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Record W2207633242 · doi:10.1186/2193-9020-2-1

The impact of sectoral minimum wage laws on employment, wages, and hours of work in South Africa

2013· article· en· W2207633242 on OpenAlexfundno aff
Haroon Bhorat, Ravi Kanbur, Natasha Mayet

Bibliographic record

VenueIZA Journal of Labor & Development · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsMargin (machine learning)Work (physics)WageEconomicsLabour economicsOrder (exchange)Minimum wageDemographic economicsEngineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.239
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations91
Published2013
Admission routes1
Has abstractyes

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