Earning and spending in South Africa
Selected findings of the 1995
income and expenditure survey
Statistics South Africa
1997
Dr FM Orkin
Head
Published byStatistics South Africa
Private Bag X44
Pretoria
0001
ISBN 0-621-27722-3
The detailed statistical tables on which this publication is based are available as ‘Income and expenditure of households’, Stats SA statistical release P0111 (South Africa as a whole), and P0111.1 to P0111.9 (the nine provinces). These can be ordered from Central Statistics, Pretoria in both printed and electronic format.
© Copyright, 1999.
Material from this publication may be applied, processed or reproduced, provided Statistics South Africa (Stats SA) is acknowledged as the source of the original data.
Author: Dr Ros Hirschowitz
Chief Director of Research and Development, Stats SA.
Statistics South Africa
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Users enquiries: (012) 310-8600 Fax: (012) 310-8500 Main switchboard: (012) 310-8911 E-mail: info@Stats SA.pwv.gov.za Stats SA website: http://www.Stats SA.gov.za
| Preliminary estimates of the size of the
South African population, based on the population census conducted in October 1996, were
issued by Stats SA in July 1997. These indicate that there are fewer people (37,9 million)
in the country, and that urbanisation (55%) has been more rapid, than was previously
realised. The new census numbers may have an effect on some of the weights and raising factors that were used in this report, since these are presently based on projections of population and household size to 1995, using the 1991 census estimates as baseline. The new Stats SA management believes that the model used to adjust the actual count of people found in the 1991 census probably overestimated population growth rates in the country, hence overestimating the size of the population and number of households. The number of people, the number of households and the percentages reported here will therefore probably need to be modified at a later date when the Stats SA has more complete information about household size and distribution of the population by race and age from Census ‘96. Nevertheless, these overall trends should be accepted as indicative of the broad income and expenditure patterns of South African households during 1995. |
Contents
Section 1: Introduction
Background 1 Reasons for conducting an income and expenditure survey 2 Focus of this report 5
Section 2: The main findings regarding incomes
Introduction 9 Average annual household incomes in 1995 9 Income distribution 14 Measures of income inequality 25 Summary 28
Section 3: The main findings regarding expenditure
Introduction 29 Average annual household expenditure 29 Expenditure by households in each expenditure quintile 30 Summary 40
Section 4: Comparing the surveys of 1990 and 1995
Introduction 43 Making the two surveys comparable 43 Income comparisons 44 Expenditure comparisons 47 Measures of income inequality 48 Summary 50
Section 5: Summary and conclusions
Income inequalities 51 Expenditure patterns 52 Comparisons between 1990 and 1995 53 Conclusions 53
References 54
Section 1
Introduction
Background
Political democracy in South Africa is, after many years of struggle, at last a reality. The new constitution (Act 108 of 1996) is founded on a set of values which embody non-racialism, non-sexism, respect for human dignity, equality, human rights and freedom for all. Explicit discrimination and denial of human rights, which formed the basis of the apartheid past, has been rejected by most South Africans.
Despite these recent advances in democracy, socio-economic deprivation and profound contrasts in life circumstances along racial, urban-rural and gender divides, persist. Although South Africa is a middle-level income country, comparable with Brazil, Chile, Malaysia, Poland, Thailand and Venezuela (World Bank/SALDRU, 1995), it is characterised by gross inequalities, partially the legacy of apartheid policies.
The government is committed to improving the life circumstances and quality of life of all South Africans, particularly those who were previously disadvantaged. To meet this challenge, and to plan and implement change, a variety of role-payers – government, the private sector, trade unions and other institutions of civil society – require accurate information on a range of aspects of South African life. The Central Statistical Service (CSS), with its vast numbers of data collections, is the most appropriate agency to provide such data.
This CSS report addresses the need for information of a particular type. It is a summary of the main findings of the October 1995 income and expenditure survey (IES), and describes the large differences in income distribution and expenditure patterns among South African households.[In this report, the term household refers to all people who live together for at least four days a week, who eat together and who share resources.]
Reasons for conducting an income and expenditure survey
There are numerous ways of collecting information on household income and expenditure. For example, people in selected households may be asked to keep receipts of all their purchases, or keep a diary of all expenditure over a specified time period. In addition to, or instead of these methods, a household survey can be conducted. Due to the relatively low level of literacy in South Africa, and the associated difficulty of record-keeping for many people, the CSS chose the route of utilising households for its October 1995 income and expenditure survey.
Through the IES, the CSS determined the proportion of expenditure in an average household, or in sub-groups of various types of households, that went towards purchasing each of a variety of goods and services, such as food, housing, transport and recreation. On the basis of this information, weights for each item of expenditure, based on household averages, or on other classification variables, were calculated.
Calculation of the CPI
The main purpose of the 1995 IES was to collect base-line information on household income and expenditure patterns for re-weighting the consumer price index (CPI).
In South Africa, the CPI is generally calculated in two stages.
Stage one
Firstly, information is collected from households in which questions are asked on:
Thereafter, the total expenditure of all households in the sample during the specified time period is raised to represent expenditure in all households in the country. From this new total, the CSS calculates the average annual expenditure per commodity or service, per household.
