Fiona Tregenna

WHAT ARE THE DISTRIBUTIONAL IMPLICATIONS OF HALVING POVERTY IN SOUTH AFRICA WHEN GROWTH ALONE IS NOT ENOUGH?

Keywords: income distribution, poverty, inequality, South Africa income distribution, poverty, inequality, South Africa. JEL codes: D30, D31, I32

What are the distributional implications of halving poverty in South Africa when growth alone is not enough?

INTRODUCTION

Sixteen years after the ending of Apartheid, poverty remains very high in South Africa. Over 10% of people in South Africa live on less than $1 a day, whereas in countries with similar levels of income per capita (such as Chile, Turkey, Malaysia, or Costa Rica) typically less than 2% of their population fall below the $1 a day line, and even comparable countries such as Brazil have less than 10% of people below this 1 Contact: ftregenna@gmail.com. This article draws on research commissioned by Trade and Industry Policy Strategies, on behalf of the Second Economy Strategy Project of the Presidency of the Republic of South Africa, and this financial support is gratefully acknowledged. Helpful comments from an anonymous referee are gratefully acknowledged. The generally unimpressive record of income poverty reduction since the advent of democracy in South Africa highlights the challenge of significantly cutting poverty. Countries such as Chile and to a lesser extent Brazil have made significant progress in reducing poverty in recent years, through dedicated programmes centred around targeting spending on the poor. Halving poverty by 2014 in South Africa, as per government's commitment, would arguably require a significant shift given the apparent stubbornness of poverty levels thusfar.

Section 2 of this paper quantifies what the 'halving of poverty' could mean, by setting out a monetary poverty line, clarifying two relevant measures of poverty, and by using the latest income and expenditure survey data to put figures to the target of halving poverty. Section 3 projects the poverty headcount ratio and poverty gap in 2014 under various growth scenarios, considering specifically whether poverty can be halved through growth under the current distributions of income and expenditure. Section 4 analyses the effects of various combinations of growth and distributional change on poverty, and section 5 discusses the broader implications of the findings. Section 6 concludes. The various datasets of the 2005/2006 Income and Expenditure Survey (IES) were utilised for this analysis. These are the official national household surveys produced by the national statistical agency, Statistics South Africa. 5 All data were inflated or deflated to March 2006 (depending on when the household was surveyed), using monthly CPI data. Both income and expenditure/consumption are shown in initial analysis, but the subsequently focus is on expenditure/consumption since this is most relevant to poverty. All calculations were undertaken on a household per capita basis, as elaborated further below.

b) The poverty line

AsgiSA does not define precisely what is meant by 'poverty' and hence what a 'halving of poverty' would actually mean. The Minister of Finance announced in his 2005 Budget Speech that a poverty line would be developed for South Africa. A process has since been underway, led by National Treasury and Statistics SA, to develop a national poverty line, and the official government targets for halving poverty are then to be framed in terms of that line. This study therefore uses the proposals contained in the official Statistics SA/National Treasury Discussion Document (2007) as a starting point to derive an appropriate line for this study. 6 This is a semi-normative poverty line based on a cost of basic needs approach. Statistics SA calculates a food poverty line at R211 [$31] 7 per capita per month (in 2000 5 The original datasets were accessed through the South African Data Archive of the National Research Foundation. In line with Statistics SA definitions and international best guidelines (see for instance United Nations University World Institute for Development Economics Research (2008)), expenditure/consumption has been calculated to include the following categories: food and beverages; tobacco and narcotics; clothing and footwear; housing, water, electricity, gas and other fuels; furnishings, household equipment and routine maintenance of the house; health; transport; communication; recreation and culture; education; restaurants and hotels; miscellaneous goods and services (which includes personal care; personal effects; social protection services; insurance; other financial services); and other services not elsewhere classified. Income has been calculated to include the following broad categories: income from work; income from capital; pensions, social insurance, family allowances; income from other individuals; other income; and income from imputed rent on owned dwelling (calculated as 7% of the value of the dwelling per annum). Not included in either income or expenditure are the estimated values of in-kind income or expenditure respectively; savings, debts, taxes, transfers made to others; loss incurred in obtaining income; and other products not consumption (such as interest on mortgage bonds; non-refundable bursaries; and the imputed costs of home production). 6 A full analysis of poverty would of course need to take into account the various monetary and non-monetary dimensions of poverty. These include not only the absolute level of income of expenditure, but also relative poverty, the meeting of basic needs, human dignity, and capabilities. The use of a monetary poverty line in the analysis that follows is not intended to undermine the importance of these aspects. However, the use of a specific line is necessary for empirical analysis of the relationship between growth, distribution, and poverty. 7 The conversion of figures from South African Rands to US$ in this article use the exchange rate in 2006 of R6.76/US$ for consistency, since the analysis is based on 2006 data. The $2 a day measure of poverty that is commonly used internationally translates to about R162 per capita per month in 2000 prices (Woolard and Leibbrandt, 2006). This is about half of the minimum poverty line which Statistics SA calculates, and is significantly below even the essential food component of the poverty line calculated as being necessary to meet minimum daily energy requirements. The $2 poverty line has been widely criticised (see for example Reddy and Pogge, 2008).

Statistics SA bases the household poverty threshold on a pooling of resources within households, with equal weighting given to all members of the household (i.e. without using any adult equivalence conversions, economies of scale, or other scaling). In other words, the poverty threshold for a household of five people would simply be [5 x R322 = R1610 per month] (in 2000 prices). An alternative approach to calculating a per capita poverty line and comparing this line to income or expenditure calculated on a per capita basis would be to take account of differences in nutritional requirements for people of different ages and genders; such an approach is discussed at the end of this section.

Drawing on the Statistics SA/National Treasury Discussion Document as well as discussions on the issue with Statistics SA officials involved in the process, this study uses as a basis the lower poverty line suggested in the Discussion Document (R322 per capita per month in 2000 prices). This R322 baseline needs to be inflated from 2000 prices to March 2006 levels for use with the 2005/6 IES data in the analysis that follows. Statistics SA inflates the 2000 figures using the CPI index for metropolitan areas only. This is flawed, particularly given the rural bias of poverty in South Africa. Further, given that CPI rates for the lower income quintiles tend to exceed those for the higher quintiles, the use of an overall CPI measure is inappropriate for inflating a measure which is relevant to people living in poverty, if the intention is to cost the same basic basket of goods deemed necessary in 2000. In order to construct an appropriate inflator index, this study thus uses the CPI rates for the lowest two quintiles for all areas In some parts of the analysis also consider the effects of using the 'food poverty line' as calculated by Statistics SA. This includes only the food items needed to meet minimum energy requirements, and excludes the costs of clothing, shelter, transport, and so on. This was calculated by Statistics SA at R211 A limitation of a household per capita poverty line, such as the one proposed by the government and updated here, is that it does not take account of differences in the demographic composition of households (specifically in terms of age and gender) and the concomitant different nutritional requirements of different people. More specifically, young children have daily nutritional requirements below those of adults, and to a lesser extent females' requirements are below those of males; it follows that households with relatively high composition of young children and/or of females will have lower per capita energy requirements. To the extent that these differences are correlated with income differences -for example if poorer households have a higher composition of young children and women than the average -this could lead to an overestimation of poverty. An alternative approach would be to calculate a poverty line for a specific demographic, such as adult females, and to calibrate household members in terms of their nutritional requirements relative to that benchmark demographic. This means that, given two households with the same income/expenditure and the same number of members but different demographic compositions, one household could be classified as falling above the poverty line and the other below it. adult equivalents in each household can be calculated, based on the age and gender of each member of the household and using the relative minimum nutritional requirements as per the Department of Health Study, and hence the income and expenditure per female adult equivalent of each household can be computed. This allows for a determination of whether members of a household collectively lie above or below the poverty line, and in the case of the latter the calculation of their poverty gap as well.

