Legacies of apartheid: the distribution of income in South Africa

LEGACIES OF APARTHEID:THE DISTRIBUTION OF INCOME

IN SOUTH AFRICA

SCOTT MCDONALD1* and JENIFER PIESSE2

1University of She�eld and University of Pretoria2Birkbeck College, University of London and University of Pretoria

Abstract: After more than a century of apartheid, the African National Congress (ANC)

inherited one of the most unequal societies in the world. The degree of inequality and

discrimination are remarkably well documented. Periodic income and expenditure

surveys during the 1980s and 1990s and annual household surveys since the early 1990s

provide a wealth of information. Gini decomposition is used to analyse levels of income

inequality by source using 1995 data, focusing on four regions, each with mixed

characteristics concerning their racial, geographical and economic base. Wage income is

found to be the major source of inequality, while other components of total household

income contribute to making the distribution more or less unequal. While the results

demonstrate that there was a clear racial component in the inequality of income

distribution in South Africa, they also demonstrate that a simple account, which

emphasizes race, ignores the complexities. They also demonstrate the magnitude of the

task facing the present government. Copyright # 1999 John Wiley & Sons, Ltd.

1 INTRODUCTION

In 1994, after more than a century of apartheid, the African National Congress(ANC) inherited one of the most unequal societies in the world. A recent report forthe O�ce of the Executive Deputy President noted, while in per capita terms, SouthAfrica is an upper-middle income country, most of the population experiencesoutright poverty or are at least vulnerable to being poor (May, 1998).

The degrees of inequality and discrimination have been remarkably well docu-mented, although in the past, the information gained was generally neglected.Periodic income and expenditure surveys (IES) during the 1980s and 1990s, andannual (October) household surveys since the early 1990s provide a wealth of

CCC 0954±1748/99/070985±20$17.50Copyright # 1999 John Wiley & Sons, Ltd.

Journal of International DevelopmentJ. Int. Dev. 11, 985±1004 (1999)

* Correspondence to: Dr S. McDonald, University of She�eld, Department of Economics, 9 MappinStreet, She�eld S1 4DT, UK. E-mail: s.mcdonald@she�eld.ac.uk

information. The latest IES which was conducted in 1995, and the 1995 OHS drewupon the same sample of households. These surveys provide details on income,expenditure, employment, housing, perceived quality of life, morbidity and mortalityrates for 30,000 households and 105,000 adults. Since classi®cation by race wasfundamental to the apartheid system, the surveys explicitly classify individuals andhouseholds by racial group.

One consequence of the apartheid system was the dualistic nature of residentiallocation, a pattern that persists. Historically, the townships were located at a distancefrom the white residential areas, existing primarily to provide services for those areas.This provides an opportunity to study groups of contiguous communities, whichshare many characteristics of the region, such as the size of the local economy, climateand environmental resources, while being highly dissimilar in others. In each case,huge divergences in income and wealth are exacerbated by a lack of basiccommodities and services that are required for an acceptable standard of living inone segment of the area while being freely available elsewhere. The ability to structurea study of inequality in this way is unique to South Africa, providing a valuableframework for comparisons of not just income and expenditure but a range of humandevelopment indicators including quality of life measures, such as access tosanitation, water, utilities, etc. Although the government is committed to povertyreduction, inequalities extend far beyond simple matters of income distribution.

The objective of this study is less ambitious. This paper reports an initialexamination of the patterns of income at the household level in South Africa. The aimis to measure the degree of inequality in the distribution of household income,disaggregated by source, in four contiguous areas that re¯ect the township±citystructure, as well as that in the country as a whole. The sub-samples are drawn fromthe provinces of Gauteng, KwaZulu Natal, Western Cape and the Free State.

The paper is organized as follows. Section 2 provides a description of the data. Theavailable censuses are wide-ranging and include information at a highly disaggregatelevel. Section 3 contains a statement of the measures of inequality used for theanalyses. The results are presented in Section 4, which starts with a series of results forSouth Africa both as a whole and by province, race and residential location (urbanversus rural). This is then repeated for the four sub-samples and includesdecomposition of the Gini coe�cients for each of the sub-samples according toincome source and race. Further results are reported as a series of tables in theAppendix. The paper ends with some concluding remarks and directions of futureresearch.

2 HOUSEHOLD SURVEY DATA FOR SOUTH AFRICA

Over the years a relatively large number of household surveys have been conducted inSouth Africa. However the value of these surveys for economic research has beenlimited, to a greater or lesser extent, by the de®nition of South Africa and therestricted geographic coverage of the surveys. From the 1970s to the mid-1990s it wascommon to ®nd South Africa to be de®ned as exclusive of the TBVC1 (homeland)states; a de®nition which e�ectively excluded the disenfranchised black populations

1 The TBVC homelands were Transkei, Bophuthatswana, Venda and Ciskei.

Copyright # 1999 John Wiley & Sons, Ltd. J. Int. Dev. 11, 985±1004 (1999)

986 S. McDonald and J. Piesse

from the surveys, and hence severely curtailed their usefulness for the study of incomedistribution. Furthermore surveys before 1995 were restricted to households in the12 major urban areas. Since the mid-1990s the surveys have encompassed the whole ofthe Republic of South Africa.

Before 1995 there were a series of Household Expenditure Surveys, the last of whichwas conducted in 1990 (HSRC, 1991). The primary purpose of these surveys was tocollect information about expenditure patterns for cost of living index purposes. Since1995 there have been two related surveys, an Income and Expenditure Survey (IES)and an October Household Survey (OHS). The IES is intended to be a periodic surveythat focuses on household incomes and expenditures, and whose primary purposes isthe collection of data for calculation of price indices. The OHS is intended to be anannual survey that provides detailed information about births, deaths, households,persons and workers within households. Despite the name, much of the data for theOHS are collected from questionnaires completed by, or for, individuals withinhouseholds, whereas the data for the IES is collected in a single questionnaire for eachhousehold. The IES and OHS for 1995 used the same sample of households.

