Social Indicators Research

, Volume 130, Issue 1, pp 277–303 | Cite as

A Multidimensional Poverty Index for Gauteng Province, South Africa: Evidence from Quality of Life Survey Data

  • Darlington Mushongera
  • Precious Zikhali
  • Phindile Ngwenya
Article

Abstract

This paper estimates a Multidimensional Poverty Index for Gauteng province of South Africa. The Alkire–Forster method is applied on Quality of Life survey data for 2011 and 2013 which offer an excellent opportunity for estimating poverty at smaller geographical areas. The results suggest that the Multidimensional Poverty Index for Gauteng is low but varies markedly by municipality and by ward, as well as across income groups. Not only are low income households more likely to be multidimensionally poor, they also suffer from higher intensities of poverty. Multidimensional poverty is highest in areas of low economic activity located on the edges of the province. However, pockets of multidimensional poverty do prevail even in better performing municipalities. Government, at all spheres, needs to devise policies that channel investments into lagging areas and avoid approaches that are indifferent to the heterogeneities that exist across localised geographical extents.

Keywords

Multidimensional Poverty Index Headcount Intensity Quality of Life survey Gauteng South Africa 

1 Introduction

Dealing effectively with poverty demands accurate, reliable and timely information at a local scale. Local level analyses are also important for evaluating the impact of government poverty reduction programmes. Significant advances have been made towards finding an appropriate measure of poverty and social wellbeing since Sen’s 1976 seminal work on measuring poverty. However, reliance on a single (typically income and/or consumption) measure of poverty is problematic because it limits policymakers’ understanding of poverty given that poverty takes dimensions beyond just income. In addition, the choice of the poverty measure has direct bearing on how poverty is understood, how it is analysed and consequently influences the types of policies that are prescribed to address it (Alkire and Foster 2011). Therefore, poverty measurement methodologies have tremendous practical and policy relevance (ibid).

The need for a multidimensional approach to poverty is widely shared as a guide to the search for an adequate indicator of poverty (Anand and Sen 1997). As (Sen 2000) rightly observed,

Human lives are battered and diminished in all kinds of different ways, and the first task, seen in this perspective, is to acknowledge that deprivations of very different kinds have to be accommodated within a general overarching framework. (Sen 2000).

Based on Sen’s observation, it is indeed clear that a multidimensional approach to measuring poverty is essential from both practical and policy perspectives. Several attempts at estimating multidimensional poverty have been made during the last two decades. Notable works include, Anand and Sen (1997), Atkinson (2003), Bourguignon and Chakravarty (2003), Kakwani and Silber (2008), and Thorbecke (2008). The conception of poverty as multidimensional also forms the basis for the Human Development Index (HDI) and the Millennium Development Goals (MDGs). Traditionally, poverty analyses across the world have favoured the money-metric measures that utilise either income or expenditure data and a given threshold (poverty line). Under this approach, individuals or households that fall below a specified poverty line are deemed poor. A numerical measure is used to determine the overall level of poverty across the entire population relative to the given poverty line (Alkire and Foster 2011).1 In spite of contestations in setting the threshold amount, the general appeal for money-metric approaches is that income is an important component of household welfare. Income facilitates access to a wide range of other items that are essential to life e.g. food, clothing, schooling, household assets, and so on.

However, money-metric measures of poverty are often criticised for limiting comparisons across countries (or surveys) given that survey designs vary across countries, and so do the purchasing power of the currency. Furthermore, money-metric measures often fail to capture the value of services that are typically not transacted on the market even though these services form a significant part of the broader multidimensional aspects of poverty. For instance, access to water, sanitation, education, health, and food contribute significantly to household welfare and the costs are often much higher than reflected in household expenditures on these items. This papers aims to address this shortcoming by computing not just a Multidimensional Poverty Index but also show the spatial variations in multidimensional poverty across Gauteng’s geographical localities. The paper deepens our understanding of poverty at sub-national level in South Africa by exploring changes in non-monetary measures of poverty and wellbeing between 2011 and 2013. The Gauteng province is used as a case study. Specifically, the paper (1) expands the analysis of poverty by adopting a multidimensional approach which focuses on non-money metric aspects of poverty, and (2) examines the spatial configuration of multidimensional poverty within Gauteng. The findings of this study are expected to inform the poverty reduction initiatives for accelerated social transformation under Gauteng’s Ten-Pillar Programme introduced in 2014.2

The rest of the paper is organised as follows: Sect. 2 provides a brief summary of poverty studies in South Africa. Section 3 gives a background to Gauteng province. The data used in the analysis is presented in Sect. 4. Section 5 discusses the methodology and the results are presented in Sect. 6. Section 7 provides a discussion of the results while Sect. 8 gives an analysis of the robustness checks and Sect. 9 concludes.