Poverty rates using this alternative measure of a poverty line are discussed in section 2(d).

c) Measuring poverty

Setting the level of a monetary poverty line answers only part of how to gauge poverty, and hence how to define what halving poverty would mean. One way of measuring poverty is the poverty headcount ratio 10 , and this is the measure that government seems inclined to use to quantify poverty.

The poverty headcount ratio measures the incidence of poverty, which is an important dimension of poverty. The simplicity of this measure may make it intuitively appealing from a policy perspective.

However, the poverty headcount ratio gives no indication of the intensity of poverty. The actual incomes of all the people falling below the poverty line do not enter into the poverty headcount ratio in any way.

The intensity of poverty can appropriately be measured not by the poverty headcount ratio but by the aggregate poverty gap, which sums the gaps between the poverty line and the income or expenditure of everyone falling below the poverty line. 11 The choice of poverty measure has significant policy implications, particularly insofar as specific targets for the reduction of poverty are part of government policy. The purpose of a 'target' is not only to evaluate outcomes but to inform policy design and implementation. The poorest people are highly unlikely to be lifted above the poverty line in the near future, and any increase in their incomes will have 10 The poverty headcount ratio H is the proportion of the population falling below the poverty line, and can be formally expressed as

∑ = = n 1 i p i ) y | y ( g n 1 H
where there are n individuals with expenditures or incomes i y arranged in ascending order such that . The poverty headcount ratio is typically expressed as 100H, showing the percentage of the population falling below the poverty line. The incidence of poverty can also be measured simply as a poverty headcount (i.e. the actual number of people falling below the poverty line, rather than as a proportion of the population). However this is less desirable than a ratio as it gives a less meaningful sense of the extent of poverty, and population changes can also obscure the interpretation of changes over time.

11 Using the same notation as in the previous footnote, the poverty gap G can be formally expressed as no impact on the poverty headcount ratio. To the extent that success in poverty reduction is measured exclusively in terms of the poverty headcount ratio, this could de-emphasise raising the incomes of the poorest people. This is particularly important when, as will be seen below, about half of South Africans can be classified as poor. Measuring the halving of poverty solely in terms of the poverty headcount ratio could imply a focus on the second quartile of the population (i.e. the second quarter from the bottom of the population) and not the poorest quartile.

∑ = = n 1 i i g G where ) 0 , y y max{ g i p i - = .
Given the important shortcomings of the poverty headcount ratio, and the information about the intensity of poverty conveyed by the aggregate poverty gap, it seems advisable that the AsgiSA target of halving poverty be framed not only in terms of halving the poverty headcount ratio but also in terms of halving the poverty gap. While this formulation may lose some of the appealing simplicity of using only the poverty headcount ratio, it seems justified by a more comprehensive standard of measure. The analysis that follows uses this dual measure of the 'halving of poverty', in terms of halving both the poverty headcount ratio and the aggregate poverty gap. The poverty gap would be R56.21 billion for expenditure and R55.6 billion for income. These figures are very similar to those derived using the household per capita poverty line, particularly for the headcount ratio. This suggests that differences in the demographic composition of the halves of the population above and below the poverty line have only a minor effect on the calculation of the poverty gap using minimum nutritional requirements averaged across age and gender (as in the household per capita method used by Statistics South Africa).

The empirical analysis in this study is thus conducted using the household per capita poverty gap. This measure is appealing in that the number of people designated as falling below a poverty line counts actual people, as opposed to adult-female-equivalents. For instance, cutting the poverty headcount measure when using the adult-female-equivalent approach does not actually mean that half as many people fall under the poverty line as previously, but rather that half as many female-adult-equivalents fall under the poverty line as previously. In the case of the household per capita measure, the fact that it refers to actual people is especially valuable given that this analysis is policy-oriented. Furthermore, the primary focus here is on taking the poverty line which government seems to be planning on adopting, as well as government's target of halving poverty by 2014, fleshing these out and most importantly looking at under what combinations of growth and distributional scenarios poverty could be halved. If poverty rates were dramatically different when calculated on the basis of a female-adult equivalent measure, this could be a reason for presenting the empirical results using this measure despite it being less heuristically appealing. But given that it does not make much difference to the empirical analysis, the results shown in the remainder of this paper are on the basis of the household per capita measure.

Given that both the income and expenditure poverty headcount ratios are in the region of 50%, the 'halving of poverty' target can be approximated as involving the following two components:

• Cutting the poverty headcount ratio to 25% of the population by 2014;

• Reducing the aggregate poverty gap to R30 billion12 [$4.4 billion] by 2014.

It is worth noting that, although about half the population is classified as poor, the poverty gap is only about 3% of GDP. The analysis of the relationship between distribution, growth, and poverty that follows is based on how these targets can be achieved. The actual policies that could be implemented to address poverty or change distribution fall outside of the scope of this article. Rather, the focus is on what the commitment in AsgiSA to halving poverty means in terms of growth and distribution, and under what growth/distributional scenarios these targets can be achieved.

CAN POVERTY BE HALVED THROUGH GROWTH?

In order to establish whether the AsgiSA target of halving poverty can be achieved through distributionally neutral growth, various growth rates are applied uniformly across each of the 47 391

192 individuals in the (weighted) dataset.

Since the poverty line is a monetary poverty line based on the cost of a basket of goods, it remains constant in real terms. This means that, with any positive growth, there will be reductions in the poverty gap and headcount ratio, so long as there is not a worsening of distribution affecting the bottom half of the population.

AsgiSA sets GDP growth targets of at least 4.5% between 2005 and 2009, and at least 6% between 2010 and 2014. We consider how poverty would evolve by 2014 with these rates and the current distributional structure. However, these rates are targets and not projections or forecasts. Furthermore, realistically it seems inconceivable that these rates will actually materialise, particularly in the light of the global economic problems.

Two sets of growth forecasts are also used. In summary, the AsgiSA growth targets translate to average annualised GDP growth of 5.43% per annum over the period 2006-2014, while the forecasts of the National Treasury and the averaged forecasts of the private banks translate to 2.94 and 3.11 respectively.

