An Examination of the Effect of Financial Inclusion on Financial Stability: Evidence From a Panel of Ten African Countries
Bibliographic record
Abstract
This paper examines the impact of financial inclusion on financial stability for a group of 10 African countries using the system Generalized Method of Moments (GMM) panel estimator for the period running from 2004 through 2019. To shortlist the financial inclusion indicators, the study used the Principal Component Analysis to construct the financial inclusion index. Economic growth and inflation variables are used as control variables. To explore the stationarity of the variables, the study applied the Im, Pesaran, and Shin, ADF Fisher, and the PP-Fisher panel unit root tests. The sample countries include Botswana, Cameroon, Kenya, Madagascar, Morocco, Mozambique, Nigeria, Uganda, South Africa, and Zambia. The results from the panel unit root tests indicate that the four variables in the system, including the financial inclusion index, bank Z-score, economic growth rate, and inflation, are level stationary. The results from Pearson correlations provided cursory evidence that financial inclusion and financial stability are significantly positively correlated. The results from the GMM panel estimator indicate that financial inclusion has a significantly positive effect on financial stability. This finding entails that access to financial services engenders bank stability. Policy implications are discussed.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".