Selected Issues

Abstract

Selected Issues

Inequality in South Africa: Trends and the Role of Fiscal Policy1

South Africa is one of the most unequal societies globally. While progress has been achieved, the legacies of apartheid continue to weigh on income distribution. Using the latest household and labor force surveys, this paper looks at key trends in poverty and inequality. It also uses fiscal incidence analysis to assess how taxes and social spending components redistribute income. While the analysis finds a large progressive impact of fiscal policy on income, low growth continues to weigh on poverty and inequality. Private-sector led growth should be favored and complemented with efficient public policies to improve the delivery and leverage social grants, and strengthen other policy interventions.

A. Poverty and Inequality: Stylized Facts

1. The aggregate data on poverty and inequality mask significant disparities across age, race, and gender. Children and the elderly are the age groups most affected by poverty. Moreover, poverty levels and severity are highest for black Africans, with black African women facing the highest levels among all groups (Figure 1). Similarly, income inequality levels are highest among black Africans (Figure 2).

Figure 1.
Figure 1.

Proportion of Population Living Below LBPL and Poverty Gap for LBPL by Sex and Population Group, 2015

(Percent)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA and IMF staff estimates.
Figure 2.
Figure 2.

Income Gini Coefficient by Population Group

(Scaled 0–1)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA and IMF staff estimates.

2. Poverty is closely correlated with access to employment. Unemployment at end-2017 stood at 26.7 percent, with youth unemployment (15–29 age group) at an alarming 52.2 percent.2 Poor households earn less than 40 percent of their income from employment, with the rest being government transfers. In contrast, income from employment accounts for 80 percent of the total income for households that move in and out of poverty. Nearly 40 percent of unemployed have never had a job, with the number rising to 60 percent for the youth. One in three of the unemployed were last employed five years ago (Figure 3), with the proportion rising to almost half for those aged between 50 and 65.

Figure 3.
Figure 3.

Percentage Number of Unemployed by Duration of Unemployment, 2018Q1

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA and IMF staff estimates.

3. Employment prospects are largely driven by education levels. The bulk of the jobs being created are high-skilled, thus favoring higher levels of education. Hence, employment status depends significantly on the level of education, with unemployment lowest for university graduates (Figure 4). The share of labor income in total income largely determines poverty and inequality levels (Leibbrandt et al, 2012).

Figure 4.
Figure 4.

Unemployment Rate by Education Status, 2017Q1

(Percent)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA and IMF staff estimates.

4. Significant spatial disparities persist. Regional gaps between the rich and the poor reflect apartheid-era restrictions on geographical settlement and the associated government spending patterns (Adato, Carter, and May, 2006). While the gaps have declined somewhat, economic and social prospects continue to be closely linked to the location of households, with significant disparities between rural and urban areas and across provinces. Poverty headcount and incidence in rural areas is about twice the level in urban areas (Figure 5). For example, GDP per capita in the poorest provinces (Limpopo and Eastern Cape) is half the level of that in Gauteng, while poverty gaps and severity (headcount and incidence together) are higher. Provinces with the lowest participation in the labor force have the highest incidence of poverty.

Figure 5.
Figure 5.

Income Distribution and Poverty Incidence by Province

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA and IMF staff estimates.Note: Colors refer to poverty severity (Red > 20%, 10% < Yellow < 20%, Green < 10%).

5. Notwithstanding efforts to promote financial inclusion, access to finance is still constrained for lower-income households, who rely on informal services. Table 1 shows that the bottom quintile of households has half the number of bank accounts of the top quintile. Many of these accounts are only used to receive social grants. However, the bottom quintile’s access to loans and credit cards is only one tenth of the access of the top quintile. Hence, bottom quintile households account for 33 percent of loans from “mashonisas” (higher-cost informal lenders) compared to 8 percent for the top quintile.

Table 1.

South Africa: Financial Inclusion by Income Decile

article image
Source: NIDS and IMF staff calculations.

