WHO ARE AFRICA’S ENTREPRENEURS? COMPARATIVE EVIDENCE FROM GHANA AND UGANDA
Bibliographic record
Abstract
Contemporary national development policy in many parts of the world is focused on the promotion of entrepreneurship. This is because policy makers see entrepreneurship as an important driver of economic development. Drawing on in-depth research in Ghana and Uganda, this paper provides a comparative analysis of the characteristics of entrepreneurs and their enterprises, their motives for choosing self-employment and the constraints to their businesses in Ghana and Uganda. Using a sample of over 1,000 micro and small entrepreneurs in each country, we found that Ghanaian entrepreneurs are much more motivated by necessity-driven motives while Ugandans are motivated by a combination of opportunity- and necessity-driven motives. Specifically, the factor analysis indicated that whereas Ghanaian entrepreneurs are significantly motived by “Work-family consideration” and “Low opportunity,” entrepreneurs in Uganda rated “Career consideration” and “Survival consideration” as their main motives for engaging in self-employment activities. On success, a much higher fraction of Ugandan entrepreneurs are found to be more successful than their Ghanaian counterparts. Comparatively, we found that Ghanaian businesses are significantly challenged with access to finance or credit; however, their counterparts in Uganda significantly face problems related to institutional weaknesses. Thus, from the factor analysis, “Financial problem” and “Institutional problem” were found to be significantly higher for Ghana and Uganda respectively. Hence, among others, Ghanaian policy makers can stimulate entrepreneurship by taking steps to reduce the level of financial constraints facing its entrepreneurs while in Uganda, much effort should be geared toward improving the business institutional environment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.004 | 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".