The CSS can also calculate the total annual expenditure, and average annual expenditure for each type of commodity or service, for various sub-groups of households – very low, low, middle, high and very high expenditure groups, for example. This can also be done for households in diverse geographic areas in different parts of the country, which can be broken down into metropolitan (metro), urban and rural areas.
In the past, the IES was conducted only among households in what were regarded as the 12 main urban areas of South Africa. [The 12 areas are the Cape Peninsula, Port Elizabeth-Uitenhage, East London, Kimberley, Bloemfontein, Free State Goldfields (Welkom-Virginia-Odendaalsrus), Durban-Pinetown, Pietermaritzburg, Pretoria-Centurion-Akasia, Witwatersrand, Vaal Triangle (Vereeniging-Van der Bijl Park-Sasolburg) and Klerksdorp-Stilfontein-Orkney.] Smaller towns and rural areas were excluded from the sample. But, in 1995, the whole country was included in the survey for the first time. This is discussed in a later section.
Stage two
In the second stage of calculating the CPI, the CSS collects the prices of all items
of expenditure from different outlets.
In the past, the prices of goods and services were obtained in selected retail outlets in the same 12 main urban areas of the country where the household survey was conducted, but these outlets have now been extended, as discussed in the following section.[Two extra urban areas were added in 1994 for the collection of retail prices, even though no information was available on buying patterns in these areas, to ensure coverage of at least one retail outlet in all of the nine new provinces of South Africa. The new areas are Nelspruit, Witbank and Pietersburg, to cover Mpumalanga and the Northern Province.]
Changes in the calculation of the CPI, based on the 1995 IES
The CSS has recently introduced, and is continuing to initiate, a series of changes in the calculation of the CPI, in both stage one and stage two.
Stage one changes
The 1995 IES differed from previous household surveys of its kind in South Africa,
since it was a countrywide survey covering metro, urban and rural areas, rather
than a more limited sub-set of households in 12 major metro/urban areas of the country
previously referred to. By extending the sample to include the whole country, a clearer
indication of the life circumstances of all South Africans in all parts of
the country can now be inferred.
Previously, only three income categories were used for the calculation of the CPI, with the lowest category including 78% of African households in the 12 main urban areas. In the 1995 IES, five approximately equal income groups (very low, low, middle, high and very high), each containing approximately 20% of households, and five expenditure groups, based on quintiles,[Quintiles divide a data set into five approximately equal groups, each group containing about 20% of the total number of households.] were derived. For reasons which will appear later in this report, income quintiles were used to describe differences in the distribution of income among various categories of households, for example households in urban versus households in rural areas; while expenditure quintiles were used to identify expenditure patterns among households falling into very low, low, middle, high and very high expenditure categories.
The effect of these changes in the 1995 IES sample, and the increase in the number of income categories, is that the country now has a clearer indication of the buying patterns of households ranging from the very poor to the very wealthy, living in metro, urban and rural areas.
Stage two changes
In the collection of information from retail outlets, the CSS now includes small
towns. Since March 1997, it has published an inflation rate for small-town areas in the
provinces, in addition to the major urban areas covered hitherto. This has involved a 50%
increase in the number of price-questionnaires issued and processed.
The importance of calculating a rural CPI
The CSS cannot, at present, collect prices from outlets in rural areas: this type of collection is very expensive and the necessary funding is not available. However, if finance can be raised, the CSS plans to measure and publish a rural CPI. As a large proportion of South Africa’s households are situated in non-urban areas, this is of obvious importance. A rural CPI will enable decision-makers to obtain as complete a picture as possible of income and expenditure patterns, and the effects of inflation, in all parts of the country, rather than just in urban areas, as was previously the case.
This is of major importance: although households in non-urban areas may spend relatively little compared to those in urban areas, inflation may have a greater effect on the ability of rural households to survive where incomes do not keep up with inflation. More extensive information on spending patterns in rural areas will facilitate planning, programme development and poverty monitoring at all levels of government – national, provincial and local.
The focus of this report
In describing the findings of the 1995 IES, this report paints a picture of how income is distributed in South Africa by using the five income quintiles. It also examines expenditure patterns in households falling into very low, low, middle, high and very high expenditure groups.
The race and gender of the head of household,[The head of household is defined here as the person who is the main breadwinner in the household, or if the main breadwinner does not live in the household, for example, if he or she is a migrant worker, the person who assumes responsibility for decision-making in the household] and other variables such as province and the location of the household in an urban or non-urban milieu, are used as explanatory variables to describe income and expenditure patterns.
The research process
The questionnaire design
The 1995 IES questionnaire, in the same vein as the previous one, contains questions
about all sources of household income. It also covers the purchase of a wide variety of
products and services, including new items such as cellular telephones.
Drawing a sample
Two surveys, namely the CSS’s annual October household survey (OHS) and
the IES were run concurrently during October 1995.
The fieldwork
Throughout South Africa, information was collected through face-to-face interviews in
the 30 000 households which formed the sample. Field workers first administered the
OHS questionnaire, and returned at a slightly later date to administer the questionnaire
for the IES.
