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Increases in poverty in South Africa, 1999–2002

Charles Meth and Rosa Dias1
Contact: methc@ukzn.ac.za

Development Southern Africa Vol. 21, No. 1, March 2004

SARPN acknowledges Development Southern Africa, Vol. 21, No. 1, March 2004 as the source of this analysis.
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Analysis of the results in the 1999 October Household Survey and the 2002 Labour Force Survey suggests that the number of people in the bottom two expenditure classes (R0–R399 and R400–R799 per household per month) increased by about 4,2 million over the period. As the boundaries of these expenditure classes remained constant in nominal terms, there is a likelihood that the number of people in poverty will have increased as well. This article attempts to discover whether this is indeed the case. The possible increase in the number of people in poverty is not equal to the increase in the number of people in these two expenditure categories. Rather, it is equal to the difference between the numbers of people in poverty in the two years. Our first crude estimate of the maximum potential number of ‘new’ poor suggests that it could be as high as 4,5 million. This estimate, which excludes any adjustments for possible underreporting of expenditure, child cost economies and household economies of scale, and the ‘social wage’, is whittled down as we attempt to make the relevant allowances. Responding to claims that poverty is increasing in the country, the government has pointed to a failure to consider the contribution of the social wage to the alleviation of poverty. Accordingly, we have also attempted to estimate the impact of the social wage.

Introduction

As the governing party, the African National Congress (ANC) knows full well that combating poverty is its most important task. Not surprisingly, the government and party spokespeople are extremely sensitive to suggestions that poverty in South Africa is worsening. Briefing Parliament’s communications committee on the work of the Government Communication and Information System (GCIS) in advance of a debate in the house earlier this year on ‘whether conditions in South Africa had improved since the democratic elections in 1994’, its CEO, Joel Netshitenzhe, said that the GCIS ‘had to correct mistaken views that the poor were worse off than they were during apartheid years’ (Business Day, 2003). He is quoted as saying that:

    …the tide had turned on the unemployment front as the economy was beginning to create jobs. A ‘social wage’ had also been introduced, reflecting government’s efforts to deal with poverty. This had contributed to an improved quality of life. The social wage included social grants, tax relief, the provision of free basic services. In addition, the acquisition of human rights had also improved the quality of people’s lives. While partial data and focus on single points in time may attract shallow claims of no delivery and increasing poverty, a contrary conclusion follows from a rounded picture of trends including the social wage, tax relief and social grants over and above cash income from employment.
If by tax relief, Netshitenzhe means the reductions in income tax rates made over the last several years, then these are of limited relevance to the people with this study is concerned. None of the households from which they come pay income tax. If tax reductions have had some impact on the wellbeing of poor households, it is most likely to have been via remittances. Given the relatively small number of remittances received, the effect is unlikely to have been very large. This is a matter that requires further research.

By about 2000, analyses of poverty and income inequality based on, or linked to, the 1996 Population Census and the 1995 Income and Expenditure Survey (IES) had reached the end of the road – further developments awaited the publication of the IES results for 2000. Taking the analyses as far as they would go, most commentators seemed to agree that between-group inequalities have fallen, while within-group inequalities have risen. Having concluded thus, the examination of South Africa’s changing income distribution in the period 1991–6 by Whiteford & Van Seventer (2000:28) argues that:
    …the rise in inequality within population groups and within society as a whole is driven, on the one hand, by rising employment of well-paid, highly-skilled persons and, on the other hand, declining employment of lower-paid, less-skilled persons who are forced into poorly remunerated informal sector employment or into unemployment.
Posing the question of whether the trends they have detected ‘which occurred in all population groups’ (ibid., 25) are likely to continue into the future the answer, they insist, has to be in the affirmative. Their analysis of labour market processes, and projections that one of the authors made in another study, has led them to predict that (ibid., 28):
    …the employment of highly skilled persons will continue to rise while the employment of less skilled persons will decline, resulting in rising unemployment. Unless there is a fundamental shift in the path along which the economy is moving, there is little hope for a reduction in inequality and income poverty.
Up to the mid-1990s, most households (72 per cent of all households and 64 per cent of African households) contained no unemployed people. By 1999, these proportions had fallen to 64 and 57 per cent, respectively. They fell still further, reaching 58 and 52 per cent, respectively, by 2002. Research (Leibbrandt et al., 2001:48) suggests that:
    …most household-level inequality [inequality between households] is driven by income dynamics within households with no unemployed members because most households do not have unemployed members and households with unemployed members tend to be crowded below the poverty line at the lower end of the household income distribution.
This conclusion no longer holds. Rising unemployment in the period since 1996 makes it likely that Whiteford & Van Seventer’s prediction on poverty and inequality would have been fulfilled. Not only has the required fundamental shift not taken place – the numbers of unemployed have climbed to record levels, almost doubling between 1995 and 2002. With some large proportion of the unemployed located in the lowest expenditure categories (we discuss the numbers below), it seems almost inevitable that poverty would have worsened.