For comparison purposes, GDP in South Africa grew at a real average annualised rate of 4.1% between 2000 and 2006, which was higher than for many years previously, and 2.4% between 2006 and 2009. These rates were reached during the recent commodities boom from which South Africa benefited, and which is unlikely to continue in the near future. The recent downturn in the world economy, which is also affecting South Africa, will in all probability result in a decline in growth rates. It goes without saying that this would make it even more difficult to attain the poverty targets than is shown here.

These three different growth rates are applied uniformly across the distribution to ascertain the effects on poverty. Note that this only means that people gain uniformly in proportionate terms; in absolute terms the wealthy of course gain many times more than the poor with a uniform growth rate.

Tables 1 and2 show what the halving of poverty would mean in terms of the poverty headcount ratio and aggregate poverty gap. Tables 3 and4 thereafter show the same using the food poverty line. These results are shown in terms of each of expenditure and income, but in the subsequent analysis the focus is on expenditure since this is most directly relevant to measuring poverty. In each case the poverty gap and headcount ratio in 2014 are projected under three growth scenarios (using AsgiSA targets, Treasury forecasts, and the banks' forecasts), given the current distribution of income or expenditure. In other words, this shows how far growth alone would go towards meeting the targets of halving poverty, under the current distributional structure.

Even with the growth rates targeted in AsgiSA, neither the poverty gap nor the poverty headcount ratio can be halved with the current distribution of income or expenditure. Growth at the AsgiSA targeted rates would make significant inroads into poverty -cutting the poverty headcount ratio by about a third and the poverty gap by around 45%. Even with the food poverty line, growth at the rates targeted in AsgiSA would result in halving the poverty gap but not the poverty headcount ratio. If actual growth between now and 2014 is closer to the rates forecast by Treasury and by the banks, the proportion of people living under either poverty line is cut by far less than half.

Note:

The poverty gap in 2006 of +R60bn is equivalent to +$8.9 billion, which is about 3% of South Africa's GDP. It can be safely concluded that it is extremely unlikely that poverty can be halved through growth alone.

This means that poverty will not be halved by 2014 in the absence of some form of pro-poor distributional change. Furthermore, these results show the effects of alternative growth rates on poverty if distribution is unchanged; were distribution to worsen then of course even fewer people would be lifted out of poverty at any of these growth rates.

TIP curves are utilised to show both the poverty gap and poverty headcount ratio under the current distributions of income and expenditure, and subsequently to explore the relationship between distribution, poverty, and growth and specifically to assess what combinations of growth and distributional change would allow for the halving of the poverty gap and poverty headcount ratio.

Derived from Jenkins and Lambert (1997), 'TIP' refers to the 'Three I's of Poverty': the incidence, The slope of the TIP curve at any given percentile equals the poverty gap for that percentile. For the subset of the population falling below the poverty line, the TIP curve is an increasing concave function of p, while for people above the poverty line the curve is horizontal (since their poverty gaps are zero).

Insofar as the curve flattens as it approaches the poverty line, this shows the decline in the poverty gap as expenditure or income increase towards the threshold.

The extent of poverty incidence, in terms of the poverty headcount ratio, is shown by the value of p at the point where the curve becomes horizontal. This is shown by the length of the non-horizontal part of the TIP curve.

The intensity of poverty is shown by the overall height of the TIP curve, since the height of the curve (at p=1) is the aggregate poverty gap averaged over the entire population. The average poverty gap amongst the population falling below the poverty line is given by the slope of a ray from the origin to (h,

TIP(g; h)).

The degree of inequality amongst the poor is shown by the degree of concavity of the non-horizontal section of the TIP curve. If all of the poor had equal incomes then the non-horizontal section of the curve would be a diagonal straight line (with a gradient equalling the difference between the poverty line and the average income of the poor). 4).

In Figure 2 below the original TIP curve for expenditure is compared with that which would result if the growth rates targeted in AsgiSA were to materialise through to 2014, given the current distribution of expenditure. The pattern of expenditure that would derive from that is shown as a dashed curve. Using the Treasury or banks' forecasts would yield TIP curves in between these two curves.

With the growth rates as hoped for in AsgiSA, the poverty gap is reduced drastically and the poverty headcount ratio also falls significantly. Despite this, it can be seen that neither the poverty gap nor the poverty headcount ratio is actually halved. Even in the highly improbable event of the AsgiSA-targeted growth rates materialising, this would be insufficient to halve poverty without some pro-poor distributional change (in the sense of distributional change that disproportionately raises the income and expenditure of the poor). With the current distribution, it would take many years of growth to halve poverty (in particular, the poverty headcount ratio). Were GDP to grow at an annual rate of 4% per annum, both measures of poverty would not be halved until the year 2022. With GDP growth of 3% per annum, only in the year 2027 would poverty be halved. This dramatically illustrates that relying on growth to bring down poverty would effectively mean that poverty would not be halved for a long time to come. The concern here is not so much a direct redistribution of income through social transfers, although this could certainly be a component of distributional change. The analysis is concerned more fundamentally with an overall shift in the growth path towards more 'pro-poor growth', in the sense of growth in which the incomes of the poor increase relatively more than do those of the non-poor. The distributional changes simulated here are intended to be indicative of the scale of 'redistribution' of incomes and expenditure that would result from a more pro-poor growth path. For instance, one in which returns to unskilled labour rose more rapidly than returns to skilled labour, and/or a relative expansion in employment opportunities. Such shift would not result in the exact distributional changes simulated here; these projections are indicative in nature and are suggestive as to what combinations of growth and a more egalitarian distribution could result in a halving of poverty.

b) Method for simulating distributional changes

The methodology used in simulating alternative distributional scenarios is set out below with reference to income for heuristic purposes, but these were undertaken with each of income and expenditure. The method is explained intuitively here, and mathematically in Appendix 2.

We begin by ranking the entire South African population from highest to lowest in terms of household per capita income. The distributional changes simulated here 'revolve' around a specific point in the distribution. In the simplest case this is the median income earner. We have also used the person at the 66.6 th percentile (i.e. where a third of people have higher incomes) and the 75 th percentile. This 'anchor' point is the only person whose income is unaffected by the distributional change. 15 Everyone with a higher income than this person loses from the distributional change and everyone below that person gains. The extent to which someone loses or gains depends on how far they are from the unaffected person: the highest income earner loses most while the lowest gains most. The simulated distributional change is generally rank-preserving because of the relatively small increments spread continuously over a population of over 47 million, with a small number of marginal rerankings.

