1. Introduction

On top of the health crisis, the coronavirus pandemic has also led to severe economic hardship via lost earnings and increased joblessness. In the case of South Africa, the NIDS-CRAM telephonic survey has provided up-to-date information on the labour market and other social impacts of COVID-19,1 including hunger. However, the impact of the pandemic on poverty and inequality at the household level is a topic for which prior evidence has not been readily available.

The purpose of this paper is to examine the impacts of the first wave of the coronavirus pandemic during the second quarter of 2020 on poverty and inequality in South Africa. This timepoint refers to the first wave of COVID-19 in the country and when a nation-wide lockdown took place. The main objective of our analysis is to investigate how well the social protection system in South Africa was able to mitigate the economic losses to the public. We take into account both the existing benefit system, which was in place before the crisis, and the influence of the new policy measures introduced in 2020 to help people cope with the crisis.

To study these issues, we use a tax–benefit microsimulation approach. This methodology has been used in many other countries (see, for example, Brewer and Tasseva, 2020; Jara et al., 2021) to examine the impacts of the crisis on households’ disposable income, as well as poverty and inequality. Tax–benefit microsimulation models combine a representative survey of incomes and other socioeconomic characteristics of the population with a modelling of tax and benefit rules, and they are used to examine the impact of tax–benefit policies on household welfare.

SAMOD, a tax–benefit microsimulation model for South Africa,2 is used in this study in the following way: first, the database underpinning the model is updated to reflect the demographic situation that was in place just before the crisis—that is, the situation in March 2020.3 Second, the dataset is adjusted for the labour market characteristics that existed at the height of the crisis during the second quarter of 2020. This is achieved by predicting the labour market situation (whether a person became unemployed or furloughed or lost part of their income) on the basis of the information in the NIDS-CRAM dataset, where a subset of the people who are represented in the dataset underpinning SAMOD were asked about their situation during the crisis.4 Third, the new benefits that were introduced in 2020 to provide households with relief during the crisis are introduced into the model. The model is then used to examine the extent to which incomes declined during the crisis, how large a share of the decline was avoided due to the social protection offered by the government, and the resulting impact on poverty and inequality. The results help to understand the success of the social protection system in mitigating the economic consequences of the crisis. They also provide pointers towards further improvements to the benefit system.

This paper is organized as follows. Section 2 describes how COVID-19 hit South Africa, including its consequences for employment. Section 3 contains a summary of the government’s policy response, detailing the new social benefits that were introduced in 2020. Section 4 reports the results of the study, while Section 5 concludes.

2. Labour market impacts of COVID-19 during the first lockdown

The first wave of COVID-19 in South Africa began in March 2020 and peaked in June 2020 (Figure 1). The second wave occurred between November 2020 and February 2021, with the third wave starting in June 2021. In late March 2020, a lockdown of the country took effect, which banned all but vital outside movement, closed down many public spaces (such as schools and shops not selling essential goods), and included, for example, a ban on alcohol sales. The most stringent lockdown was in place until the end of April 2020. Under these conditions the majority of industries were closed down, and people other than essential workers were not allowed to leave their homes to work. Clearly this meant that the South African labour market was severely affected in the first half of 2020.

Figure 1. COVID-19 cases in South Africa, 5 March 2020 – 2 June 2021. Source: Our World in Data.

Some surveys provide insight into the labour market effects of the crisis over our study period (March – June 2020). NIDS-CRAM is a broadly representative national panel survey implemented using computer-assisted telephone interviewing (CATI) that focuses on adult individuals’ responses to the pandemic and lockdown. The first wave of interviews was conducted across May and June and collected retrospective employment information for April (during the strictest period of lockdown – level 5) and February (before the COVID-19 shock) (see Jain et al., 2020). The other significant source of information on the employment shock comes from the second-quarter round of the Quarterly Labour Force Survey (QLFS) (Statistics South Africa, 2020). The inability to conduct interviews during the pandemic meant that the release of the second quarter of the survey was delayed and that the survey had to be changed to be run telephonically.