Data capture
Data capture of both the 1995 OHS and the IES took place at the head office of the
CSS. Where possible, this process involved linking the information contained in the 1995
OHS with that contained in the IES.
Raising the sample to the population
Data collected on households were raised to the estimated number of households in the
country in the various provinces, according to the proportions found in urban and
non-urban areas in the 1991 census. All further discussions in this report are based on
these raised figures.
Calculating new weights for the CPI
For the sample as a whole, weights were allocated for each item of expenditure
according to the proportion of annual disbursements for that particular item by the
average household. In addition, the same procedure was followed for households in each
quintile.
Identifying income and expenditure
quintiles
Two different sets of quintiles were obtained – those based on annual household
income and those based on annual household expenditure.
To calculate income quintiles, information obtained on all sources of annual income for each household was used. This total annual income was divided, as closely as possible, into five groups or income categories, as indicated in Table 1. To calculate annual expenditure quintiles, the same procedure was used.
Table 1: Annual income and expenditure quintiles
Quintile 5 (bottom quintile) Range |
Quintile 4 Range |
Quintile 3 Range |
Quintile 2 Range |
Quintile 1 (top quintile) Range |
|
| Income | R400-6 868 |
R6 869-12 660 |
R12 691-23 940 |
R23 941-52 800 |
R52 801 + |
| Expenditure | R332-6 340 |
R6 341-11 589 |
R11 590-21 908 |
R21 909-49 497 |
R49 498 + |
Undeclared income and expenditure in the process of identifying quintiles was dealt with in the following way:
Data analysis and report writing
After data processing, a series of tables and cross-tabulations were obtained. This
summary report is based on those tables.
Raising factors and weights used for
analysis of the 1995 IES
As already indicated, estimates using the 1991 census formed the basis for the
calculation of raising factors and weights.
However, preliminary estimates based on the October 1996 population census have shown that the population of 37,9 million people in South Africa is smaller, and urbanisation more rapid, than was previously thought. These preliminary estimates are based on a limited set of variables from Census ‘96. For example, the CSS does not as yet know the number of households in the country, only the number of questionnaires that were completed during Census ‘96. Since this particular data set looks specifically at household incomes and expenditure, it is not at this stage possible to take the new 1996 census-based population estimates into account. The numbers and percentages in this report should, therefore, be regarded as indicative of patterns and trends, rather than as definitive numbers or proportions.
One in twelve (8%) urban male-headed households are found in the bottom income category, compared with about one in four (26%) non-urban, male-headed households.
One in five (19%) African, female-headed households in urban areas are in the bottom income category, compared with as many as almost four in every ten (37%) female-headed households in non-urban areas.
Income among female-headed non-urban African households is extremely low. For example, 37% are in the bottom income quintile, and a further 28% are in the second lowest quintile.
A very small proportion of African, non-urban, male-headed (7%) and female-headed (3%) households are in the top income category.
Among Africans, non-urban households are the poorest in the country. There are proportionately fewer female-headed households in urban areas in the lower income categories, compared to male-headed non-urban households.
Coloured households
In households where the head is coloured, a similar pattern emerges although, overall, these households tend to have higher incomes than African households.
In urban areas, one in twenty (5%) coloured, male-headed households are in the bottom income category, compared with one in seven (15%) coloured, female-headed households.
In non-urban areas, one in five (20%) coloured, male-headed households falls into the bottom income category, compared with one in three (31%) female-headed ones. These figures should, however, be treated with caution, because of the small sample size.
A large proportion of non-urban, male-headed, coloured households (39%) are found in the second lowest income category, while a very small proportion (3%) is in the top category.
White households
White, male-headed households are amongst the most affluent in the country, while white, female-headed households are less affluent.
About three-quarters of white, male-headed households living in both urban (73%) and non-urban (75%) areas are found in the top income category.
Nevertheless, a substantial proportion of white, female-headed households in both urban (30%) and non-urban (17%) areas are found in the three bottom income quintiles, with relatively few male headed households (7% in non-urban, and 8% in urban areas) in these three categories.
This demonstrates that not only absolute, but also relative, comparisons are important considerations in understanding South African income distributions. White households generally have the highest incomes in the country, but within the category of white households, there are significant gender inequalities. White, female-headed households in both urban and non-urban areas are relatively well-off, compared with African and coloured households in these areas. But when compared with white, male-headed households, they are relatively poorer.
Differences in income distributions by province
Figure 6 demonstrates the uneven distribution of income within the provinces.
Proportionately more households are found in the bottom income category in the Eastern Cape (32%) and the Free State (31%), followed by the Northern Province (26%), the North West (24%) and the Northern Cape (23%). There are proportionately even fewer households in the bottom income category in Mpumalanga (17%) and KwaZulu-Natal (12%), while the smallest proportion of the poorest households are found in the Western Cape (6%) and Gauteng (5%).
On the other hand, proportionately more households in the Western Cape (30%) and Gauteng (42%) are found in the top income categories while there are relatively few households in the top income category in the other seven provinces: the Eastern Cape (11%), the Free State (13%), the North West (14%), the Northern Cape (14%), the Northern Province (15%), KwaZulu-Natal (19%) and Mpumalanga (12%).
