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The Impact of the Canada-Korea Free Trade Agreement as Negotiated

2014· article· en· W802421308 on OpenAlexaboutno aff
Dan Ciuriak, Jingliang Xiao

Bibliographic record

VenueEast Asian Economic Review · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsRestrictivenessTrade diversionFree trade agreementInternational tradeInternational economicsRevenueFree tradeTrade barrierCommercial policyEconomicsComparative advantageInternational free trade agreementBusiness

Abstract

fetched live from OpenAlex

This paper analyzes the impact of the Canada-Korea Free Trade Agreement on the basis of the published text and agreed schedule of commitments. We find that the Agreement reinforces existing patterns of comparative advantage between Canada (agriculture and resource-based sectors) and Korea (autos and other industries). The sensitive sectors that held up the deal for years - autos into Canada and beef into Korea - witness major trade gains, but are not unduly disrupted. In both economies, the major output gains otherwise come in non-traded services sectors, driven by income effects. We find that trade diversion effects are quite significant; this lends support for the domino theory of major free trade agreements - since the Korea-EU agreement broke the ice, the pressure has intensified on third parties to re-level playing fields by striking their own deals. The study breaks new ground in modelling services trade by developing policy impacts based on the extent to which the text of the Agreement modifies Korea's and Canada's scores on the OECD's Services Trade Restrictiveness Index and by providing estimates of Mode 3 Services trade impacts. The analysis of the Agreement as negotiated, the present study, in our view, is a step forward in understanding the impact of modern free trade agreements.

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 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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.029
GPT teacher head0.216
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations2
Published2014
Admission routes1
Has abstractyes

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