Both the QLFS and NIDS-CRAM revealed dramatic losses of employment at the height of the national lockdown (estimates of around 2.2 million and close to 3 million jobs lost, respectively). Further, research from NIDS-CRAM showed that this job loss was found to be especially severe among women (Casale and Posel, 2020), the youth, and lower-income workers (Ranchhod and Daniels, 2020).5 The employment effects were also more serious for informal workers (Rogan and Skinner, 2020), who were prohibited from working or trading and simultaneously excluded from some of the social support measures introduced in response to the crisis. In addition to this large-scale loss of employment, there was also an unprecedented increase in the number of workers who became furloughed, working zero hours and earning no pay, and in the number of workers who were placed on paid leave (Jain et al., 2020), and many workers faced reduced earnings.

3. Policy responses in 2020

South Africa has a well-established tax and benefit system that was already in place prior to the pandemic. This meant that it was possible for the government to make swift changes to the existing arrangements to mitigate the effect of the pandemic on people’s incomes. In addition, new policies were introduced to support groups that were not covered by existing policies.

Table 1 lists the tax and benefit policies that are simulated in SAMOD, most of which existed prior to the pandemic (see column 2). The pandemic was declared a national disaster on 15 March 2020 and a national lockdown was announced on 23 March 2020. One of the first policy responses to be introduced, on 26 March, was an adjustment to the Unemployment Insurance Fund (UIF), with the establishment of the COVID-19 Temporary Employer/Employee Relief Scheme (TERS) (Republic of South Africa, 2020). TERS was payable by application of employers to the Department of Labour on behalf of their furloughed workers who were unable to go to work due to the lockdown but who had not been made redundant. In some cases the earnings of these employees were suspended, while in other cases their salaries were reduced. The TERS payment is calculated on a sliding scale, ranging from R3,500 to R6,500 per month.

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.

Source: Authors’ compilation.

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.

With respect to the social benefits, four were amended to be paid at a higher level for May through to October 2020: the payments for the Old Age Grant, Disability Grant, Foster Child Grant, and Care Dependency Grant were each increased in value by R250 per month. As the payment systems for these grants were already in place, it was technically straightforward to implement these top-up payments.

The Child Support Grant (CSG) was initially amended in a similar way, with the value of the CSG being increased by R300 per month in May. However, this was then removed and replaced by a dedicated benefit for the primary caregivers of children in low-income families: each primary caregiver in receipt of CSG for their child(ren) became eligible for a new benefit, called the Caregiver Social Relief of Distress (Caregiver-SRD), which was paid at R500 per month for May through to October 2020. As with the other grants, the CSG payment system was already in place and it was technically straightforward for the 7.1 million primary caregivers to receive the Caregiver-SRD for themselves in addition to the CSG for their children. This was a particularly important policy change because when the CSG was first introduced in 1998 to replace the former State Maintenance Grant (SMG), the caregiver component of the SMG was not carried through to the CSG, leaving caregivers of working age with no social assistance unless they were disabled.6

Another significant response was the introduction of a new benefit called the COVID Social Relief of Distress (COVID-SRD), which was paid at R350 per person per month to people of working age who were unemployed and had no income.7 This was a more difficult group to get on to the payment system and its roll-out was therefore slower than for the other grants mentioned above. Nevertheless, it was set up at great speed and has been iteratively extended in duration (its current end date is the end of April 2021). Again, this was an important policy change as prior to the pandemic there had been no social assistance in South Africa for unemployed people of working age unless they were disabled, apart from the short-term Social Relief of Distress, used sparingly in exceptional circumstances such as natural disasters or incarceration of one’s spouse.

All of these policy adjustments and innovations were simulated in SAMOD for the relevant months and are referred to here as ‘the COVID-19 policies’. As this study focuses on March, April, May, and June 2020, the COVID-19 policies can be summarized as comprising 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).8

Within SAMOD, a separate system (set of tax and benefit rules) was prepared for each of the four months March to June 2020, and in such a way that the COVID-19 policies could be either included or excluded in the running of the model. This enables one to estimate the extent to which poverty and inequality were affected by the lockdown in a scenario that includes all of the actual policies that were in place in each month, and in a hypothetical scenario with no COVID-19 policies.