6. On current trends, South Africa runs the risk of not achieving its target of reducing income inequality (Gini) to 0.6 by 2030. Meeting this target would require a radical pick-up in growth from the current anemic levels. Inequality reductions of similar magnitudes have been achieved in other EMs, but over longer periods (Figure 6). Except for Argentina (where a post-crisis recovery contributed to a rapid decline in inequality levels), the other countries (Brazil, Chile, Colombia, and Mexico), took between 17 and 23 years to achieve similar gains.

Figure 6.
Figure 6.

Evolution of Gini Coefficient, Selected Countries

(Scaled 0–100)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Source: WDI and IMF staff calculations.

7. Several initiatives have been rolled out to foster a more inclusive society since the end of apartheid. The government has invested significantly to bridge the poverty and inequality gap. Social spending benefits have been expanded over time, and now include targeted direct cash transfers (old age, disability, and child grants), indirect transfers (e.g. provision of water and sanitation services and housing subsidies for the poor), and in-kind transfers (provision of health and education services). The share of households with access to electricity and water has increased consistently (Figure 7). The coverage of social grants has expanded from 3 million in 2000 to 17 million in 2016, reaching over 30 percent of the population (Figure 8). The Black Economic Empowerment Program was put in place to allow diversification of ownership and increase employment opportunities.

Figure 7.
Figure 7.

Percent of Households with Access to Electricity and Water

(Percent)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA and IMF staff estimates.
Figure 8.
Figure 8.

Social Grants Disbursed: 2000–16

(Beneficiaries, millions)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: South African Social Security Agency (SASSA) and SOCPEN Database (2000–2016).

B. Why Does Inequality Matter?

8. The relationship between inequality and growth has gained renewed attention. While endogeneity issues are not fully resolved, inequality can affect growth through various channels. Credit constraints and market imperfections are likely to be more common in unequal societies, limiting low-income households’ ability to invest in physical as well as human capital and to mitigate shocks (Galor and Zeira, 1993; Corak, 2013). Carter and Barett (2006) argue that insufficient access to loans and insurance can inhibit the ability of poor households to accumulate assets, leading them to remain trapped in poverty, while the rich are able to get the financial resources they need to move further ahead. High inequality (including spatial inequality) can give rise to socio-political instability and deepen ethnic tensions with potentially deleterious effects on social cohesion and the sustainability of growth (World Bank, 2009).

9. Given the demographic trends, South Africa needs to create jobs both to absorb new entrants, and to employ low-skilled workers. The working age population is expected to increase by over 2 million until 2030 and over 6 million by 2050 (Table 2). Job creation, at the average levels observed between 2010–16, would imply a rise in the unemployed to 8 million by 2030, and in the worst-case scenario (where job creation remains at a lower level, 2016 elasticity), exceed 10 million. Such an increase in the number of unemployed could fuel social tensions and increase the demand for fiscal transfers.

Table 2.

South Africa: Demographics and Employment

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Source: STATS SA, Labour Market Dynamics in South Africa, 2016, page 114.

10. Growth in South Africa is pro-poor, but has not contributed to reductions in inequality. Periods of extended growth contribute to declines in poverty (Figure 9). However, with most jobs created in the high-skills sector, inequality has increased as income levels have gone up (Figure 10). This emphasizes the role of fiscal policy as a redistributive instrument.

Figure 9.
Figure 9.

Income per Capita Growth and Poverty

(Impact of a 1-percent increase in per capita income on poverty headcount, in percent)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: World Bank, PovcalNet, IMF, World Economic Outlook Database, and staff calculations.
Figure 10.
Figure 10.

Income per Capita Growth and Inequality

(Impact of a 1-percent increase in per capita income on Gini)

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: World Bank, PovcalNet, IMF, World Economic Outlook Database, and staff calculations.

