An Assessment of the Banking Sector Development in Economic Performance: A Case of Selected Countries
Bibliographic record
Abstract
Purpose: The study examines economic growth and banking sector development in some G8 countries (United States, United Kingdom, Canada, Japan, and Germany) and three African countries (Nigeria, Ghana, and South Africa). Approach/Methodology/Design: Study objectives include filling the gap occasioned by a lack of literature on this topic, especially as it concerns the selected countries. As a check for stationarity, we used the Levin-Lin-Chu and Im-Peseran-Shun unit root tests. In addition to Pedroni, long-run relationships between variables are also tested. Because the study is a cross-country study, it was necessary to perform the Hausman test to determine if random effect panel analysis is consistent and effective and to test long-run cointegration using the ARDL Bound test. Findings: According to the results, banking sector development, and exchange rate contribute positively to economic growth while CPI contributes negatively. In contrast, the results indicate a long-run relationship between economic growth, banking, and other determinants. Originality/value: The study recommends that G8 countries and most African countries consider improving their banking sector and incorporating it into their economic development as one of the determinants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".