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Record W3123805600

Trade Disputes, Quality Choice, and Economic Integration

2010· preprint· en· W3123805600 on OpenAlexaff
Richard Chisik

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTransparency (behavior)Quality (philosophy)International economicsOrder (exchange)Promotion (chess)BusinessWork (physics)Trade barrierEconomicsTrade promotionInternational tradeFree tradeFinance
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Recent work demonstrates the importance of developing high quality output in order to compete in export markets and other recent studies verify the prevalence of fixed and ongoing trade costs while participating in those markets. I consider the joint choice of quality and export promotion costs when trade relationships are subject to temporary disputes. When transparency is low and macroeconomic instability is high, disputes arrive more frequently and, therefore, firms may inefficiently choose lower levels of quality and export promotion. These, in turn, build shallower trading relationships with less trade volumes and higher tariffs, and generate greater trade reductions during the more common trade disputes. Several institutional features of the WTO that are generally lacking in preferential trade agreements such as improved transparency, dispute investigation, and the provision to recommend asymmetric continuation payoffs can ameliorate these inefficient quality choice outcomes. Hence, lower quality output and lower quality trading relationships may be more endemic to countries that depend on preferential trading areas as opposed to the WTO.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0020.005
Scholarly communication0.0060.004
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.093
GPT teacher head0.324
Teacher spread0.231 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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