From Rural to Microfinance Banking: Contributions of Micro Credits to Nigeria’s Economic Growth – An ARDL Approach
Why this work is in the frame
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Bibliographic record
Abstract
Given current emphasis on potentials of micro credits as a means of addressing poverty alleviation and improved economic growth especially in developing economies, this study seeks to evaluate the nature of long-run relationship and the direction of causality between economic growth and micro credits disbursed by private sector led micro finance institutions in Nigeria. Covering the period 1982 – 2011 (30 years), the Autoregressive Distributed Lag (ARDL) technique was employed in analyzing the time series data. The study finds significant long-run relationship between Nigeria’s economic growth and micro credits disbursed, while causality runs from economic growth to micro credits (unidirectional). Accordingly, increase in the quantum of micro credits as well as development of long tenured micro credit products are recommended as strategies to enhance the contributions of micro credits to Nigeria’s economic growth.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it