The skew distribution is confirmed by a Gini coefficient of 0.67 for South Africa, which is one of the highest in the world. This coefficient provides an indication of the level at which income is equally or unequally distributed throughout a population. A Gini coefficient of ‘1′ is an indication of complete income inequality with one person having all the income, while a Gini coefficient of ‘0′ is indicative of complete equality with everybody earning an equal income.

Table 1 – Personal Income by Province and Income Group (R Million), 2009

Table 2 – Adult Population by Province and Income Group, 2009

Metro dwellers earn more

Table 1 provides a breakdown of personal income by province and income group in monetary terms. About R1 102 billion (65.6% of total personal income) is earned in three provinces, namely Gauteng, KwaZulu-Natal and the Western Cape. A total of R661bn (39.4% of total personal income) is earned by people earning R300 000 pa or more.

The results of the study also show that there is a strong relationship between work status, area of residence and income. Workers residing in metropolitan areas are the biggest income earners, while the rural unemployed constitute the poorest of the poor. The results further show that about 46.2% of all income in South Africa during 2009 accrued to metro dwellers, while 86.9% of all income accrued to urban dwellers (metro and non-metro), leaving rural dwellers with 13.1% of all income. When area of residence is taken into account, workers with the highest probability of earning R300 000+ pa are those in metro areas, who constitute 66.1% of R750 000+ pa earners.

Middle-aged adults earn more

The 35-49-year-old age group earns 44% of total personal income in South Africa. This percentage is even higher with regard to the R300 000-R500 000 income group, where nearly 52% of all people in this income group fall within the 35-49-year age group. This group also constitutes 51.7 % of all R300 000-R500 000 pa income earners, 50.1% of R500 000-R750 000 pa income earners and 53.1% of R750 000+ income earners.

There appears to be a strong correlation in the data between ageing and personal income up to the 35-49-year-old age group, after which this relationship weakens, leaving many older people with insufficient funds for daily expenses and for retirement.

Education predicator of success

The analysis also shows that educational status is a strong predictor of income. People with a secondary education or lower earn the bulk of personal income in the R0-R50 000 and R50 000-R100 000 income categories. People with a secondary or tertiary education earn the bulk of income in the R300 000-R500 000 category, while people with a tertiary qualification earn the bulk of personal income in the R750 000+ category.

The strong correlation between education and income in South Africa is also evident. Whereas 89.4% of adults with no schooling earn R50 000 pa or less, only 27.4% of tertiary-level educated adults are in this income group. Conversely, while only 0.2% of adults with no schooling earn R300 000+ pa, 25.6% of adults with a tertiary qualification earn such an income.

Other research results

More detailed marketing data on the various income groups was also generated to describe the different income groups. An example of such a description of the different income groups was in respect of the use of financial instruments by different income groups (see table 3).

Table 3 – Percentage of Adult Population Making Use of Financial Instruments by Income Group

*Mzansi accounts resulted from the financial sector charter encouraging banks to provide low-income transactional accounts with the aim of ensuring broader access to financial services.

Higher income, higher policy take-up

It appears from table 3 that there is a strong relationship between income group and percentage usage of certain financial instruments. Strong positive relationships are found between income and medical aid scheme membership, debit cards, cheque accounts, unit trusts, credit cards, investment accounts, share portfolios, petrol/garage cards, home loans and vehicle finance agreements.

As regards policy take-up by income group, the results of the study show that while 82.4% of adults in the lowest income group did not make use of any policies, only about 14% of adults in the highest income group did not make use of any policy instruments. The policy instruments mostly used by the lowest income group were firstly funeral insurance, followed by life cover policies.

Internet usage

Turning towards Internet usage patterns by income group, it appears that a very high percentage of high income (R750 000+ pa) earners make commercial use of the Internet by making reservations on the Internet (36.4%), and engaging in Internet shopping (27.0%) and banking (40.6%). Generally, there appears to be a very strong positive relationship between Internet usage and income group with the R300 000 pa income earners being high percentage Internet users.

Entrepreneurs earn more

Turning towards the employment status of adults in different income groups it appears that income shows a strong negative correlation with unemployment, but has a strong positive correlation with full-time employment. It is interesting to note that there is no correlation between income and part-time employment since the majority of part-time jobs are not high-paying jobs.

A very interesting trend in the data is the strong correlation between income and self-employment: While 34.5% of all employed earning R300 000-R500 000 pa were self-employed, 62.3% of all employed earning R750 000+ pa were self-employed.

This 62-page research report no 387 is available from the Bureau of Market Research, PO Box 392, UNISA 0003.