2 Poverty in South Africa

In spite of major redistributive policy and legislative interventions enacted since the fall of apartheid in 1994, alleviating poverty and lowering inequality remain the major challenges facing the South African government. The post-apartheid government prioritised the reduction of poverty and inequality as reflected in several policies and pieces of legislation, in particular, the Reconstruction and Development Program (RDP) of 1994 (RSA 1994). Twenty years on, reducing poverty and inequality alongside unemployment still stands as the main objective of the current government. As such the poverty alleviation and inequality reduction objectives occupy a central place in the new National Development Plan, 2030 (NDP) published in November 2011.

In addition to pursuing economic growth as a broad measure for alleviating poverty and lowering inequality, the South African government also uses the budget to pursue these goals through the social wage3 (RSA 2013; Stats 2014). The social wage—which constituted around 60 % of total government spending in 2013—provides the poor, the previously disadvantaged, and marginalized communities access to basic services under the Free Basic Services programme (FBS) and other social protection initiatives (RSA 2013). Free basic services include subsidised access to electricity, water, sanitation, and refuse removal. Social protection comprises mainly of social grants, covering a number of areas such old age, child support, disability, social relief and so on. Social spending on primary health care, education, enhancing access to productive assets by the poor (e.g. housing and land), and job creation through the Expanded Public Works Program (EPWP) also form part of the social wage.

2.1 Poverty Analysis in South Africa

In South Africa, a number of studies focusing on poverty and inequality have been conducted e.g. Stats (2014), Sekhampu (2013), Ngepah (2011), Tregenna (2011), Leibbrandt et al. (2010), Leibbrandt and Levinsohn (2011), Bhorat et al. (2007), Seekings and Nattrass (2005), Woolard and Leibbrandt (2006). Most of these studies are based on either national or provincial level data. Key national datasets used, either alone or in combination, include the Income and Expenditure Surveys (IES), the October Household Surveys (OHS), the Quarterly Labour Force Surveys (QLFS), National Income Dynamics Study (NIDS), and National Census. A number of sub-national surveys have also been undertaken notably the KwaZulu-Natal Income Dynamics Study (KIDS) and the Cape Area Panel Study (Noble et al. 2006). Detailed but succinct summaries of findings from some of the studies are given in Noble et al. (2006).

A range of poverty lines have been applied to assess the incidence, intensity and severity of poverty e.g. Hoogeveen and Özler (2006), Martins (2003) and Woolard and Leibbrandt (2001). Each of the poverty lines is unique, based on particular assumptions by the analysts. However, the use of money metric measures and the various poverty lines send conflicting messages about the level of and changes in poverty in South Africa since 1994. There is also a marked difference in poverty estimates between money and non-money metric approaches that show an increase in welfare (Schiel 2000). For example, Bhorat et al. (2009) showed that in South Africa, people’s access to public assets such as formal housing, piped water, electricity for lighting and cooking and certain private assets such as radios and televisions, increased remarkably after 1994, particularly among the previously disadvantaged groups. Using a range of socio-economic and demographic indicators in 21 geographical nodes across South Africa that are known for their high levels of poverty, Everatt (2009) also showed that after 1994, poverty levels improved significantly in the 21 nodes although challenges still exist. As such, unidimensional measures tend to underestimate both the levels of and changes in welfare.

