International migrants in Johannesburg's informal economy
Bibliographic record
Abstract
Some 70% were men and 30% were women; 96% were aged between 20 and 49 years; 29% had primary schooling or less, almost 40% had some secondary education, 23% had completed secondary school, and 9% had at least some tertiary education.• They came from 27 countries of which 21 were in Africa.The majority were born in SADC countries (65%), particularly Zimbabwe (30%) and Mozambique (14%).Some were from Nigeria (7%), the DRC, Lesotho, and Pakistan (5% each), and India (4%).• At least 46% were asylum seekers, refugees, or permanent residents with permits that allow them to own and operate businesses in South Africa.Another 20% held work permits, mostly Zimbabwean Special Dispensation Permits which again allowed them to operate a business.Another 12% held visitors' permits, while only 12% had no official documentation.• Less than 5% had arrived in South Africa in 1994 or before.Around 80% had arrived since 2000, with a third arriving between 2000 and 2004, 30% between 2005 and 2009, and 15% between 2010 and 2014.Migrant entrepreneurs are often perceived to have advantages in business skills and experience compared to South Africans.At the same time, entrepreneurs in the informal economy, regardless of nationality, are often seen as survivalists without entrepreneurial aspirations and skills.As regards these perceptions, the survey found that:• Over half (56%) of the entrepreneurs had been unemployed before coming to South Africa.However, only 5% were involved in informal entrepreneurial activity and only 2% had owned a business in the formal economy in their home country.• Almost half (47%) had been unemployed in South Africa before starting their business.Just over a quarter had done semi-skilled or unskilled manual work.However, 5% were professional workers, suggesting that the informal economy offers opportunities not always found in the formal economy.• Only a minority of the entrepreneurs had prior entrepreneurial experience in South Africa, with 13% having operated a previous informal economy business and 5% owning a business in the formal economy before starting their current business.• Challenging perceptions that migrant entrepreneurs arrive in South Africa armed with skills that give them advantages over South Africans, 56% said their skills were selftaught, 19% had learned from friends and relatives, and 10% had learned from previ-migration policy series no.71
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".