Unfortunately, the statistical basis on which reliable judgements about poverty and inequality in the period after 1996 were to be based – the 2000 IES (StatsSA, 2000b) – turned out to be deeply flawed. An analysis of its results, presented in Earning and spending in South Africa (StatsSA, 2002), which shows an increase in poverty and inequality over the period 1995–2000, was dismissed by the government.

This article uses a variant of the headcount method to attempt to discover what happened to the numbers of people in poverty between 1999 and 2002. There is an excellent discussion of the advantages and limitations of the various estimates of poverty that can be made in Woolard & Leibbrandt (2001). By comparison with that work, the estimates presented in this study are crude in the extreme. We make no apology for this – our intention is to measure the extent of poverty between two well-defined groups in society, not its intensity. We are also aware of the difficulties of using expenditure estimates. The way in which we deal with this difficulty will become clear below. We could, in addition, have performed a consistency test on our results by attempting to estimate the incomes of the households whose results we are working with in the study.

The usual technique for conducting a headcount is to establish a poverty line (PL) and then to count the number of individuals whose expenditure or income falls below this level. In order to do so, data on the distribution of households by expenditure level are required, as are data on the age distribution of individuals within households. The latter are used to adjust the size of those households containing children to lower costs (i.e. estimate adult equivalents), and to make allowance for economies of scale in those households containing more than one individual. The number of people in poverty is the total number of people in those households below the PL. Rather obviously, to measure changes in poverty, one estimates and compares the numbers below the PL at the beginning and end of the period in which one is interested or for which one has the relevant data.

Each study will have peculiarities imposed upon it by both the nature of the inquiry undertaken and by the availability of data. In the case of the present study, a major feature is the allowance to be made for in-kind consumption (the social wage). Another feature of this study is that rather than attempting to measure changes in poverty in the nation as a whole, it aims to count the total number of people in poverty in the two bottom expenditure categories, i.e. those in households where expenditure lies between R0–R399 and R400–R799 per month, respectively.

As far as data constraints are concerned, although detailed information is available on household composition from the relevant surveys, the Labour Force Survey (LFS) for September 2002 (StatsSA, 2003) and the October Household Survey (OHS) for 1999 (StatsSA, 2000a), nothing is known about the distribution of households by expenditure level. In order to overcome this hurdle, it has been necessary to construct the relevant distributions by assumption. This is a less hazardous process than may be thought – estimates of the number of poor appear to be relatively insensitive to quite wide variations in the assumed distributions of expenditure. This is tested by allowing mean expenditure in the lowest category (R0–R399 per month) to vary.

The investigation was conducted in stages:

  1. An estimate was made of the change in the number of poor between 1999–2002, using the data extracted from the two data sets.
  2. Allowing for child costs and for household economies of scale, an estimate of maximum potential consumption levels for different types of household was made. Estimates were made of daily maximum potential consumption levels of people living in households containing adults and children, in the bottom two expenditure categories, while allowing for underreporting of expenditure. The social wage was still excluded.
  3. An attempt was made to value the social wage. Maximum potential consumption levels were established, allowing for child costs and household economies of scale, including the social wage but not allowing for underreporting errors.
  4. Estimates of changes in the numbers of the poor, taking the social wage into account, were made. The estimates show the effects of underreporting of household expenditure on the likely numbers of ‘new poor’.
Our results, the basic data from which they were generated and the simple devices used to perform operations such as adult equivalence calculations, social wage valuation and expenditure underestimation corrections, are contained in four linked spreadsheets called ‘Poverty-0.xls’, ‘Poverty-50.xls’, ‘Poverty-100.xls’ and ‘Poverty-150.xls’. Using these spreadsheets, a large number of simulations that deliver estimates of the numbers in poverty may be performed. These make use of a wide variety of assumptions about some of the variables about which our knowledge is hazy. The spreadsheets are available on the website of the School of Development Studies at the University of Natal. Using them, a person can make any changes to the assumptions that we have made. By this means, one can test the sensitivity of our results to variations in those assumptions.


Footnote:
  1. Respectively, Research Fellow, School of Development Studies; and Lecturer, Division of Economics, both of the University of Natal, Durban, South Africa. We are grateful for helpful comments made by the discussant of our paper, David Fryer of Rhodes University, to Nicoli Nattrass of the University of Cape Town, to Ingrid Woolard of the Human Sciences Research Council (HSRC) and to Michael Noble of the Centre for the Analysis of South African Social Policy (CASASP) at the University of Oxford. Thanks are due as well to colleagues and friends who attended a seminar in the School of Development Studies at the University of Natal, where we test-flew the pre-conference version of the article. The usual disclaimer applies – the remaining errors are all our own.


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