Income distribution by gender and area within each province
In Table 5, we examine income distribution differences among male-headed and female-headed households in urban and non-urban areas in each province, starting with the province that has the largest proportion of households in the lowest income category, and ending with the province that has the smallest.
Table 5: Income distribution by gender and urban/non-urban areas within each province
| Income quintile by province | Non-urban female | Non-urban male | Urban female | Urban male | Total |
%* |
%* |
%* |
%* |
%* |
|
| Eastern Cape: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
2 7 11 28 53 100 |
5 9 20 35 31 100 |
10 21 22 22 25 100 |
31 23 19 17 11 100 |
11 13 17 27 32 100 |
| Free State: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
2 5 7 27 60 100 |
4 5 14 30 47 100 |
6 14 23 28 29 100 |
27 25 18 20 10 100 |
13 14 17 25 31 100 |
| Northern Province: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
6 14 18 24 37 100 |
17 18 20 24 22 100 |
16 25 27 21 11 100 |
40 28 15 9 8 100 |
15 18 19 23 26 100 |
| North West: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
3 11 16 32 38 100 |
5 11 18 32 34 100 |
11 23 23 21 22 100 |
33 25 24 12 6 100 |
14 17 20 25 24 100 |
| Northern Cape: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
7 15 18 22 38 100 |
13 9 12 34 34 100 |
6 16 27 27 25 100 |
21 23 27 17 13 100 |
14 17 22 24 23 100 |
| Mpumalanga: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
3 15 31 28 23 100 |
9 18 30 24 19 100 |
14 25 23 18 20 100 |
33 29 19 11 8 100 |
12 19 28 22 17 100 |
| KwaZulu-Natal: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
4 17 26 30 22 100 |
7 21 31 26 15 100 |
22 29 26 16 7 100 |
42 31 16 8 3 100 |
19 24 25 20 12 100 |
| Western Cape: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
30 24 18 20 8 100 |
12 14 33 32 9 100 |
20 28 26 15 11 100 |
38 29 20 10 3 100 |
30 27 23 14 6 100 |
| Gauteng: Quintile 1 (top) Quintile 2 Quintile 3 Quintile 4 Quintile 5 (bottom) Total |
19 18 16 26 21 100 |
32 12 25 21 9 100 |
27 32 22 12 7 100 |
50 25 16 7 3 100 |
42 26 18 10 5 100 |
* Due to rounding off, figures may not always add up to exactly 100%
Eastern Cape
Table 5 shows that, in the Eastern Cape, incomes are highly unequally distributed by gender of the head of household and by urban or non-urban place of residence.
In total, 32% of households are in the bottom income quintile, and 27% are in the second lowest, whilst only 11% are in the top income quintile.
More than half (53%) of all non-urban, female-headed households are in the bottom income category in this province, as against one in ten (11%) urban, male-headed households.
At the other extreme, three in every ten (31%) urban male-headed households are in the top income category, as against one in every fifty (2%) non-urban, female headed households.
Free State
Incomes are even more unequally distributed by gender and by urban versus non-urban place of residence in the Free State than they are in the Eastern Cape.
Table 5 shows that, in total, 31% of households are in the lowest income quintile, and 25% in the second lowest, whilst only 13% are in the top income quintile in this province.
However, as many as six in every ten (60%) non-urban, female-headed households are in the bottom income category in the Free State, as against one in ten (10%) urban, male-headed households.
On the other hand, just over a quarter (27%) of urban male-headed households are in the top income category, as against one in every fifty (2%) non-urban, female headed, and one in every twenty-five (4%) non-urban, male-headed households.
When comparing all provinces, income distribution in the Free State is the most unequal in the country.
The Northern Province
There is a similar pattern of income distribution in the Northern Province as in the Eastern Cape.
Altogether, 26% of households are in the lowest income quintile, and 23% in the second lowest, whilst only 15% are in the top income quintile.
Almost four in every ten (37%) non-urban, female-headed households are in the bottom income category, as against one in thirteen (8%) urban, male-headed households.
Four in every ten (40%) urban male-headed households are in the top income category, as against one in every eighteen (6%) non-urban, female headed, and one in every six (17%) non-urban, male-headed households.
The North West Province
Income distribution in the North West Province, and inequalities in incomes, are very similar to the Northern Province.
In total, 49% of households are in the lowest two quintiles, and only 14% are in the top income quintile.
However, as many as almost four in every ten (38%) non-urban, female-headed households are in the bottom income category, as against one in eighteen (6%) urban, male-headed households.
The Northern Cape
Almost half of all households (47%) in the Northern Cape are in the lowest two quintiles.
In common with all other provinces, non-urban, female-headed households are the poorest in this province, with about four in every ten of these households (38%) falling into the bottom income category.
Also in common with the other provinces, urban male-headed households are the most affluent, with 21% falling into the top income category.
Mpumalanga
Female-headed households in non-urban areas in this province tend to be relatively better off than their counterparts in most other non-urban areas, since fewer than one in four (23%) fall into the bottom income category.
