The impact of sectoral minimum wage laws on employment, wages, and hours of work in South Africa
Bibliographic record
Abstract
This paper attempts to investigate the impact of sectoral wage laws in South Africa. Specifically, we examine the impact of minimum wage laws promulgated in the Retail, Domestic work, Forestry, Security, and Taxi sectors using 15 waves of biannual Labour Force Survey data for the 2000–2007 period. Using a quasi-experimental approach, we apply two alternative specifications of a difference-in-differences model to estimate the impact of multiple minimum wage laws in South Africa on employment, wages, and hours of work. In order to assess whether the changes experienced by workers in the sectors analysed were unique to those sectors, a unique control group is identified for each sector. We find some evidence of a significant increase in real hourly wages in the post-law period in four of the five sectors examined. Our results also suggest that whilst there was no significant impact of the laws at the extensive margin, there was some evidence of an adjustment at the intensive margin in certain sectors. We also find that in three of the five sectors, increases in real hourly wages were sufficient to outweigh intensive margin adjustments so that workers in these sectors experienced an improvement in real monthly income as a result of the law. C21, J23, J30, J31, J38.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".