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Analysis of the Distributional Effects of COVID-19 and State-led Remedial Measures in South Africa

  1. Helen Barnes
  2. Gabriel Espi-Sanchis
  3. Murray Leibbrandt
  4. David McLennan
  5. Michael Noble
  6. Jukka Olavi Pirttilä
  7. Wynnona Steyn
  8. Brenton Van Vrede
  9. Gemma Wright  Is a corresponding author
  1. Southern African Social Policy Research Insights, United Kingdom
  2. Southern Africa Labour and Development Research Unit, South Africa
  3. University of Helsinki, Finland
  4. South African Revenue Service, South Africa
  5. Department of Social Development, South Africa
  6. College of Graduate Studies, South Africa
Research article
Volume 14(2) Summer 2021
Cite this article as: H. Barnes, G. Espi-Sanchis, M. Leibbrandt, D. McLennan, M. Noble, J. Olavi Pirttilä, W. Steyn, B. Van Vrede, G. Wright; 2021; Analysis of the Distributional Effects of COVID-19 and State-led Remedial Measures in South Africa; International Journal of Microsimulation; 14(2); 2-31. doi: 10.34196/ijm.00234
9 figures and 13 tables

Figures

Figure 1
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COVID-19 cases in South Africa, 5 March 2020 – 2 June 2021. Source: Our World in Data.
Figure 2
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Mean monthly household disposable income by decile in March, April, May, and June 2020 (includes pre-COVID-19 and COVID-19 policies). Note: Simulated receipt of COVID-SRD benefit was dampened to match actual receipt (applicable to May and June only). Source: Authors’ analysis of output datasets from SAMOD V7.3-COVID.
Figure 3
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Change in mean monthly household disposable income by decile since March in April, May, and June 2020 (includes pre-COVID-19 and COVID-19 policies). Note: Simulated receipt of COVID-SRD benefit was dampened to match actual receipt (applicable to May and June only). Source: Authors’ analysis of output datasets from SAMOD V7.3-COVID.
Figure 4
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Percentage change in mean monthly household disposable income by decile since March in April, May, and June 2020 (includes pre-COVID-19 and COVID-19 policies). Note: Simulated receipt of COVID-SRD benefit was dampened to match actual receipt (applicable to May and June only). Source: Authors’ analysis of output datasets from SAMOD V7.3-COVID.
Figure 5
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Change in mean monthly household disposable income by decile between March and June 2020. Note: Simulated receipt of COVID-SRD benefit was dampened to match actual receipt (applicable only to June in this figure). Source: Authors’ analysis of output datasets from SAMOD V7.3-COVID.
Figure B1
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Reweighting from original dwt to rebased dwt_2017q2pl. Source: Authors’ construction.
Figure B2
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Reweighting from rebased dwt_2017q2pl to new dwt_2020q1pl. Source: Authors’ construction.
Figure B3
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Effects of reweighting from original 2017 weight (dwt) to match the population estimate for mid-2017 and the QLFS labour market profile for 2017 Q2 (dwt_2017q2pl). Note: dwt: Original 2017 survey weights in SAMOD input dataset (i.e. NIDS Wave 5). dwt_2017q2pl: Reweighted to 2017 Q2 external controls for population estimates (‘p’) and labour market (‘l’). dwt_2017q2p: Reweighted to 2017 Q2 external controls for population estimates (‘p’) only. dwt_2017q2l: Reweighted to 2017 Q2 external controls for labour market (‘l’) only. Source: Authors’ construction.
Figure B4
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Effects of reweighting from rebased 2017 weight (dwt_2017q2pl) to match the population estimate for end of March 2020 and the QLFS labour market profile for 2017 Q2. Note: dwt_2017q2pl: Reweighted to 2017 Q2 external controls for population estimates (‘p’) and labour market (‘l’). dwt_2020q1pl: Reweighted to 2020 Q1 external controls for population estimates (‘p’) and labour market (‘l’). dwt_2020q1p: Reweighted to 2020 Q1 external controls for population estimates (‘p’) only. dwt_2020q1l: Reweighted to 2020 Q1 external controls for labour market (‘l’) only. Source: Authors’ construction.

