The Role of Savings in Reducing the Effect of Oil Price Volatility for Sustainable Economic Growth in Oil Based Economies: The Case of GCC Countries
Bibliographic record
Abstract
We investigate the simultaneous links between oil price changes, national savings, legal and institutional development, and economic growth in the Gulf Cooperation Council (GCC) countries. Our study includes six GCC countries namely, Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates. We base our analysis on annual data that covers the period from 1980 to 2011. We implement different methodologies on time series cross sectional data: First, we test our model using fixed effect and random effect model techniques; Second, we employ Arellano-Bond/Blundell-Bond estimator to reduce the endogeneity problem that is common in this kind of studies. Results reveal a nonlinear and concave relationship between saving rates and economic growth. This result suggests that, at low level of economic growth, the increase in savings leads to high economic growth. However, as the countries’ revenues and surpluses increase significantly (at higher levels in revenues and savings), high level of savings lead to lower growth in the economy. This might due to the lack of absorption capacity of the GCC markets. In addition, controlling for different factors, oil price changes explain the variability in the economic growth of the GCC countries. Economic globalization affects growth negatively, while institutional quality plays no role in economic growth of the GCC markets.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".