Exploring Factors Affecting Growth of Micro and Small Enterprises: Evidence from Ethiopia
Bibliographic record
Abstract
Micro and small businesses (MSEs) create jobs at a low cost and assist society's progress toward wealth and growth. The aim of the study is to examine major factors that determine the growth of MSEs in the Siltie Zone, in southern Ethiopia. Out of the total 2,244 MSEs units, 488 respondents were sampled for the study using stratified and simple random sampling techniques. Descriptive statistical tools and a binary logit model are applied. Employment was employed as a growth indicator in the study. The findings of the study showed that out of the total sample, 49% of MSEs were growing and 51% of MSEs were not growing in terms of employment. The results showed that individuals and their relatives are the main source of finance for the majority of MSEs for two major reasons. The first and the most important reasons are due to the religion factor. A majority of the respondents replied that access to credit is forbidden to Muslim followers. The second reason is unwillingness to access credit; they fear the high interest rate of debt, the complexity of the procedure, and the lack of collateral. In addition to that access to infrastructure, access to finance and government policy are the major factor affecting MSEs engaged in construction sector. The major independent variables affecting growth of MSEs engaged in manufacturing sector are access to infrastructure, working premises, government policy and market linkage. Access to finance, market linkage and business management capacity are the determinant factor hindering the growth of MSEs operating trade sector. Access to infrastructure, market place and government policy were the determinant factor affecting the growth of MSEs operating service sector. Access to working premises, market linkage, government policy and owner motivation are affecting the growth of MSEs operating urban agriculture sector. Hence, government organizations concerned with the promotion and development of MSEs needs to work with the factors in each sector for economic growth.
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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 | 0.001 | 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 | 0.001 |
| 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".