Male-headed, urban households continue to be the most affluent, with 33% in the top income quintile.
KwaZulu-Natal
A relatively small proportion of all households in KwaZulu-Natal (12%) are in the bottom income quintile.
Instead, incomes tend to cluster into the third (25%) and fourth (24%) quintiles.
Both female- and male-headed, non-urban households are relatively well off compared to households in non-urban areas in other provinces, with 22% of female- and 15% of male-headed households being found in the bottom quintile.
The Western Cape
The Western Cape is relatively wealthy, with only 6% of households falling into the bottom, and 14% in the second-lowest income quintile. On the other hand, one in every three (30%) households are in the top income quintile.
Female-headed households in non-urban areas, whilst remaining the poorest in the province, are relatively less poor than their counterparts in other provinces. There are only 8% in the bottom income category. A relatively large proportion (30%) of female-headed households in non-urban areas are, for the first time, found in the top income quintile.
Gauteng
Findings regarding non-urban distributions of household income in Gauteng should be treated with caution, since the income distribution patterns among both female- and male-headed households in non-urban areas are based on a small number of households in the sample. Nevertheless, the picture that emerges is consistent with the overall picture in other provinces.
There are relatively few households in the lowest (5%) or second lowest (10%) income quintiles in Gauteng.
At the upper end of the scale, as many as 68% of all households in the province are found in the two highest quintiles (26% in the second highest, and a substantial 42% in the highest income quintile).
A large proportion of male-headed households in both non-urban (32%) and urban areas (50%) are found in the top income quintile, compared with relatively few female-headed households in either non-urban (19%) or urban (27%) areas of Gauteng.
This establishes that income distributions, even in the wealthiest province, tend to be highly unequal.
Measures of income inequality
Two additional measures of income inequality, namely Lorenz curves and Gini coefficients, further demonstrate the extent of income disparities in South Africa.
A Lorenz curve is a graph showing the cumulative income distribution in a given population, as illustrated in Figure 7. The relevant population in this case is the number of households in the country. The cumulative percentage of households, arranged from poorest to most affluent (from 0% to 100%), has been plotted on the horizontal axis, while the cumulative percentage of income, arranged from least to most, (also from 0% to 100%) has been indicated on the vertical axis.
A cut-off point of 20% on the horizontal axis indicates the poorest 20% of households, while a cut-off point of 60% indicates the bottom 60% of households. A cut off point of 20% on the vertical axis indicates 20% of income while a cut-off point of 60% indicates 60% of income. A diagonal line joins the vertical and horizontal axes.
In a Lorenz curve, the vertical axis on the right-hand side represents one side of a triangle, while the horizontal axis represents the second, and the diagonal connecting the two axes represents the third side of the triangle. The Lorenz curve is drawn within this triangle. The curved line in Figure 7 is the actual Lorenz curve.
The nearer this curve is to a straight diagonal line, the more equal the income distribution. The more curved the line, the less equal the income.
A Gini coefficient involves a convenient short-hand way of indicating the relative degree of income inequality, based on the Lorenz curve. It can vary from the value of zero, indicative of absolute equality in income distribution, to the value of one, indicative of absolute inequality. It is essentially a ratio. The area between the Lorenz curve and the diagonal forms the enumerator, while the total area of the triangle in the Lorenz curve forms the denominator.
Figure 7 clearly indicates that income distribution in South Africa is highly unequal. It shows that the poorest 10% of households in the country received as little as 1% of all household income in 1995, while the poorest 20% received only 3%. The poorest 30% of households received only 5% of all household income, while the poorest 50% received only 11%.
Sixty percent of households in South Africa received only 16% of all household income in 1995, while 80% of households had 35%.
The most affluent 20% of households had as much as 65% of all household income in 1995, while the most affluent 10% received as much as 48%.
In other words, the richest 20% of households have 65% of all household money at their disposal, while the poorest 20% have only 3%.
Table 6 gives the Gini coefficient for the country as a whole, and for various sub-groups of households.
Table 6: Gini coefficients of different types of South African households
| Type of household | Gini coefficient |
| All households | 0,59 |
| Race of head of household: African Coloured Indian White |
0,52 0,50 0,44 0,49 |
| Gender of head of household: Male Female |
0,75 0,55 |
| Type of area: Urban Non-urban |
0,57 0,55 |
The Gini coefficient for the country as a whole was 0,59 in 1995. This value is high, and is comparable to other countries with a high degree of inequality in income distribution such as Brazil and Ecuador (Todaro, 1989).
Within race groups, income distribution is less unequal among Indian (Gini coefficient = 0,44) households than among white, coloured or African ones.
Among male-headed households, income distribution is highly unequal (Gini coefficient = 0,75), but it is less unequal among female-headed households (Gini coefficient = 0,55).
Income distribution is slightly more unequal among urban households (Gini coefficient = 0,57), compared to non-urban ones (Gini coefficient = 0,55).
Summary
Income in South Africa is distributed in a highly unequal manner. Annual household incomes vary by race, gender and province; within province; and by urban and non-urban environments. African female-headed and male-headed households in non-urban areas are the poorest. Indeed, African households generally tend to be the least affluent, followed by coloured and Indian households, while the most affluent are white. Female-headed households in urban areas are better off than male-headed households in non-urban areas.