Tables

Table 1
Tax and benefit policies that are included in SAMOD for 2020
Tax–benefit policy Existed prior to COVID-19? Changes introduced due to COVID-19? Summary of the changes that were introduced due to COVID-19, if applicable
Old Age Grant (OAG) ✓ ✓ OAG top-up of R250 in May–October 2020 inclusive
Disability Grant (DG) ✓ ✓ DG top-up of R250 in May–October 2020 inclusive
Child Support Grant ✓ ✓ CSG top-up of R300 per child for May 2020 only
Care Dependency Grant (CDG) ✓ ✓ CDG top-up of R250 in May–October 2020 inclusive
Foster Child Grant (FCG) ✓ ✓ FCG top-up of R250 in May–October2020 inclusive
Caregiver Social Relief of Distress (Caregiver-SRD) X ✓ New A payment of R500 was made to each CSG caregiver (irrespective of number of children) for June–October 2020 inclusive
COVID-19 Social Relief of Distress (COVID-SRD) X ✓ New COVID-19 SRD payment of R350 from May 2020 to end of April 2021
Personal income tax main policy ✓ ✓ But not implemented in SAMOD A proportion of PAYE (paid to the South African Revenue Service by employers) could be deferred. Tax relief was also introduced for provisional tax (for the self-employed, individuals running their own small businesses with gross income below R100 million).
Income tax rebates ✓ X N/A
Income tax on lump sums ✓ X N/A
Medical tax credits ✓ X N/A
Unemployment Insurance Fund contributions ✓ X N/A
Temporary Employer/Employee Relief Scheme X ✓ New UIF introduced TERS (or ‘COVID UIF’) payments for furloughed employees in April 2020, which had a minimum payment of R3,500 per month (even if usual salary is less than this) up to R6,500 per month on a sliding scale.

  1. Source: Authors’ compilation.

  2. Note: The Skills Development Levy and the Employment Tax Incentive are not modelled in SAMOD as these concern employers rather than employees. Grant-in-aid and the War Veterans Grant are not simulated due to lack of information in the input dataset with which to model the policy. UIF contributions are simulated in SAMOD but receipt of the main UIF benefits is not modelled due to lack of data on past contributions. CSG was also increased from 1 October 2020 by R10 to R450.

Table 2
Poverty headcount ratio (P0) and poverty depth (P1) in March, April, May, and June 2020 under different assumptions
Poverty line Scenario March April May June
FPL  Existing policies (COVID-SRD dampened) P0 0.206 0.263 0.209 0.188
  P1 0.091 0.129 0.083 0.070
Existing policies (COVID-SRD not dampened) P0 N/A N/A 0.164 0.177
  P1 N/A N/A 0.047 0.049
All policies apart from COVID-19 policies P0 N/A 0.321 0.321 0.321
  P1 N/A 0.158 0.158 0.158
LBPL  Existing policies (COVID-SRD dampened) P0 0.326 0.379 0.343 0.307
  P1 0.145 0.188 0.143 0.123
Existing policies (COVID-SRD not dampened) P0 N/A N/A 0.276 0.291
  P1 N/A N/A 0.099 0.105
All policies apart from COVID-19 policies P0 N/A 0.452 0.452 0.452
  P1 N/A 0.229 0.229 0.229
UBPL  Existing policies (COVID-SRD dampened) P0 0.482 0.525 0.527 0.475
  P1 0.233 0.278 0.245 0.215
Existing policies (COVID-SRD not dampened) P0 N/A N/A 0.461 0.468
  P1 N/A N/A 0.192 0.199
All policies apart from COVID-19 policies P0 N/A 0.593 0.593 0.593
  P1 N/A 0.329 0.329 0.329

  1. Source: Authors’ analysis of output datasets from SAMOD V7.3-COVID.

  2. Note: FPL, food poverty line (R561 in April 2019 rands); LBPL, lower-bound poverty line (R810 in April 2019 rands); UBPL, upper-bound poverty line (R1,227 in April 2019 rands). Simulated receipt of COVID-SRD benefit was dampened to match actual receipt (applicable to May and June only). The poverty lines were inflated from April 2019 rands to March, April, May, and June 2020 rands using the consumer price index and then averaged.

