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Social Distress and (Some) Relief: Estimating the Impact of Pandemic Job Loss on Poverty in South Africa

  1. Ihsaan Bassier  Is a corresponding author
  2. Joshua Budlender  Is a corresponding author
  3. Maya Goldman  Is a corresponding author
  1. University of Surrey; and SALDRU, University of Cape Town, South Africa
  2. SALDRU, University of Cape Town, South Africa
  3. World Bank Equity and Policy Lab; and SALDRU, University of Cape Town, South Africa
Research article
Volume 18(1) Spring 2025
Cite this article as: I. Bassier, J. Budlender, M. Goldman; 2025; Social Distress and (Some) Relief: Estimating the Impact of Pandemic Job Loss on Poverty in South Africa; International Journal of Microsimulation; 18(1); 1-32. doi: 10.34196/ijm.00312
4 figures and 11 tables

Figures

Figure 1
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Poverty increase in NIDS by matching QLFS employment changes.
Figure 2
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Poverty increase 2020 Q1 to 2021 Q4 with and without SRD, in NIDS by matching on employment changes.
Figure F1
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Poverty rates in LCS, matching on changes.
Figure F2
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Poverty rates in NIDS, matching on levels.

Tables

Table 1
Poverty estimates by method, 2020 Q1 to 2021 Q4.
Increase in poverty rates
Data and methodBaselineEmployment loss onlySRD
Headcount (%)Gap (%)Headcount (percentage points)Gap (percentage points)Population (million)Headcount (percentage points)Gap (percentage points)Population (million)
Upper-bound poverty line
NIDSChanges40.418.25.23.83.13.41.52.0
Levels44.021.43.02.51.81.10.20.7
LCSChanges44.522.24.03.02.4
Food poverty line
NIDSChanges15.65.74.72.62.81.40.70.8
Levels19.68.13.31.72.0−0.3−0.3−0.2
LCSChanges21.58.63.42.02.0

  1. Note: the 2021 FPL is ZAR624 per month and the UBPL is ZAR1,335 per month.

  2. Source: authors’ estimates based on QLFS 2017, 2021 Q4; NIDS 2017; LCS 2014/15.

Table 2
Comparison with existing poverty estimates.
Poverty headcount ratio estimates (%)
Poverty lineBarnes et al.Van den Heever et al.NIDS levels
Mar 2020April 20202020 Q42020 Q12020 Q22020 Q42021 Q4
FPL20.626.321.219.622.521.622.9
UBPL48.252.548.944.046.746.047.0

  1. Note: authors’ estimates calculated here using most comparable method (NIDS levels); authors’ estimates and 2020 Q2 broadly comparable estimates not including simulated allocations of the Special COVID-19 SRD; broadly comparable poverty rate estimates modelled using income aggregate; authors’ estimates based on 2021 poverty lines; broadly comparable estimates based on 2019 lines adjusted using CPI to 2021; differences likely to be minimal.

  2. Source: author’s estimates based on QLFS 2017, 2020 Q4, 2021 Q4; NIDS 2017; broadly comparable estimates for April 2020 based on Barnes et al. (2021) (including simulated TERS receipt) and for 2020 Q4 on Van de Heever et al. (2021).

Table A1
Number of individuals and households, by QLFS dataset
QLFS datasetIndividualsHouseholds
2015 Q172,56120,828
2017 Q169,35320,529
2020 Q166,65719,913
2020 Q247,10313,408
2020 Q448,99014,242
2021 Q439,07311,502

  1. Source: authors’ calculations based on QLFS 2015 Q1, 2017 Q1, 2020 Q1, 2021 Q4.

Table B1
Year-on-year GNI growth (LCS).
YearPer capita GNIYear-on-year growth
2014/1577,062
2015/1681,4741.06
2016/1785,6261.05
2017/1889,1721.04
2018/1992,3091.04
2019/2091,6810.99
2020/2196,2291.05

  1. Source: authors’ estimates based on SARB (2022).

Table B2
Year-on-year GNI growth (NIDS).
YearPer capita GNIYear-on-year growth
201786,633
201890,0181.04
201993,0721.03
202091,2170.98

  1. Source: authors’ estimates based on SARB (2022).

Table C1
Employment rates by demographic characteristic in QLFS and NIDS, matching on changes.
Employment
Demographic characteristics2017 rates2021 rates2017–21 (% change)
QLFSNIDSQLFSNIDSQLFSNIDS
All58.063.248.551.0−16.4−19.3
Race
African54.961.845.549.0−17.1−20.7
Coloured62.763.652.251.9−16.7−18.4
Indian/Asian68.066.551.658.0−24.1−12.8
White79.679.078.970.4−0.9−10.9
Gender
Female65.072.955.158.1−15.2−20.3
Male51.153.841.944.0−18.0−18.2
Rural/urban
Rural44.952.337.242.9−17.1−18.0
Urban63.168.153.454.6−15.4−19.8
Education
Less than matric48.854.140.442.3−17.2−21.8
Matric62.964.150.449.7−19.9−22.5
Tertiary81.982.073.971.2−9.8−13.2

  1. Note: supercolumn (a) shows the employment rates in the original 2017 data, for NIDS and QLFS, supercolumn (b) the employment rates for 2021 Q4 in the updated dataset, and supercolumn (c) the % change in the QLFS and NIDS from 2017 to 2021 Q4; we disaggregate into the usual four racial groups rather than the aggregated two we use for the updating algorithm; restricted to ages 25–55.