The database used for this study was the IES for 1995. This database containsrecords for 29,595 households with some 960 data ®elds for each household of whichabout 815 are independent. The sample of households was strati®ed by race, province,district and residential location (urban and non-urban), using information from the1991 population census. However, the 1996 population census produced results thatsuggest previous censuses may have been unreliable.2 The sample provides data onhouseholds classi®ed by 9 provinces and 362 districts, with between 24 and 78 districtsin each province. The census indicates the extent to which the racial composition andthe residential locations of populations vary across provinces. For instance, theEastern Cape province has a relatively large non-urban population (65 per cent) and alarge black population (85 per cent), while the Western Cape is predominantly urban(86 per cent) and has a large coloured population (57 per cent) but a small blackpopulation (19 per cent).3 The questionnaires were compiled through face-to-faceinterviews, `due to the low level of literacy', and then coded as ®xed width ASCII data®les (CSS, 1997a, 1997b).

The samples used in the analyses reported in this paper were drawn from the IESdatabase. Preliminary analysis of the full sample provides an overview of incomedistribution in South Africa. In the second stage, four samples are drawn from thedatabase. These samples were chosen to illustrate the imposed structure of residentiallocation within South Africa. In drawing the samples it was necessary to choosesamples that contained a mix of populations and in which each population group wassu�ciently large. Details for each sample are in Table 1.

The sample from the Western Cape is of a mainly rural wine growing area with amix of small towns and farms. There is a relatively large coloured population, smallerblack and white populations and virtually no Asians. The other three samples are forpredominantly urban centres each with a di�erent racial composition. The majority ofSouth Africa's Asian population lives in KwaZulu Natal and this is re¯ected by theDurban area sample, the only one with an appreciable number of Asian households.

2 The results of the 1996 population census have called into question a number of South African economicstatistics, e.g. benchmarking National Account statistics has been delayed.3 This may be an underestimate since the apartheid policy of restricting residence by blacks in the WesternCape may have distorted the 1991 population estimates.


Legacies of Apartheid 987

The black population dominates the Bloemfontainn area in the Free State, while inthe Pretoria area the black and white populations are of a much more similar size.

The samples drawn do not re¯ect the population structure of South Africa. Theblack population is severely underrepresentated (47.3 per cent against a true ®gure of75 per cent) while the Asian, coloured and white populations are over represented(6.5 per cent against 2.5 per cent, 17.9 per cent against 8.5 per cent, and 28.2 per centagainst 14 per cent respectively). Nevertheless, each sample provides an opportunityto examine income inequality within a structure of imposed household location.

The unit of analysis in this paper is the household. This seems appropriate since thefocus is on household income. The IES data severely limit the ability to disaggregateincome by individual. The IES for 1995 provides information on the total `direct'income for each household with details for 5 members of each household (head ofhousehold, spouse and members 1 to 3).4 There are 14 sources of `direct' incomeidenti®ed in the IES for each of the 5 members and a 15th category, other income,identi®ed for the whole household. For this analysis, the 14 `direct' income categorieswere aggregated to form 5 income categories and these were then aggregated acrossthe 5 separately identi®ed household members. Table 2 shows how income is de®ned,with increasing levels of disaggregation across the columns from left to right. In somecases the calculated `direct' incomes for a household did not equal the publishedhousehold totals. This is because for some households more than 5 members wereresponsible for generating `direct' income. The unaccounted `direct' income wasallocated pro rata across the `direct' income categories for each household.5

The IES also record details of 28 forms of òther' income received by householdsthat are not attributable to individual household members. These range frominheritances to lobola/dowry. For each of the samples other income is a relativelyimportant source of household income, but none of the various forms that otherincome takes is su�ciently large to justify separate identi®cation.

Table 3 provides some detail on the share of each income component that makes updirect income for the four samples. This shows appreciable di�erences in the structure

Table 1. Regional samples.

Stellenbosch area(Western Cape)

Durban area(KwaZulu Natal)

Bloemfontein area(Free State)

Pretoria area(Gauteng)

Sample Size 495 608 359 532

Asian 0.4% 16.3% 0.0% 5.5%

Black 23.6% 51.8% 71.9% 47.7%

Coloured 46.7% 10.4% 8.1% 6.4%

White 29.3% 21.5% 20.1% 40.4%

Districts Paarl Durban Botshabelo Pretoria

Stellenbosch Richmond Bloemfontein Soshanguve

Somerset-West Pietermaritzburg Thaba'Nchu Wonderboom

Robertson

Worcester

4 There are up to 21 members of each household in the total IES sample, although the IES only identi®esthe ®rst 10 members.5 It was assumed that any other `direct' income accrued from the same mix of sources as all other `direct'income.



of household incomes although wages and salaries are by far the most importantsource for all areas.

3 MEASURES OF INEQUALITY AND GINI DECOMPOSITION BYINCOME COMPONENTS

Measures of Inequality

The emphasis of the analyses reported in this paper is on the Gini coe�cient and itsdecomposition between income components (sources). However, all summarymeasures of inequality are open to degrees of criticism. Consequently a series ofmeasures of inequality have been calculated from the data series used for this study.These are speci®ed below.

Table 2. Sources of household income.

Aggregate income categories Sample income categories IES income categories

Direct income comprising of: Salaries and wages Salaries and wages

Pro®ts and investments Pro®t income

Letting income

Royalties

Interest received

Dividends

Pensions and annuities Pension

Annuities

Welfare income Old age and war pensions

Disability grants

Family allowances

Insurance fund income

Alimony and remittances Alimony

Remittances

Other income Other income Other income

Total income

Table 3. Relative scale of income sources.

