Further Evidence on Export-Led Growth in the United Arab Emirates: Are Non-Oil Exports or Re-Exports the Key to Economic Growth?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".