Estimating the Impact of Minimum Wages on Employment, Wages, and Non‐Wage Benefits: The Case of Agriculture in South Africa
Bibliographic record
Abstract
Assessments of the impact of minimum wages on labor market outcomes in Africa are relatively rare. In part this is because the available data do not permit adequate treatment of econometric issues that arise in such assessments. This paper, however, attempts to estimate the impact of introducing a minimum wage law in the agriculture sector in South Africa, based on 15 waves of the biannual Labor Force Survey conducted between September 2000 and September 2007. The chosen sample includes six waves before the legislation's effective date (March 2003) and nine afterwards. To assess whether the changes experienced by farm workers are unique, we identify a control group that has similar characteristics to the treatment group. Our econometric approach involves using two alternative specifications of a difference‐in‐differences model. We test whether employers reduced employment, and whether they responded at the intensive margin by reducing hours of work. The results suggest a significant employment reduction in agriculture from the minimum wage (and particularly a noticeable move away from employment of part‐time workers), an increase in wages on average, and a rise in non‐wage benefits compliance. Our analysis also indicates that, firstly, overall average of hours worked fell in the post‐law period, suggesting that employers adjusted to some extent on the intensive margin. Secondly, it appears that hours of work increased more in areas where wages were lower in the pre‐law period, driven largely by the fall in part‐time employment.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".