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Record W2902760884 · doi:10.1111/1758-5899.12614

Global Value Chains and Product Differentiation: Changing the Politics of Trade

2018· article· en· W2902760884 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGlobal Policy · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMcGill University
Fundersnot available
KeywordsTariffProduct differentiationEconomicsProduct (mathematics)Free tradeValue (mathematics)LiberalizationInternational tradeInternational economicsArgument (complex analysis)Commercial policyProduction (economics)Goods and servicesTrade barrierInternational free trade agreementMicroeconomicsMarket economyWelfare

Abstract

fetched live from OpenAlex

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 ( PTA s). On the other hand, we use a data set with highly disaggregated tariff data from 61 PTA s 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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.243
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it