Table 3
Poverty in March, April, May, and June 2020 for household subgroups, with and without the COVID-19 policies: food poverty line
Household subgroup Scenario March April May June
Female-headed households Existing policies (COVID-SRD dampened) 0.243 0.263 0.204 0.190
All policies apart from COVID-19 policies N/A 0.351 0.351 0.351
Households with older people Existing policies (COVID-SRD dampened) 0.096 0.121 0.008 0.009
All policies apart from COVID-19 policies N/A 0.156 0.156 0.156
Households with children Existing policies (COVID-SRD dampened) 0.225 0.279 0.193 0.179
All policies apart from COVID-19 policies N/A 0.339 0.339 0.339

  1. Source: Authors’ analysis of output datasets from SAMOD V7.3-COVID.

  2. Note: Simulated receipt of the COVID-SRD benefit was dampened to match actual receipt (applicable to May and June only). The household subgroups are not mutually exclusive. The food poverty line (R561 in April 2019 rands) was inflated from April 2019 rands to March, April, May, and June 2020 rands using the consumer price index and then averaged.

Table 4
Income inequality in March, April, May, and June 2020 under different assumptions
Scenario Gini coefficient
  March April May June
Existing policies (COVID-SRD dampened) 0.644 0.648 0.631 0.613
Existing policies (COVID-SRD not dampened) N/A N/A 0.600 0.603
All policies apart from COVID-19 policies N/A 0.676 0.676 0.676

  1. Source: Authors’ analysis of output datasets from SAMOD V7.3-COVID.

  2. Note: Simulated receipt of COVID-SRD benefit was dampened to match actual receipt (applicable to May and June only). The first row shows results for all simulated tax and benefit policies including COVID-19 policies. The COVID-19 policies comprise TERS (applied in April, May, and June); benefit increases (in May and June with the exception of the CSG increase, which was only in May); and new benefits (COVID-SRD in May and June; Caregiver-SRD in June). No results are shown for March and April in the middle row as the COVID-SRD benefit was only introduced in May. No results are shown for March in the bottom row as there were no COVID-19 policies in place.

Table 5
Percentage of households with earnings and mean earnings, by household income decile in March and April 2020
Decile Percentage of households with earnings Mean monthly earnings (rands)
March April March April
1 (poorest) 13.0 8.6 368 354
2 48.4 28.2 1,133 1,022
3 57.4 41.2 1,981 1,682
4 68.3 49.6 3,076 2,291
5 80.7 62.4 4,688 3,870
6 87.8 69.2 6,213 4,893
7 78.2 65.3 8,669 7,059
8 93.7 80.1 13,392 10,656
9 94.3 83.9 20,213 16,733
10 (richest) 92.4 85.5 49,289 41,423

  1. Source: Analysis of input datasets from SAMOD V7.3-COVID.

  2. Note: Earnings are defined in this table as income from employment or self-employment. The April dataset includes the labour market shock induced by the pandemic and lockdown using predictions based on NIDS-CRAM Wave 1 (for more details, see Appendix C).

