The Impact of International Migration on Skills Supply and Demand in South Africa
Bibliographic record
Abstract
This study investigated the demographic, education and labour market characteristics of three groups: (1) immigrants into South Africa; (2) natives who remained in the country; (3) emigrants into the top five destination countries (Australia, Canada, New Zealand, the UK and USA). The empirical findings were used to examine the extent of migration to and from the country, from the perspectives of skills supply and demand. Emigrants were most educated, enjoyed the lowest unemployment probability (about 10%), and were most likely to be involved in high-paying skilled occupations and tertiary sector activities as full-time employees (if employed). The immigrants fared worse than the emigrants but better than natives. These immigrants, mainly originating from the other African countries, were slightly more educated, but enjoyed higher LFPR (75%) and lower unemployment likelihood (20%), compared with the natives (55% and 30% respectively). Furthermore, for both above-mentioned two groups, they were distinguished into long-term, medium-term and short-term migrants, and it was found that long-term migrants fared relatively better in the labour markets of their respective host countries. Overall, the findings strongly indicated brain drain out of South Africa, and exodus of highly educated and skilled people is not complemented by a rapid increase of supply of equally educated and skilled labour force entrants in the country. The study recommended four policy suggestions: ease up regulations to attract skilled immigrants, promote entrepreneurial activities of immigrants, better develop and retain skills of the natives, improve migration and vacancy data capture, availability, usage and analysis.
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.
How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".