Lastly, an important consideration when modelling the policy responses is the extent to which the simulations of the policies reflect actual receipt of the benefits and insurance payments in practice. The two main discrepancies that were identified are summarized here:

  • SAMOD simulates more than twice the number of recipients of the COVID-SRD benefit for May and June than received it in practice. This is likely to be due to implementation challenges associated with the sudden roll-out of a new benefit. For this reason, a switch was added in SAMOD that enables the user to dampen receipt of the COVID-SRD to reflect actual numbers of beneficiaries in May and June, as reported by the South African Social Security Agency. This enables one to compare a situation in which all eligible recipients receive the benefit (the ‘de jure’ scenario), with the actual (or ‘de facto’) scenario.

  • In contrast, SAMOD simulated many fewer recipients of TERS than received the benefit in practice: 44, 60, and 64 per cent of the actual number of recipients in April, May, and June, respectively (Department of Employment and Labour, 2020). A decision was made not to adjust for this under-simulation, on the basis that it can be assumed that a subset of those who reported earnings in NIDS-CRAM Wave 1 were actually reporting income derived from TERS.9 As a consequence, the findings about the impact of the COVID-19 policies will be understated with respect to the role of TERS; however, it should not affect results on the combined impact of the shock and all policies on distributional incomes as the income sources are not differentiated.

4. Results

This section presents the main findings from the study. Results are provided with respect to changes in disposable income, poverty, and inequality, and the extent to which the COVID-19 policies helped protect household incomes and provide additional support for those already in poverty during the first few months of the pandemic.

As described in Appendix C, the labour market shock induced by the pandemic and lockdown was incorporated in the simulation by modelling loss of employment and earnings among those who were employed going into the lockdown (using predictions based on NIDS-CRAM Wave 1). This modelling reflects predicted outcomes at the height of the crisis and lockdown (in April), and remains static throughout all of the months considered here (up to June). This means that these results can be interpreted as a counterfactual showing what poverty and distributional outcomes would have been had these different policy regimes (from different months) been in place at the height of the lockdown.

4.1. Change in mean disposable income between March and June 2020

Figure 2 shows the distribution of household per capita disposable income by decile for March, April, May, and June 2020, in rands. Disposable income refers to incomes after the deduction of simulated personal income tax payments and UIF contributions, and having added all relevant simulated benefits. The deciles are deciles of disposable income for March 2020, and are held constant for the other three months.

Figure 2. 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.

Mean disposable income fell for the wealthier deciles and ultimately increased for the poorer deciles. The first column in each decile (in green) shows the situation in March 2020 prior to the shock, while the results for the other three months are based on the shocked input dataset in which those in employment prior to the shock had been assigned different statuses. Here, the most notable change is the reduction in mean monthly household disposable incomes in the top (richest) deciles.

Figure 3 shows the change in mean monthly household disposable income by decile in rands. As can be seen, deciles 7–10 experienced a fall in disposable income in April, May, and June when compared with the baseline in March. The wealthiest (tenth) decile experienced the largest fall in disposable income. In contrast, in May and June deciles 1–6 experienced a rise in disposable income.

Figure 3. 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.

The increase in disposable income in the lower deciles that is observed in Figure 3 is small in rand amounts, but when expressed as a percentage of March’s mean disposable incomes the change is more striking. This is shown in Figure 4, which shows that the mean disposable income of those in the first (poorest) decile increased by just over 100 per cent in April and by almost 200 per cent in May and June compared to March. As will be elaborated below, the notable increases for the lower deciles are a result of the introduction of social assistance for people of working age.

Figure 4. 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.

Although the wealthiest (tenth decile) loses the most in absolute terms, the eighth and ninth deciles lose slightly more in relative terms.

4.2. Change in income poverty and inequality between March and June 2020

Table 2 shows how the poverty rates changed across the four time points, using Statistics South Africa’s three poverty lines. It should be recalled that the input dataset for April, May, and June is held constant in the simulations and so the only drivers of any changes are the simulated policy responses to the pandemic and associated lockdown.

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

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

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.

