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Economy
Thursday, April 1, 2010
BMR Research Report

Negative Gross Domestic Product and employment growth during 2009 meant that personal incomes went down in real terms by 1.2%.

This is according to Carel van Aardt (Research Director) and Marietjie Coetzee (Senior Computer Scientist) of the Bureau of Market Research in their Research Report no 387 at the University of South Africa.
Total personal income increased to R1 681 billion in 2009, which was a 5.9 % increase in nominal terms during the period 2008 to 2009, before taking inflation into account.
The first table provides a breakdown of personal income by province and income group. 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 R661 billion (39.4 % of total personal income) is earned by people earning R300 000 pa or more. The fact that 1 189 472 income earners (constituting 3.8 % of the adult population) earn 39.4 % of total personal income is further proof that South Africa has a very skewed income distribution. This is confirmed by a Gini coefficient of 0.65 for South Africa (2005), which is one of the highest in the world (see accompanying box). It was better in 1994 (0.593).


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. Nearly 44 % of total personal income in South Africa is earned by the 35–49-year-old age group. This percentage is even higher with regard to the R300k–R500k income group, where nearly 52 % of all people in this group fall are between the ages of 35–49-years old. There appears to be a strong correlation in the data between ageing and personal income up to the 35-49-year-old age group, where after this relationship weakens, leaving many older people with insufficient funds for daily expenses and for retirement.
The analysis also shows that educational status is a strong predictor of income. Whereas the bulk of personal income in the R0–R50k and R50k–R100k income categories is earned by people with a secondary education or lower, the bulk of income in the R300k–R500k category is earned by people with a secondary or tertiary education, while the bulk of personal income in the R750k+ category is earned by people with a tertiary qualification.
The strong correlation between education and income in South Africa is also evident in the following figures. Whereas 89.4 % of adults with no schooling earn R50k 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 R300k+ pa, 25.6 % of adults with a tertiary qualification earn such an income.


Turning towards the number of adults in each of the income groups, it appears from the second table that while only 1 189 472 adults (or 3.8 %) are in the R300k+ income groups, about 6 492 589 adults (or 20.8 %) are in the R50k–R300k (or emerging middle class) income groups, leaving about 23 503 512 adults (or 75.4 % of adults) earning less than R50k pa.
With regard to the relationship between age group and income, the 35–49-year age group is the biggest personal income earning group in South Africa. It constitute 51.7 % of all R300k–R500k pa income earners, 50.1 % of R500k–R750k pa income earners and 53.1 % of R750k+ income earners.

Turning to work status, there is a preponderance of those in the higher income categories, with workers constituting 91.8 % of all adults earning R300k–R500k pa, 92.5 % of all adults earning R500k–R750k pa and 94.0 % of all adults earning R750k+ pa. When area of residence is taken into account, workers with the highest probability of earning R300k+ pa are those in metro areas, who constitute 66.1 % of R750k+ pa earners.
Besides the information provided in this study with respect to 2009 personal income patterns as described above, more detailed marketing data pertaining to the various income groups used 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).

Life insurance

It appears from this table 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.
Turning towards Internet usage patterns by income group, it appears that a very high percentage of high income (R750k+ pa) earners make commercial use of the Internet by making reservations (36.4 %), 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 R300k pa income earners being high percentage Internet users.
Turning towards the employment status of adults in different 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 R300k–R500k pa were self-employed, 62.3 % of all employed earning R750k+ pa were self-employed.
Note: The report, ’Personal income patterns and profiles for South Africa, 2009’, consists of 62 pages, and is available from the Bureau of Market Research, PO Box 392, UNISA 0003.

The 'Gini' Coefficient


The ‘Gini coefficent’ is a somewhat complicated set of mathematical computations established by an Italian statistician by the name of Corrado Gini. It is measured on a scale 0 to 1, although is sometimes represented as a multiple of 100 in a range of 0 to a 100.
Essentially it seeks to reflect the relative equality amongst a group, usually of a country, with ‘0’ meaning complete equality and ‘1’ meaning total and complete inequality, and mainly in terms of household income. So South Africa’s 0.65 is really bad. Indeed, only one other country in the world (Namibia) ranked worse. See table four for some examples.

 

Copyright © Insurance Times and Investments® Vol:23.4 1st April, 2010
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