HOW LARGE IS THE PRIVATE SECTOR IN AFRICA? EVIDENCE FROM NATIONAL ACCOUNTS AND LABOUR MARKETS
Bibliographic record
Abstract
Abstract In recent years, the private sector has been recognised as a key engine of Africa's economic development. Yet, very little is known about its size and characteristics. We present novel estimates for 50 African countries and show that the private sector accounts for about two thirds of total investments, four fifths of total consumption and three fourths of total credit. Countries with small private sectors include a sample of oil exporters and some of the poorest countries in the continent. Surprisingly, the size of the private sector does not appear to be significantly correlated with growth performance. Labour market data reinforce the idea of a large private sector, which provides about 90% of total employment opportunities. However, most of this labour is informal and characterised by low productivity: permanent wage jobs in the private sector account on average for only 10% of total employment. South Africa is the notable exception, with formal wage employment in the private sector representing 46% of total employment. Finally, we find evidence of negative private sector earning premiums (−13% on the average), suggesting that market distortions abound. These are likely to prevent the efficient allocation of human resources and to reduce the overall productivity of the African economies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".