For each of the three poverty lines, the first row shows the poverty headcount ratio with all policies switched on (that is, taking into account both the set of policies that existed prior to the pandemic and the set of policies that were introduced as COVID-19 policies to mitigate the impact of the pandemic), but with receipt of the COVID-SRD benefit dampened to match reported numbers of beneficiaries. Using all three poverty lines, poverty is higher in April and May when compared to March. Poverty reached its height in April when only the TERS had been introduced and no changes had been made to the benefit system. For example, using the food poverty line, poverty rose from 0.206 in March to 0.263 in April, at which point over one-quarter of people in South Africa were below the food poverty line.

Notably for all three poverty lines, in June poverty fell to a level lower than in March. This is reflected in the poverty headcount and the poverty depth summary measure (Table 2). The two policy differences between May and June were the switch from CSG top-up payments to a dedicated Caregiver-SRD, and an improved roll-out of the COVID-SRD from 4.4 million beneficiaries in May to 5.1 million in June.

4.3. The role of the COVID-19 policies in preventing income poverty and inequality from rising to much higher levels

In Table 2, for each of the poverty lines the poverty headcount ratio is shown for a hypothetical scenario in which the COVID-19 policies are switched off (the row ‘All policies apart from COVID-19 policies’). These results are the same for April, May, and June as the input dataset and non-COVID-19 policies remain constant, but are shown for each month for completeness. In a hypothetical situation with no COVID-19 policies, it can be seen that poverty would have risen to 0.321 each month using the food poverty line (a 56 per cent increase from the baseline in March), and to 0.452 using the lower-bound poverty line (a 37 per cent increase from the baseline in March), and to 0.593 using the upper-bound poverty line (a 23 per cent increase from the baseline in March).

The COVID-19 policies played a particularly vital role for female-headed households, and households containing children or older people. Table 3 shows the poverty headcounts for three particularly vulnerable subgroups. The same overall pattern is observed as for the population as a whole: that is, poverty increases between March and April and then falls to levels lower than in March, though for these subgroups the fall to a level lower than in March occurs sooner (May) than for the population as a whole (June). For households containing one or more older people, poverty (as measured using the food poverty line) is almost obliterated. This will be driven by the R250 increase to the Old Age Grant from May onwards. The fall in poverty between the months of May and June will be due to the combined impact of the transition from an increase to the Child Support Grant payment (which occurred only in May) to a Caregiver SRD benefit (which started in June), and an improved role-out of the COVID-SRD benefit, as the number of beneficiaries increased from 4.4 million in May to almost 5.1 million people in June.

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

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

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.

The COVID-19 polices greatly reduce the extent of poverty that would otherwise have existed: without them, poverty in female-headed households would have risen to 0.351 (a 44 per cent increase from the baseline in March), and poverty in households containing one or more older people would have risen to 0.156 (a 62 per cent increase from the baseline in March), and poverty in households containing one or more children would have risen to 0.339 (a 51 per cent increase from the baseline in March).

Table 4 shows the Gini coefficient for each month. From the first row it can be seen that inequality increased very slightly in April compared to March, but in May and June it fell to levels lower than in March. This was due to the reduced earnings in the top deciles, and increased incomes (mostly from the COVID-SRD and Caregiver-SRD) in the bottom deciles (as reflected in Figures 1–3). In the absence of any COVID-19 policies, inequality would have increased to 0.676.

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

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

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.

As the COVID-19 benefit changes only commenced in May, it is possible to attribute the reduction of inequality in April from 0.676 (in the hypothetical situation of no COVID-19 policies) to 0.648 wholly to the TERS income received by furloughed workers. Similarly, as TERS is applied in a constant way in April, May, and June, the further reductions in inequality in May and June can be attributed to the COVID-19 benefits.

4.4. Summary of the distributional impact of the pandemic and COVID-19 policies between March and June 2020

Figure 5 shows the overall change between March and June 2020 in mean monthly household disposable income, by decile and for South Africa as a whole. The figure decomposes the changes into three parts: changes due to loss of earnings caused by the pandemic (shown in dark grey); the cushioning effects of automatic stabilizers—that is, the tax–benefit system in place prior to the pandemic (shown in light blue); and the additional effects of the newly introduced COVID-19 policies (shown in dark blue). Overall (the final column), mean household disposable income fell between March and June by 11.0 per cent. If this change is decomposed, the change in earnings accounts for a 24.7 per cent drop in disposable income; the change in automatic stabilizers accounts for a 4.1 per cent rise in disposable income; and the introduction of new COVID-19 policies (including TERS) accounts for a 9.6 per cent rise in disposable income.

