Financial Development and Inclusive Growth in Nigeria: A Multivariate Approach
Bibliographic record
Abstract
Financial development is a multidimensional concept that constitutes a potentially important mechanism for long run growth in an economy. However, short-run gains at the expense of long-run growth coupled with various exogenous factors could have precipitated economic fluctuations in Nigeria. Therefore, efforts to moderate these fluctuations by successive federal authorities must have prompted them to adopt various economic policy measures including Stabilization Policy, 1981- 1983, Structural Adjustment Programe (SAP), 1986-1992; Medium Term Economic Strategy, 1993-1998 and the Economic Reforms 1999-2007, on the basis that such policy actions can engender economic growth in the long run. This was eventually the driving force behind various financial policy reforms in Nigeria. However, in spite of all these reforms, the associated problems that exist still include: inefficiency in the allocation of funds to the productive sectors, lack of long-dated funding and decline in domestic credit to the private sector. All these frustrate inclusive growth experience in the country. Therefore, the important issues of concern are: what level of financial development is required for growth to be inclusive? How can the economy create and support inclusive growth through the financial sector? Hence, the objective of the paper is to examine the impact of financial development on inclusive growth in Nigeria using a multivariate model (Bound testing approach), this study obtained new evidence for the finance-growth nexus 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".