Sample income categories Stellenboscharea

Durban area Bloemfonteinarea

Pretoria area

Direct income 88.8 83.7 89.4 87.7

Salaries and wages 64.2 53.4 68.9 60.8

Pro®ts and investments 13.1 22.1 9.5 18.8

Pensions and annuities 7.4 3.8 3.8 6.7

Welfare income 2.7 3.0 5.1 1.1

Alimony and remittances 1.5 1.4 2.1 0.3

Other income 11.2 16.3 10.6 12.3



Coe�cient of Variation

The coe�cient of variation, C, is derived from the variance, V, as

C �Xni�1

�m ÿ yi�2n

!1=2�m

� V1=2=m:

�1�

This measure satis®es what Sen (1997) calls the Pigou±Dalton condition6 and isindependent of the mean income level (unlike the variance).

Standard Deviation of Logarithms

The coe�cient of variation attaches the same weight to transfers between individualsirrespective of the income levels. If income transfers at the lower end of the incomedistribution are deemed to be of greater importance, then one measure thatincorporates this is the standard deviation of logarithms of the income series, i.e.

H �

Xni�1�log m ÿ log yi�2

n

0B@1CA

1=2

: �2�

This measure satis®es the Pigou±Dalton condition, but while the logarithmictransformation has some appeal it is essentially arbitrary, as is the squaring of thedi�erences (a criticism that can also be levelled against the coe�cient of variation).

Gini Coe�cient

The Gini coe�cient can be de®ned as the ratio of the area between a Lorenz curve andthe diagonal and the total area under the diagonal, where the Lorenz curve is de®nedby the cumulative shares of income/wealth attributable to proportions of thepopulation. Consequently the Gini coe�cient is bounded between 0 and 1, where0 identi®es absolute equality of income distribution and 1 absolute inequality ofincome distribution. Mathematically the Gini coe�cient can be expressed as (see Sen,1997, p. 31)

G � 1

2n2m

� �Xni;j

jyi ÿ yjj

� 1 � 1

n

� �ÿ 2

n2m

� ��y1 � 2y2 � � � � � nyn�

�3�

for y1 5 y2 5 � � � 5 yn, which is half the relative mean di�erence, or the mean of theabsolute di�erences between all pairs of income.

6 The Pigou±Dalton condition requires that any transfer from a poorer to a richer person, ceteris paribus,increases the measured degree of inequality, in this case the variance.



Sen (1997) discusses the welfare interpretations of these three (C, H and G)measures of income distribution.

Theil's Entropy Measure

Theil's entropy measure is derived from thermodynamics where entropy is a measureof disorder, and is de®ned as

T �Xni�1

xi log nxi �4�

where xi is the share of income going to individual i. If income is transferred from apoorer (richer) to a richer (poorer) person then T increases (decreases). While Tsatis®es the Pigou±Dalton condition and can be aggregated over groups, the formulais arbitrary and lacks intuitive appeal.

Atkinsons's Measure

Atkinson (1970) proposed a measure that explicitly introduces the distributionalconcerns of society. The measure is

A � 1 ÿXni�1

yim

� �1ÿe1

n

" #1=�1ÿe��5�

where the parameter e represents the distributional objectives of society. The value ofe ranges from 0, where society is indi�erent to the distribution of income within it, toin®nity where society's only concern is the income of the least well o� individual orgroup.

Gini Decomposition by Income ComponentsWhere information is available on the composition of income it may be useful todetermine the contribution of di�erent sources of income and wealth to inequality.An alternative expression for the Gini coe�cient, derived by Stuart (1954), is

G � 2 cov�y;F�y��m

�6�

where y is a measure of income and F(y) is the cumulative density function of incomewhich is uniformly distributed between [0, 1]. De®ning yk such that y � SK

k�1yk,where k refers to the di�erent sources of income, (6) can be re-written as

G �2XKk�1

cov�yk;F�y��

m�7�



which can be rearranged (see Lerman and Yitzhaki, 1985, pp. 151±2) to give

G �XKk�1

cov�yk;F�y��cov�yk;F�yk��

� 2 cov�yk;F�yk��mk

� �mkm

� ��

�XKk�1

Rk � Gk � Sk

�8�

Rk is known as the `Gini correlation' and is bounded between ÿ1 and �1 (Lermanand Yitzhaki, 1985, p. 152). Gk is the Gini coe�cient for the kth source of income andSk is the share of total income attributable to the kth source. Fei and Ranis (1974)de®ned the products of the ®rst two terms (Rk � Gk) as pseudo-Gini coe�cients. If thepseudo-Gini coe�cient for the kth income component, that is, the product of Rk � Gk ,is constructed in a single stage the income series are not ranked monotonically butassume the ranking of the total income series. Since Rk is bounded between ÿ1 and�1 the pseudo-Gini coe�cients can take a negative value.

Thus, overall inequality for the area can be estimated as a function of the degree ofinequality of each income source, the extent of correlation between income from thatsource and total income, and the contribution of that source to total householdincome.

4 RESULTS

The World Bank estimates that South Africa is among the most unequal societies inthe world. As measured by Gini coe�cients the World Bank only identi®es4 countries,7 out of 88 for which Gini coe�cients are recorded, that are less equal(World Bank, 1998).8 The patterns of income distribution, and hence of inequality, inSouth Africa are closely related to the socio-political structure produced by theapartheid system. The analyses reported below provide an initial examination ofthe nature of inequality in South Africa. The discussion starts with a brief look at thesituation in South Africa as a whole and then discusses the relationships within thefour sub-regions. Additional tables in an appendix supplement the tables of results inthe main text.

