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

Figures & data

Table 1. Average characteristics of the agricultural and retail sectors and their control groups.

Year^a	2000	2001	2002	2003	2004	2005	2006	2007
Agricultural sector
N	1659	2793	3027	2768	2866	2754	2855	2717
Education	4.91*	4.90*	5.00*	5.17*	5.29*	5.66*	5.68*	5.90*
Age	34.93*	35.71*	34.93*	35.08*	35.12*	34.94*	34.99*	35.8*
Proportion African	0.61*	0.66*	0.66*	0.62*	0.66*	0.67*	0.66*	0.67*
Proportion male	0.63*	0.67*	0.65*	0.66*	0.66*	0.68*	0.62*	0.64*
Hours per week	52*	51*	52*	50*	49*	49*	49*	49*
Real hourly wage	2.57*	2.58*	2.36*	2.69*	3.05*	3.38*	3.52*	3.66*
Control group for the agricultural sector
N	1848	2336	2282	2887	3109	3471	3801	3677
Education	6.43	6.66	6.77	6.85	6.86	6.93	7.15	7.36
Age	35.19	35.03	35.41	35.71	35.83	35.99	35.98	35.74
Proportion African	0.75	0.79	0.80	0.82	0.83	0.83	0.82	0.83
Proportion male	0.56	0.59	0.59	0.67	0.70	0.69	0.69	0.67
Hours per week	49	48	47	45	44	45	44	44
Real hourly wage	4.23	3.98	4.03	3.84	4.12	4.31	4.79	4.85
Retail Sector
N	890	1191	1170	1191	1059	1032	1181	1140
Education	9.89	10.02*	10.01	10.17*	10.38*	10.20*	10.39*	10.18*
Age	33.40*	33.41*	33.24*	33.73*	33.68*	34.20*	32.93*	34.11*
Proportion African	0.59*	0.62*	0.62*	0.60*	0.64*	0.62*	0.63*	0.67*
Proportion male	0.50*	0.47	0.48	0.50	0.50	0.52*	0.49	0.51
Hours per week	48.44	48.82*	49.10*	48.15*	48.86*	49.06*	47.88*	47.64*
Real hourly wage	8.62*	7.87*	7.08*	7.92*	7.35*	7.66*	7.61*	8.06*
Control group for the retail sector
N	9393	12 995	12 848	12 579	12 047	10 876	11 678	11 839
Education	6.69	6.78	6.75	6.85	6.95	7.15	7.31	7.51
Age	36.93	37.11	37.11	37.31	37.26	37.15	37.09	37.20
Proportion African	0.76	0.79	0.79	0.80	0.82	0.83	0.83	0.82
Proportion male	0.44	0.45	0.45	0.48	0.50	0.49	0.48	0.49
Hours per week	47.94	47.79	48.05	46.41	45.90	46.19	44.97	45.07
Real hourly wage	5.09	4.94	4.38	4.46	4.67	4.75	5.13	5.39

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Notes: The dashed line indicates the timing of the law. *p < 0.05, indicating significant differences across treatment and control groups.

^aGiven that the paper only used the September round of 2000, the sample sizes for the year 2000 are substantially smaller than the subsequent years which used the March and September rounds.

Table 2. The number of observations across district councils for the agricultural and retail sectors, by firm size and time period.

	No restriction		<20 workers		<50 workers		≥20 workers		≥50 workers
	Pre	Post	Pre	Post	Pre	Post	Pre	Post	Pre	Post
Agricultural sector
Minimum	36	37	32	31	30	33	32	37	30	39
Mean	248	348	148	191	196	265	156	238	105	162
Median	152	203	84	130	100	181	93	148	73	94
Maximum	1820	1975	439	610	1279	1322	1366	1495	526	635
Total	8925	12 514	4134	5354	6662	9007	4360	6657	1886	2913
Retail sector
Minimum	31	35	32	30	34	33	30	42	36	37
Mean	102	131	74	89	88	114	76	78	73	55
Median	79	123	65	90	72	108	50	61	78	55
Maximum	404	308	230	150	315	234	168	159	98	74
Total	3859	4995	2450	2948	3080	3999	764	778	290	220

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Table 3. Average wage gaps for the agricultural and retail sectors.

Agricultural sector		Retail sector
Entire sector	0.50	Entire sector	0.23
<20 employees	0.54	<20 employees	0.24
≥20 employees	0.50	<50 employees	0.23
<50 employees	0.51
≥50 employees	0.45

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Table 4. Probability of employment in the agricultural sector.