In 2011, Stats SA published the Living Condition Survey (LCS) 2008/09. This survey was the first survey designed by Stats SA with the sole objective of measuring poverty. The LCS emerged from earlier attempts by government to find a suitable measure of poverty. These attempts include (1) the Key Indicators of Poverty in South Africa, 1995 (2) Participative Poverty Assessment—South Africa Report, 1998, (3) Poverty and Inequality Report, 1998, (4) Taylor Committee on the State of Poverty in South Africa, 2002 and (5) Towards an Anti-Poverty Strategy for South Africa, 2008. In 2012, South Africa published a set of three national poverty lines for use in assessing poverty in the country (Stats 2014). These lines were labelled with the following threshold amounts (1) the Food Poverty Line (FPL)—R305, (2) Lower-Bound Poverty Line (LBPL)—R416 and (3) Upper-Bound Poverty line (UBPL)—R577. In the same year the three poverty lines were applied to the 2008/09 LCS. Despite using the three poverty lines, the Stats SA poverty profiles based on the 2008/9 LCS data were largely unidimensional even though thresholds are set in such a way as to relate to specific baskets of goods and services. However, having an income equivalent to any of the three thresholds does not imply access and hence overly assumed what actually happens in reality. For example, the prices of goods and services are not uniform across the different municipalities.

Apart from being predominantly unidimensional and money-metric, most studies on poverty in South Africa have a number of other shortcomings that render their findings less relevant at subnational levels such as provinces and local municipalities. Most critically, they tend to be pitched at a national level owing to the nature of available datasets that constrain analysis of poverty at subnational level. Alternative approaches are therefore needed to complement the money-metric measures as well as to focus attention on poverty dynamics at subnational and localised levels (the spatial dimension). The geography of apartheid in South Africa strongly influenced the socio-economic configuration in the country, deliberately creating spaces of poverty and spaces of affluence. During apartheid, the then government of South Africa pushed for a policy of separate development along racial lines with the Black/African population being highly prejudiced and marginalised. These racial imbalances still persist in many parts of the country despite efforts by government during the last 20 years.

3 Gauteng Province: An Overview

In Gauteng province, poverty reduction is high on the policy agenda of the provincial government. During 2014, the Gauteng Provincial Government (GPG), adopted the “Multi-Pillar Programme for Radical Transformation” for the province. One of the ten pillars in the programme is the ‘Accelerated Social Transformation’ (AST); poverty reduction forms a greater part of this pillar (GPG 2014a). In addition, to the AST, GPG launched the Ntirhisano (working together) Service Delivery War Room strategy (NSDWR). This strategy aims at establishing a cohesive and integrated network of service response structures that connect all levels of administration from provincial down to wards. The strategy also aims at creating a shift in how people’s needs are identified, responded to and resolved (GPG 2014b). A ‘public complaints’ and response system and a household profiling system were put in place during 2014 and a team of community-based field workers was deployed to monitor service delivery at local level (GPG 2014b). These initiatives are indicative of the commitment by the provincial leadership of Gauteng to accelerate social transformation and deal with the various aspects that define poverty at local level. A strategy for ensuring that the various local municipalities and other development agencies work in tandem was also drawn up as a way of avoiding duplication of efforts in the form of what the premier described as ‘GCR institutions’, i.e. Gauteng City Region institutions. GPG also took advantage of existing partnerships with research institutions, in particular the Gauteng City-Region Observatory (GCRO), in pursuance of the ward profiling process. This ward profiling process is expected to facilitate the identification of areas that are lagging behind in terms of infrastructure provision and ensure development initiatives target needy communities. However, limited knowledge exists on the nature, depth and severity of the various dimensions of poverty at provincial, municipal and ward level to enable the provincial government and its local municipalities to craft appropriate and effective policy interventions at those levels.

3.1 Location of Gauteng

Gauteng province is one of South Africa’s nine provinces and is centrally located in the northern part of the country. The province owes its origin to the discovery of gold in the late nineteenth century and subsequent gold mining activities of the early to mid-twentieth century. The scale of gold mining was so large that it attracted a lot of investment from across the world and the demand for both skilled and unskilled labour was very high. Within a space of a century, Gauteng had evolved into a very large urban landscape characterised by diversity of cultures and socio-economic disparities. Gauteng is small in physical size, stretching 18,182 km2 or just over 1 % of South Africa’s total land area. It shares its border with four other provinces namely Limpopo to the north, Mpumalanga to the East, the Free State to the south and North West to the west. Gauteng comprises of ten local municipalities three of which are among the largest metropolitan cities in South Africa by both population and economic activity. These three metropolitan cities are, City of Johannesburg (CoJ)—the financial capital, City of Tshwane (CoT)—the administrative capital commonly known as Pretoria, and Ekurhuleni Metropolitan Municipality (EMM)—a major industrial hub and home to the country’s major airport—O.R. Tambo International Airport. As such, Gauteng is largely an urban province. Figure 1, shows the map of Gauteng and its ten local municipalities.
Fig. 1

