The Effect of Financial Inclusion and Competitiveness on Financial Stability: Why Financial Regulation Matters in Developing Countries?
Bibliographic record
Abstract
This study aims to assess the effect of financial inclusion and competitiveness on banks’ financial stability, considering the moderating role of financial regulation. To do so, we compare the effects of these variables in Sub-Saharan African (SSA) and Latin American and Caribbean (LAC) countries. Our results suggest that inclusion enhances bank stability in SSA and LAC countries, and financial regulation contributes to increasing financial stability in LAC countries, while we find no statistical significance in the effect of financial regulation on financial stability in SSA countries. Moreover, competitiveness negatively impacts financial stability, and financial regulation moderates the negative effect of competitiveness on financial stability in SSA and LAC countries. We also find that financial inclusion reduces credit risk in SSA countries, and for LAC countries financial inclusion increases credit risk and reduces bank profitability. Regarding the practical implications, this study shows that fostering financial inclusion in the countries under study contributes significantly to improving the welfare of households and especially to the stability of the financial system. The present study allows expanding of the scarce literature by examining the effect of financial inclusion and market structure on financial stability in two different samples, consisting of 41 countries in the SSA region and 31 countries in the LAC region, throughout 2005–2018.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".