Determinants of Sovereign Credit Rating in BRICS, G7 and NEXT11 Countries: A Comparative Analysis
Bibliographic record
Abstract
This paper tries to investigate the determinants of sovereign credit ratings from 2016 to 2020. This has been conducted using S&P, Fitch and Moody’s ratings in 23 countries, including G7 (i.e. Canada, France, Germany, Italy, Japan, United Kingdom and United States), BRICS (i.e. Brazil, Russia, India, China, South Africa) and NEXT11 (i.e. Bangladesh, Egypt, Philippines, South Korea, Turkey, Vietnam, Indonesia, Iran, Mexico, Nigeria and Pakistan). Using panel analysis according to GMM technique, results reveal the importance of macroeconomic variables in explaining the sovereign credit rating, where GDP per Capita, GDP Growth, Economic Growth, External Debt, Unemployment Rate, Foreign Reserves and Inflation Rate seem to have significant effects on sovereign credit rating. In addition, robustness checks - using country types as control variables – have supported the significant effects of macroeconomic variables in explaining the sovereign credit rating, with explanation power ranged from 71.1% to 92.5%. Country type seems to have different effects, where including in G7 may have a negative effect on S&P rating, while including in BRICS may have a negative effect on Fitch rating. In addition, including in NEXT11 may have a positive effect on Moody’s rating. This needs to be more elaborated using additional analysis for each country, where investors and policymakers may consider the potential changes in credit rating when taking investment decisions and reviewing market regulations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".