	(I)	(II)	(III)	(IV)	(V)
	no restriction	<20	<50	≥20	≥50
1st set of results
Post	−0.124	−0.097	−0.087	−0.066	−0.046
	(0.097)	(0.067)	(0.064)	(0.060)	(0.059)
Wage Gap	−0.127	−0.139*	−0.232***	−0.343***	−0.175**
	(0.095)	(0.077)	(0.073)	(0.075)	(0.086)
Wage Gap* Post	−0.009	−0.110	−0.081	−0.002	0.033
	(0.137)	(0.098)	(0.093)	(0.109)	(0.114)
Controls	No	No	No	No	No
Constant	0.551***	0.592***	0.643***	0.754***	0.691***
	(0.066)	(0.051)	(0.048)	(0.038)	(0.040)
R-squared	0.022	0.041	0.046	0.044	0.009
N	41 822	19 502	30 093	16 747	7022
N treatment	20 545	9298	15 085	10 162	4498
N control	21 277	10 204	15 008	6585	2524
DC	38	29	35	27	18
	(VI)	(VII)	(VIII)	(IX)	(X)
	no restriction	<20	<50	≥20	≥50
2nd set of results
Post	0.031	0.085**	0.054**	−0.070*	−0.092
	(0.024)	(0.038)	(0.025)	(0.038)	(0.057)
Wage Gap	10.403***	−0.345***	−0.941***	−0.357	−0.494
	(0.497)	(0.074)	(0.227)	(0.514)	(1.001)
Wage Gap* Post	−0.062*	−0.072*	−0.066**	0.027	0.032
	(0.034)	(0.043)	(0.032)	(0.052)	(0.081)
Controls	Yes	Yes	Yes	Yes	Yes
Constant	−2.134***	0.142	0.257	0.554	0.334
	(0.298)	(0.359)	(0.321)	(0.572)	(0.793)
R-squared	0.245	0.219	0.225	0.206	0.138
N	41 812	19 499	30 088	16 744	7021
N treatment	20 544	9298	15 084	10 162	4498
N control	21 268	10 201	15 004	6582	2523
DC	38	29	35	27	18

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Notes: Standard errors were clustered at district council level and appear in parentheses. All regressions are weighted using the cross entropy weights. Controls include age, race, education agricultural real GDP, year fixed effects and district-council dummy variables. Asterisks denote ***p < 0.01; **p < 0.05; *p < 0.1.

Figure 1. Median amount of capital in the agricultural sector by firm size. Source: Authors’ calculations using the SARS-NT panel for the 2011 tax year.

Notes: The SARS-NT panel only starts from the 2008 tax year, but figures are only reliable a few years into the dataset and thus, figures for the 2011 tax year are shown here. Amounts are shown in South African Rands.

Figure 2. Median ratio of wage costs relative to turnover for the agricultural sector by firm size. Source: Authors’ calculations using the SARS-NT panel for the 2011 tax year.

Notes: Firm size categories are based on turnover measures comparable to those used in the Agricultural Survey in 2016.

Table 5. Probability of employment in the retail sector.

	(I)	(II)	(III)
	no restriction	<20	<50
Post	0.005	−0.005	0.005
	(0.006)	(0.006)	(0.006)
Wage Gap	−0.019	−0.016	−0.035**
	(0.013)	(0.013)	(0.014)
Wage Gap* Post	0.006	0.017	0.008
	(0.017)	(0.017)	(0.018)
Controls	No	No	No
Constant	0.097***	0.099***	0.102***
	(0.005)	(0.004)	(0.004)
R-squared	0.000	0.000	0.001
N	99 237	58 545	81 349
N treatment	8249	5002	6997
N control	90 988	53 543	74 352
DC	37	31	37
	(IV)	(V)	(VI)
	no restriction	<20	<50
Post	0.006	0.009	0.011
	(0.007)	(0.010)	(0.007)
Wage Gap	0.480***	−0.222***	−0.354***
	(0.088)	(0.057)	(0.065)
Wage Gap* Post	−0.004	0.001	−0.004
	(0.013)	(0.014)	(0.013)
Controls	Yes	Yes	Yes
Constant	−0.042***	0.034***	0.033***
	(0.010)	(0.011)	(0.012)
R-squared	0.077	0.084	0.086
N	99 192	58 526	81 318
N treatment	8246	5002	6996
N control	90 946	53 524	74 322
DC	37	31	37

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Notes: Standard errors were clustered at district council level and appear in parentheses. All regressions are weighted using the cross entropy weights. Controls include age, race, education, year fixed effects and district-council dummy variables. Asterisks denote ***p < 0.01; **p < 0.05; *p < 0.1.

