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Record W2121413862 · doi:10.1108/17538250910953435

Optimal tariffs of preferential trade agreements and the tariff complementarity effect

2009· article· en· W2121413862 on OpenAlexaff
Kamal Saggi, Halis Murat Yildiz

Bibliographic record

VenueIndian Growth and Development Review · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTariffEconomicsCournot competitionWelfareComplementarity (molecular biology)International economicsCommercial policyMarket accessMicroeconomicsInternational tradeMarket economy

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to evaluate and contrast the welfare effects of free trade agreements (FTAs) and customs unions (CUs) on member and non‐member countries when tariffs of both members and non‐members are endogenously determined. It also aims to provide sufficient conditions under which both types of preferential trade agreement (PTA) are likely to lower tariffs on non‐members relative to that under most favored nation (MFN). Design/methodology/approach The paper employs a three country Cournot oligopoly model of trade with segmented markets. Findings It is shown that under symmetry CU members enjoy higher welfare relative to that under an FTA or MFN. Furthermore, the non‐member country gains from the formation of a PTA so long as the PTA's external tariff falls below a certain threshold. However, for FTA members to necessarily gain, their external tariff needs to be greater than this threshold but smaller than twice their MFN tariffs. Outside this tariff range, welfare effects of FTAs are ambiguous in the absence of further assumptions. The paper also isolates sufficient conditions under which a PTA member is less likely to impose a positive tariff on the non‐member relative to that under MFN. Originality/value Unlike existing literature, we do no assume demand linearity to obtain our main welfare results and use this assumption only for illustrative purposes. Another contribution of the paper is to provide sufficient conditions under which a PTA member is less likely to impose a positive tariff on the non‐member relative to that under MFN.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.037
GPT teacher head0.221
Teacher spread0.184 · 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 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

Citations12
Published2009
Admission routes1
Has abstractyes

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