Financial Sector Development in Nigeria: Do Financial Reform, Output Size and Resource Dependence Matter?
Bibliographic record
Abstract
The study examined the determinants of financial sector development in Nigeria in an error correction modelling framework, and with OLS for robustness checks, using data from 1980 to 2017. The results show that, banking sector reform, gross capital formation, government expenditure, interest rate spread, output size and trade openness were significant determinants of financial sector development in both the short- and long run. Proxy for economic misery was only significant in the ECM equation, while literacy and human development metric was significant in the long-run equation. Natural resource dependence, proxy by ratio of natural resource rent to GDP, was negatively related to financial sector development in Nigeria, though the coefficient was not significant at conventional levels. Economic misery, interest rate spread and inflation were observed to undermine financial development in Nigeria. The study recommends the continuation of the process of financial liberalization because of its immerse benefits of promoting competition amongst financial institutions with attendant positive effects of reducing interest rate gap. Domestic output, measured by the real GDP, should be enhanced with appropriate stabilising policy, whether fiscal or monetary policy. Additionally, efforts should be enhanced to limit the effects of macroeconomic instability on financial sector development. Lastly, the study recommends efficient management of natural resources to enjoy a non-declining contribution to the development of an inclusive financial system in Nigeria.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".