Regulatory Capital Requirements and Risk Taking Behaviour: Evidence from the Malawi Banking System
Bibliographic record
Abstract
Proponents of stringent regulation argue in favor of higher capital requirements that it promotes financial stability, while opponents argue that capital requirements might not enhance stability but might in fact increase a bank’s riskiness. In this paper, we test this hypothesis with a dynamic panel data model for eight Malawian commercial banks using GMM estimation technique. Our results reveal that there is high persistency in risk-taking behavior of Malawian banks. Further, the study finds that high capital ratios reduce risk-taking behavior of Malawian banks through reduction in NPLs ratio and investment in high risky-assets. Based on these results, imposition of stringent penalties on banks that fail to meet minimum capital requirements and strict enforcement of regulation is key to ensuring that all banks sustain sufficient capital buffers and hence safeguard stability of banking system. However, contrary to corporate governance propositions, the study finds that the structure of board of directors does not significantly influence the impact of capital regulation on bank risk taking in Malawi.
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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 | 0.001 |
| 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) | 0.002 | 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".