Determinants of Economic Development: A Case of Gulf Cooperation Council (GCC) Countries
Bibliographic record
Abstract
The main objective of this research is to identify the determinants of economic development in Gulf Cooperation Council (GCC) countries over the period of 1996-2016. The economic growth of GCC countries has slowed down due to a sharp drop in oil prices as GCC countries are depending on oil exportation for their economies. The GCC countries preferred to diversify their economies through the strategic plans called Vision 2030. The Vision 2030 for Gulf countries started in Saudi Arabia in 26 April 2016 when the Crown Prince (Mohammad bin Salman Al-Saud) declared that Saudi Arabia has to not depend on oil exportation substantially and that the diversification of oil is a must. The economic growth can be measured through the gross domestic production (GDP). Higher GDP indicates a better economy and higher standards of lives (welfare). Based on this, this research is finding the main indicators of economic development through regressions of fixed-effects model (FEM), random-effects model (REM), generalized methods of moments (GMM) and generalized least squares (GLS) models. The results show that production and rule of law strongly support the economy. In contrast, political instability and a larger population impact economic growth significantly and negatively. In addition, the global financial crisis (GFC) also decreased the economic strength significantly. This study helps the policymakers in economics sector to focus on the positive determinants and to avoid (or reduce) the implementation of the negative factors. In addition, the researcher on economics can be benefited from this study.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".