Table A1
Summary of datasets and tax–benefit systems in SAMOD V7.3-COVID
Era and dataset name Month in 2020 System name System summary(tax and benefit policies)
Pre-crisis
SA_2017_b3_pre_2		 March sa_2020_march Actual policies in March
Crisis
SA_2017_b3_April		 April sa_2020_april Actual policies in April
sa_2020_april_noters Existing policies excluding those introduced because of COVID
May sa_2020_may Actual policies in May
sa_2020_may_damp_bsaon Existing policies but COVID-SRD dampened to actual figures
sa_2020_may_nocovidpols Existing policies excluding those introduced because of COVID
June sa_2020_june Actual policies in June
sa_2020_june_damp_bsaon Existing policies but COVID-SRD dampened to actual figures
sa_2020_june_nocovidpols Existing policies excluding those introduced because of COVID
Table C1
Coefficients from the multinomial logit for different employment outcomes (relative to remaining employed without a drop in earnings)
April employment outcome
Employed with a reduction in earnings Furloughed No longer employed
15–24 years 0 (.) 0 (.) 0 (.)
25–34 years 0.289 (0.66) 0.0120 (0.03) 0.120 (0.43)
35–44 years 0.492 (1.07) −0.0866 (−0.22) −0.0969 (−0.32)
45–54 years 0.179 (0.38) 0.106 (0.28) 0.0263 (0.08)
55+ years 0.403 (0.84) 0.563 (1.27) −0.0676 (−0.17)
Urban −0.0868 (−0.37) −0.189 (−0.71) 0.0450 (0.24)
1.Managers 0 (.) 0 (.) 0 (.)
2.Professionals −0.199 (−0.48) −0.688 (−1.11) −0.327 (−0.57)
3.Technicians and associate professionals −0.0749 (−0.16) −0.355 (−0.51) −0.0279 (−0.05)
4.Clerical support workers 0.355 (0.71) −0.115 (−0.18) 0.254 (0.47)
5.Service and sales workers 0.0806 (0.18) 0.270 (0.45) 0.0418 (0.08)
6.Skilled agricultural, forestry, and fishery workers 0.416 (0.60) −0.555 (−0.59) −0.226 (−0.35)
7.Craft and related trades workers 0.330 (0.72) 0.670 (1.01) 0.0275 (0.05)
8.Plant and machine operators, and assemblers 0.0751 (0.14) −0.0198 (−0.03) 0.774 (1.48)
9.Elementary occupations 0.00397 (0.01) 0.123 (0.19) 0.294 (0.60)
Female −0.131 (−0.09) 0.767 (0.47) −0.833 (−0.61)
African 0 (.) 0 (.) 0 (.)
Coloured/Asian/Indian 0.501 (1.42) −1.582* (−2.54) −0.546 (−1.15)
White 0.457 (1.08) −0.113 (−0.19) −0.00196 (−0.00)
No education 0 (.) 0 (.) 0 (.)
Primary 0.852 (0.67) 1.359 (0.92) 0.138 (0.11)
Incomplete secondary −0.270 (−0.22) 1.771 (1.23) 0.202 (0.17)
Matric −0.0546 (−0.04) 1.131 (0.78) 0.445 (0.38)
Tertiary −0.108 (−0.09) 2.010 (1.37) 0.0400 (0.03)
Earning quintile 1 0 (.) 0 (.) 0 (.)
Earning quintile 2 −0.283 (−0.45) −0.904 (−1.93) −0.643 (−1.85)
Earning quintile 3 0.511 (1.01) −1.099** (−2.77) −1.600*** (−5.03)
Earning quintile 4 0.543 (1.10) −1.737*** (−3.97) −1.137*** (−3.43)
Earning quintile 5 0.847 (1.67) −1.579** (−2.79) −2.550*** (−5.45)
2.Female # African/Black 0 (.) 0 (.) 0 (.)
2.Female # Coloured/Asian/Indian −1.158 (−1.87) 1.793 (1.95) 0.451 (0.68)
2.Female # White 0.712 (1.10) 0.599 (0.78) 0.488 (0.63)
2.Female # None 0 (.) 0 (.) 0 (.)
2.Female # Primary −0.988 (−0.63) −0.684 (−0.40) 0.990 (0.66)
2.Female # Incomplete Secondary 0.739 (0.48) −1.668 (−1.00) 1.002 (0.72)
2.Female # Matric 1.076 (0.70) −0.493 (−0.29) 1.428 (1.03)
2.Female # Tertiary 1.282 (0.84) −0.827 (−0.49) 1.432 (1.02)
Female # Earning quintile 1 0 (.) 0 (.) 0 (.)
Female # Earning quintile 2 0.722 (1.04) 0.219 (0.35) −0.183 (−0.44)
Female # Earning quintile 3 −0.900 (−1.49) 0.200 (0.38) 0.374 (0.88)
Female # Earning quintile 4 −1.016 (−1.48) 0.505 (0.63) −0.396 (−0.91)
Female # Earning quintile 5 −2.229** (−2.95) −1.237 (−1.37) 0.0898 (0.13)
Constant −2.289 (−1.59) −2.406 (−1.53) −0.646 (−0.50)

  1. Source: Authors’ calculations based on NIDS-CRAM and SAMOD data.

  2. Note: t statistics in parentheses. All equations are relative to the baseline outcome of ‘employed with no drop in earnings’. * p < 0.05, ** p < 0.01, *** p < 0.001.

