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Record W3113902941 · doi:10.3390/su13010164

Trade Facilitation and Its Impacts on the Economic Welfare and Sustainable Development of the ECOWAS Region

2020· article· en· W3113902941 on OpenAlexaff
Shahrzad Safaeimanesh, Glenn P. Jenkins

Bibliographic record

VenueSustainability · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsQueen's University
Fundersnot available
KeywordsTrade facilitationWelfareEconomicsSustainable developmentContext (archaeology)EstimationPartial equilibriumEconomic integrationPrincipal (computer security)General equilibrium theoryTrade barrierInternational tradeInternational economicsMacroeconomicsGeography

Abstract

fetched live from OpenAlex

The facilitation of trade is a principal objective in the context of increasing regional trade integration for the achievement of sustainable development goals. The purpose of this study is to estimate the potential annual economic gain to be had from trade facilitation by the coastal countries of the Economic Community of West African States (ECOWAS). These measures would decrease border and documentary compliance time and costs of the administration of international trade. A partial equilibrium welfare economics framework is used that employs sets of export supply and import demand elasticities for each country that are derived using a general equilibrium estimation method. The annual economic welfare gains resulting from the reduction of excessive trade compliance costs for the region are estimated to between US$1.6 billion to US$2.7 billion (2019 prices). This is between 0.24% and 0.42% of the combined GDPs of these countries. The welfare gain is between 6% and 10% of the combined governments’ budgets assigned for education, and is between 33% and 58% of their budgets allocated for health. In the absence of reform, these inefficient practices waste an amount equal to between 15% and 26% of the annual net official development assistance these countries receive.

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)0.001
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 score0.830
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.043
GPT teacher head0.214
Teacher spread0.171 · 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 designTheoretical or conceptual
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

Citations28
Published2020
Admission routes1
Has abstractyes

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