Inequality in South Africa by race, province and residential locationThe data in the IES for 1995 indicate that South Africa was even more unequal in1995, an estimated Gini coe�cient of 0.607, than had been estimated by the WorldBank for 1993 (0.584), see Table 4. However, the overall Gini coe�cient disguises theextent to which income distribution varies across the country. Inequality in ruralareas is markedly greater (0.642) than in urban areas (0.544), but inequality within theracial groups, while pronounced, is less, ranging between 0.484 and 0.531.

Similarly the variations in inequality across the 9 provinces of South Africa as awhole are appreciable (see Table 4). In part this re¯ects the relative sizes of the urbanand rural populations, but as the Gini coe�cients for each province by residential

7 The countries are Brazil (0.601), Guatemala (0.596), Paraguay (0.591) and Sierra Leone (0.629).8 Comparisons of the degree of inequality between countries or regions or over time based on Ginicoe�cients imply that the weighting structure used to compile the Gini coe�cient are accepted.



location indicate, the patterns are far from uniform. For most provinces, income inrural areas is more unequally distributed than in the urban areas, with the exceptionof Mpumalanga and the Free State. The pattern of the Gini coe�cients by race andprovince is much more similar. With only one exception, the Gini coe�cients withinracial groups are smaller than for households in total.

All these Gini coe�cients indicate that South African society is characterized bysubstantial inequalities. The evidence from the means and standard deviations of thedata, indicates that there are systematic biases in terms of the levels of absolutehousehold incomes across races, provinces and residential locations, see Table A4i.While the most striking feature of the results in Table 4 is the extent to which averageincomes for white households exceed those of the other racial groups, the largestandard deviations, see Table A4i, reported for white households indicate the highnumber of poor white households in South Africa. There are major inequalities withinracial groups, provinces and residential locations, not just between the racial groups.This suggests that the inequalities bequeathed to the democratic state of South Africaare far more complex than a simple tale of racial biases alonewould suggest. Indeed theredistribution objectives of the government's Growth, Employment and Redistribu-tion (GEAR) programme may be more di�cult to achieve given the extent to whichrelative incomes are important to individuals' perceptions of their well-being.

The results from the Gini coe�cient estimates are supported by the estimates of thecoe�cients of variation, standard deviation of logarithms and Theil entropy measuresreported in Tables A4ii to A4iv (see the Appendix). These additional estimates dohowever add a potentially important insight. The dispersion of income across whitehouseholds is clearly appreciably greater than that across non-white households.

Measures of inequality for the four sample areasThe measures of inequality as a whole support the view that the distribution ofhousehold income in South Africa is complex, and that variation across regions maybe as important as variation across households. The Gini coe�cients by incomesources for the four sample areas provide a number of interesting insights and theseare shown in Table 5.

The Gini coe�cients for total household income indicate that while incomedistribution within the sample areas may be somewhat less unequal than for the

Table 4. Gini coe�cients for income per household in South Africa by race, province andresidential location.

All households Urban Rural Black Coloured Asian White

South Africa 0.6037 0.5443 0.6417 0.5309 0.4911 0.4842 0.4943

Eastern Cape 0.6144 0.5710 0.5979 0.5288 0.4902 0.4415 0.5147

Gauteng 0.5377 0.5235 0.6382 0.4872 0.4324 0.5386 0.4307

KwaZulu Natal 0.5657 0.4801 0.6049 0.4807 0.4357 0.4350 0.5141

Mpumalanga 0.5386 0.5119 0.4693 0.4385 0.4008 0.3466 0.4164

Northern Cape 0.6061 0.5738 0.6650 0.4578 0.5026 0.5569 0.4878

Northern Province 0.6210 0.5422 0.6304 0.5964 0.5696 0.4205 0.4743

North West Province 0.6634 0.5665 0.7436 0.5455 0.4919 0.5061 0.6493

Orange Free State 0.5943 0.5564 0.5230 0.4853 0.4593 n/a 0.4540

Western Cape 0.5581 0.5177 0.6940 0.4507 0.4476 0.3465 0.4919



respective provinces, it remains highly unequal. For the Bloemfontain and Durbansamples direct incomes are less equally distributed that other incomes, while for theother two samples there is little di�erence. Since other income captures a number ofinformal economic activities it might appear that these activities reduce inequality.

Salaries and wages are more unequally distributed across all four samples thantotal income, while the other four categories of direct income (pro®ts and invest-ments, pensions and annuities, welfare income and alimony and remittances) are evenmore unequally distributed than earned income. The extreme inequality in thedistribution of pro®t and investment income accords with expectations given theconcentration of wealth. The unequal distribution of other sources of direct incomeappears to re¯ect the limited proportions of households that receive income fromthese sources. This suggests that the reason the Gini coe�cients for direct incomein each of the four samples are less than all the components individually is aconsequence of the distributions of the components being inversely correlated. This isexamined further below.

The other measures of inequality indicate that the patterns of inequality portrayedby the Gini coe�cients are robust. The rank orders for the standard deviations oflogarithms and Theil's entropy measures are consistent with the Gini's, although thecoe�cients of variation do di�er (see Table 6). The Atkinson measures of inequalityinevitably increase as the epsilon parameter increases. However, it is interesting tocompare the Atkinson measures since they display di�erent patterns as epsilonincreases. For instance, while the measures for Pretoria and the Stellenbosch samplesare very similar for the ®rst three values of epsilon, the Pretoria measure increasesmore sharply between e � 1.5 and e � 2.0. This indicates the extent to which incomesat the lower end of the spectrum are more skewed in Pretoria and hence the well-beingof the least well o� becomes increasingly important. In contrast the patterns for theBloemfontain and Durban samples diverge continuously, which is consistent with amore polarized nature of income distribution in these areas.

