Contributions of Micro, Small and Medium Enterprises (MSMEs) to Income Generation, Employment and GDP: Case Study Ethiopia
Bibliographic record
Abstract
The pillar goals of this research are to review the conditions of MSMEs, their contribution to employment creation, income generation, poverty alleviation, contributions to the local, regional and national GDP, stimulating entrepreneurial climate and the challenges and opportunities in the design, implementations, marketing opportunities, linkages, financial sources, dynamics, survival and policy landscape. To achieve the presented purposes, we collected primary and secondary data through a survey, focus group discussions and documents reviews. We used qualitative and quantitative approaches to analyse the collected data using various statistical programs. We used descriptive and econometric statistical analysis to process the data, obtain the relevant estimation results and fully discuss the purposes under the study. We firmly maintain that the systems we presented, and the methods applied enabled us to tackle the aims of the study. MSMEs in Ethiopian are the chief sources of job, income, significantly contribute to the local, regional and national GDP and key policies to eliminate poverty. In the log-linear regression, we found that MSMEs initial capital, BDS, access to credit facility are the key determinants of MSMEs performance. Majority of the MSMEs produce for local and regional markets; few for national markets and none for international markets. Besides, we found that sex of MSMEs owner/manager, BDS, access to credit and capital size strongly determine the survival of MSMEs. Based on this study, the major obstacles of MSMEs in Ethiopia are the question of sustainability, lack of credit, weak market linkage, insufficient training, weak human resources development schemes, dependency on government and spoon-feeding mentality, oscillations in government policies, price variations, weak links and poor market and product development strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".