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Record W2990732713 · doi:10.1515/rmeef-2019-0007

Further Evidence on Export-Led Growth in the United Arab Emirates: Are Non-Oil Exports or Re-Exports the Key to Economic Growth?

2019· article· en· W2990732713 on OpenAlexaff
Athanasia Stylianou Kalaitzi, Trevor W. Chamberlain

Bibliographic record

VenueReview of Middle East Economics and Finance · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCointegrationCausality (physics)EconomicsGranger causalityWald testJohansen testError correction modelShort runEconometricsVector autoregressionOrder (exchange)Autoregressive modelUnit rootMacroeconomicsMonetary economicsStatistical hypothesis testingMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract This study investigates the relationships between exports and economic growth in the United Arab Emirates. Understanding these relationships is important for purposes of establishing appropriate growth and development policies and strategies. The study uses an augmented Cobb–Douglas production function to examine the causality between non-oil exports, re-exports and economic growth over the period 1981–2012. To investigate the existence of a long-run relationship between the variables, the study performs the Johansen cointegration test, while the direction of the short-run causality is examined by applying the Granger causality test in a vector error correction model framework. A modified Wald test in an augmented vector autoregressive model is applied in order to find the direction of the long-run causality. This research provides evidence in support of an indirect short-run uni-directional causality from economic growth to re-exports, through physical capital accumulation and imports. As for long-run causality, the results show that a bi-directional causality exists between re-exports and economic growth in the UAE.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.624
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.076
GPT teacher head0.232
Teacher spread0.156 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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