Gini decompositions for four sample areasThe Gini coe�cients in Table 5 indicate that total income inequality is highest inBloemfontain, followed by Durban, then Stellenbosch and lowest in Pretoria. The

Table 5. Gini coe�cients by sample, income source and racial group.

Bloemfontainarea

Pretoria area Durban area Stellenboscharea




Welfare income 0.8321 0.9199 0.8476 0.8616


Direct Income 0.5503 0.4980 0.5740 0.5111

Other Income 0.7909 0.7997 0.7620 0.8326

Total Income 0.5352 0.4997 0.5301 0.5076

Black 0.5134 0.4154 0.4010 0.3294

Coloured 0.4496 0.3330 0.4175 0.4156

Asian n/a 0.5545 0.4170 n/a

White 0.3327 0.4177 0.4773 0.3934



Gini coe�cients by racial groups within the four sample areas, Table 5, suggest thatthe racial pro®les of the regions considerably in¯uence the overall rankings.

The largest Gini coe�cient in the Bloemfontain sample is for the black population,0.5134, which also is the largest population group, 72 per cent, suggesting that thisgroup has the most impact on the level of inequality overall.9 A di�erent situationexists in Durban and the surrounding areas. The Gini coe�cients for the racial groupsshow less variation, 0.401 to 0.477, although the overall Gini is similar. The whitepopulation returns the highest Gini, 0.4773, while that for the black population is thelowest. The Gini coe�cients for the coloured and Asian households are virtuallyidentical, 0.4175 and 0.4170 respectively. The situations are di�erent again for theStellenbosch and Pretoria samples. In the Stellenbosch areas the Gini coe�cients areleast for the black households and greatest for the coloured households, with whitehouseholds in between.10 In Pretoria black and white households return very similarGini coe�cients.

These preliminary analyses of income distribution within the four sample areassuggest that the image of complex income distribution relations found for SouthAfrica as a whole also exists at the sub-provincial level. However, the patternssuggested by the results presented so far only tell part of the story. Far greater insightscan be obtained by decomposing the Gini coe�cients in order to identify thecontributions of di�erent sources of income to measured inequalities. Decompositionby income source can provide useful information about both the contribution of eachincome source and also the direction of the e�ect of each component on total incomedistribution. Perhaps more importantly, it also provides information about theinteraction of the di�erent income sources and hence how segments of the populationwould bene®t from a change in any single income source.

Table 6. Other inequality measures by area.

Bloemfontainarea


Cope�cient of variation

Direct income 1.2130 1.2497 1.7469 1.1676

Total income 1.1605 1.2275 1.5092 1.1856

Standard deviation of logarithms

Direct income 1.1505 0.9658 1.0934 0.9884

Total income 1.0832 0.9732 0.9949 0.9712

Theil's entropy measure

Direct income 0.5293 0.4680 0.6653 0.4620

Total income 0.4948 0.4633 0.5481 0.4600

Atkinson's measure

e � 0.5 0.231 0.206 0.233 0.209

e � 1.0 0.423 0.372 0.404 0.376

e � 1.5 0.570 0.506 0.531 0.504

e � 2.0 0.680 0.615 0.623 0.597

9 There are insu�cient Asian households in the Bloemfontain sample to produce meaningful Ginicoe�cients.10 There are insu�cient Asian households in the Stellenbosch sample to produce meaningful Ginicoe�cients.



The results presented in Tables 7 and A7 were derived using the decompositionmethod suggested by Lerman and Yitzhaki (1985). The results in Table 7 refer to thecontributions of di�erent types of income according to both income source and racialgroup,11 whereas the results in Table A7 refer to the contributions of di�erent incomesources within racial group. The results are derived using the formula in (8). The largerthe product of the three components, Rk � Gk � Sk, the greater the contribution of thekth income source to total income inequality.

Table 7. Gini correlations (Rk).

Bloemfontainarea


All households




Welfare income ÿ0.186 ÿ0.105 ÿ0.295 ÿ0.247Alimony and remittances 0.217 ÿ0.168 ÿ0.109 0.163

Other income 0.628 0.750 0.591 0.705

Black

Salaries and wages 0.493 0.018 0.136 ÿ0.312Pro®ts and investments 0.792 0.268 0.225 0.066

Pensions and annuities ÿ0.158 ÿ0.026 0.190 ÿ0.420Welfare income ÿ0.420 ÿ0.279 ÿ0.389 ÿ0.450Alimony and remittances 0.121 ÿ0.391 ÿ0.337 ÿ0.574Other income 0.261 0.094 0.111 ÿ0.108

Coloured


Pro®ts and investments 0.000 ÿ0.115 0.810 0.414

Pensions and annuities 0.186 0.000 ÿ0.152 0.356

Welfare income ÿ0.191 ÿ0.112 ÿ0.137 ÿ0.182Alimony and remittances ÿ0.393 ÿ0.898 0.399 ÿ0.271Other income 0.514 0.474 0.639 0.164

Asian

Salaries and wages n/a 0.633 0.579 n/a

Pro®ts and investments n/a 0.922 0.794 n/a

Pensions and annuities n/a ÿ0.040 ÿ0.114 n/a

Welfare income n/a ÿ0.709 ÿ0.040 n/a

Alimony and remittances n/a ÿ0.073 0.028 n/a

Other income n/a 0.654 0.623 n/a

White




Welfare income 0.672 0.268 ÿ0.144 ÿ0.001Alimony and remittances 0.625 0.485 0.054 0.544

Other income 0.825 0.838 0.789 0.897

11 This is legitimate for these samples since the racial groups to which household members belong arehomogenous. Indeed for the 1995 IES less than 2 per cent of households contain members who areclassi®ed as belonging to a di�erent racial group than the `head of household'.