In the simplest case in which distributional change revolves around the median income earner, the change is symmetrical around that point. The loss of the highest income earner is the exact gain of the lowest; the loss of the second highest income earner is the gain of the second lowest; and so on. In this case the distributional change is both mean-preserving and median-preserving. 15 Since weights are being used this is not necessarily an actual individual, but the principle is the same. In a slightly more complex variation, the point around which the distributional change revolves is not the median income-earner (i.e. the 50 th percentile), but the person at for instance the 66.6 th or 75 th percentile. In these cases the distributional changes simulated are mean-preserving but not median preserving, and the distributional change is not symmetrical around the person whose income remains constant. If for example the change in the distribution of revolves around the 75 th percentile, the gain of the bottom three income earners must be matched by the loss of the top income earner, the gain of the next three income earners must be matched by the loss of the second highest income earner, and so on.

One parameter of these transformations is the 'scale' of the distributional change, in terms of how much income is redistributed. The simplest way to think about this is to set by how much the income of the lowest earner should grow through the distributional change. Simulations have been run here in which the income or expenditure of the bottom income earner grows by amounts ranging between R50 [$7.40] and R300 [$44.4] per month. While this would constitute a very significant increase in income for someone at the lowest end of the distribution, the negative effect at the top of the distribution is but a miniscule fraction of the income of the highest earners.

For example, in the case of a distributional change in which the income of the lowest-income person rises by R50 and the distributional change revolves around the median, the income of the highestincome person would decline by R50. The income of the second-lowest-income person would rise by just under R50 and that of the second-highest-income by fall by just under R50 and so on, with the amounts falling uniformly from both sides until reaching zero at the median. In the case of a distributional change of a maximum R50 but revolving around the 75 th percentile, the income of the lowest-income person rises by R50 while the income of the highest-income person declines by R150, with the absolute amounts declining from both ends (but in larger increments for the top quarter of the distribution) until reaching zero at the 75 th percentile.

An alternative way of modelling distributional changes would have been simply to apply different growth rates to different parts of the distribution spectrum -for instance, that the income or expenditure of the bottom decile grows at 7%, that of the next decile at 6.5%, and so on. However, such a method is much cruder than the one have employed in this paper. The method used here avoids an outcome where the income or expenditure of the person at the top end of the bottom decile grows significantly more than that of the person just above them at the bottom of the next decile. In the method employed here, the growth rates vary not by income category (e.g. deciles) but by individual, resulting in a much more continuous distributional change across the distributional spectrum. This analysis does not model the causal relationships between growth and distribution. It uses microdata to simulate distributional changes and to combine these changes with various growth rates in order to quantify the effects on poverty.

c) Projected poverty outcomes under various growth/distributional scenarios

Growth rates averaging between 1% and 7% per annum through to 2014 are considered here. While the upper growth scenarios are not at all likely to materialise, they are included here for the purposes of comparing various growth/distribution combinations.

We thus simulate the effects on the poverty gap and headcount ratio of eighty-four different combinations of growth and distributional change, for each of income and expenditure. These scenarios combine seven alternative growth rates (1%, 2%, 3%, 4%, 5%, 6%, and 7% annual average growth rates through to 2014) with four different 'intensities' of pro-poor distributional change (in which the income of the lowest-income person rises by R50, R100, R200, or R300) and in which distributional change revolves around each of the median, the 66.6 th percentile, and the 75 th percentile. This allows for a consideration of the effects on poverty of combining growth with change in distribution that benefits the poor.

Poverty outcomes under two such scenarios are shown in Figure 3 for illustrative purposes. The solid line shows the expenditure pattern that would result from 6% GDP average growth per annum through to 2014, combined with a progressive distributional change in which the poorest South African is just R50 better off than they would otherwise have been. The dashed line shows a scenario in which growth is fairly low at 2% per annum but there is a more intensive distributional change, with the lowestexpenditure person gaining an additional R200 per month (with decreasing amounts thereafter, as explained earlier). The poverty gap is halved in both of these scenarios (as can be seen by the fact that both curves lie below the horizontal dotted line). However, while the poverty headcount ratio is reduced in both cases, this is by less than half (both curves flatten out a bit to the right of the vertical dotted line). Neither of these particular growth/distribution combinations is quite enough to halve the proportion of people living below the poverty line. Figure 4 shows two growth/distributional scenarios in which both the poverty gap and the poverty headcount ratio are halved. In the scenario depicted with a solid line, GDP grows at 4% per annum, while in terms of distribution the expenditure of the poorest person is R200 per month higher than would otherwise be the case. The dashed line shows a scenario of GDP growth of 3% per annum with distributional change where the expenditure of the poorest person is raised by R300 per month. The TIP curves for both scenarios fall well below the horizontal dotted line, indicating that the poverty gap is actually cut by much more than half (in the second scenario it actually falls as far down as to 12%). Both curves flatten out to the left of the vertical dotted line, showing that the poverty headcount ratio is cut by at least half (in the second scenario, the poverty gap is actually cut by almost 80%). In these growth/distribution scenarios the target of halving poverty is thus achieved on both counts. Table 5 summarises whether the targets of halving the poverty headcount ratio and the poverty gap could be met under a range of growth/distribution scenarios. While these results are shown for expenditure, there is only very minor variation for income. The effects of GDP growth through to 2014 at averages of 1-7% per annum are considered. These growth rates are shown here combined with four different pro-poor distributional scenarios. Following the method described earlier, in the most 'intensive' distributional change the maximum gain is R300 per month, which benefits the very poorest person, with the gains decreasing from there. In the least 'intensive' distribution scenario shown here, the lowest-expenditure person gains by only R50 per month; intermediate scenarios of R100 and R200 are also shown. The results shown here are for distributional changes revolving around the 66.6 th percentile. For each scenario Table 5 indicates whether or not the target of halving poverty is met. Since the halving of poverty is being considered in terms of halving both the poverty headcount ratio and the poverty gap, in each scenario an 'H' indicates that the poverty headcount ratio is (at least) halved while a 'G' indicates that the poverty gap is (at least) halved. The eleven scenarios in which both dimensions of poverty are halved are shaded in.

Even under a (unrealistically optimistic) scenario of 7% annual growth through to 2014, the poverty headcount ratio cannot be halved without some distributional change. Conversely, even with growth as low as 2% per annum, both the poverty gap and poverty headcount ratio can be halved with distributional change in which the poorest person consumes an additional R300 per month.

Distribution R300 R200 R100 R50 None Growth 7% H, G H, G H, G -,G -,G 6% H, G H, G -,G -,G -,G 5% H, G H, G -,G -,G -, - 4% H, G H ,G -,G -, - -, - 3% H, G -,G -,G -, - -, - 2% H, G -,G -, - -, - -, - 1% -,G -,G -, - -, - -, -

Notes:

Growth refers to the average annualised growth rate between 2006 and 2014 under the various scenarios. Distribution refers to the distribution scenarios as set out in the text. R300 means that the expenditure of the lowest-income person is R300 per month higher than it would otherwise have been (with amounts decreasing from there as income rises); similarly for R200, R100, and R50.

For each scenario (growth/distribution combination), H means that the poverty headcount ratio is at least halved and G indicates that the poverty gap is at least halved; -means that those measures are not halved.

