Global Value Chains and Product Differentiation: Changing the Politics of Trade
Bibliographic record
Abstract
Abstract Both global value chains and trade in differentiated goods have become increasingly important in the international economy. We argue that these two developments interact in changing the political economy of trade. For finished goods, product differentiation facilitates trade liberalization because the adjustment costs of liberalization are lower when countries trade varieties of the same good. By contrast, for goods that are used as inputs in the production process, product differentiation makes trade liberalization more difficult. We find support for this argument in two tests. On the one hand, we look at patterns of lobbying on US preferential trade agreements (PTAs). On the other hand, we use a data set with highly disaggregated tariff data from 61 PTAs signed between 1995 and 2013. The paper contributes to the long‐standing debates on endogenous tariff formation and the consequences of intra‐industry trade, and a nascent literature on the relationship between global value chains and trade policy.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".