Sectoral minimum wages in South Africa: Disemployment by firm size and trade exposure

ABSTRACT

This paper measures the impact of South African minimum wages on small and large firm employment in a sector that is exposed to international competition (agriculture) and one that is not (retail). Small farm employment is most vulnerable to minimum wage legislation. In contrast, large farm employment was shielded from employment losses. While this shift represents a short-run response to minimum wages, it may intensify the long-run movement towards fewer, larger, and more capital-intensive farms. Retail employment experienced no changes in employment, regardless of firm size. These results are in line with the idea that firms exposed to international markets cannot easily increase prices when their employees’ wages increase while non-tradable sectors can more readily shift the burden of higher labour costs onto consumers by increasing prices. Implementation of a uniform national minimum wage ignores this type of heterogeneity, and could lead to intra-industry changes in concentration and inequality.

KEYWORDS:

• Minimum wages
• employment effects
• firm size
• international trade
• concentration

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Marlies Piek http://orcid.org/0000-0002-0276-1749

Dieter von Fintel http://orcid.org/0000-0001-6771-1315

Notes

1 Prior to the national minimum wage, two independent mechanisms determined minimum wages. Sectoral determinations are legislated directly by central government, but only in selected sectors that employ the poorest workers in the labour market (such as agricultural, retail and domestic employees). Collective agreements, on the other hand, are agreed upon by industry bargaining councils – a collective of union representatives and firms who negotiate wages of better-paid workers (such as metal and clothing workers). These agreements can be extended to uncovered firms within the same sector and jurisdiction at the discretion of the Minister of Labour, regardless of whether employers and employees in these firms were party to the original agreement. These industry agreements, therefore, function in the same way as a conventional minimum wage.

2 Until recently, South Africa had 124 different minimum wage structures (Cassim, Jourdan & Pillay, 2015).

3 Union and government workers were excluded from the sample since their wages are often subject to collective bargaining agreements and including them would have confounded the two types of wage legislation.

4 These are available on the Department of Labour’s website, www.labour.gov.za.

5 Defined as earning below R10 000 per month in 2000 Rands.

6 Since the majority of farmworkers are African and of mixed race, the control group was restricted to these race groups. It is furthermore known that different wage-determination processes operate for various race groups (Burger et al., 2016).

7 We conducted a sensitivity analysis by defining the control groups for both sectors in various ways (by including and excluding the narrowly unemployed). The signs were the same for either definition, although with varying levels of significance.

8 The employed people in this control group were not restricted by specific skill-levels, since the retail treatment group also included occupations over a range of different occupations and skill-levels.

9 Opting to use only employed individuals in our control groups was based on wanting the treatment and control groups to be as similar as possible, since employed and unemployed individuals often have different characteristics.

10 This estimation strategy essentially measures the intention to treat effect. As we show in the appendix, wages did increase as a result of the minimum wage legislation and thus gives traction to our estimation strategy. Future research could use actual treatment instead, as recent research by Bhorat et al. (2019) suggests.

11 South Africa is divided into 53 district councils. Minimum wages do not vary greatly by geography, except where these districts contain greater numbers of workers in either area A or B municipalities (which are smaller geographic units than district councils).

12 We use the earnings variable which is a consistent income variable across waves and the recommended variable to analyse labour incomes in the PALMS dataset (Kerr & Wittenberg, 2017). This variable, however, does not adjust for bracket responses, we therefore, weight the earnings variable by the bracket weight when creating median wages.

13 Both the median and minimum wages are real hourly wages in 2000 Rands.

14 To construct the wage gap variable, each individual in the dataset had to be assigned the hourly real minimum wage for the district council they reside in. However, minimum wage regions (areas A and B) are defined by smaller municipal demarcations which cannot be identified in the data. District councils are the smallest demarcation that can be consistently identified throughout the period of analysis. A district council could therefore comprised of only area A municipalities, only area B municipalities or a mixture of area A and area B municipalities. Population estimates from the 2007 Community Survey were used to calculate the percentage of the district councils’ populations that lived in local municipalities classified as A or B. Minimum wages for mixed district councils were then calculated as follows: E(Minimum wage)_j = % of people living in area A * area A minimum wage + % of people living in area B * area B minimum wage. Each individual is assigned this weighted minimum wage according to their district of residence. This was then used to construct the wage gap variable. A similar method was followed for the retail sector, the main difference being that the retail minimum wage varied across three areas instead of two.

15 As mentioned previously, the retail minimum wage ranges across certain occupations, one of which include managers. Since managers are often relatively more educated and remunerated, another treatment group was created to see whether mean characteristics of the treatment group changed. Regressions with this treatment group were also run, but as with the mean characteristics, there were no substantial differences. For future research, one could perhaps split the retail minimum wage into a relatively more skilled and relatively less skilled groups, to get better control groups.

16 A linear probability model was used for the employment regressions for both sectors.

17 This is in line with the descriptive figures from Table 2, where large farm employment does not seem to have been negatively affected by the introduction of minimum wages.

18 This is apparent when analysing the number of years of education of farm workers and their wages by firm size; the average (and median) number of years education and wages is significantly higher in larger farms compared to smaller farms. Please see Table A5 for more details.

19 Since the sample sizes for the largest two firm sizes were too small (as depicted in Table 2), the retail sector analysis was not split by these two firm size categories.