The Effect of Risk Rating Agencies Decisions on Economic Growth and Investment in a Developing Country: The Case of South Africa
Bibliographic record
Abstract
Over the last decade, the South African economy has endured prevailing economic challenges, including weak economic growth, unreliable electricity supply, rising fiscal deficits, declining investment inflows and the inexorable rise in government debt alongside the expected impact of the coronavirus pandemic. Credit ratings have significantly evolved, making them key elements in the modern financial markets because of their creditworthiness opinions, as many investors across the globe rely heavily on their opinions. A quantitative research approach was followed using data from 1994Q1 to 2020Q2. The analysis entailed a descriptive and econometric analysis where two models were estimated using the autoregressive distributed lag (ARDL) model. The findings reveal long-run relationships between economic growth (GDP), risk rating index, foreign direct investment (FDI), exchange rate, gross fixed capital formation and lending rates. The results also reveal a bi-directional causality between economic growth and the rating index and between FDI and the rating index. This study’s findings suggest that investments and economic growth in the country need to be stimulated significantly to impact risk rating agencies decisions. Policymakers need to redirect resources towards effective and efficient capital-forming initiatives and development projects to improve the country’s sovereign risk rating to re-ignite 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 | 0.003 |
| 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".