Banking Sector Development and Corporate Leverage: Empirical Evidence from South African Firms
Bibliographic record
Abstract
The banking sector in Africa dominates the financial system which makes bank credit an important source of external finance for firms within the economy. For this reason, financial policy makers and regulators implement development measures in the banking sector to ease a firm’s access to bank credit. This paper investigates the effect of banking sector development on capital structure decisions of publicly listed firms in South Africa with the use of a dynamic panel data estimator. We use the two-step system generalised method of moments (GMM) in the investigation and find that as the banking sector develops, publicly listed firms in South Africa use less debt in their capital structure. This is consistent with the notion that as the banking sector develops in emerging markets; the resultant risk management process which tightens lending process gives a better pricing for risk and makes the cost of bank credit higher. The policy implication for financial policy makers and regulatory bodies is that they should implement policies that will put in place an efficient risk management process which at the same time, reduces cost of bank credit.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".