Section 3
The main findings regarding expenditure
Introduction
This section focuses on the goods and services which households purchase, and examines expenditure patterns among poorer households, compared to more-affluent ones.
In general, when describing household purchases, we shall make use of annual household expenditure, rather than annual household income quintiles.
It would have been possible to have used either measure because, when comparing annual household income versus annual household expenditure, we found a high correlation (r=0,98; p<0,001) between the two measures. However, it made more sense to talk about each type of product or service purchased as a percentage of total expenditure for a household, rather than as a percentage of the total income of that household. For this reason, it was decided to use expenditure quintiles rather than income quintiles to describe purchasing patterns of households. This approach also conforms with international standards.
Average annual household expenditure
A large proportion of expenditure in the average South African household goes towards buying essential products and services, such as food and housing.
Figure 8 indicates that, on average, 59% of annual household expenditure goes towards paying for four items – food (18%), housing (16%), income tax (15%) and transport (10%).
On average, 5% of annual expenditure goes on clothing and footwear, while 4% goes on health care and 3% on personal care.
A relatively small proportion, on average, of household expenditure goes towards investments and saving (2%, including saving through informal sources, for example stokvels or savings clubs), and 2% goes towards pensions.
Expenditure by households in each expenditure quintile
The average expenditure pattern gives an overall indication of how ‘average’ South African households spend their money. But this is an aggregate concept, and does not give a clear indication of differences in expenditure patterns among poorer households, compared to more affluent ones.
The purchasing power of each expenditure quintile
Total expenditure of households in South Africa on goods and services differs greatly, depending on the quintile in which a household falls.
Figure 9 shows that households in the bottom expenditure quintile account for only 3% of total annual household expenditure in the country, while those in the second
lowest quintile (quintile 4) account for 6%, those in the third lowest for 10%, and those in the fourth lowest for 20%.
Households in the top expenditure quintile account for 61% of total annual expenditure.
The richest 20% of households therefore spend more than 60% of all the money in the country available for expenditure, while the poorest 20% of households spend only 3%.
Expenditure is therefore very unevenly distributed in the country, with the vast majority of households able to buy very little.
How households in each expenditure quintile spend their money
We now examine the proportion of expenditure in each quintile that goes towards purchasing selected goods and services, and how this proportion differs according to quintile.
Figure 10 indicates that households in the bottom expenditure quintile spend as much as 51% of their total annual average expenditure on food.
As the amount of money available for expenditure increases, so the proportion of expenditure on food decreases. Households in the second lower quintile spend 43% of their total expenditure on food, decreasing to 33% in the third lower quintile and even further to 23% in the second higher quintile, and to only 12% of total annual expenditure in the top quintile.
Overall, the proportion of expenditure on housing, on average, is more evenly distributed across quintiles than expenditure on food. Nevertheless, those in the highest quintile tend to spend proportionately more on housing (17%) than those in the other quintiles.
There is an increase in the proportion of average annual expenditure on transport from 3% to 12% in each successively higher expenditure quintile (including the purchase of vehicles).
The poorest households in the country are therefore spending more than half the money they have at their disposal on food, while the more affluent households can afford to purchase a much wider variety of goods and services, since a smaller proportion of available money goes towards buying food.
Proportion in each expenditure quintile spent on fuel and power, furniture and household operation (including cleaning materials, furniture polish, etc.)
Poorer households spend a relatively large proportion of their available money on fuel and power for heating and lighting – paraffin, candles or electricity, for example – compared to more affluent households.
Figure 11 (in which percentages in the graph are shown to one decimal place because of the small proportions involved) indicates that, on average, households in the bottom expenditure category spend as much as 5,1% of total annual expenditure on fuel and power, compared to only 0,1% spent by the most affluent households.
This does not take into account household resources used for collecting firewood and other energy sources.
Households in the bottom quintile also tend to spend proportionately more on materials for household operation, for example cleaning and washing materials (3,9%), compared to households in the other quintiles.
Households in expenditure quintiles 3 and 2 (5,4% and 5,7% respectively) spend a larger proportion of available money on furniture, on average, compared to households in the other quintiles.
Percentage in each expenditure quintile spent on footwear and clothing, personal care and recreation
Figure 12 (in which percentages in the graph are shown to one decimal place because of the small proportions involved) shows that more affluent households tend to spend proportionately more on recreational activities such as reading, sport, holidays and restaurants and proportionately less on footwear and clothing than other households.
Households in the bottom expenditure category spend less than 1% on average of total expenditure on recreational activities, compared to 4% spent by the most affluent households.
Households in all quintiles, except the top quintile, tend to spend roughly the same proportion, on average, on items for personal care.
Households in quintiles 4 (8,1%) and 3 (8,1%) spend a larger proportion of available money, on average, on footwear and clothing, compared to households in the other quintiles.
Expenditure on income tax
As would be expected, households in the higher expenditure quintiles pay more income tax than those in the lower quintiles. But the extent of these differences in income tax payments across quintiles is striking.