Tables A1-A3, in Appendix 3, show what inequality of expenditure (household per capita) would look like under the various growth/distributional scenarios. The Gini coefficient of the current distribution of expenditure is 0.67, and without any distributional change this would of course remain the same irrespective of the growth rate. 16 Before considering growth, the last row of the tables shows how much the Gini would be brought down to under each of the distributional scenarios.

Distributional change in which the poorest person gains an additional R50 per month, with decreasing gains for each person going up the distribution, would already cut the Gini to 0.65 (for distributional change around the 75 th or 66 th percentiles) or 0.66 (for distributional change around the 50 th percentile).

The most intensive distributional change modelled here, in which the poorest person consumes an additional R300 per month, could bring the Gini down as far as to 0.56. While this level of inequality would be a significant improvement on current levels, it would still be extremely high by international standards, bringing South Africa to about the current level of inequality in Brazil.

Finally, it can be noted that, while the point at which distributional change revolves does not really affect the impact on poverty, it does affect overall distributional outcome. As would be expected, the 16 The only reason why the Gini varies across growth rates under a given distributional scenario is that the distributional changes were implemented after applying the growth rates, so that the value of a distributional change differs relative to the post-growth income or expenditure values. Had the distributional changes been applied prior to the respective growth rates, the Gini would be constant for any given distributional scenario, irrespective of the growth rate. However, this would mean that the scale of the distributional change would not be identical for any given distributional scenario, as the growth would also affect the size of the effective distributional change, e.g. the poorest person would gain not just R300 under the 'R300' distributional scenario, but R300 inflated by a growth rate, cumulative over the eight year period. higher the point in the expenditure spectrum around which distributional change revolves, the greater the reduction in poverty (for any given growth rate and maximum gain at the bottom). For instance, under 4% annual growth and with distributional change in which the poorest person consumes an additional R200 per month, the current Gini coefficient of 0.67 falls to 0.63 in the case where distributional change revolves around the median, to 0.62 where distributional change revolves around the 66.6 th percentile, and to 0.61 where distributional change revolves around the 75 th percentile.

DISCUSSION

Some important implications emerge from these scenarios concerning meeting the target of halving poverty. First, the target of halving poverty by 2014 does appear to be feasible, under growth rates that are a bit lower than in recent years and with quite mild distributional change. It might be suggested therefore that this target should not be given up upon or treated as some distant goal or rhetorical aspiration. This is reinforced by the fact that the entire poverty gap in South Africa (using the poverty line specified here) is just 3% of GDP.

Second, however, it is highly improbable that the AsgiSA poverty reduction target will be attained in the absence of a pro-poor shift in the growth trajectory. Growth alone will not enable the halving of poverty. Furthermore, it is unlikely that the growth path would endogenously evolve in a sufficiently pro-poor way, without active policy interventions designed to achieve this shift.

Third, these scenarios warn that any worsening of inequality will make the meeting of the AsgiSA poverty targets virtually impossible. Specifically, should distribution worsen for the bottom half of the population, unrealistically high growth rates would be needed to halve poverty by 2014. Given that income and expenditure include non-earnings sources, economic growth would in itself not necessarily be distributionally neutral in the absence of policy measures to ensure that the unemployed also benefit. Growth which failed to carry along those in the lower part of the distribution would not even have the poverty-reducing effects shown earlier for growth alone. South Africa thus cannot afford any worsening of inequality if poverty is to be halved by 2014.

Fourth, given that government is still finalising the level of the national poverty line, the possible temptation for policymakers to set this too low should be avoided. It currently appears that, notwithstanding the background research by Statistics SA into the minimum amount which could be used for a poverty line, government is considering setting it even lower than this level. This might be motivated at least in part by the realisation of just how many people would fall under such a line, and perhaps a concern that it would be difficult to halve that number of people within a reasonable timeframe. One insight that emerges from this analysis is that even middling growth with no distributional change goes a long way towards halving of poverty by 2014, and with what might be considered fairly mild pro-poor distributional change the halving of poverty appears to be feasible.

While a poverty line in the region of R450 [$67] per capita per month (as used in this analysis) means that about half of all South Africans would currently be classified as poor, this should not necessarily motivate the choice of a lower poverty line given the feasibility of dramatically cutting poverty over the next few years.

Given South Africa's levels of income per capita and status as an upper-middle income country, the scale of poverty is associated more with distributional patterns than with the total amount of resources available. Poverty in South Africa would be far lower than it is, were distribution to be at anything approaching a typical level of inequality by international standards.

While decent rates of growth could make some inroads into poverty, given the scale of poverty growth alone will fall short. 17 Internationally, [START_REF] Bourguignon | The poverty-growth-inequality triangle[END_REF] emphasises that distribution matters for poverty reduction, and that comparative international evidence indicates that over the medium-run distributional changes can account for significant increases or decreases in poverty. Highlighting the country-specificity of this relationship, he suggests that changing the distribution is likely to be more important than growth for reducing poverty in middle-income and inegalitarian countries. South Africa is a classic instance of such countries. The simulations of the effects of various growth/distributional scenarios suggest that halving poverty by 2014 requires a 'pro-poor' shift in the growth trajectory (over and above the distributional policies currently in place), such that distribution becomes less unequal.

Conversely, any worsening of inequality will put the AsgiSA poverty reduction targets beyond reach.

A broader issue involves the causal relationship between growth and distribution. There is a corpus of literature on the effects of inequality on growth, and another concerning the effects of growth on inequality. The impact of growth on distribution is a priori indeterminate, and would be contingent on country-specific factors such as relative factor endowments and rewards (e.g. to labour and capital, and to various skills categories). The relationship is also affected by the sectoral character of growth, in 17 In terms of the relationship between growth and poverty, [START_REF] Adams | Economic growth, inequality and poverty: estimating the growth elasticity of poverty[END_REF] finds that economic growth reduces poverty in developing countries, but the rate of this reduction depends heavily on how growth is defined and measured. For African countries, [START_REF] Fosu | Inequality and the growth-poverty nexus: specification empirics using African data[END_REF] finds that greater income equality would positively affect the impact of growth on poverty reduction, although there is a high degree of heterogeneity among countries. Fanta and Upadhyay (2009) find that, for a sample of 16 African countries, growth reduces poverty, with estimated elasticities between -0.5 and -1.1. For studies of the growthinequality-poverty relationship in other parts of the world, see for example Wodon (2000), Enders and Hoover (2003) and [START_REF] Gibson | The impact of growth and distribution on poverty in Papua New Guinea[END_REF]. which growth is highest. Broader political economic and institutional issues also affect the way in which growth affects distribution, including factors such as the power of various groups, and the impact of growth on institutions and culture. Empirically, according to [START_REF] Bourguignon | The poverty-growth-inequality triangle[END_REF] there is too much country specificity in the impact of growth on inequality to allow for any generalisation. Similarly, [START_REF] Ravallion | Growth and poverty: evidence for developing countries in the 1980s[END_REF] finds that there are no systematic effects of growth on poverty.