Map of Gauteng and its municipalities, Source: Gauteng City Region Observatory

3.2 Population

Although physically smaller than the other provinces, Gauteng is the most populous province in the country with an estimated population of over 12 million (Stats 2011). It is therefore, close in size to metropolitan Los Angeles, which has an estimated 12.9 million people in an area of 14 764 km2, and metropolitan Paris with 11.7 million people in a region of 12 012 km2 (GCRO 2012). National Census 2011 showed that 23.1 % of South Africa’s population lives in Gauteng. Projecting forward at current annual average population growth rates, Gauteng may have as many as 15.6 million people by 2020, at which point it would house 26.5 % of the country’s population (GCRO 2012). 85 % of Gauteng’s population is located in the three metropolitan municipalities of CoJ, CoT and Ekurhuleni. Although all municipalities are predominantly Black/African, there is a higher concentration of Black/Africans in areas with low economic activity such as Westonaria, Merafong City and Emfuleni. Here the Black/African population constitutes 92, 87 and 86 % respectively. Under apartheid, Black/Africans were forced to live in overcrowded and underserviced townships where high levels of deprivation were and are still found. Census 2011 also showed that 78 % of Gauteng’s population is Black/African. Given such a skewed population distribution and the policy of separate development pursued by the apartheid government, poverty and inequality trends correlate highly with race. This is a distinctive feature of development in South Africa generally.

Census 2011 also revealed that there are 3.9 million households in Gauteng (24.4 % of total households nationally), having increased by nearly 1.2 million since the Census of 2001. As an economically vibrant region, Gauteng attracts a large number of migrants from other provinces, neighbouring countries, the African continent and the world generally. The majority, particularly those from other provinces are largely poor young people looking for economic opportunities that Gauteng is perceived to offer (Landau and Gindrey 2008). This is reflected distinctly in the population structure of the province where the size of 16–36 years cohort is very large. There are huge challenges in Gauteng related to unemployment, migration, pressure on service delivery, poverty, and urbanisation more generally. Municipalities in Gauteng are therefore under extreme pressure to maintain and improve existing levels of service delivery while extending services to cater for the growing population and rapid urbanisation.

The ensuing analysis is made possible by the availability of three recent and uniquely generated datasets from GCRO’s Quality of Life (QoL) surveys that focus exclusively on Gauteng. These datasets permit a more nuanced analysis of poverty given their emphasis on aspects that directly affect people’s wellbeing. In addition, the datasets permit analysis down to sub-place level (i.e. ward level). Such level of analysis is impossible using datasets such as the General Household Surveys (GHS), Income and Expenditure Surveys (IES) and Quarterly Labour Force Survey (QLFS) among others, which allow only for national and provincial level analyses. Given that the mandate for service delivery in South Africa falls directly on the local sphere of government, these national surveys have limited value to policy makers at the local level.

3.3 Poverty, Inequality and Unemployment

With an income Gini of 0.69, South Africa is among the countries with very high levels of income inequality (World Bank 2012). Such levels of income inequality are more acute in urban environments and Gauteng is no exception. High income inequality coupled with high levels of deprivation and a large population exerts pressure on government to deliver services. Municipalities are also hard-pressed to assist the poor and indigent members of society who cannot afford to pay for services. Spatial data from the GCRO’s 2013 QoL Survey indicate that income inequality is very high in the three metropolitan areas of Johannesburg (income Gini of 0.74), Ekurhuleni (0.77) and Tshwane (0.72). In a country like South Africa, with low levels of social cohesion (GCRO 2012), high-income inequality is a potential source of socio-economic tension and extreme incidences of violence such as xenophobia.

In spite of the existence of a large industrial base, Gauteng faces a high unemployment rate of 25.5 % as of the second quarter of 2014. Although the unemployment rate was lower than the national average (36 %), Gauteng faces a more serious problem given the size of its population (the 25.5 % unemployment translates to about 1.8 million people who are unemployed). Data from Quantec suggests that although the unemployment rate is generally high across all municipalities, it is particularly worse for places like Ekurhuleni (27.8 % for 2013), Merafong City (28.4 %), Emfuleni (39.2 %) and Westonaria (42.0 %). Only Tshwane and Mogale City have unemployment rates of <23 %.

