The Effects of COVID-19 Pandemic on the Economies of the Gulf Cooperation Council States due to Low Oil Prices
Bibliographic record
Abstract
This paper shows the effect of a drop in oil price on the economic growth of GCC states as a result of the Covid-19 pandemic using monthly data for (2019/2020 M1 – 2019/2020 M12) where oil priceis an explanatory variable and economic growth is the affected variable. Because the economies of the GCC countries are centered mostly on oil, the spread of COVID-19 pandemic has become a serious concern since they depend on the outside world in diverse ways. The confirmed number of cases in the GCC countries is eliciting fear about security in these countries. This paper focuses on analyzing theoretical and empirical literature reviews to show the effects of oil price on economic growth and explaining this effect in GCC states for this period using the autoregressive distributed lag (ARDL) technique in Eviews program. This paper concluded that there are negative and significant effects of oil price on the economic growth of Kuwait and Qatar,but insignificant effects of oil price on the economic growth of Bahrain, Oman and the United Arab Emirates and a positive and significant effect of oil price on the economic growth of Saudi Arabia by using monthly data for (2019/2020 M1 – 2019/2020 M12) at a significance level of 5%.Also, this paper reaches a recommendation of the GCC states to improve their economies through other sectors and not by relying on oil to enhance their economic growth.
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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 | 0.000 | 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) | 0.002 | 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".