The classical conception of the relationship between inequality and growth was expressed in Kuznets' inverted-U curve, according to which inequality initially rises with income per capita but falls at higher levels of income per capita. Especially in the earlier literature, this was interpreted as pointing to a trade-off between growth and equity, up to the turning point of the inverted-U. According to Bourguignon and Morrison (1998), the inverted-U hypothesis was borne out empirically in the 1970s but not since then. [START_REF] Zweimüller | Inequality, redistribution, and economic growth[END_REF] points to increases in inequality in several major OECD over the past three decades or so, but significant variation among countries. According to Deininger and Squire (1998) there is no very little support for a Kuznets curve when considering time-series data for most countries of the world.

In terms of the causal relationship from inequality to growth, it has been argued in the literature that inequality can be beneficial for growth, inter alia through the incentivisation of work effort and risktaking; the promotion of innovation; higher savings rates being associated with higher inequality; concentration of wealth allowing for the large initial investments required for some types of production.

Recent empirical work supporting a positive relationship between inequality and growth includes [START_REF] Forbes | A reassessment of the relationship between inequality and growth[END_REF] who finds evidence that, in the short and medium term, an increase in a country's level of income inequality has a significant positive effect on subsequent growth.

Conversely, an extensive theoretical and empirical literature has emerged that points to a negative causal relationship from inequality to growth; see for instance [START_REF] Alesina | Distributive politics and economic growth[END_REF], [START_REF] Aghion | Inequality and economic growth: the perspective of the new growth theories[END_REF], Persson and Tabellini (1994), [START_REF] Clarke | More evidence on income distribution and growth[END_REF], and Deininger and Squire (1998). Channels identified by various authors for such a relationship include imperfections in capital, credit and insurance markets associated with inequality that negatively affect investment and growth; that high inequality can encourage policies that are detrimental to growth; negative effects of inequality on work effort and productivity; conflict and instability and weak social capital associated with high inequality; and inefficient allocation of education resources with a deleterious effect on human capital formation. [START_REF] Ferreira | Inequality and economic performance: a brief overview to theories of growth and distribution[END_REF] characterises the recent state of the inequality-growth debate as being that income inequality might not directly affect growth but that it proxies for wealth inequality, and that wealth Given that South Africa is one of the most unequal countries in the world, this could suggest that reducing inequality could be beneficial not only for reducing poverty but also for increasing growth. To the extent that a reduction in inequality could contribute to raising the growth rate, this could suggest that pro-poor distributional changes such as those simulated here might support higher growth, thus making the scenarios which combine a reduction in inequality with higher growth easier to achieve than would otherwise be the case.

The actual effects of a specific conjuntural reduction of inequality on growth would of course be contingent on the nature of that reduction and how it came about. The types of policies that could be considered for the reduction of inequality in South Africa might include land reform and other forms of asset redistribution; a more progressive taxation system; increased expenditure directed at improving the incomes and capabilities of the poor; a more equitable education system; measures to reduce unemployment and in particular to increase employment opportunities for the unskilled and semiskilled; labour market interventions to narrow the wage gap; and measures to influence the sectoral structure of the economy in favour of sectors from which low-income earners derive a greater share of income. A consideration of the specifics of policy options for reducing inequality, and the implications of such options for growth, fall beyond the scope of this study. More pertinently, insofar as reducing the extremely high level of inequality in South Africa would support a higher rate of growth, then such measures could be considered not only for the purposes of reducing inequality and poverty but also for improving growth performance. This is also germane to the feasibility of scenarios combining reduced inequality with higher growth rates.

Distributional changes would of course not in practice materialise in the manner modelled here, but these simulations are indicative of the scale of distributional changes needed to halve poverty. The most important dynamic underlying actual distributional changes is likely to be through the labour market, in terms of both employment creation (or losses) and the distribution of earnings amongst the employed.

Social spending certainly has a role to play in ameliorating inequality and poverty, particularly in the short-medium term. However, South Africa's inequality is unlikely to be brought down to 'decent' levels -at least to 'normal' standards of inequality internationally -through social spending, but rather through increased demand for low-and semi-skilled labour and through a closing of wage gaps. dynamics. However, the magnitude of the reduction in inequality that would be required to bring South Africa anywhere in line with international norms is realistically not going to happen without policies dedicated to that end. The distributional changes analysed here would not even bring South Africa down to typical levels of inequality for a middle-income country, but to the range of highly unequal countries such as Brazil.

A stylised fact of distributional changes internationally, at least in recent decades, is what might be termed a 'downward stickiness' of inequality (see [START_REF] Palma | Globalizing inequality: 'centrifugal' and 'centripetal' forces at work[END_REF]. Increases in inequality are much less reversible than are decreases. For instance, in countries where a government has come into power which instituted conservative economic policies that worsened income distribution, followed by the election of a government that switched to more 'progressive' policies, the distribution of income typically hardly comes down and certainly not down to the initial levels. Even where the intention is genuinely to improve income distribution, this often turns out to be far more difficult than anticipated. This is not surprising, as the wealthy are generally far better able to protect their income than are the poor, as well as being better placed to reverse any 'unfavourable' changes in distribution that do occur.

This asymmetry in distributional changes underlines the point that a significant improvement in income distribution is highly unlikely to materialise without strong policy interventions geared towards that goal. Improving income distribution is possible, but it takes effort.

With the poverty line as defined here, the aggregate poverty gap is only about 3% of GDP. This suggests that poverty in South Africa should not be viewed as an insurmountable problem. In fact, given that half of the population falls below that line, 3% of GDP is a comparatively small amount, and is smaller than what might have been expected before analysing the data. Of course the actual cost of eliminating poverty would significantly exceed this amount if considered in terms of direct transfers (given issues of targeting and administration).

Nonetheless, considering the huge scale of poverty in terms of its incidence, in conjunction with the rather small scale when considered in terms of GDP, does suggest the feasibility of dramatic reductions in poverty. If this proves intractable through a shift in the growth path, direct transfers could prove effective (as they have been in the case of Brazil). The extreme levels of inequality in South Africa would seem to suggest that there is considerable scope for pro-poor distributional change.

In this vein it might be suggested that the reduction of inequality be placed as a more central and explicit goal of government policy than is currently the case, both for its own sake and in order to 27 significantly reduce poverty. Whether the reduction of inequality is a desirable goal in its own right is obviously a political issue. An associated consideration, if indeed the reduction of inequality is a public policy objective, is how strongly and in what ways this is to be pursued insofar as there are tensions between this and other policy goals.