It is ironic that Gauteng faces problems of poverty and unemployment when it has a very large and diverse economy. The GDP for Gauteng far exceeds that of other provinces, a trend that has been maintained over the last 20 years. In 2012 alone, Gauteng contributed 35 % to total GDP for South Africa. In real terms, Gauteng’s GDP has increased from R379 249 million to R693 530 million between 1995 and 2012 (an increase of 83 %). In spite of its large population, per capita GDP is also much higher compared to other provinces and it increased significantly from R46 115 in 1995 to R55 565 in 2012 (an increase of 20 %). However, given the high levels of inequality, the massive wealth generated in Gauteng is only enjoyed by a smaller proportion of the population.

4 Data

This paper utilises GCRO QoL survey data for 2011 and 2013. The QoL survey is conducted biennially and is deliberately designed to focus solely on Gauteng province. Given this unique focus on Gauteng, the QoL survey is an important source of information for municipalities in the province. This information helps municipalities gauge the impact of service delivery efforts on communities as well as understand people’s perceptions on service delivery and governance more generally. A key feature of QoL data is that it can be disaggregated to ward level. In addition, the QoL surveys have been designed to ask specific questions about Quality of Life not often included in many national surveys. As such the GCRO QoL survey is best suited to a multidimensional approach to poverty analysis with potential to drill down to sub-place areas.

The sample size for QoL was 17,289 respondents in 2011, and 27,490 respondents in 2013. This makes the GCRO QoL survey the largest single living conditions survey in the country. The 2013 QoL survey is particularly important for this analysis for several reasons. First, the sample size is fairly large, with 27 490 respondents. Secondly, there was more emphasis on the metropolitan areas of Johannesburg, Tshwane and Ekurhuleni to allow for more nuanced analysis because the majority of Gauteng’s population is located in these three areas. City of Johannesburg had the largest proportion of interviews (36.1 %), followed by Ekurhuleni (25.9 %) and Tshwane (23.8 %). Lesedi had the smallest sample size and this stems from the fact that it is largely farming areas and the population size is very small. Thirdly, the data is ward representative and therefore a fairly accurate picture of sub-place level characteristics can be generated. The technique used was simple random sampling, with probability proportional to size. The 508 wards in Gauteng (which formed the Population Sampling Units, PSU) were broken down into Small Area Layers (SALs) permitting a balanced sample distribution across each ward. Probability Proportional to Size (PPS) sampling was used to determine the distribution of population in each SAL and every fifth stand was selected for interview. In cases where there were multiple dwellings on a single stand, random sampling was used to select a household for the interview. Fifthly, the data was geocoded in a way that allows one to locate respondents within a 50-m radius of their dwelling. This is useful in cases where further analysis about the respondents is required in which neighbourhood characteristics matter. In addition, the geocoding permits the generation of ward level maps for the MPI results. Lastly, the 2013 dataset was weighted to reflect the Census 2011 population distribution in terms of race, sex and other biometric characteristics.

It is important to note that the QoL interview is conducted with the head of household or any member of the household who is 18 years or above who is present at the time of the interview should the head of household be absent. The assumption is that these individuals are able to give information that truly reflects the status of that household and of the household members.

5 Methodology

Estimation of the MPI was based on the ‘counting’ methodology developed by Alkire and Foster (2008, 2011). This method has several advantages. First, the Alkire–Foster (AF) method is flexible, allowing for the inclusion of any number of dimensions. Secondly, the method follows a counting approach in its determination of who is multidimensionally poor, which is a better approach when dealing with ordinal dimensions. Lastly, the AF method employs a more rigorous way of identifying the poor—combining the counting approach to identify the poor, and then ‘adjusting’ that finding with measures of the breadth, and depth of the said poverty.