In the current context the most interesting insights are provided by the Ginicorrelation.12 The Gini correlation is a measure of the extent of the relationshipbetween the distribution of each income source and that household's overall rankingwith respect to total income. As noted above, this is bounded between ÿ1 and 1.Thus, when Rk is less than zero, income from source k is negatively correlated withtotal income and thus lowers the overall Gini measure for the sample, and vice versa.Table A8 reports the share weights, Sk .

For all households within the four samples the correlations are highest for incomesfrom wages and salaries and pro®ts and investments. Thus these sources are mostin¯uential in determining income distribution. This is not surprising. However, it issurprising to discover that the Gini correlation for other income is strongly positivelycorrelated with overall inequality. The negative correlation on welfare income suggeststhat public transfers reduce the level of inequality, while pensions and annuitiescontribute substantially to inequality, especially in the Bloemfontain and Stellenboschsamples. The correlations for alimony and remittances are inconsistent across thesamples: this may be a re¯ection of the populations in Pretoria and Durban makingnet payments whilst those in Stellenbosch and Bloemfontain are net recipients.Without additional and di�erent analyses this conclusion must be tentative.

When the contributions to inequality by income source and racial group areexamined major di�erences appear. The impacts of income sources by whitehouseholds are similar to those for all households. The Gini correlations for salariesand wages, pro®ts and investments, pensions and annuities and other income are allstrongly positive. More surprisingly the correlations for welfare income are positive intwo areas and only substantially negative in one case: this indicates that whitehouseholds are relatively well served by welfare payments. The overall pattern thatemerges is one in which all sources of white income, with two minor exceptions,contribute appreciably to income inequality.

For black income sources a quite di�erent pattern emerges. The Gini correlationfor salaries and wages is close to zero for two regions, higher for Blomfontainalthough not by white or Asian standards, and in the Western Cape wine region,negative. In this rural area, earned income is reasonably equally distributed amongstthose who are in work and overall the e�ect is to reduce the inequality of total income.Also for this racial group, remittances play an important part in total income forsome families. In three out of the four regions, this component contributes toreducing the level of inequality. Again, singling out the Western Cape wine region,remittances are received by households at the lower end of the total incomedistribution, a pattern suggestive of appreciable levels of out migration.

In the two regions that have a signi®cant Asian population, Pretoria and Durban,the Gini correlations re¯ect those of the white households, especially with respect tosalaries and wages and pro®ts and investments. Much of this is accounted for by thepredominance of self-employed families in largely urban areas where there are a largenumber of small traders and service sector enterprises. Low remittances are alsoevident; where they exist, they have no e�ect on the overall distribution of income.

12 It should be noted that these analyses are output intensive. The results presented in Tables 7 and A7 arelimited to the Gini correlations. Full sets of results are available from the authors.



CONCLUSION

The results presented in this paper largely conform to expectations. There is apronounced racial component in the ranking of incomes within South Africa, and thisconclusion is very robust across di�erent measures of inequality and across di�erentsamples. The results also demonstrate that the patterns of income distribution arecomplex and a simple story based solely on race may be misleading. While whiteincomes are on average at least twice those of the next racial group, and more oftennearly three times as great, the unequal distribution of white incomes is verypronounced. Clearly, there are large numbers of poor whites in South Africa. And yetthese results may understate the full extent of inequality because systematic di�erencesin household sizes exist across all the samples used.

It is tempting to interpret the results from the decomposition analyses as clearevidence of discrimination, especially in the labour market (salary and wage incomes)and in the distribution of wealth (pro®ts and investment and pension and annuityincomes). However, much more analysis is necessary to determine the extent to whichthe Gini correlations are re¯ections of demand or supply side phenomena. In thiscontext the ability to link the IES and OHS for 1995 may be particularly useful sincethe OHS provides large amounts of information on employment by skill category andindustry, and about education and training for individual members of households.These surveys clearly provide important and under exploited sources of informationabout South Africa. Moreover, they contain information that future surveys maydeliberately exclude. For understandable reasons, Statistics South Africa intend todiscontinue collecting data that di�erentiates racial groups, although this will lead to aserious loss of information about the progress of the reforms aimed at those mostvulnerable to poverty.

There are limitations to this paper. First, the data are for a single year and theanalyses are therefore static. No historical data is comparable and none are likely to beexactly comparable in the future. The analyses are not concerned with redistributionand therefore no inference is made about the direction andmagnitude of redistributionpolicies. However, the results presented are the ®rst known analyses to use data fromthe 1995 IES for SouthAfrica to decompose household income into source componentand ethnic group. While there is substantial awareness of the issue of incomedistribution in South Africa, and although much is known, there remains a pressingneed for a better understanding of the micro level di�erences, if e�ective redistributionpolicies are to be formulated. These results should contribute to an increasedunderstanding.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge research assistance by Lindsay Chant.

REFERENCES

Atkinson, A. (1970). Òn the measurement of inequality', Journal of Economic Theory, 2,

244±263.



CSS (1997a). Income and Expenditure Survey, 1995. Pretoria: CSS.

CSS (1997b). Earnings and Spending in South Africa: Selected Findings of the 1995 Income and

Expenditure Survey. Pretoria: CSS.

Fei, J. and Ranis, G. (1974). Ìncome inequality by additive factor components', Economic

Growth Center Discussion Paper, No. 207, Yale: Yale University.

Fei, J., Ranis, G. and Kuo, S. (1978). `Growth and the family distribution of income by factor

components', Quarterly Journal of Economics, XCII, 17±53.

HSRC (1991). Survey of Household Expenditure, 1990. Pretoria: CSS.

Lerman, R. I. and Yitzhaki, S. (1985). Ìncome inequality e�ects by income source: a new

approach and applications to the United States', Review of Income and Wealth, 67, 151±156.