C. Fiscal Policy and Inequality

11. A fiscal incidence analysis is employed to assess how the types of taxes and components of social spending redistribute income, and thus affect inequality. As discussed in Inchauste et. al. (2015), the analysis takes the market income distribution per decile as a starting point and the Gini coefficient associated with it. The Gini coefficient is then recomputed for several definitions of income that reflect the impact of groups of taxes and benefits covered in the analysis to capture their effect on inequality. When the effect of all taxes and benefits being analyzed is reflected together on income (i.e., final income), the Gini coefficient of the resulting income distribution is compared to the market one to get the estimated effect of fiscal policy on inequality.

12. The accounting approach of fiscal incidence analysis is used to compute post-tax and benefit income distributions. This approach examines what is paid and received by households without assessing the behavioral responses that taxes or public spending may trigger. Annex I discusses methodological aspects including the definition of relative and absolute progressivity, the data sources, and relevant caveats.

Figure 11.
Figure 11.

Moving from Market Income to Final Income

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Source: Inchauste and Lustig, 2015.

13. The incidence analysis focuses on a subset of tax and benefit categories given available information. The analysis covers the personal income tax (PIT), unemployment insurance fund levy (UIF), and the skills development levy (SDL) on the direct tax front, and the VAT, fuel excises, and alcohol and tobacco specific excises for indirect taxes. On the social spending side, the analysis covers the incidence of specific components of health and education expenditure, the largest social grants (old age, disability and child grants), and some indirect subsidies (housing subsidy, free water, electricity, and sanitation for the poor).

14. The tax system is mildly progressive. The progressivity of direct taxes (due to a relatively high exemption threshold, progressive rates, and rebates) combined with some progressivity of the general fuel excise in most waves (consumption of fuels is more heavily concentrated among the non-poor) more than offsets the relatively regressive specific excises on alcohol and tobacco (more heavily consumed by the poor) and the relatively less regressive VAT given that basic food items are VAT exempt or face a lower rate (Figure 12). This mildly progressive impact has remained broadly stable across the four waves, averaging 0.02 (Table 3).

Figure 12.
Figure 12.

South Africa: Indirect Taxes

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA, NIDS, and IMF staff estimates
Table 3.

South Africa: Impact of Tax System on Inequality

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Source: IMF staff estimates

15. Social spending is progressive in absolute terms. The most progressive components are the direct cash transfers followed by primary and secondary education and all health expenditure. Nonetheless, tertiary education spending is still progressive in relative terms because of the very skewed market distribution (see Figures 13, 14, and 15). Adding the impact of cash benefits, health, education, free services and housing subsidy benefits to market income results in a significant reduction of the market Gini by an average of 0.18 percent in the four waves. These benefits considerably increase the incomes of the lowest deciles of the distribution (Table 4). The progressivity of social spending has also been increasing mainly due to the expansion of the well-targeted social grants over this period.3 However, given quality concerns with health and education spending, the results may overstate the extent of the actual benefit to the vulnerable from health and education.

Figure 13.
Figure 13.

South Africa: Social Grants

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA, NIDS, and IMF staff estimates
Figure 14.
Figure 14.

South Africa: Education

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA, NIDS, and IMF staff estimates
Figure 15.
Figure 15.

South Africa: Health

Citation: IMF Staff Country Reports 2018, 247; 10.5089/9781484371473.002.A002

Sources: Stats SA, NIDS, and IMF staff estimates
Table 4.

South Africa: Impact of Social Expenditure on Inequality

article image
Source: IMF staff estimates

16. The impact of fiscal policy on income inequality is among the largest in emerging markets. The Gini coefficient is reduced on average by 0.2 or about 30 percent of the market income in the four NIDS waves. This compares favorably with an average reduction of 0.06 in a sample of countries analyzed in the Commitment to Equity Initiative (World Bank (2014)) which includes emerging markets such as Brazil, Indonesia, Mexico, and Peru. Despite the favorable impact of fiscal policy on income inequality, South Africa remained the country with the highest Gini for the final income in the sample given the highly unequal market distribution. The estimates below suggest that inequality, as measured by market income, worsened with the global financial crisis, to then go back broadly to still very high pre-global financial crisis levels.

