Examining the Factors Affecting Sovereign Credit Rating of Gulf Cooperation Council Countries
Bibliographic record
Abstract
Despite the controversy surrounding the credibility of credit rating agencies’ rating systems, these agencies' ratings still play a crucial role in determining the premium paid by governments on their bonds. As a result, obtaining a high sovereign credit rating would lower borrowing costs and more demand for their bonds. In order to do so, policymakers should be aware of the factors that mostly affect the sovereign credit rating of their countries. While there are many factors credit rating agencies consider when assigning a sovereign credit rating for any country, this study aims to identify the factors that mostly affect Gulf Cooperation Council (GCC) countries’ sovereign credit ratings assigned by the biggest three credit rating agencies, Standard and Poor’s (S&P), Moody’s, and Fitch. This study is based on the Gulf Cooperation Council (GCC) data 2012–2018. Results obtained from this research show that interest rate, government debt to GDP ratio, GDP per capita, and the labeling of the country as developed or developing country was the variables that mostly affect the S&P rating. GDP per capita and government debt to GDP were the factors that most influenced Moody’s scores. In contrast, GDP, interest rate, transparency score, government debt to GDP, and GDP per capita were the factors that most affect Fitch's credit rating scores. The results also revealed that in 2018, Kuwait was the most overrated country, while Oman was the most underrated country.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".