5.1 The Alkire–Foster (AF) Method

The method identifies the poor using a two-stage cut-off process. Prior to the application of these cut-offs, a set of nine indicators d was identified on the basis that each indicator is generally accepted as essential for human wellbeing. These indicators were classified into four broad dimensions (T), which were equally weighted. The indicators were in turn assigned equal weighting based on the weight of the dimension such that the weight attached to indicator j, with j = (1, 2,…, d) is illustrated in Eq. (1).4
$$w_{j}^{d} = \frac{1}{T} \cdot \frac{1}{d}$$
(1)
The first cut-off process relates to deprivation cut-offs for each of the 9 indicators. These are defined in Table 1. The cut-off point is a normative minimum level that a household i needs to achieve in order to be defined as non-poor. The deprivation cut-offs is represented by a vector, z = (z1, z2,…zd). A household is defined as deprived if its achievement (i.e. what each household obtains) is less than the cut-off.
Table 1

Dimensions, indicators and deprivation cut-offs for the GMPI

Dimension (T)

Indicator (d)

Deprivation cut-off (z)

Weights

Standard of living

Housing

Household dwelling is a shack/informal dwelling (Informal dwelling = both in backyard and not in backyard)

1/24

Housing

Overcrowded: 2 persons per room

1/24

Water

No access to piped water in dwelling or in yard

1/24

Sanitation

No access to a flush toilet

1/24

Energy

No access to electricity for lighting

1/24

Communication assets

Household has no more than one of radio, TV, and telephone

1/24

Food security

Food

At least one household member had to skip a meal

1/4

Economic activity

Unemployment

No one in the household is employed

1/4

Education

Years of school attendance

Respondent has 5 or less years of schooling

1/4

Source Authors’ calculations

The second cut-off step is to choose poverty cut-off (k), the number of deprivations that a household must experience in order to be considered multidimensionally poor. The choice of k is such that 1 ≤ k ≤ d so that poverty is neither defined as being deprived in only one indicator k = 1 nor is it defined as being deprived in all indicators k = d. k can be chosen normatively, either based on previous studies or based on what society would consider reasonable. In such instances, k can take on a real number. It can also be chosen to reflect a country’s or province’s specific policy goal. In the ensuing, a household is considered multidimensionally poor if they are deprived in at least one-third of the weighted indicators used in the calculation of the Multidimensional Poverty Index (MPI). The count of the weighted number of deprivations in which the household is deprived is represented by ci such that if ci ≥ k then household i is considered multidimensionally poor. So the two-stage identification process can be summarised as follows:
$$q = \sum {w_{i} \rho_{k} (y_{i} ;\,z)}$$
(2)
where q is the number of poor households; wi are the weights; ρk is the identification of households; yi = (yi1, yi2,,yid) is household i achievements across d indicator dimensions; z = (z1, z2,….,zd) is a vector of poverty lines, comprising of a collection of thresholds below which a household is considered poor i.e. \(y_{ij} \left\langle {z_{j} } \right.\).
This was then used to estimate the poverty headcount ratio:
$$H = \frac{q}{n}$$
(3)
where q is the number of multidimensionally poor households, and n is the total population. However H on its own violates two of the properties of a multidimensional index. First, it is not dimensionally monotonous, meaning that it is not sensitive to the number of dimensions that a poor person is deprived in. Dimensional monotonicity means that if a household becomes newly deprived in another dimension, overall poverty should increase. H is also not decomposable, which means that it is not possible to breakdown H to show the contribution of each dimension to poverty. Therefore an adjustment factor for H is necessary, to correct for these weaknesses. The adjustment factor A is estimated as
$$A = \frac{1}{qd}\sum\limits_{i = 1}^{n} {w_{i} c_{i}^{*} }$$
(4)
and \(c_{i}^{*}\) are the counted deprivations for households achieving ci ≥ k.
It can thus be said that the Multidimensional Poverty Index is based on the dimension adjusted headcount ratio because it is a product of two main components:
$$MPI = H \times A$$
(5)
where H is the poverty headcount ratio, and A is the average number of weighted deprivations that multidimensionally poor households suffer. A is a measure of the intensity of poverty.