Table 5.

South Africa: Combined Impact of the Tax System and Social Expenditure on Inequality

article image
Source: IMF staff estimates

17. Several factors account for the significant impact of fiscal policy on inequality in South Africa. The 2017 Fiscal Monitor and World Bank (2014) suggest the following factors account for the favorable outcome: (1) most other EMs spend less in direct cash transfers, which is facilitated by favorable revenue levels in South Africa; (2) direct cash transfers are relatively well targeted given that categorical targeting in South Africa works well as the majority of the poor are children and the old; (3) given how unequal the market distribution of income is compared to other EMs, education and health spending tends to be better targeted to the lower deciles of the income distribution, and contributes to reduce the Gini more; and (4) South Africa collects more of its tax revenue through direct taxes, with a progressive PIT as its main tax instrument, while most other EMs do so with indirect taxes, which tend to be either slightly progressive or regressive.

18. South Africa’s spending on targeted transfers is cost effective in reducing income inequality compared to other EMs. Spending on targeted social grants for about 3 percent of GDP during the years of the NIDS waves achieved a reduction in the Gini coefficient averaging 0.1 (i.e. difference between the net market income and disposable income columns), which is more than what most other countries in the Commitment to Equity sample achieved with all the benefits they provide.

D. Policy Considerations

19. The findings stress the importance of achieving inclusive growth to reduce inequality. Given the already significant fiscal spending used to address inequality, the limited fiscal space, and the substantial role of fiscal policy in reducing income inequality in South Africa compared to other EMs, the scope to scale up public resources to address inequality is severely constrained. The highly unequal market income distribution and the marked difference in inequality reduction between the periods when growth was relatively strong on a sustained basis and the most recent period of protracted low growth underscore the critical importance of increasing growth and job creation to reduce income inequality.

20. There is room to improve the efficiency of fiscal policy to reduce income inequality in the currently constrained resource environment. The options include:

  • Reconsidering the reduction in cost recovery in tertiary education. While tertiary education subsidies are relatively progressive in South Africa given a highly unequal market income distribution compared to other EMs, they are a relatively inefficient spending instrument to reduce inequality. Moreover, the benefits of tertiary education are typically captured by the students in the form of higher wages, so those that can afford to pay for their studies will do so without any government intervention. Focusing the limited government resources on those that are qualified but without the means to study would likely allow to provide better support at a lower cost and achieve better results.

  • Improving the efficiency of spending in education and health. South Africa seems to be spending more resources but getting weaker outcomes in education and health than other countries that spend less. Given that basic education expenditures (i.e. primary and secondary) and health expenditure are progressive in absolute terms, the poor benefit disproportionately from them. Increasing the quality of basic education and health and improving outcomes would likely contribute to increase their impact in reducing inequality.

  • Seeking opportunities to leverage social grants. This refers to exploring opportunities to use social grants to incentivize beneficiaries to engage in desirable behaviors that are conducive to improving their level of education and health. For example, in Brazil and Mexico, cash transfers are successfully linked to enrollment of family members in school and attendance at nutrition and health clinics. Such links help reduce human capital inequalities (and thus future income inequalities) and current income inequalities.

21. Opportunities to achieve significant progressivity gains from the design of the personal income tax going forward will likely be more difficult. This is because of the limited buoyancy observed in recent years, and the fact that the highest marginal tax rate for higher incomes has been increased to 45 percent in the 2017 budget. Such rate is already reaching the range where the literature suggests that revenues from high income individuals are maximized (50–60 percent) (see IMF (2014)). Further increases may provoke increased tax avoidance efforts, which may result in lower overall revenue, especially in a context of significant capital mobility. Nevertheless, there may be some opportunities to improve progressivity by seeking additional revenue from property taxes, which have favorable distributional properties. Addressing weaknesses in tax administration could also provide additional revenues to finance progressive expenditure.

