Islamic banking and economic growth: fresh insights from Nigeria using autoregressive distributed lags (ARDL) approach
Bibliographic record
Abstract
Purpose The paper examines the relationship between Islamic banking and the growth of the economy in Nigeria in both the short run and long run. Design/methodology/approach The study employs quarterly secondary time series data for Islamic banking as well as major macroeconomic variables to study the contribution of Islamic banking to the economy of Nigeria. It employs autoregressive distributed lags (ARDL) and error correction model (ECM) approaches from 2013 quarter 1 up to 2020 quarter 2. Findings The results show that Islamic banking has a positive contribution to Nigeria's economy in both short run and long run, but this contribution is insignificant. Practical implications Policymakers should endeavor to redesign the country's financial architecture and come up with policies that can support the growth of Islamic finance sector. This will significantly strengthen Nigeria's position as one of the leading Islamic finance hubs in Africa. Originality/value This is the first study to examine the contribution of Islamic banking to the Nigerian economy according to the best knowledge of the authors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".