Effect of Bank Diversification on Economic Growth in Nigeria
Bibliographic record
Abstract
The study investigated the effect of bank diversification on economic growth in Nigeria. Ten (10) commercial banks were randomly sampled for the study data used were sourced from the annual reports of the selected commercial banks spanning from 2013-2016. The study gathered data on real gross domestic product, diversification of income, diversification of loan and diversification of deposits. The study employed Panel data estimations including pooled OLS, fixed effect, and random effect estimations approach to test the relationship existing between the exogenous and endogenous variables in the study. The result of the finding explored that diversification measured in terms of diversification of income, diversification of loan and diversification of dividends has positive impact on economic growth in the study as measured in terms of Gross Domestic Product, meaning diversification has the capacity to boost the level of performance of the economy. Based on this, the study recommended that there should be sustainability of government policies in order to stimulate the much desired growth in the nation’s economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".