Entrepreneurship and Economic Performance in Africa: A Sectoral Analysis with Focus on the Role of Finance, Institutions and Globalization
Bibliographic record
Abstract
The main aim of the paper was to investigate the role of entrepreneurship on economic performance but with focus on sector-wide growth in 12 selected African countries during the period 2006-2016. Overall, the results suggest that while the quantitative impact of entrepreneurship on economic growth is positively significant, there is a differential effect on the sectors. The service sector in particular is associated positively with entrepreneurship whereas there is no evidence in the data that the growth in the manufacturing and agriculture sectors is influenced by entrepreneurship activities. A further analysis that includes interactions in the model supports the conditionality hypothesis that globalization as well as the quality of institutions and financial development matter in the entrepreneurship-growth nexus. In addition, while internet access and government consumption appear beneficial for the manufacturing and service sectors, the role of personal remittances is observed important for the agriculture sector contribution to GDP whereas trade in services matters for each sector but most significantly in the latter sector. In light of the findings policy recommendations are suggested.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".