  2. Source: authors’ estimates based on QLFS 2017, 2021 Q4; NIDS 2017.

Table C2
Employment sectors in QLFS and NIDS, matching on changes.
Employment sectorEmployment
2017 rates (%)2021 rates (%)2017–21 (% change)
QLFSNIDSQLFSNIDSQLFSNIDS
Formal/informal
Informal28.332.528.031.9−1.1−1.8
Sector
Agriculture8.110.98.411.93.79.2
Util./fin.21.818.324.220.311.010.9
Industry20.319.017.015.4−16.3−18.9
Trade19.416.319.715.91.5−2.5
Services22.225.322.225.6-1.2
Private households8.210.28.611.04.97.8

  1. Note: supercolumn (a) shows the proportions of employed in the original 2017 data, for NIDS and QLFS, supercolumn (b) the proportions of employed for 2021 Q4 in the updated dataset; and supercolumn (c) the % change in the QLFS and NIDS from 2017 to 2021 Q4; data restricted to ages 25–55.

  2. Source: authors’ estimates based on QLFS 2017, 2021 Q4; NIDS 2017.

Table C3
Employment rates by province in QLFS and NIDS, matching on changes.
Employment
Province2017 rates2021 rates2017–21 (% change)
QLFSNIDSQLFSNIDSQLFSNIDS
Eastern Cape49.555.640.546.1−18.2−17.1
Free State52.962.150.148.9−5.3−21.3
Gauteng64.669.553.155.9−17.8−19.6
KwaZulu-Natal52.161.644.852.0−14.0−15.6
Limpopo53.757.042.045.4−21.8−20.4
Mpumalanga58.062.946.749.1−19.5−21.9
Northern Cape50.662.245.150.4−10.9−19.0
North West53.560.243.349.2−19.1−18.3
Western Cape67.463.158.948.6−12.6−23.0

  1. Notes: supercolumn (a) shows the employment rates in the original 2017 data, for NIDS and QLFS, supercolumn (b) the employment rates for 2021 Q4 in the updated dataset, and supercolumn (c) the % change in the QLFS and NIDS from 2017 to 2021 Q4; data restricted to ages 25–55.

  2. Source: authors’ estimates based on QLFS 2017, 2021 Q4; NIDS 2017.

Table C4
Income growth in NIDS and national accounts.
YearPer capita national income
GNI in national accountsDisposable income
Matching on changesMatching on levels
Growth
2017 to 20191.071.021.03
2020 Q1 to 2021 Q41.090.940.97
2020 Q1 to 2021 Q4 + SRD0.940.99

  1. Source: authors’ calculations based on SARB (2022) and NIDS 2017.

Table D1
Employment rates in QLFS and LCS, matching on changes.
Demographic characteristics2015 rates (%)2021 rates (%)2017–21 (% change)
QLFSLCSQLFSLCSQLFSLCS
All57614953−15−14
Race
African54584650−16−13
Coloured63685255−17−19
Indian/Asian65705261−20−14
White788379771−7
Gender
Female65685559−15−14
Male50554247-16-14
Rural/urban
Rural45463740-17-14
Urban62685358-14-14
Education
Less than matric49524045−17−14
Matric62675056−19−15
Tertiary81877478−9−10

  1. Notes: supercolumn (a) shows the employment rates in the original 2015 data, for LCS and QLFS, supercolumn (b) the employment rates for 2021 Q4 in the updated dataset, and supercolumn (c) the % change in the QLFS and LCS from 2015 to 2021 Q4; we disaggregate into the usual four racial groups rather than the aggregated two we use for the updating algorithm; restricted to ages 25–55.

  2. Source: authors' estimates based on QLFS 2015, 2021 Q4; LCS 2014/15.

Table D2
Employment rates in QLFS and NIDS, matching on levels.
Demographic characteristics2017 rates (%)2021 rates (%)2017–21 (% change)
QLFSNIDSQLFSNIDSQLFSNIDS
All58634947−16−26
Race
African55624644−17−29
Coloured63645248−17−24
Indian/Asian68675263−24−5
White80797968−1−14
Gender
Female65735551−15−30
Male51544242−18−22
Rural/urban
Rural45523737−17−30
Urban63685351−15−25
Education
Less than matric49544037−17−31
Matric63645046−20−28
Tertiary82827468−10−18

  1. Notes: supercolumn (a) shows the employment rates in the original 2017 data, for NIDS and QLFS, supercolumn (b) the employment rates for 2021 Q4 in the updated dataset, and supercolumn (c) the % change in the QLFS and NIDS from 2017 to 2021 Q4; we disaggregate into the usual four racial groups rather than the aggregated two we use for the updating algorithm; restricted to ages 25–55.

  2. Source: authors' estimates based on QLFS 2017, 2021 Q4; NIDS 2017.

Data and code availability

The raw data associated with this research is publicly available from Statistics South Africa (https://www.statssa.gov.za/) and DataFirst (https://www.datafirst.uct.ac.za/). The code associated with this paper and any supplementary data is publicly available on Joshua Budlender’s website (https://www.joshbudlender.co.za/) and Github repo (https://github.com/jbudlender).

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