Figure 5. 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.

These numbers can be used to calculate the so-called income stabilization coefficient.10 When comparing March and April it amounts to 40 per cent;11 this measures the extent to which the automatic stabilizers and the TERS policy protected households from declines in market income. When comparing March and June, the income stabilization coefficient rises to 53 per cent, meaning that more than half of the drop in market income was avoided by the combined effects of automatic stabilization, TERS, and the COVID-19 benefit changes that were in place in June.

As can be seen, the mean disposable income increased in the lowest five deciles, remained largely unchanged for decile 6, and fell for deciles 7–10. The changes were driven by a combination of the introduction of the COVID-19 policies, a fall in earnings, and to a much lesser extent the role of the automatic stabilizers. Effects of the pre-COVID tax–benefit system (shown in light blue) at the top of the income distribution are mostly driven by the reduction in contributions to UIF and payments of personal income tax after earnings shocks.

The figure shows the important redistributional effect of the COVID-19 policies. However, although the percentage increases in mean disposable income are highest in the lowest deciles, the actual increases in rand amounts are very low (Figure 2).

Table 5 provides more detail about the profile of households in each of the deciles shown in Figure 5 in respect of earnings in March 2020 and April (and May and June) 2020. Only 13 per cent of households in the first (poorest) decile had earnings prior to the pandemic in March 2020, and this fell to under 9 per cent of households in April 2020. The mean earnings of those in the first decile are very low at both time points, which explains why the change in disposable income is so great in Figure 5 for the first decile between March and June: a COVID-SRD benefit of R350 per month is more or less equivalent to the mean monthly earnings of this decile.

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

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

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).

It should also be kept in mind that SAMOD simulated many fewer recipients of TERS than received the benefit in practice (Department of Employment and Labour, 2020); as explained above in more detail, it is assumed that a subset of those who reported earnings in NIDS-CRAM Wave 1 were actually reporting income derived from TERS. As a consequence, the findings about the impact of the pandemic on earnings (shown in dark grey) is likely to be understated (that is, there will have been greater drops in earnings), and the counterbalancing impact of TERS (shown in dark blue) is also likely to be understated for those working in the formal sector (that is, TERS will have played a larger role than shown in protecting people’s incomes). Nevertheless, this should not affect the combined impact of the shock and all policies on mean distributional incomes (the white dots in Figure 5) as the income sources are not differentiated.

In summary, the COVID-19 policies not only served to mitigate the impact of the pandemic and lockdown to a great extent, but also represent a long-overdue change in policy approach by providing social assistance to low-income adults of working age.

4.5. Comparison to some earlier analyses

This study has examined the impacts on poverty and inequality of a package of COVID emergency policies that were implemented by the South African government in late March 2020 in response to the arrival of the pandemic in South Africa. In the weeks preceding the announcement of this package, the government engaged intensively with South Africa’s research community over a range of possible interventions. Bassier et al. (2021) is one such study, originally written for the presidency in March 2020. It sought to use information from the NIDS Wave 5 survey of 2017 to investigate a set of emergency policies to support informal workers whose employment and earnings would be halted by a lockdown but who would not receive any relief through systems such as the UIF that rely on formal registration of employment. The evidence from NIDS Wave 5 was used to show that a very large proportion of such informal workers reside in households in the bottom deciles of the income distribution in which there are recipients of existing social grants, in particular the CSG. This made a case therefore that this grant could be re-purposed to provide substantial emergency relief to these workers and their households. They also showed that, if it were feasible to implement, a ‘Special COVID-19 Grant’ broadly targeting the unemployed and those in informal employment would be very effective in assisting these informal workers and mitigating COVID-19-associated poverty.

Quantitative work such as that of Bassier et al. (2021) used the 2017 situation to inform policy options by using ex-ante simulations of the impacts of various policy options. In this study, we have worked hard to ground our assessment on a base situation that prevailed in the country in 2020 on the eve of the pandemic and the lockdown. Then, we have incorporated reliable information on actual labour market outcomes between April 2020 and June 2020 in order to ensure that our simulations are assessing as closely as possible the impacts for incomes, poverty, and inequality of what actually happened in the labour market over this period.