Table C2
Estimated proportional earnings reductions in race and sex subgroups (NIDS-CRAM)
Mean earnings reduction (%) Standard error n
African males 27.7 30.34 200
Coloured/Asian/Indian males 40.49 27.07 27
White males 39.62 32.22 29
African females 23.92 27.2 228
Coloured/Asian/Indian females 33.54 32.24 33
White females 40.83 30.73 27

  1. Source: Authors’ calculations based on NIDS-CRAM and SAMOD data.

  2. Note: These proportions are estimates of the extent of the reduction in earnings among those who faced earnings reductions. A 15 per cent threshold was used for earnings reductions.

Table C3
April employment outcomes among those who were employed in February (weighted estimates)
NIDS-CRAM April employment outcome Simulated April employment outcome in SAMOD
  Estimated percentage Estimated total N Estimated percentage Estimated total N
Employed with no drop in earnings 51.13 9,386,097 1,718 53.71 8,875,967 5,333
Employed with a drop in earnings 12.24 2,247,465 411 14.21 2,348,882 1,411
Furloughed 11.88 2,181,499 399 11.51 1,902,303 1,143
No longer employed 24.75 4,543,719 832 20.56 3,397,428 2,041

  1. Source: Authors’ calculations based on NIDS-CRAM and SAMOD data.

Table C4
Distribution of simulated April outcomes by actual NIDS-CRAM April outcome
Simulated April employment outcome (SAMOD)
Actual April employment outcome (NIDS-CRAM) Employed with no drop in earnings Employed with a drop in earnings Furloughed No longer employed Total percentage (N)
Employed with no drop in earnings 57.04 15.21 8.47 223 100 (N = 1157)
Employed with a drop in earnings 55.97 13.58 9.05 21.4 100 (N = 243)
Furloughed 49.37 15.06 13.81 21.76 100 (N = 239)
No longer employed 47.81 13.36 12.73 26.1 100 (N = 479)

  1. Source: Authors’ calculations based on NIDS-CRAM and SAMOD data.

Table C5
April employment outcomes among the February employed in different race and education groups (NIDS-CRAM)
April employment outcome (%)
  Employed with no drop in earnings Employed with a drop in earnings Furloughed No longer employed
Race
African 49.2 10.69 13.38 26.72
Coloured/Asian/Indian 55.65 13.22 6.94 24.19
White 58.86 21.34 7.38 12.43
Education level
Less than matric 46.61 9.63 14.12 29.64
Matric 48.84 12.08 9.03 30.05
More than matric 56.23 14.46 11.77 17.54

  1. Source: Authors’ calculations based on NIDS-CRAM data.

Table C6
Modelled April employment outcomes among the February employed in different race and educationgroups (SAMOD data)
April employment outcome (%)
  Employed with no drop in earnings Employed with a drop in earnings Furloughed No longer employed
Race
African 53.43 11.83 12.57 22.17
Coloured/Asian/Indian 56.27 16.85 8.61 18.27
White 53.06 24.21 6.8 15.93
Education level
Less than matric 51.48 11.23 13.47 23.81
Matric 50.7 17.31 6.09 25.9
More than matric 58.4 15.21 12.12 14.27

  1. Source: Authors’ calculations based on SAMOD data.

Table C7
Comparison of aggregate employment outcomes in NIDS-CRAM and SAMOD
NIDS-CRAM Wave 1 (April) SAMOD April simulated estimates
  Estimated total Estimated proportion Estimated total Estimated proportion
NEA 7,872,015 22.44 12,931,312 32.94
Unemployed 11,564,228 32.96 13,277,751 33.82
Employed 15,648,103 44.6 13,050,449 33.24
Total 35,084,347 100 39,259,512 100

  1. Source: Authors’ calculations based on NID-CRAM and SAMOD data.

  2. Note: Based on a sample of individuals aged 18 and above. NEA, not economically active.

Data and code availability

The NIDS and NIDS-CRAM datasets are freely available for download and can be applied for and accessed via this portal: https://www.datafirst.uct.ac.za/dataportal/index.php/catalog

SAMOD is proprietary but is made available to not-for-profit, government and academic users free of charge and subject to certain conditions. For more information contact info@saspri.org

For access to the EUROMOD microsimulation software contact: https://euromod-web.jrc.ec.europa.eu/contact

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