5.2 Choice of Indicators

The choice of dimensions, indicators and deprivation cut-off points to include in the Gauteng Multidimensional Poverty Index (GMPI), was guided by (1) Statistics South Africa’s South African Multidimensional Poverty Index (SAMPI) analysis for 2014, which in turn took its cue from the Global Multidimensional Poverty Index, (2) relevance of indicators to South Africa’s socio-economy in general and Gauteng province in particular and (3) limitations imposed by the QoL survey data. Food, water, sanitation, energy, and housing are considered basic needs for humans. In South Africa, large segments of the population were previously excluded from adequately accessing these basic needs. The democratic government took a deliberate policy step to close the gap by providing housing, water, sanitation and electricity to previously disadvantaged households. Access to basic services was institutionalised as a basic human right in the 1996 Constitution of the Republic of South Africa. Measuring progress in the delivery of these services is important for informing policy in the country given the wide gaps that still exist in the provisions of housing and basic services. For example, Census 2011 shows that there were 524,786 backyard housing structures and 58.2 % of them were informal. The extent of service delivery protests also indicates challenges in terms of the quantity and quality of services. Table 1 shows a list of the indicators and the respective weights based on equal weighting of dimensions.

A redundancy test was carried to determine which indicators are highly associated and which ones have low associations for final choice of indicators. Under food security, association between “skipping meals” and “no money to feed children” were highly associated therefore the latter was dropped. Concerning assets, other studies use radio, television (TV), telephone, refrigerator and motor vehicles. However, due to limitation of the data, radio, TV, and telephone (either a landline or cellular phone) were used but collapsed into a single variable called communication. Although health is an important dimension for policy in South Africa, it could not be used in the analysis because of data limitations: the QoL survey does not collect data on health. In place of health, the food security indicator was used. The analysis expanded the standard of living dimension to include dwelling type, and overcrowding as the poverty indicator.

6 Results

This section presents the findings from the analysis.

6.1 What Proportion of Gauteng Households is Deprived Per Indicator?

Table 2 provides an overview of the proportion of Gauteng households that are deprived in each of the indicators specified in Table 1. Both the uncensored and censored5 results are shown for 2011 and 2013.
Table 2

Proportion deprived in each indicator—uncensored versus censored

Dimension

Indicator

Uncensored headcount

Censored Headcount (k = 33 %)

2011

2013

2011

2013

Standard of living

Household dwelling is a shack/informal dwelling

10.0

14.3

5.3

6.0

Overcrowded: 2 persons per room

25.3

17.4

6.1

4.3

No access to piped water

8.1

8.7

4.8

4.1

No access to a flush toilet

10.2

10.9

5.8

5.2

No access to electricity

10.5

7.2

4.9

3.7

Household has no more than one of radio, TV, and telephone

2.0

0.5

1.0

0.2

Food security

At least one household member had to skip a meal

19.9

14.3

12.0

8.3

Economic activity

None of the household members are employed

37.8

27.2

12.4

9.4

Education

Respondent has 5 or less years of schooling (%)

6.9

3.9

4.3

2.6

Source Authors’ calculations based on GCRO’s 2011 and 2013 Quality of Life surveys

In 2013, 14.3 % of households lived in informal dwellings—loosely referred to as shacks—a 4.3 percentage point rise compared to 10.0 % in 2011. Living in shacks is directly linked to poverty because typically it is poor households that tend to use this type of accommodation because it is perceived to be more affordable. Yet, living in a shack could also trap a household in poverty because service delivery infrastructure in these areas is typically poor, lacking, or difficult to provide. The increase in the proportion of households living in shacks suggests that access to housing remains a significant challenge for many households in Gauteng. Shacks are an attractive particularly for poor migrants because they are more affordable.

At the more than two persons-per-room standard, 17.4 % of the Gauteng population was defined as being overcrowded in 2013. This followed a 7.9 percentage points decrease from 25.3 % in 2011. Individuals living under overcrowded conditions often suffer from poor health and education outcomes (Leventhal and Newman 2010; Lund et al. 2011). As a result, overcrowding is often viewed as a good indicator of persistent poverty because it is less susceptible to fluctuations compared to other measures of poverty.

With respect to economic activity, 27.2 % of households had none of their members employed. This followed a marked improvement from 37.8 % in 2011. This is consistent with the challenges of unemployment that the country as a whole is currently battling with. The encouraging news is that this indicator recorded the fastest decline between 2011 and 2013. The other encouraging statistic is the relatively low incidence of households with no access to electricity, which was 7.2 % in 2013.

The food security indicator registered improvements between 2011 and 2013. The proportion of households for which at least one household member had to skip a meal fell by 5.6 percentage points from 19.9 to 14.3 %. The province has improved in the education indicator: there has been a reduction in the proportion of respondents with <5 years of schooling from 6.9 to 3.9 %.