Another useful point of comparison is a study by Zizzamia et al. (2020) based on NIDS-CRAM that matched job losers with observably similar job retainers in order to estimate poverty effects of COVID-induced job loss. Stressing that their results are highly approximate, they estimated that one million job losers (and three million people accounting for dependents) fell into poverty in April as a result of the COVID employment shock. This estimate incorporates grant receipt on the basis of survey responses and differs sharply to the poverty findings of this paper, but it must be borne in mind that the time point of April means SRD receipt would still have had a minimal effect on ameliorating poverty in their estimate.

5. Conclusions

This study examined the impacts of the coronavirus pandemic on household incomes, poverty, and inequality in South Africa during the first wave of infections in April–June 2020. We made use of information from the NIDS-CRAM survey to predict job and income losses for the representative sample of the general population that underpins the tax–benefit microsimulation model for South Africa, SAMOD. The changes made to existing social benefits and the new policies, introduced in 2020 to assist households to weather the pandemic, were included in the modelling. Households’ economic situations were then compared to the pre-crisis conditions in early March 2020.

The results indicate that while a decline in earnings would have caused a 25 per cent drop in disposable income on average, the overall drop in disposable income in June was much smaller at 11 per cent. Automatic stabilization played a role due to households losing income, paying less tax and becoming eligible for social grants. But the main contributor to the protection was the package of augmented and new benefits that was introduced, including the COVID-19-SRD, Caregiver-SRD, and TERS. Overall, the drop in disposable incomes was highest in absolute terms among higher-income households (Figure 3); conversely, mean disposable incomes increased for the poorest income deciles (Figures 3 and 4), although only by a small rand amount.

We estimate that poverty increased in May when compared to the pre-crisis levels: the poverty headcount went from 0.33 in March to 0.34 in May using Statistics South Africa’s lower poverty line. It dropped further in June to 0.31. This is because of the COVID-19 policies, which for the first time brought social benefits available to non-disabled adults not eligible for unemployment insurance. Of all the grants in the package, only the COVID-SRD required substantial new implementation systems to be put in place. Poverty reduction would have been greater if all those eligible for COVID-SRD had benefited from it; in other words, if its roll-out and take-up had been 100 per cent. Overall, the South African tax–benefit system provided considerable support for households during the first wave of the pandemic, even in an international comparison.12

We have not been able to address all facets of the pandemic or the policy response to it. We have concentrated on earnings and the role of direct taxes and transfers, whereas a full analysis would also need to take into account changes in capital income. Tax-paying firms also had the opportunity to defer tax payments, which has probably contributed in a significant way to their ability to survive and pay salaries during the crisis.13 We have not been able to capture the contribution of this policy in our analysis.

This study focussed on the time period of the first wave of the pandemic in South Africa, up to June 2020. Since then, the Caregiver-SRD grant and the increased monthly payments of the existing benefits by government ceased in October 2020. Furthermore the COVID-SRD benefit was terminated at the end of April 2021. Our work here suggests that the poverty situation therefore probably worsened in late 2020 compared to our study period. The country is now (in June 2021) embarking on a serious third wave during the winter season, and so the vital support that was provided during the first wave continues to be sorely missed.

One of the main takeaways of this analysis is the need to develop the South African social protection system further for the post-COVID world. The success of especially the COVID-19 benefit changes in poverty reduction underscores the need to have similar transfers in place in more normal times as well. That said, the present system was implemented as an emergency response and it should be further developed if it is to be made more permanent. For example, the COVID-SRD was put in place in great haste to fill a gap in emergency funding, and its application procedures are not easy to work with for potential beneficiaries. They could be simplified within a design framework that splices this grant into an integrated system of grants. Similarly, the means test for receiving SRD is exceptionally stringent (requiring applicants to have zero income) and it would need to be reconsidered and harmonized with those being applied to the other social grants. Introducing new benefits is costly, of course, but financing options exist. Also, in the spirit of this study, going forward there is so much to be learned for policy prioritization by careful evaluation of the effectiveness of policies in guiding the country through the COVID pandemic.

