The effect of capital ratios on the risk, efficiency and profitability of banks: Evidence from OECD countries
Bibliographic record
Abstract
Using a sample of 1,992 banks from 39 OECD countries during the 1999–2013 period, we examine whether the imposition of higher capital ratios is effective in reducing risk and improving the efficiency and profitability of banking institutions. We demonstrate that while risk- and non-risk based capital ratios improve bank efficiency and profitability, risk-based capital ratios fail to decrease bank risk. Our results cast doubts on the validity of the weighting methodologies used for calculating risk-based capital ratios and on the efficacy of regulatory monitoring. The ineffectiveness of risk-based capital ratios with regard to bank risk is likely to be exacerbated by the adoption of the new Basel III capital guidelines. While Basel III requires banks to hold higher liquidity ratios along with higher capital ratios, our findings suggest that imposing higher capital ratios may have a negative effect on the efficiency and profitability of highly liquid banks. Our results hold across different subsamples, alternative risk, efficiency, and profitability measures and a battery of estimation techniques.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".