CONCLUSION

This study has investigated how poverty rates in South Africa might evolve under various combinations of growth and distributional change. Specifically, whether the target set by the South African government of halving poverty by 2014 can be achieved through growth alone. This target has not yet been fully fleshed out, but it is proposed here that it be defined in terms of both the poverty gap and the poverty headcount ratio, and an updated monetary poverty line was set out here. The target of halving poverty by 2014 was thus concretised here as cutting the poverty headcount ratio to a quarter of the population and reducing the aggregate poverty gap to R30 billion (in March 2006 Rands, equivalent to $4.4 billion). With the current distribution of income and expenditure, these targets cannot be reached even were the highly ambitious growth goals set by government before the economic crisis to materialise, and with growth rates similar to those forecast by government and the private sector the poverty reduction targets would be even further off. Simulations indicate that relying on growth alone, were the current distributional structure to remain, would mean that poverty would only be halved by the year 2027 under growth rates of 3% per annum or by 2022 under growth rates of 4% per annum.

This underlines the imperative of pro-poor distributional change if poverty is to be halved by 2014. A different scale is used in this case as from the other TIP curves (in Figures 1234).

APPENDIX 2: METHOD USED FOR SIMULATING DISTRIBUTIONAL CHANGES

To set out the method described in section 4 more formally, let x i denote the income or expenditure of person i where the population is ranked from lowest to highest in terms of variable x, for i = 1,2,…,n.

That is, x 1 is the lowest income or expenditure and x n the highest. Note that this ranking will differ for income and for expenditure. In this analysis, n = 47 391 192. Let i θ be the value of the distributional change affecting person i such that:

0 i > θ for ρ < i ; 0 i < θ for ρ > i
; and

0 i = θ for ρ = i .
Select ω , the value of the gain to the person with the lowest income or expenditure [i=1].

The range of values to be redistributed will be:

φ θ ω ≤ ≤ i
where φ is the maximum loss to the person with the highest income or expenditure , the distributional gain will be:

1 ) i ( 1 ) 1 i ( i - - = - - - = ρ ρ ω ρ ω ω θ .
It can be seen that for the poorest person, the distributional gain will be the full value of the maximum distributional change, ω , while for the person just below the unaffected person (e.g. immediately below the median person in the simplest case) the distributional gain will be positive but close to zero. The post-distributional-change income of person i will thus be:

  between 1995 and 2000, but a decline in poverty between 2000 and 2004. The UNDP (2003) finds a small reduction in the poverty headcount ratio between 1995 and 2002. A review recently put out by the South African government (Government of the Republic of South Africa, 2008) claims declines in both the poverty gap and the poverty headcount ratio between 1995 and 2005, and attributes this decline primarily to government's social welfare grants.

  This is intended to represent the minimum amount required to purchase enough food to meet an average person's basic daily food-energy requirements over a month.8 Statistics SA then estimates the non-food component of a poverty line as R111 [$17] per capita per month. 9 This yields a total poverty line of R322 [$48] per capita per month in 2000 prices.

  other urban, and rural) in order to inflate the 2000 poverty line to March 2006 prices. The mean of these CPI rates for the lowest and second-lowest quintiles are used, given that these are most relevant to the basket of goods consumed by the poor. The use of this inflator indices results in the poverty line of R322 in 2000 prices being converted to a line of R450.48 [$67] in March 2006 prices (as opposed to R422.46 when the overall CPI for urban areas is used). March 2006 is used because this is the month to which the 2005/06 figures are calibrated. The baseline poverty line used in the analysis which follows is thus R450 [$67] per capita per month (or R5 400 [$800] per capita per annum) in March 2006 prices.

  per capita per month (in 2000 prices) which translates to R295 [$44] per month in March 2006, for use with the 2005/6 IES data.

Following

  this alternative approach, a 'female adult equivalent' poverty line was also derived, based on a study of average daily energy requirements by age and gender produced by the national Department of Health. That study estimates the minimum daily nutritional requirement of an adult female as 2 145 kilocalories, which was costed (based on the same costing model utilised by Statistics South Africa), the non-food component added in, and inflated to March 2006 prices (in the same manner as described a female adult equivalent poverty line of R435 per month. The number of female

  Firstly, the growth forecasts put out by the National Treasury for the years2010-2012 (National Treasury, 2010), combined with the actual growth rates for 2007-2009. Since official forecasts are not available for the years 2013 and 2014, the 2012 forecasts are extended for these two years. Secondly, we use the growth forecasts put out by the major private banks in South Africa for the years 2010 onwards, again combined with actual growth rates for 2007-2009.13

  with intermediate points derived through linear interpolation).

Figure 1

 1 Figure1shows the TIP curve for current expenditure (on a household per capita basis, per month). The curve plots over 47 million individual points, the cumulative poverty gaps of every South African (weighted from the original survey data). It can be seen that about half of the population currently falls under the poverty line of R450 per capita per month. Halving the poverty headcount ratio would mean cutting it to about a quarter. This target for the headcount ratio is shown by the dotted vertical line at around 0.26. The mean poverty gap per capita over the whole population is about R105 per capita per month. Halving the poverty gap would mean bringing it down to about R53 per capita, and this target is shown by the horizontal dashed line. Meeting the targets of halving both the poverty gap and the

Figure

  Figure 1: TIP curve of expenditure

Figure 2 :

 2 Figure 2: TIP curve of expenditure and expenditure with AsgiSA targeted growth rate

  4. POVERTY OUTCOMES UNDER ALTERNATIVE GROWTH/DISTRIBUTIONAL SCENARIOS a) Distributional changes Having established the improbability of growth alone leading to the halving of poverty by 2014, given the current distribution of income and expenditure, we thus examine what growth-distribution scenarios could produce the result of halving poverty by 2014.There is an almost infinite variety of hypothetical distributional changes that could result in a halving of poverty. Distributional changes across the entire population are considered in the scenarios that follow, as explained below. Of course this is not how distributional change occurs in practice, and it would be very difficult to design policies to effect these outcomes with any degree of precision (and redistributional changes would of course also incur significant administrative costs and other types of transaction costs).

  'losers' from the distributional change, at the upper end of the distribution, do not actually suffer any net loss of income or expenditure in the scenarios set out below, as these simulated distributional changes are combined with various growth scenarios. The income or expenditure at the top still grows considerably in every scenario (and far more than other people in absolute terms), but slightly less than it would in the absence of the equalising distributional change.

  Figure 3: TIP curve of expenditure under alternative growth/distribution scenarios

  Figure 4: TIP curve of scenario halving poverty gap and headcount ratio

  productivity in different sectors and the distributional characteristics of the sectors in

  a significant negative effect on growth. While the debate concerning the nature of the influence of inequality on growth remains open, the preponderance of arguments and evidence particularly in the past fifteen years or so points to negative effects on inequality (especially very high inequality) on growth.

  distribution rarely come about without active measures targeted specifically at lessening inequality. Moderate decreases in inequality may well materialise as a by-product of other

  Scenarios combining various growth scenarios with a range of simulations of pro-poor distributional change point to the feasibility of halving poverty by 2014. For instance, with growth rates of 2% combined with distributional change in which the poorest person can consume a maximum of R300 more per month, or alternatively growth of 4% per annum combined with distributional change where the poorest person consumes a maximum of R200 more per month. Inequality in South Africa is extremely high by international standards. If policymakers in South Africa are serious about halving poverty, active interventions would be needed to avoid any worsening of inequality and actually to reduce inequality -specifically, the shares of income and expenditure going to the bottom half of the population. Finally, the method developed here for simulating distributional changes could be applied to investigating the impact of various combinations of growth and pro-poor distributional change in other countries; this could be particularly valuable in the light of the Millennium Declaration goal of halving extreme poverty internationally by 2014.