Footnotes

1.

See https://cramsurvey.org.

2.

See Appendix A for a description of the model.

3.

See Appendix B for details of this part of the analysis.

4.

See Appendix C for details of this part of the analysis.

5.

See Table C5 for estimates of how employment transitions varied along the dimensions of race and education level based on NIDS-CRAM.

6.

Although people of working age (including caregivers) are not eligible for social assistance unless they are disabled, the social insurance scheme (Unemployment Insurance Fund) does exist, but this is time limited and depends on sufficient contributions having been made.

7.

The monthly R350 payment is USD 24.53, EUR 20.60, and GBP 17.66 (xe.com 28 June 2021), and is similar to the value of the mean monthly earnings of households in the poorest household income decile (see Table 5 below).

8.

Although most of the main tax and benefit policies that affect people’s incomes at the individual level are simulated in SAMOD V7.3-COVID, certain policies are not: value-added tax, grant in aid, the War Veterans Grant, and the usual UIF (i.e. non-TERS) payments. The only COVID-19 policy response that is not simulated is the introduction of tax payment deferrals.

9.

As mentioned earlier, TERS was generally distributed to workers through their employers, and so income derived from TERS would have been reported as earnings by many respondents. In addition, the NIDS-CRAM questionnaire makes no distinction between different sources of earnings and does not explicitly tell respondents to exclude TERS.

10.

See Dolls et al. (2012) for a description of the methodology.

11.

Calculated as one minus the change in disposable income divided by the change in market income.

12.

A similar analysis for Ecuador (another upper-middle-income country) shows, for example, a dramatic increase in poverty despite the introduction of a new social benefit (Jara et al., 2021). In the UK, while the income losses were smaller, the role of government protection is at roughly the same level as in South Africa (with a mean disposable income drop of 7 per cent against a corresponding reduction in earnings of 13 per cent in the UK) (Brewer and Tasseva, 2020).

13.

On the basis of the information in the 2021 budget review, taxpayers had used tax deferrals with a total value of R40 billion until mid-February 2021.

14.

See www.nids.uct.ac.za/nids-data/documentation/overview-documentation/wave-5.

15.

The pandemic was declared a national disaster on 15 March 2020 and a national lockdown was announced on 23 March 2020.

16.

In practice, it was not possible to ascertain which respondents from NIDS Wave 5 were ‘farmers’ and so this category was not coded in either the SAMOD input dataset or the QLFS external controls.

17.

All children aged 0–14 were assigned code 99 on the ‘les’ classification, irrespective of whether they had been assigned a different economic status in the raw NIDS data or raw QLFS data.

18.

In practice, it was not possible to identify armed forces personnel in the QLFS data, so this category was excluded. There were 15 respondents coded as ‘armed forces’ occupation in the NIDS data and these were recoded to ‘elementary occupation’.

19.

All children aged 0–14 were assigned code 999 on the composite ‘les_loc’ variable, irrespective of whether they had been assigned a different economic status in the raw NIDS data or raw QLFS data.

20.

This restriction to positive earners follows the precedent of Brewer and Tasseva (2020).

21.

A more sophisticated model that does not rely on the assumption of the ‘independent of irrelevant alternatives’ (IIA) property could be used in future work.

22.

Due to small numbers in the Asian/Indian group, a three-category race variable was used for regressions that collapsed the Coloured and the Asian/Indian groups into one category.

23.

Due to a lack of available data, marriage status was not included as a regressor.

24.

Note that this classification means that a few individuals who reported zero earnings in April but still worked positive days will be placed in the reduced earnings group rather than the furloughed group.

25.

Note that the earnings variables in the input data have no missing values, which means there is no distinction between zero-earners and missing data (in contrast with NIDS-CRAM). The multinomial logit is restricted to February positive earners, which attenuates the effect this difference has, while the inability to distinguish between zero-earners and missing in April is largely consistent with the coding in NIDS-CRAM, which treated missing as sufficient to classify people as having zero earnings or earnings reductions.

26.

This is in line with the method of Brewer and Tasseva (2020). Once again, a 15 per cent threshold is used for what counts as an earnings reduction.