HOW LARGE IS THE PRIVATE SECTOR IN AFRICA? EVIDENCE FROM NATIONAL ACCOUNTS AND LABOUR MARKETS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In recent years, the private sector has been recognised as a key engine of A frica's economic development. Yet, very little is known about its size and characteristics. We present novel estimates for 50 A frican 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. S outh A frica 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 A frican economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it