Table A1. Number of hours worked in the agricultural sector.

	(I)	(II)	(III)	(IV)	(V)
	no restriction	<20	<50	≥20	≥50
1st set of results
Post	−2.138***	−2.232**	−2.136***	−1.482	−2.258
	(0.783)	(1.029)	(0.795)	(0.999)	(1.364)
Wage Gap	8.095***	8.604***	8.207***	8.757***	5.069*
	(1.228)	(1.376)	(1.182)	(1.696)	(2.847)
Wage Gap* Post	−0.926	−1.788	−1.371	−2.034	1.697
	(1.459)	(1.704)	(1.363)	(2.148)	(3.591)
Controls	No	No	No	No	No
Constant	48.001***	48.508***	48.266***	47.234***	48.052***
	(0.691)	(0.852)	(0.690)	(0.865)	(1.210)
R-squared	0.042	0.043	0.044	0.046	0.025
N	20 525	9287	15 069	10 154	4495
DC	38	29	35	27	18
	(VI)	(VII)	(VIII)	(IX)	(X)
	no restriction	<20	<50	≥20	≥50
2nd set of results
Post	−2.319**	−1.918	−2.415**	−2.040	−3.043*
	(1.018)	(1.266)	(1.032)	(1.378)	(1.811)
Wage Gap	−12.860	11.982**	3.647	−4.818	7.642
	(38.088)	(5.044)	(9.986)	(18.456)	(33.309)
Wage Gap* Post	−0.962	−2.154	−1.699	−1.137	3.490
	(1.379)	(1.613)	(1.305)	(1.926)	(3.050)
Controls	Yes	Yes	Yes	Yes	Yes
Constant	67.012***	43.435***	62.724***	84.477***	95.590***
	(14.381)	(13.431)	(14.788)	(20.478)	(24.835)
R-squared	0.057	0.068	0.061	0.081	0.089
N	20 524	9287	15 068	10 154	4495
DC	38	29	35	27	18

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Notes: Standard errors were clustered at district council level and appear in parentheses. All regressions are weighted using the cross entropy weights. Controls include age, race, education agricultural real GDP, year fixed effects and district-council dummy variables. Asterisks denote ***p < 0.01; **p < 0.05; *p < 0.1.

Table A2. Log of real hourly wages^a in the agricultural sector.

	(I)	(II)	(III)	(IV)	(V)
	no restriction	<20	<50	≥20	≥50
1st set of results
Post	0.212***	0.161***	0.198***	0.182***	0.222***
	(0.045)	(0.052)	(0.049)	(0.052)	(0.074)
Wage Gap	−0.986***	−1.031***	−0.953***	−1.092***	−0.930***
	(0.057)	(0.062)	(0.062)	(0.068)	(0.100)
Wage Gap* Post	0.307***	0.392***	0.326***	0.410***	0.323**
	(0.075)	(0.080)	(0.079)	(0.092)	(0.133)
Controls	No	No	No	No	No
Constant	1.114***	1.144***	1.097***	1.157***	1.099***
	(0.033)	(0.040)	(0.037)	(0.035)	(0.052)
R-squared	0.263	0.258	0.251	0.308	0.259
N	20 395	9246	14 981	10 075	4462
DC	38	29	35	27	18
	(VI)	(VII)	(VIII)	(IX)	(X)
	no restriction	<20	<50	≥20	≥50
2nd set of results
Post	0.042	0.004	0.034	0.047	0.114*
	(0.032)	(0.038)	(0.033)	(0.043)	(0.061)
Wage Gap	0.839	−0.912***	−2.238***	−2.542***	1.227
	(1.192)	(0.148)	(0.361)	(0.640)	(0.890)
Wage Gap* Post	0.316***	0.385***	0.321***	0.351***	0.276***
	(0.046)	(0.050)	(0.046)	(0.057)	(0.085)
Controls	Yes	Yes	Yes	Yes	Yes
Constant	0.205	1.108***	0.947*	0.470	−1.064
	(0.475)	(0.420)	(0.488)	(0.625)	(0.683)
R-squared	0.332	0.325	0.327	0.380	0.363
N	20 394	9246	14 980	10 075	4462
DC	38	29	35	27	18

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Notes: Standard errors were clustered at district council level and appear in parentheses. All regressions are weighted using the cross entropy weights. Controls include age, race, education agricultural real GDP, year fixed effects and district-council dummy variables. Asterisks denote ***p < 0.01; **p < 0.05; *p < 0.1.

