting interests on the part of firms, generating incentives to lobby for specific, targeted measures against their closest competitors. To test whether firms’ preferences are translated into trade policies pursued by governments, I use data on trade barriers imposed by Brazil, Canada, China, the European Union, India, Japan, Russia, and the United States. I find compelling evidence that the levels of IIT and to a lesser extent trade in GVCs positively affect the decision to implement selective trade measures—such as bilateral tariffs and antidumping duties—rather than broader forms of trade protection. This result suggests that IIT and GVCs have structurally altered firms’ attitudes toward trade barriers and, consequently, the way in which countries protect their domestic markets against foreign competition.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".