May, J. (1998). Poverty and Inequality in South Africa. Report prepared for the O�ce of the

Executive Deputy President and the Inter-Ministerial Committee for Poverty and Inequality,

Praxis, Durban, RSA.

Sen, A. (1997). On Economic Inequality (Expanded edition with annex by Foster JE, Sen A.)

Oxford: Clarendon.

Stuart, A. (1954). `The correlation between variate-values and ranks in samples from a

continuous distribution', British Journal of Statistical Psychology, 7, 37±44.

Theil, H. (1967). Economics and Information Theory. Amsterdam: North-Holland.

World Bank (1998). World Development Report, 1998±99. Oxford: Oxford University Press.



Table A4ii. Coe�cients of variation for South Africa by race, province and residentiallocation.


South Africa 2.146 1.479 3.655 0.571 0.146 0.049 1.241

Eastern Cape 2.052 1.709 2.501 0.594 0.200 0.018 0.952

Gauteng 1.476 1.450 1.698 0.536 0.062 0.049 0.946

KwaZulu Natal 2.300 1.213 3.835 0.566 0.046 0.145 1.427

Mpumalanga 1.349 1.162 1.304 0.603 0.027 0.025 0.668

Northern Cape 1.799 1.703 2.050 0.316 0.374 0.013 1.021



Free State 1.622 1.316 2.671 0.559 0.102 0.784

Western Cape 1.692 1.342 2.915 0.278 0.397 0.018 1.101

APPENDICES

Table A4i. Mean incomes and standard deviations for South Africa by race, province andresidential location.

Allhouseholds

Urban Rural Black Coloured Asian White

South Africa 39,774 49,562 26,720 12,457 38,847 54,044 133,823

85,354 73,304 97,660 7,114 5,688 2,652 166,113

Eastern Cape 26,851 36,927 18,083 10,107 39,963 58,323 129,866

55,095 63,124 45,225 6,004 8,006 1,067 123,589

Gauteng 68,646 69,459 62,345 21,596 51,558 62,623 152,133

101,339 100,727 105,878 11,576 3,180 3,086 143,906

KwaZulu Natal 41,031 52,747 29,964 16,683 42,475 58,305 158,037

94,384 63,988 114,918 9,437 1,960 8,433 225,556

Mpumalanga 33,407 48,449 20,920 15,937 42,438 46,739 100,835

45,080 56,316 27,274 9,603 1,132 1,170 67,310

Northern Cape 32,930 34,781 28,851 5,474 16,235 32,053 90,837

59,243 59,219 59,156 1,728 6,064 410 92,713

Northern Province 36,210 54,855 29,857 21,984 95,858 99,489 207,554

77,674 77,281 76,800 21,184 1,526 715 212,378

North West Province 40,012 46,281 32,550 14,377 49,956 59,608 176,174

148,860 77,461 203,252 10,090 4,001 884 365,601

Free State 25,690 33,435 12,562 9,440 29,620 82,758

41,670 43,998 33,549 5,275 3,019 64,850

Western Cape 52,968 54,036 48,397 7,665 23,903 47,108 121,101

89,612 72,502 141,082 2,129 9,500 844 133,272



Table A4iii. Standard deviation of logs for South Africa by race, province and residentiallocation.


South Africa 1.099 1.057 0.982 0.624 0.146 0.049 0.533

Eastern Cape 1.049 1.082 0.892 0.610 0.198 0.018 0.514

Gauteng 1.044 1.009 1.229 0.632 0.062 0.049 0.517

KwaZulu Natal 0.995 0.921 0.903 0.629 0.046 0.144 0.511

Mpumalanga 0.985 1.022 0.819 0.666 0.027 0.025 0.464

Northern Cape 1.080 1.036 1.121 0.397 0.363 0.013 0.614



Orange Free State 1.039 1.047 0.748 0.571 0.102 0.532

Western Cape 1.013 0.981 1.047 0.323 0.400 0.018 0.566

Table A4iv. Theil's entropy measure for South Africa by race, province and residentiallocation.


South Africa 0.755 0.558 1.095 0.160 0.011 0.001 0.304

Eastern Cape 0.788 0.627 0.902 0.165 0.020 0.000 0.236

Gauteng 0.552 0.524 0.786 0.148 0.002 0.001 0.240

KwaZulu Natal 0.700 0.422 1.060 0.159 0.001 0.010 0.355

Mpumalanga 0.536 0.461 0.433 0.178 0.000 0.000 0.155

Northern Cape 0.739 0.661 0.935 0.055 0.067 0.000 0.307



Orange Free State 0.683 0.556 0.783 0.147 0.005 0.203

Western Cape 0.620 0.496 1.204 0.041 0.077 0.000 0.294



Table A4v. Atkinson's measure for South Africa by race, province and residential location.

