Investigating Finance-Growth Nexus: Further Evidence from Nigeria
Bibliographic record
Abstract
This study investigates the influence of financial sector development on economic growth in Nigeria during the period 1982 to 2015. As such, the study obtained annual secondary data from the Central Bank of Nigeria statistical bulletins and World Bank financial database. The empirical model for this study examines growth in savings, growth in exchange rate, growth in government expenditure, growth in stock market capitalization, growth in credit to private sector, growth in gross capital formation, growth in trade openness and growth in broad money on economic growth in Nigeria. The multiple regression output reveals that growth in government expenditure and growth in gross capital formation are statistically significant on economic growth in Nigeria at 1% and 10% respectively under the period under investigation while other regressors in the model prove to be statistically insignificant. VAR test shows that there is considerable short-run causality running from lags of regressors to economic growth in Nigeria except for lag 1 of growth in exchange rate and lag 2 of growth in credit to private sector. The granger causality test reveals the existence of bi-directional causality between financial sector development and economic growth in Nigeria during the period under investigation. Hence, this study supports the ‘feedback hypothesis’ view on finance-growth. Based on these empirical results, this study recommends effective channeling of funds to the private sector and autonomy of the Central Bank of Nigeria in the use of monetary policy tools.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".