The average household pays 14,7% of expenditure on income tax (percentages are shown to one decimal place in the graph, because of the small proportions of expenditure involved).
Figure 3 indicates that households in the bottom expenditure quintile pay 0,5%, on average, of their total expenditure on income tax. This proportion rises to 3,6%, on average, in the second lowest quintile, then to 8,5% in the third, 12,1% in the second highest and 17,7% in the highest quintile.
Expenditure on value-added tax (VAT), which affects households in all quintiles, is not taken into account in these estimations.
Savings and investments
In general, a very small proportion of total expenditure in the average South African household goes on savings (1,3%), investments (0,8%), pension funds (1,6%) or insurance (3,3%) (decimal places are shown because of these small proportions). But, in spite of such small proportions, there are large variations when comparing expenditure quintiles.
Figure 14 shows that, for households in the bottom quintile, no money is spent on pensions and investments, and very little, on average, is spent on insurance (0,3%) or savings (0,2%). This proportion increases as overall expenditure increases.
In the top quintile, proportionately more money, on average, is spent by households on insurance (4,3%) than on pension funds (2,1%), savings (1,5%) or investments (1,1%).
It thus appears that, once the vast majority of households has spent its available money on basic requirements such as food, housing, clothing and fuel, there is very little left for savings, including insurance, pension funds and investments. Only the more affluent seem able to save and, even in the top quintile, this represents a small proportion, on average, of total expenditure.
A closer examination of food expenditure
In an earlier section, we observed that more than half of all expenditure, on average, goes towards buying food in the poorest households. In this section, we examine the amount of money in rands, on average, that is spent by households in each quintile on food, and the type of food that is purchased by households in each quintile.
Food expenditure in rands by quintile
The average South African household spends R6 531 on food per annum. But this amount varies by expenditure quintile. Although, the poorest households spend more than half of their available money on food, on average, the actual amount they spend is rather small.
Figure 15 shows that the average household in the bottom expenditure quintile spends R2 190 per annum on food. This amount, as we have seen, is 51% of their total expenditure.
Households in the top quintile spend an average amount of R12 718 on food per annum. This, as we have seen is only 12% of their total expenditure.
This spending pattern does not take into account household size. The figures therefore probably overestimate consumption levels of households in the bottom quintiles, since poorer households are likely to contain more people than more affluent ones. We have seen, for example, in Section 2 of this report, that average earnings are lower in those households with six or more people than they are in households with two to five people.
Type of food products that the average household purchases
In this section, we look at household expenditure on food as being 100%. We then calculate the percentage of food expenditure, on average, on each food group.
Figure 16 indicates that, on average, the main items of food expenditure are meat, including chicken, (27%), grain products (23%), vegetables (10%) , and milk and dairy products (10%).
Proportion of total food expenditure spent on selected food items in each quintile
We now examine the proportion of total food expenditure, on average, that is spent on selected food items, by households in different expenditure quintiles.
Figure 17 shows that households in the bottom quintile tend to spend 36% of their total food expenditure on grain products such as mealie meal, bread and rice. On the other hand, they spend a relatively small proportion (19%), on average, on meat and fish.
As household income increases, the proportion of total food expenditure on grain tends to decrease, and the proportion of food expenditure on meat and fish tends to increase.
In the top quintile, only 17%, on average, of total food expenditure goes towards buying grain products, while as much as 36% goes towards purchasing meat and fish.
Dietary patterns are therefore very different in rich and poor households. This, in turn, may be reflected in the health and nutritional status, patterns of disease and life-expectancy of household members. For example, malnutrition is more likely to occur among those households which cannot purchase sufficient food to meet the basic requirements, in calories, of each household member. In some countries, an absolute index of poverty has been determined, whereby the cost of a basket of food containing a minimum amount of calories required for a healthy life-style by each member of a given household is calculated. Poverty, purchasing power, diet, health and life circumstances are all closely interlinked.
Summary
The amount of money South African households spend differs widely by quintile. The poorest 20% of households spend only 3%, while the most affluent 20% of households spend as much as 61% of total national household expenditure. Poor households tend to spend proportionately much more of their available money on food than more affluent ones, even though the actual amount spent on food is comparatively little. In addition, in poor households, proportionately more of the total food expenditure goes towards purchasing
cereals and grain products, while in more affluent households proportionately more money is spent on meat and fish. In general, relatively little money finds its way into savings, even among more affluent households.
Thus, the substantial income inequalities described in the previous chapter translate into different expenditure patterns, with poorer households buying a smaller variety of essential goods and services, and more affluent households spending a smaller proportion on essential purchases, and buying a wider range of non-essential products and services.
Section 1
Introduction
Background
Political democracy in South Africa is, after many years of struggle, at last a reality. The new constitution (Act 108 of 1996) is founded on a set of values which embody non-racialism, non-sexism, respect for human dignity, equality, human rights and freedom for all. Explicit discrimination and denial of human rights, which formed the basis of the apartheid past, has been rejected by most South Africans.
