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Fiscal expenditure incidence in South Africa, 1995 and 20001
A report for the National Treasury


Servaas van der Berg
SvdB@sun.ac.za

Department of Economics, University of Stellenbosch

21 February 2005

SARPN acknowledges the South African Treasury website as the source of this document: www.treasury.gov.za
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[Download Appendices - 239Kb ~ 1 min (84 pages)]

Introduction

In 1999, the National Treasury (then still known as the Department of Finance) requested a team of researchers to investigate shifts in fiscal expenditure incidence for the period 1993 to 1997. This study consisted of two parts, the one dealing with expenditure incidence and the other with tax incidence. The previous study was linked to a related study of tax incidence by Simkins, Woolard & Thompson (2000), using the same welfare measure (income per capita before social transfers). The expenditure incidence, or benefit incidence, side of this project was undertaken to systematically investigate who benefits from public expenditure (Van der Berg 2000a & b). The expenditure study focused on about 60 per cent of expenditure – education (both at school and at universities and technikons), health, social grants, water provision and housing – between 1993 and 1997. It concluded that the first years after the political transition to democracy saw a large and significant shift of social spending from the affluent to the more disadvantaged members of society. Spending had become relatively well targeted to poor people, as a result of shifts of government spending to social services, changes in composition of social spending, shifts between programmes, and better targeting. In particular, the extent of rural targeting was found to be extremely high for a developing country. The results of the study were used by the government inter alia in the 2000/01 Budget Review and to inform the Ten Year Review process by way of a paper to a workshop held by the Presidency (Van der Berg 2002) and were also incorporated into two journal articles (Van der Berg 2001a &b).

At the time of the original study, government made clear its intention of undertaking regular updates of this work in order to inform the policy process. Consequently, the present study was commissioned in 2004 from the same researchers. The terms of reference required determining incidence of public expenditure in 1995 and 2000 in key selected areas of particular concern to poor households.2 The objective was to determine whether and to what extent there had been a shift in public expenditure incidence between 1995 and 2000 and who were beneficiaries of such shifts. Since the previous study, the long term impact of policies adopted earlier has increased, e.g. greater equity in teacher-pupils ratios and the move towards primary health care. Some new policies designed to improve the situation of the poor had not yet had their full impact (e.g. the introduction and rapid expansion of child support grants) or were only implemented later (e.g. subsidies for basic municipal services).

The previous incidence study largely utilised the Income and Expenditure Survey (IES) linked to the October Household Survey (OHS) of 1995, referred to hereafter as IES/OHS95. The 2000 Income and Expenditure Survey linked to the Labour Force Survey of Statistics South Africa (hereafter IES/LFS2000) provides income and expenditure data that should in principle have enabled comparative analysis to be undertaken regarding changes in expenditure incidence between 1995 and 2000. However, there are severe credibility problems regarding IES/LFS2000, inter alia because the results published in a Statistics SA document (South Africa, Statistics South Africa 2002) appear to show large inconsistencies with the IES/OHS95 and with national accounts trends.3 In discussions Statistics South Africa blamed incomparability with the 1995 surveys on poor sampling and subsequent weighting in IES/OHS95, rather than in IES/LFS2000, implicitly admitting that their comparison of the results of the two surveys was not credible. However, there are many additional data problems relating to this survey:
  • The magnitude and relative magnitudes of income components are incompatible with national accounts data.


  • Matching the IES and LFS data does not produce consistent information about the race, age or gender of many individuals.4


  • There are large differences in the weights for the IES and the LFS.5
The General Household Survey (GHS) of 2002 and 2003 do not contain the systematic income and expenditure data needed both to rank households by their economic welfare into quintiles or deciles, and to determine the distribution of taxation across products and income sources. It could thus not be used as primary data source, but rather to supplement IES/LFS2000. Thus it became necessary to derive an alternative source of data for comparing the 1995 and 2000 datasets. For this purpose, additional work had to be carried out on the 2000 IES/LFS, to arrive at estimates that would be comparable.

Although this report is mainly concerned with expenditure incidence, it first sets out briefly the situation with the income distribution model, which formally resorts under the tax incidence sub-project but is also an essential input for this sub-project. The appendices provide a summary of the cost information gathered at the sectoral (programme)6 level as inputs to the final report. Given South African history, this incidence analysis should ideally consider at least the incidence of public spending by race group, income class and urban/rural location.

