Table 4: Comparing the Gini coefficient for all households vs. households excluding the top 1% of income households

Where this is the case, the analysis uses other proxies to segment the market. Examples of these alternative proxies include Living Standard Measures (or LSMs, which themselves reflect key dimensions of inequality such as access to services), area or area type, race as well as education levels. The unit of analysis relative to which inequality is calculated varies throughout the report.

Excluding the top 1% from income inequality calculations only marginally reduces overall inequality. The contribution of the top 1% of the income distribution is a topic of global relevance and concern.7 Removing the top 1% of the income distribution’s income from calculations of the Gini coefficient can indicate to what extent overall income inequality is driven by the phenomenon of the “top 1%”, as the literature often refers to this group. As shown in Table 4, excluding this group’s income from the Gini only marginally decreases overall inequality. It generally lowers the Gini from 0.60 and above to just below, although using GHS income data lowers it to as low as 0.52 – which is still a very high level of inequality. Given that good data on the total population’s wealth is not accessible, this exercise was not repeated with wealth data.

Table 4: Comparing the Gini coefficient for all households vs. households excluding the top 1% of income households Sources: NIDS and GHS

The evidence on whether the inclusion of older generations (>65 years) in income inequality calculations lead to higher inequality is inconclusive. Older generations have theoretically had more time to accumulate wealth from non-labour market income in the form of both financial and non-financial assets such as housing. However, different older population groups in South Africa would also have been more heavily subject to the inequalities generated by apartheid. Depending on the data source, the Gini coefficient for the population aged 65 and below can be higher or lower than the Gini coefficient for the population older than 65 years (Table 5). Data from the GHS generally shows a marginally lower trend in inequality for the population aged 65 and younger, while data from NIDS shows higher inequality among the population 65 and younger. The inter-group Gini coefficient for different South African race groups (Table 6) shows much higher inequality among Black and Coloured South Africans compared to White and Indian/Asian South Africans. Specifically for Black South Africans aged 65 years and younger, there also was a trend of increasing inequality in later years. Among Coloured South Africans, the inter-group Gini coefficient seems to have decreased between 2015 and 2019. This raises questions about which income distribution brackets within the group experienced increased or decreased incomes. Further investigation is required to answer this question.