Islamic Financing and Economic Growth in Nigeria: The Moderating Role of Corruption
Bibliographic record
Abstract
This study examined the relationship between Total Islamic Financing and Economic growth of Nigeria, with a particular emphasis on the role of governance, proxied by control of corruption indicator of the Worldwide Governance Indicator’s Institutional Quality Index, which moderates the link. The growing significance of Islamic finance in developing nations has generated interest in its effectiveness under various institutional contexts, especially in economies marked by not a very strong governance and institutions. Notwithstanding the increasing prevalence of Islamic financial institutions in Nigeria, empirical research incorporating institutional quality remains scarce. This research aims to evaluate the extent to which total Islamic financing (TF) significantly impacts economic growth and whether governance quality, as indicated by control of corruption (CORR), affects this relationship. The research uses quarterly time series data from the first quarter of 2014 to the first quarter of 2024 and implements the Autoregressive Distributed Lag (ARDL) bounds testing method to identify both short-term and long-term relationships. Real GDP functions as the dependent variable, whereas TF, CORR, the exchange rate (EXR), and foreign direct investment (FDI) act as explanatory factors. Research indicates that Islamic finance significantly helps long-term economic growth, especially when corruption is adequately managed. In the short term, however, the impacts are ambiguous and contingent upon the lag structure of the variables. The interaction term (TF × CORR) is statistically significant alone in the long term, highlighting the significance of governance quality. The findings indicate that institutional reforms are crucial for optimizing the developmental impact of Islamic finance 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 | 0.002 | 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".