Economic Diversification and Bilateral Trade Agreements in the United Arab Emirates: A Gravity Model Analysis
Bibliographic record
Abstract
The United Arab Emirates (UAE) has experienced a remarkable economic shift—from a reliance on pearl diving, fishing, and agriculture to becoming a global trade hub. This study explores the development of the UAE’s bilateral trade agreements within its oil-based economy, especially after the 1970s oil boom. It highlights key strategies such as the Comprehensive Economic Partnership Agreements (CEPAs) and Dubai’s diversification efforts, including the Jebel Ali Free Zone (JAFZ) and tourism expansion. Dubai’s non-oil trade growth has positioned it as a significant global re-export center. Using a gravity-type trade model and pooled data from 1970 to 1997, the study evaluates the UAE’s trade with nine major partners: India, China, the U.S., Canada, South Africa, Egypt, Kenya, the U.K., and Germany. Variables include GDP, distance, exchange rates, FDI, and trade openness. Results show that GDP, FDI, and openness boost trade, while distance and risk reduce it. The model accounts for about 90% of trade variation, offering insights for sustaining growth through strategic diversification.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".