^aIn order to include those which reported incomes in brackets, we merged in data from the supplementary palmsv3.2incomes and assigned the midpoint of the bracket to those who reported with brackets. However, in our sample we only have 67 observations which responded in brackets. Of these 67 observations, only 12 have an indication of hours worked in the past week, which is needed to create hourly wages. Such a low figure is unsurprising since low-income earners (which is the sample we focus on) are less likely to report in brackets as shown by Posel & Casale (2006) and Von Fintel (2007).

Table A3. The number of hours worked in the retail sector.

	(I)	(II)	(III)
	no restriction	<20	<50
Post	−0.190	−0.499	−1.202
	(0.770)	(0.999)	(0.770)
Wage Gap	11.763***	7.615***	8.649***
	(1.938)	(1.918)	(1.641)
Wage Gap* Post	−4.240*	−3.369	−2.501
	(2.468)	(2.657)	(2.181)
Controls	No	No	No
Constant	46.542***	48.963***	48.467***
	(0.637)	(0.772)	(0.592)
R-squared	0.024	0.012	0.018
N	8238	4993	6987
DC	37	31	37
	(IV)	(V)	(VI)
	no restriction	<20	<50
Post	0.971	0.284	−0.063
	(1.137)	(1.418)	(1.167)
Wage Gap	−16.321	−7.421	0.806
	(14.053)	(8.067)	(8.224)
Wage Gap* Post	−3.845**	−2.591	−1.961
	(1.877)	(2.420)	(1.966)
Controls	Yes	Yes	Yes
Constant	49.339***	52.530***	50.704***
	(1.943)	(2.006)	(1.850)
R-squared	0.097	0.096	0.094
N	8235	4993	6986
DC	37	31	37

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Notes: Standard errors were clustered at district council level and appear in parentheses. All regressions are weighted using the cross entropy weights. Controls include age, race, education agricultural real GDP, year fixed effects and district-council dummy variables. Asterisks denote ***p < 0.01; **p < 0.05; *p < 0.1.

Table A4. Log of real hourly wages in the retail sector.

	(I)	(II)	(III)
	no restriction	<20	<50
Post	0.008	0.011	0.050
	(0.043)	(0.051)	(0.045)
Wage Gap	−1.064***	−0.945***	−0.913***
	(0.116)	(0.110)	(0.095)
Wage Gap* Post	0.349**	0.283**	0.266**
	(0.153)	(0.138)	(0.123)
Controls	No	No	No
Constant	1.846***	1.757***	1.758***
	(0.035)	(0.042)	(0.037)
R-squared	0.087	0.083	0.076
N	8197	4980	6954
DC	37	31	37
	(IV)	(V)	(VI)
	no restriction	<20	<50
Post	−0.015	0.028	0.009
	(0.034)	(0.042)	(0.040)
Wage Gap	0.945	−0.463	−0.908**
	(0.613)	(0.451)	(0.456)
Wage Gap* Post	0.267***	0.172*	0.140*
	(0.083)	(0.091)	(0.079)
Controls	Yes	Yes	Yes
Constant	0.691***	0.863***	0.857***
	(0.107)	(0.127)	(0.111)
R-squared	0.337	0.342	0.344
N	8194	4980	6953
DC	37	31	37

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

Notes: Standard errors were clustered at district council level and appear in parentheses. All regressions are weighted using the cross entropy weights. Controls include age, race, education agricultural real GDP, year fixed effects and district-council dummy variables. Asterisks denote ***p < 0.01; **p < 0.05; *p < 0.1.

Table A5. descriptive statistics on farm workers’ education level and hourly wages by firm size.

		Number of employees
		2–5	5–9	10–19	20–49	50 upwards
Years of education	mean	4.18	4.83	5.38	5.60	5.83
	median	4	5	6	6	6
Hourly earnings	mean	2.73	2.84	3.03	3.09	3.10
	median	2.37	2.45	2.67	2.74	2.70

Source: Authors’ calculations based on the PALMS dataset (Kerr et al., 2017).

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