22. Policy responses to spatial inequality need to balance the need to reduce disparities against the need to preserve gains from agglomeration. Kanbur and Venables (2005) suggest a two-pronged strategy of de-concentration of economic activity through the development of human and physical infrastructure, complemented with the removal of impediments of migration of individuals and households to areas of high and rising well-being. For South Africa, this translates into allowing households with limited access to markets and public services to commute to the centers of economic activity and share the benefits of growth. The role of network industries is critical in this regard. Alleviating constraints to urban land and housing will also facilitate the migration of workers from rural areas to the economic centers.

23. Finally, interventions aimed at addressing apartheid legacies need to acknowledge potential unintended perverse consequences. Regulatory and policy uncertainty arising from the implementation of the otherwise well-intended government policies, such as the Black Economic Empowerment Program (BEEP) and land reform, need to balance the negative impact on private investment and growth. These policies can unintentionally contribute to a deterioration in distributional indices by worsening the wage premium or creating incentives for rent-seeking.

Annex I. Definitions of Progressivity, Data Sources, and Caveats

1. Progressivity is measured by comparing the share of a specific tax/benefit that is collected/paid by each decile of the income distribution with the share of total income each decile receives and the 45-degree line. Relative and absolute progressivity of a tax or a benefit is defined as follows:

  • A tax is progressive/regressive in relative terms if the cumulative share of the tax paid by the households accounting for X percent of households is lower/higher than their share in income as show by the market income curve. A tax is progressive/regressive in absolute terms if the cumulative share of the tax paid by X percent of the households is less/more than X percent.

  • A benefit is progressive/regressive in relative terms if the cumulative share of the benefit received by the households accounting for X percent of households is higher/lower than their share in income as shown by the market income curve. A benefit is progressive/regressive in absolute terms if the cumulative share of the benefit received by X percent of the households is more/less than X percent.

2. The National Income Dynamics Survey (NIDS) waves and fiscal data from the National Treasury and the SARB are the main data sources. The NIDS cover four 12 months periods in 2008, 2010, 2012, and 2014 (i.e., Waves 1 through 4, respectively) and contains data on household income, expenditures, cash benefits, and utilization of education and health services. The information is complemented with budget spending data primarily to estimate the value of certain benefits such as the average amount paid for several grant types, the average spending on several levels of education and health. Since there is limited information on the amounts paid in taxes in the surveys, taxes such as the personal income tax are computed based on the corresponding tax schedule for the given year obtained from the corresponding national budgets.

3. The methodology is subject to important caveats. For health, education, and other free services more generally, the value of government services does not consider quality. Therefore, the value to recipients may be considerably less than the cost to the government if quality is poor. A similar concern is valid for cash benefits if only a fraction were to get to the intended beneficiaries. The accounting approach used does not ponder behavioral responses that changes in taxes and spending may trigger among individuals or households. Due to data and methodological constraints, the analysis also excludes important taxes (i.e., corporate income, international trade, and property taxes) and spending categories (i.e., infrastructure investments and the certain grants which reach a lower number of beneficiaries such as the care dependency grant).

References

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1

Prepared by Alejandro Simone and Vimal Thakoor; reviewed by Ana Lucía Coronel.

2

Including discouraged workers, the rates increase to 36.4 percent and 67.4 percent for the youth.

3

The coverage of the vulnerable population by the social grants has been expanded by lowering and equalizing the age in which men become eligible for the grant from 65 to 60 years old between FY 2008/09 and 2010/11, and increasing the age until which children can get a child grant from 14 years old in FY 2008/09 to 18 years of age by the 2012/13 budget.

South Africa: Selected Issues
Author: International Monetary Fund. African Dept.