The beneficiaries of certain goods provided by government can be relatively accurately determined when determining public expenditure incidence, e.g. education, health services, social transfers, social welfare spending, and housing. The incidence of other functions is far more difficult to evaluate, e.g. police or defence spending. Various conventions have been followed in the expenditure incidence literature when allocating the benefits of the latter, but the results arrived at are largely driven by the assumptions made (e.g. that such functions are allocated in proportion to income, or in proportion to population share.7) Generally speaking, recent attempts internationally have ignored less easily allocable functions (usually those with a greater public goods character) and concentrated on spending that can be so allocated.

Income distribution dataset

A usable income distribution dataset was a first requirement for both the expenditure and tax sides of the fiscal incidence project. The Global Insight version of the 2000 Income & Expenditure Survey (IES) was used as the starting point for this work. This version was created by a private consultancy group, Global Insight, evaluating the expenditure data item for item and line for line. Because the documentation was hard-to-follow and incomplete, several weeks were consumed in trying to fully understand what had been done to “clean” the dataset. Global Insight focused exclusively on the expenditure side of the survey, whereas this project requires both the income and expenditure components. Once the dataset was as clean as possible, the data were purged of records regarded as unusable. For this purpose, “expected” per capita income and “expected” per capita expenditure were estimated separately and the point estimates compared to these predicted values. Where the point values were more than 2 standard deviations from the expected values and there was an apparent mismatch between income and expenditure, the record was discarded.

Both the 1995 and 2000 IES datasets were then re-weighted, for three reasons:
  • the original weights did not gross up to population totals;


  • the original weights were released prior to the release of the 2001 Census, which found significantly different population totals from what had been assumed in some provinces; and


  • to compensate for the records purged from the dataset.
Data for several years provided by StatsSA by age, province and gender were validated and the assumptions underlying StatsSA’s demographic model assessed, before the IES datasets were re-weighted. Comparisons were then made between the 1995 & 2000 survey results and the 1996 and 2001 census results.

[Download Appendices - 239Kb ~ 1 min (84 pages)]


Footnotes:

  1. This research was funded by USAID/South Africa through Nathan Associates SEGA/MESP Project (contract number: 674-0321-C-00-8016-00). I also wish to thank the Alexander von Humboldt Stiftung for the funding that allowed me to do much of the writing of this report whilst on sabbatical at the Arnold Bergstraesser Institut, Freiburg, Germany.


  2. The Terms of Reference for this study set out that the fiscal expenditure incidence should be determined for school education, tertiary education, health services, social assistance, housing, free water and free electricity. It turned out that free water and free electricity were not yet funded nationally or provincially in 2000, thus the preliminary work in this regard was discontinued after discussions with National Treasury.


  3. For instance, the 33% reduction in per household income and 43% reduction in per household expenditure in Gauteng are highly unlikely even if massive population shifts had taken place (which was not the case), and these reductions are inconsistent with modest real growth in retail sales and a 22% real increase in Gross Geographic Product of this province in the period 1995-2000 contained in other Statistics South Africa data series.


  4. Between the two datasets, 103 732 observations match, but there are 1 639 unique to the LFS dataset and 421 unique to the IES. Of the matched observations, there are 268 cases for which the race variable from the two datasets does not match, 839 for which gender does not match, and 1 263 for which age does not match (in only 178 of these is the age difference one year, which can probably be ignored). Altogether, for 2087 of the matched observations between the two dataset one or more of these variables (race, gender, age) do not match between the two dataset, and 8984 individuals are members of households for which one or more of these variables do not match across the two datasets, leaving only 96 808 individuals in households without some matching problems (91.5% of 105 792 observations in the two datasets, or 92.9% of the 104 153 in the IES person dataset).


  5. For Gauteng, the number of black household heads is 43% higher in the IES than in the LFS, and for coloureds 27%.


  6. The terms “programme” or “sector” will be used interchangeably, to refer to the expenditures covered. Neither term is fully accurate, as some but not all of these expenditures are indeed programmes.


  7. See in this regard McGrath 1983




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