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Record W2180196658 · doi:10.5539/jsd.v8n9p129

Has the East African Community Regional Trade Agreement Created or Diverted Trade? A Gravity Model Analysis

2015· article· en· W2180196658 on OpenAlexvenueno aff
Isaac M. B. Shinyekwa

Bibliographic record

VenueJournal of Sustainable Development · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsGravity model of tradeTrade creationInternational tradeTrade diversionRegional tradeTreatyInternational economicsEconomicsBilateral tradeInternational free trade agreementGravity equationTrade barrierPanel dataEconomic integrationVariable (mathematics)EstimationFree tradeGeographyEconometricsPolitical scienceMathematics

Abstract

fetched live from OpenAlex

The paper investigates the potential impact of the EAC trade agreement (a south-south Regional grouping) on trade creation and diversion. The paper seeks to establish whether the EAC RTA has diverted or created trade using an expanded (augmented) gravity model. The paper departs from the conventional estimation approach that uses average combined trade flows as the dependent variable which is prone to errors and uses exports. We estimate static and dynamic random effects models using a panel data set from 2001 to 2011 on seventy countries that trade mainly with the EAC partner states. Results suggest that indeed the implementation of the EAC treaty has created trade contrary to widely held views that South-South RTAs largely divert trade. There is thus evidence that the EAC, a south-south RTA has been a more trade creating than trade diverting as espoused in the literature.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.186
GPT teacher head0.239
Teacher spread0.053 · 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 designSimulation or modeling
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

Citations5
Published2015
Admission routes1
Has abstractyes

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