Macroeconomic Drivers of Private Equity Penetration in Sub-Saharan African Countries
Bibliographic record
Abstract
The objective of this study is to investigate in to the drivers of private equity penetration in Cameroon, Nigeria, Ghana, Kenya and South Africa. Secondary data was collected from private equity and venture capital data bases (CapitalIQ, Preqin, Burgiss and Mergermarket), World Bank development indicators, regional private equity and venture capital associations and country specific stock market websites. The Panel Two-Stage Least Squares Instrumental Variables (2SLS IV), Panel Corrected Standard Errors (PCSE) and Feasible Generalised Least Squares (FGLS) estimation techniques were used. This was due to potential problems of endogeneity and spherical errors of serial correlation, heteroskedasticity, cross sectional dependence and multicollinearity. The results using the 2SLS IV estimation technique show that stock market capitalisation, GDP per capita, banking credit to private sector, real exchange rate and private investments are key macroeconomic drivers of private equity penetration in the selected Sub-Saharan African countries. Inflation had negative and insignificant effect on private equity penetration in the selected countries. The results using the PCSE and FGLS estimation techniques show that the signs of all the variables remain the same as was the case in the 2SLS IV estimation technique though the magnitudes were different. However, the results of PCSE and FGLS estimation techniques show that banking credit to private sector is significant in the FGLS model while private investments is significant in the PCSE model. GDP per capita, real exchange rate, stock market capitalisation and inflation are significant in both the PCSE and FGLS estimation techniques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".