Effects of Participation of Micro and Small Enterprises in Microfinance on Their Performance in Kenya
Bibliographic record
Abstract
The development of the microfinance sub-sector in Kenya is seen as a favourable catalyst for increasing performance of Micro and Small Enterprises (MSEs). Despite the development, MSEs continue to suffer from high levels of financial exclusion and shortage of operating funds. This scenarios raise policy questions on whether participation in microfinance has effects on performance of MSEs. While past studies on this relationship have demonstrated that the effects are mixed, an understanding of the effects on participation of microfinance on different segments on MSEs - especially the youth and women owned businesses and age of businesses, is necessary in designing relevant policy changes in the MSE subsector. To address this, the study used the 2016 FinAccess Dataset and estimated these effects using the propensity score matching technique. This model was considered suitable since it accounted for potential endogeneity biases associated with self-selection into participation, unobserved entrepreneurial abilities and risk taking behaviour of MSEs. Apart from showing that participation in microfinance has positive effects on performance of MSEs, the study has demonstrated that there is presence of constraints limiting the impact of microfinance especially in firms owned by the youth and women. As such, there is need for policy and product designs to address these hindrances even as participation in microfinance is encouraged. Based on the results, it is recommended that government and microfinance providers should design policies and products that increase firm participation in microfinance. This may be through scaling up financial literacy programmes and encouraging acquisition of permits. Finally, policy should address obstacles that hinder the youth and women owned MSEs from benefiting from microfinance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".