Despite these recent advances in democracy, socio-economic deprivation and profound contrasts in life circumstances along racial, urban-rural and gender divides, persist. Although South Africa is a middle-level income country, comparable with Brazil, Chile, Malaysia, Poland, Thailand and Venezuela (World Bank/SALDRU, 1995), it is characterised by gross inequalities, partially the legacy of apartheid policies.
The government is committed to improving the life circumstances and quality of life of all South Africans, particularly those who were previously disadvantaged. To meet this challenge, and to plan and implement change, a variety of role-payers – government, the private sector, trade unions and other institutions of civil society – require accurate information on a range of aspects of South African life. The Central Statistical Service (CSS), with its vast numbers of data collections, is the most appropriate agency to provide such data.
This CSS report addresses the need for information of a particular type. It is a summary of the main findings of the October 1995 income and expenditure survey (IES), and describes the large differences in income distribution and expenditure patterns among South African households.
In this report, the term household refers to all
people who live together for at least four days a week, who eat together and who share
resources.
1
Reasons for conducting an income and expenditure survey
There are numerous ways of collecting information on household income and expenditure. For example, people in selected households may be asked to keep receipts of all their purchases, or keep a diary of all expenditure over a specified time period. In addition to, or instead of these methods, a household survey can be conducted. Due to the relatively low level of literacy in South Africa, and the associated difficulty of record-keeping for many people, the CSS chose the route of utilising households for its October 1995 income and expenditure survey.
Through the IES, the CSS determined the proportion of expenditure in an average household, or in sub-groups of various types of households, that went towards purchasing each of a variety of goods and services, such as food, housing, transport and recreation. On the basis of this information, weights for each item of expenditure, based on household averages, or on other classification variables, were calculated.
Calculation of the CPI
The main purpose of the 1995 IES was to collect base-line information on household income and expenditure patterns for re-weighting the consumer price index (CPI).
In South Africa, the CPI is generally calculated in two stages.
Stage one
Firstly, information is collected from households in which questions are asked on:
Thereafter, the total expenditure of all households in the sample during the specified time period is raised to represent expenditure in all households in the country. From this new total, the CSS calculates the average annual expenditure per commodity or service, per household.
The CSS can also calculate the total annual expenditure, and average annual expenditure for each type of commodity or service, for various sub-groups of households – very low, low, middle, high and very high expenditure groups, for example. This can also be done for households in diverse geographic areas in different parts of the country, which can be broken down into metropolitan (metro), urban and rural areas.
In the past, the IES was conducted only among households in what were regarded as the 12 main urban areas of South Africa. Smaller towns and rural areas were excluded from the sample. But, in 1995, the whole country was included in the survey for the first time. This is discussed in a later section.
Stage two
In the second stage of calculating the CPI, the CSS collects the prices of all items
of expenditure from different outlets.
In the past, the prices of goods and
services were obtained in selected retail outlets in the same 12 main urban areas of the
country where the household survey was conducted, but these outlets have now been
extended, as discussed in the following section.
Changes in the calculation of the CPI, based on the 1995 IES
The CSS has recently introduced, and is continuing to initiate, a series of changes in the calculation of the CPI, in both stage one and stage two.
Stage one changes
The 1995 IES differed from previous household surveys of its kind in South Africa,
since it was a countrywide survey covering metro, urban and rural areas, rather
than a more limited sub-set of households in 12 major metro/urban areas of the country
previously referred to. By extending the sample to include the whole country, a clearer
indication of the life circumstances of all South Africans in all parts of
the country can now be inferred.
Previously, only three income categories were used for the calculation of the CPI, with the lowest category including 78% of African households in the 12 main urban areas. In the 1995 IES, five approximately equal income groups (very low, low, middle, high and very high), each containing approximately 20% of households, and five expenditure groups, based on quintiles, were derived. For reasons which will appear later in this report, income quintiles were used to describe differences in the distribution of income among various categories of households, for example households in urban versus households in rural areas; while expenditure quintiles were used to identify expenditure patterns among households falling into very low, low, middle, high and very high expenditure categories.
The effect of these changes in the 1995 IES sample, and the increase in the number of income categories, is that the country now has a clearer indication of the buying patterns of households ranging from the very poor to the very wealthy, living in metro, urban and rural areas.
Stage two changes
In the collection of information from retail outlets, the CSS now includes small
towns. Since March 1997, it has published an inflation rate for small-town areas in the
provinces, in addition to the major urban areas covered hitherto. This has involved a 50%
increase in the number of price-questionnaires issued and processed.
The importance of calculating a rural CPI
The CSS cannot, at present, collect prices from outlets in rural areas: this type of collection is very expensive and the necessary funding is not available. However, if finance can be raised, the CSS plans to measure and publish a rural CPI. As a large proportion of South Africa’s households are situated in non-urban areas, this is of obvious importance. A rural CPI will enable decision-makers to obtain as complete a picture as possible of income and expenditure patterns, and the effects of inflation, in all parts of the country, rather than just in urban areas, as was previously the case.
This is of major importance: although households in non-urban areas may spend relatively little compared to those in urban areas, inflation may have a greater effect on the ability of rural households to survive where incomes do not keep up with inflation. More extensive information on spending patterns in rural areas will facilitate planning, programme