Informal Finance: A Boon or Bane for African SMEs?
Bibliographic record
Abstract
The aim of this study was to ascertain what can be done by the informal finance sector to close the credit gap in order to improve access to finance by SMEs. SMEs are the backbone of many economies as a result of generating employment and improving GDP. Despite playing such a major role in African economies, SMEs have been excluded from the financial systems. The informal finance sector plays a vital role by providing finance to small businesses. The study employed a literature survey with a primary focus on empirical studies that have been conducted in the African context. The study found that, generally, there are two circumstances under which most small businesses depend on informal finance. Firstly, informal finance is used as a last resort by SMEs that fail to access credit from the formal finance sector, owing to, among other issues, information asymmetry, lack of collateral security and perceived high default rates. Further, low financial literacy and the absence of credit bureaus in developing countries also contribute to the failure to access finance from formal institutions. Secondly, some entrepreneurs opt for informal finance even if they are eligible for formal finance as a result of its flexibility, convenience and simple administrative procedures. Notwithstanding the above benefits of informal finance, informal lenders are regarded as exploiting the clients by charging high interest rates. In addition, this sector suffers from limited resources; hence, it fails to fully service SMEs that require larger funding and are not eligible for formal finance. Invariably, all the studies that have been carried out confirm that access to finance is a major obstacle to the growth and development of SMEs. The development and empowerment of SMEs cannot be ignored as an important driver of the developmental agenda of most economies globally. The main policy recommendations that flow from this study, based on the policy syndrome of improving access to finance (financial inclusion) by the SME sector, include (1) the establishment of a suitable regulatory framework which will nurture the informal finance sector while promoting consumer protection, and (2) linking the formal and informal sector. On the other hand, SMEs should improve their risk management practices and also embrace FinTech platforms in order to access credit.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".