The COVID-19 crisis and the South African informal economy: A stalled recovery
Bibliographic record
Abstract
This paper seeks to identify the differentiated impacts of the crisis on specific groups of informal workers. The analysis draws on official nationally representative labour force surveys collected quarterly by South Africa’s national statistical agency (Statistics South Africa). Based on an analysis of six quarters of labour market data (with the first quarter of 2020 as the ‘pre-COVID’ baseline), the paper aims to identify the labour market impacts of the first three waves of the pandemic and of one of the world’s strictest ‘lockdowns’ (as it was described at the time—in April 2020). In investigating the contours of the pandemic’s impact on the South African informal economy, the paper focuses, in particular, on the different impacts by gender, sector, and status in employment. The findings show that both relative and absolute job losses have been greater in the informal economy, while the rate and level of recovery have been greater for formal employment. Further, the data suggest uneven impacts within the informal economy with women informal workers, those working in the informal sector and those in retail and community and social services being particularly hard hit. The pandemic period has thus widened pre-existing inequalities and fault lines. In policy terms, this suggests that the informal economy should be a priority in economic recovery efforts but also that support requires differentiated approaches and a range of measures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".