Abstract
The segmented labor market model describes the impacts of minimum wages on covered and uncovered sectors. This paper examines the impacts of an industry-specific minimum wage in South Africa, a state characterized by high unemployment, a robust union movement, and the presence of a large informal sector. Under the industry-specific wage law, formal agricultural and household workers are covered, while workers in other sectors are not. The unique aspect of this paper lies in the ability to compare the impacts of minimum wage legislation on formal covered, informal covered, formal uncovered, and informal uncovered workers. This natural experiment allows us to test whether industry-specific minimum wage legislation leads to higher wages, whether wage increases are restricted solely to covered formal sectors or if there are spillover effects, and whether such legislation manifests in disemployment effects. We find evidence of higher wages yet disemployment among black workers in formal markets. In informal markets we find no employment effects, but higher wages in formal markets appear to have spilled over into informal markets in covered sectors.
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Notes
In South Africa, the racial designation coloured refers to individuals having both African and white ancestry. Individuals of Asian descent are also an officially recognized population group. However, small sample sizes precluded an analysis of these workers.
The predecessor to the LFS was the October Household Survey, which was conducted annually from 1995 to 1999. In 2008, Statistics South Africa began producing the Quarterly Labour Force Survey. However, neither of these publications was strictly comparable with the LFS, therefore we restricted our analysis to the surveys conducted between 2000 and 2007. To eliminate possible seasonal variation we simply used the September surveys.
The real annual average pay estimate given here was computed as reported hourly pay multiplied by the hours the respondent normally works per week times 50 weeks per year and converted to US dollars using the 2000 exchange rate.
Weight data from Branson (2009) can be found at: https://www.datafirst.uct.ac.za/
The last of the locality variables, hours spent fetching water per week, serves as a proxy for local economic development and the urban/rural status of the individual’s residence.
Experience is estimated as the individual’s age less their time in school minus the assumed age for entering school (5). However, since many people in the sample had so few years of schooling, we assumed a lower bound of 15 which means we assumed that any work experience before the age of 15 did not contribute to the market value of their adult work.
While the selection of this treatment group is straightforward it is imperfect and comes with two caveats. First, workers in the treatment group include some workers in those industries who may have been directly covered legislatively and others who may have benefited only indirectly from wage spillovers. The estimated coefficients represent the average impacts across the industries covered. Second, depending on labor mobility across industries, for example from agricultural to transportation, the legislation could have an indirect impact on workers not included in this treatment group at all. Thus the impacts of the legislation may accrue to those in these industries and be lost to our analysis.
We performed the Heckman (1979) two-step correction for sample selection bias in this analysis. As part of this approach we therefore included the Inverse Mills Ratio (λ) as derived from the selection equation discussed previously. The selection equation used to calculate λ was similar to the employment equations reported in Tables 2 and 3 and the results are shown in the Appendix.
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Millea, M.J., Rezek, J.P., Shoup, B. et al. Minimum Wages in a Segmented Labor Market: Evidence from South Africa. J Labor Res 38, 335–359 (2017). https://doi.org/10.1007/s12122-017-9241-z
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DOI: https://doi.org/10.1007/s12122-017-9241-z