FINANCING SMALL AND MEDIUM ENTERPRISES (SMES) IN GHANA: CHALLENGES AND DETERMINANTS IN ACCESSING BANK CREDIT
Bibliographic record
Abstract
Access to credit is crucial for the growth and survival of Small and Medium-sized Enterprises (SMEs). Thus policy makers attempt to pursue financial sector policies to propel financial intermediaries to extend more credit to SMEs. Access to credit still remains a challenge to SMEs especially those in developing economies and continues to dominate discussions both within business circles and at the corridor of various governments. In Ghana, for instance, a survey by the Association of Ghana Industries (AGI) for the second quarter of 2011 indicated that lack of adequate access to credit topped the factors hampering the growth of small businesses in Ghana. The ability of SME’s to grow depends highly on their potentials to invest in restructuring, innovation etc. All of these investments need capital, and therefore access to finance. Against this background the consistently repeated complaint of SME’s about their problems regarding access to finance is a highly relevant constraint that endangers the economic growth of countries. The general objective of this study is to examine the challenges and determinants of access to bank credit in Ghana by focusing on SMEs in the Wa Municipality. The study employed the quantitative approach to research in which the probability sampling criteria specifically the stratified and simple random sampling was employed to select eighty entrepreneurs from the Wa Municipality. The major findings for the study indicated that there exist significantly,
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".