Allhouseholds

Urban Rural Black Coloured Asian White

South Africa e � 0.5 0.3013 0.2436 0.3597 0.0813 0.0053 0.0006 0.1163

e � 1 0.4938 0.4285 0.5220 0.1633 0.0106 0.0012 0.1878

e � 1.5 0.6169 0.5654 0.6118 0.2436 0.0159 0.0018 0.2365

e � 2 0.6970 0.6639 0.6704 0.3206 0.0211 0.0024 0.2725

Eastern Cape e � 0.5 0.3101 0.2668 0.3142 0.0819 0.0098 0.0001 0.0964

e � 1 0.4939 0.4576 0.4637 0.1616 0.0196 0.0002 0.1623

e � 1.5 0.6034 0.5898 0.5473 0.2378 0.0291 0.0002 0.2099

e � 2 0.6735 0.6803 0.6040 0.3106 0.0384 0.0003 0.2458

Gauteng e � 0.5 0.2393 0.2270 0.3345 0.0769 0.0009 0.0006 0.0981

e � 1 0.4209 0.4007 0.5537 0.1589 0.0019 0.0012 0.1647

e � 1.5 0.5576 0.5346 0.6824 0.2437 0.0028 0.0018 0.2126

e � 2 0.6573 0.6356 0.7559 0.3278 0.0038 0.0024 0.2488

KwaZulu Natal e � 0.5 0.2689 0.1891 0.3323 0.0809 0.0005 0.0052 0.1278

e � 1 0.4389 0.3432 0.4778 0.1636 0.0011 0.0103 0.1965

e � 1.5 0.5550 0.4691 0.5638 0.2453 0.0016 0.0154 0.2390

e � 2 0.6370 0.5707 0.6243 0.3236 0.0021 0.0204 0.2685

Mpumalanga e � 0.5 0.2337 0.2135 0.1826 0.0905 0.0002 0.0002 0.0685

e � 1 0.4051 0.3914 0.3153 0.1819 0.0003 0.0003 0.1218

e � 1.5 0.5268 0.5303 0.4167 0.2703 0.0005 0.0005 0.1640

e � 2 0.6126 0.6314 0.4964 0.3517 0.0007 0.0006 0.1982

Northern Cape e � 0.5 0.3006 0.2710 0.3658 0.0292 0.0329 0.0000 0.1281

e � 1 0.4905 0.4512 0.5622 0.0631 0.0646 0.0001 0.2166

e � 1.5 0.6097 0.5744 0.6635 0.1036 0.0944 0.0001 0.2793

e � 2 0.6891 0.6635 0.7219 0.1542 0.1221 0.0001 0.3255

Northern Province e � 0.5 0.3188 0.2431 0.3328 0.1856 0.0001 0.0000 0.1101

e � 1 0.5143 0.4298 0.5158 0.3437 0.0001 0.0000 0.1805

e � 1.5 0.6338 0.5684 0.6232 0.4663 0.0002 0.0000 0.2278

e � 2 0.7087 0.6656 0.6913 0.5570 0.0002 0.0000 0.2614

North West e � 0.5 0.3804 0.2657 0.5165 0.1098 0.0016 0.0001 0.2297

Province e � 1 0.5613 0.4482 0.6548 0.2117 0.0031 0.0001 0.3382

e � 1.5 0.6629 0.5748 0.7145 0.3019 0.0047 0.0002 0.3969

e � 2 0.7257 0.6626 0.7509 0.3794 0.0063 0.0002 0.4336

Free State e � 0.5 0.2851 0.2484 0.2584 0.0729 0.0026 0.0886

e � 1 0.4694 0.4341 0.3774 0.1439 0.0052 0.1564

e � 1.5 0.5827 0.5628 0.4481 0.2122 0.0078 0.2093

e � 2 0.6552 0.6499 0.5008 0.2783 0.0104 0.2512

Western Cape e � 0.5 0.2562 0.2185 0.4136 0.0216 0.0385 0.0001 0.1179

e � 1 0.4323 0.3873 0.5882 0.0452 0.0761 0.0002 0.1956

e � 1.5 0.5543 0.5159 0.6692 0.0715 0.1121 0.0002 0.2507

e � 2 0.6397 0.6123 0.7145 0.1013 0.1457 0.0003 0.2923



Table A7. Gini correlations within racial groups (Rk).

Bloemfontainarea


All Households




Welfare income ÿ0.186 ÿ0.105 ÿ0.295 ÿ0.247Alimony and remittances 0.217 ÿ0.167 ÿ0.110 0.163

Other income 0.628 0.750 0.591 0.705

Black



Pensions and annuities ÿ0.050 0.428 0.595 0.209

Welfare income ÿ0.230 0.094 ÿ0.029 0.111

Alimony and remittances 0.323 ÿ0.031 ÿ0.001 ÿ0.101Other income 0.533 0.632 0.655 0.581

Coloured


Pro®ts and investments 0.000 ÿ0.333 0.807 0.596

Pensions and annuities 0.178 0.000 ÿ0.538 0.501

Welfare income ÿ0.434 ÿ0.316 ÿ0.524 ÿ0.120Alimony and remittances ÿ0.496 ÿ1.000 0.198 ÿ0.228Other income 0.568 0.539 0.573 0.330

Asian

Salaries and wages n/a 0.687 0.729 n/a

Pro®ts and investments n/a 0.936 0.755 n/a

Pensions and annuities n/a ÿ0.286 ÿ0.364 n/a

Welfare income n/a ÿ0.855 ÿ0.304 n/a

Alimony and remittances n/a ÿ0.317 ÿ0.285 n/a

Other income n/a 0.392 0.587 n/a

White



Pensions and annuities 0.033 ÿ0.143 ÿ0.027 ÿ0.218Welfare income 0.141 ÿ0.178 ÿ0.533 ÿ0.636Alimony and remittances ÿ0.097 0.117 ÿ0.575 ÿ0.027Other income 0.450 0.745 0.568 0.752



Table A8. Gini decomposition share weights (Sk).

Bloemfontainarea


All households




Welfare income 0.0512 0.0106 0.0298 0.0270


Other income 0.1058 0.1233 0.1632 0.1119

Black




Welfare income 0.0368 0.0054 0.0175 0.0080


Other income 0.0515 0.0264 0.0647 0.0087

Coloured




Welfare income 0.0052 0.0018 0.0045 0.0147


Other income 0.0029 0.0053 0.0119 0.0284

Asian




Welfare income 0.0000 0.0003 0.0036 0.0002


Other income 0.0000 0.0049 0.0332 0.0008

White




Welfare income 0.0092 0.0031 0.0042 0.0041


Other income 0.0514 0.0867 0.0534 0.0740



Documents

Legacies of apartheid: the distribution of income in South Africa