The Role of Microfinance on Growth of Small-Scale Agribusinesses in Malawi: A Case of Lilongwe District
Bibliographic record
Abstract
The emergence and proliferation of Microfinance Institutions (MFIs) in Malawi gave rise to the need for empirical research to assess their role on growth of small-scale agribusiness entrepreneurs. The paper gives the details of the results of a study which was conducted in Malawi to analyze the role of microfinance on the growth of small-scale agribusinesses in Lilongwe District. A financing constraint approach was applied using logit model to determine factors affecting investments of small-scale agribusiness entrepreneurs. The approach stipulates that entrepreneurs in areas with significant presence of MFIs (unconstrained) rely less on internal funds (average profits) for their investment decisions than areas with limited presence of MFIs (constrained). A T-test was also used to compare investment levels of unconstrained and constrained firms to support the results obtained from the financing constraint approach. Loans were among the products which were found to be offered by MFIs although their accessibility was affected by, among others, high interest rates. The logit model revealed that for each additional profit the probability of investment decreased by 46 percent in constrained firms and 39 percent in unconstrained firms. However, the T-test results revealed no significant difference in levels of investments between unconstrained firms and constrained firms. These results show no significant role of MFIs on growth of small-scale agribusiness entrepreneur. The results have insinuated the review of MFI loans conditions such as interest rates if they are to have a significant role on growth of small-scale agribusiness entrepreneurs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".