  TIP CURVE USING FOOD POVERTY LINEFor comparison purposes, FigureA1shows the TIP curve using the food poverty line (set at R295[$44] per capita per month). Using this lower line means that the poverty headcount ratio is significantly lower, at around 34% of the population. Furthermore, the poverty gap is significantly lower, just about R36 per capita per month when averaged over the entire population. The dotted horizontal and vertical lines show what the targets for halving the poverty gap and headcount ratio respectively if the food poverty line is used.

Figure

  Figure A1: TIP curve of expenditure, using food poverty line

29

  Select ρ , the point around which the distributional change will revolve. For symmetrical distributional change around the median,

  a general expression which mathematically balances the aggregate loss to upper-income earners with the aggregate gain to lower-income earners who gain from distributional change, no matter around which point the distributional change revolves. this exposition we have selected the maximum gain to the person with the lowest income or expenditure [i=1], ω , then calculated the gain to the other people in subset ρ < i ; this allowed for the derivation of the loss to the people in subset ρ < i. The method could equally have begun by selecting the loss to the person with the highest income or expenditure [i=n], φ , and calculating

  INEQUALITY UNDER ALTERNATIVE GROWTH/DISTRIBUTION SCENARIOS

Table 1 :

 1 Poverty projections under alternative growth scenarios -Expenditure

		Poverty headcount ratio (%)	Poverty gap (R billion)
	2006 actual	52.46	59.82
	Target: halving poverty	26.23	29.91
	Growth scenarios to 2014:
	AsgiSA targets	34.33	32.00
	Treasury projections	42.40	43.61
	Banks projections	41.28	42.75

Page 11 of 33 Editorial

	Submitted Manuscript
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Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table 2 :

 2 Poverty projections under alternative growth scenarios -Income

		Poverty headcount ratio (%)	Poverty gap (R billion)
	2006 actual	49.57	59.65
	Target: halving poverty	24.79	29.83
	Growth scenarios to 2014:
	AsgiSA targets	33.75	33.98
	Treasury projections	41.03	44.81
	Banks projections	40.55	44.02

Table 3 :

 3 Poverty projections [using food poverty line] under alternative growth scenarios -Expenditure

		Poverty headcount ratio (%)	Poverty gap (R billion)
	2006 actual	34.36	21.02
	Target: halving poverty	17.18	10.51
	Growth scenarios to 2014:
	AsgiSA targets	17.52	8.76
	Treasury projections	24.61	13.42
	Banks projections	24.19	13.05

Table 4 :

 4 Poverty projections [using food poverty line] under alternative growth scenarios -Income

		Poverty headcount ratio (%)	Poverty gap (R billion)
	2006 actual	33.86	22.31
	Target: halving poverty	16.93	11.16
	Growth scenarios to 2014:
	AsgiSA targets	18.57	10.57
	Treasury projections	25.05	15.14
	Banks projections	24.69	14.78

Page 12 of 33 Editorial

  , and inequality of poverty. TIP curves plot the cumulative sum of the poverty gaps per capita (y-axis) against the cumulative population share (x-axis).Formally the TIP curve can be denoted (following Jenkins and Lambert) as TIP(g; p) where p is the

			Submitted Manuscript
	cumulative population share with	0	≤	p	≤	1	and p on the x-axis is plotted against ∑ = k 1 i	i n g . Thus
	F o
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Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK intensity

Table 5 :

 5 Meeting of poverty targets under alternative growth/distribution scenarios

Table A1 :

 A1 Distributional change around 50 th percentile

				Distribution
		R300	R200	R100	R50	None
	Growth
	7%	0.62	0.64	0.65	0.66	0.67
	6%	0.62	0.64	0.65	0.66	0.67
	5%	0.61	0.63	0.65	0.66	0.67
	4%	0.61	0.63	0.65	0.66	0.67
	3%	0.61	0.63	0.65	0.66	0.67
	2%	0.60	0.62	0.65	0.66	0.67
	1%	0.59	0.62	0.64	0.66	0.67
	-	0.59	0.62	0.64	0.66	0.67

Note: Expenditure inequality, measured with Gini coefficient.

Table A3 :

 A3 Distributional change around 66.6 th percentile

			Submitted Manuscript
		F o
		r
		P
			e e r
				R e v i e w
				Distribution
		R300	R200	R100	R50	None
	Growth
	7%	0.61	0.63	0.65	0.66	0.67
	6%	0.60	0.62	0.65	0.66	0.67
	5%	0.60	0.62	0.65	0.66	0.67
	4%	0.59	0.62	0.64	0.66	0.67
	3%	0.58	0.61	0.64	0.66	0.67
	2%	0.58	0.61	0.64	0.65	0.67
	1%	0.57	0.60	0.64	0.65	0.67
	-	0.56	0.60	0.63	0.65	0.67
						31

Note: Expenditure inequality, measured with Gini coefficient. Page 30 of 33 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

Table A3 :

 A3 Distributional change around 75 th percentile

				Distribution
		R300	R200	R100	R50	None
	Growth
	7%	0.60	0.62	0.65	0.66	0.67
	6%	0.59	0.62	0.64	0.66	0.67
	5%	0.59	0.61	0.64	0.66	0.67
	4%	0.58	0.61	0.64	0.65	0.67
	3%	0.57	0.61	0.64	0.65	0.67
	2%	0.57	0.60	0.64	0.65	0.67
	1%	0.56	0.59	0.63	0.65	0.67
	-	0.55	0.59	0.63	0.65	0.67
	Note: Expenditure inequality, measured with Gini coefficient.
		F o F o
		r r
		P P
			e e r e e r
				R e v i e w R e v i e w

This measure is based on the daily energy requirement of 2 261 kilocalories per capita, as recommended by the South African Medical Research Council. Statistics SA then calculated the cost of meeting this minimum energy requirement, in the light of the types of foods commonly available to low-income South Africans. As discussed later, this measure does not take account of differences in nutritional requirements by age or gender.

This is based on the assumption that the non-food items typically purchased by a household that spends about R211 per capita per month on food can be treated as essential, as such households are effectively forgoing food consumption to purchase these non-food items. The overall poverty line is approximately one-and-a-half times the food poverty line. In Latin American countries, the poverty line is typically set as double the food poverty line.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

In March 2006 Rands.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript