Impact of Monetary Policy on Small Scale Enterprises Financing in Nigeria
Bibliographic record
Abstract
Small and Medium Scale Enterprises play vital roles in the economy which are usually instrumental in achieving macroeconomic goals. This has attracted the attention of monetary authorities to institute policiesto boostconducive environment for SMEs to thrive. This study therefore empirically investigates the impact of monetary policy on SMEs financing in Nigeria spanning from the first quarter of 1992 to the last quarter of 2016. The time series data were subjected to unit root test to ascertain the stationarity of the variables and thereafter, cointegration and Error Correction Model (ECM) technique were used for the analysis. The residuals of the analysis were further subjected to various diagnostics tests. The result revealed that interest rate has a positive and significant impact on the SMEs financing in Nigeria. On the other hand, inflation rate was found to have a significant but negative impact on SMEs financing in Nigeria. Money supply and exchange rate were found to be insignificant in impactingSMEs financing. Based on this finding, the study recommends that, monetary authorities should give special attention to SMEs in specific sectors by creating special windows through various financial institutions to grant low interest rate so as to grant SMEs access to funds.This will boost business growth and consequently achieve macroeconomic goals.
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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 | 0.000 |
| 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".