Overall, Table 2 suggests difficulty when it comes to provision of basic services as reflected in the standard of living indicators. All indicators in this category recorded an increase between 2011 and 2013 with the exception of the proportion of households living in overcrowded conditions at the more than two persons-per-room standard as well as the proportion of households with no access to electricity.

An interesting policy question is how multidimensional deprivation varies across income groups. In addition to other indicators of wellbeing, the QoL surveys collected information on household income. The variable is an interval variable with equal and constant distances between values. An adjustment to the original intervals was made, resulting in six categories of income groups one of which captured households with no income. The aim is to show how deprivation levels vary with income.

Table 6 in the “Appendix” provides insights into this with a focus on 2013. Widespread disparities in the multidimensional poverty indicators are revealed, with poorer households exhibiting higher incidences of deprivation. The indicator that has the highest disparity is one that captures whether any of the members are employed. While 74.3 % of households with no income had none of their members employed, the corresponding figure for households with an income of more than R12 801 per month was only 8.5 %. A similar story holds with respect to the rest of the indicators: the proportions of the deprived decline with income and the pattern holds both in 2011 and 2013.

6.2 Deprivation Amongst the Multidimensionally Poor

Notable differences prevail in multidimensional deprivation across municipalities. Using 2013 to illustrate these differences, Westonaria is clearly the worst affected when it comes to multidimensional deprivation.6 In 2013, this municipality had the highest proportion of multidimensionally poor affected in all indicators except two: communication assets and education. 17.1 % of Westonaria residents that were deprived in more than 33 % of weighted indicators lived in a shack/informal dwelling in 2013, 11.1 percentage points higher than the Gauteng average of 6.0 %. Westonaria, is also worst affected in terms of food security having the highest proportion of multidimensionally poor households in which a member had to skip a meal (15.7 %). Midvaal lags behind with respect to education (5.2 %) as shown in Table 3.
Table 3

Censored headcount by municipality, 2013

Dimension

Indicator

Ekurhuleni

Emfuleni

Johannesburg

Lesedi

Merafong

Midvaal

Mogale City

Randfontein

Tshwane

Westonaria

Total

Standard of living

Dwelling is a shack

7.1

5.0

5.3

4.1

9.5

7.8

6.8

6.3

5.2

17.1

6.0

 

Overcrowded

4.9

4.2

4.6

3.6

6.1

6.2

4.4

6.2

3.1

8.5

4.3

 

No piped water

4.9

2.9

3.2

4.8

8.3

9.6

5.6

6.2

3.7

16.1

4.1

 

No flush toilet

5.1

4.1

3.9

3.8

8.9

13.0

5.0

6.5

6.7

15.9

5.2

 

No electricity

4.9

3.2

2.9

4.5

6.4

8.7

4.6

7.0

2.9

14.8

3.7

 

One of radio, TV, & telephone

0.5

0.1

0.1

0.2

0.2

0.7

0.0

0.0

0.1

0.2

0.2

Food security

At least one member skipped a meal

10.3

14.4

6.8

7.1

8.0

13.9

8.3

11.5

6.4

15.7

8.3

Education

5 or less years of schooling

2.7

4.6

1.9

3.7

4.4

5.2

2.5

2.9

2.8

2.9

2.6

Economic activity

No members employed

11.7

13.7

8.0

8.1

11.9

9.2

8.2

12.6

7.8

13.9

9.4

Source Authors’ calculations based on GCRO’s 2013 Quality of Life survey

6.3 Multidimensional Poverty Index (MPI), Headcount and Intensity

As highlighted above, the cut-off for poverty is 33 % meaning a household is deemed to be multidimensionally poor (or MPI poor) if it is deprived in at least a third of the weighted indicators listed in Table 1. Analogous to use of the money-metric poverty line, the MPI headcount ratio or incidence of poverty (H) is the proportion of the Gauteng population that is MPI poor. The intensity of the poverty of the poor (A) is defined as the average share of weighted indicators in which poor households are deprived. Multiplying H by A gives the MPI i.e. MPI = H × A.

Analyses by municipality shows that, consistent with the picture shown in Table 3, Westonaria drives multidimensional poverty observed in Gauteng. Table 4 shows that Westonaria had the highest headcount ratios in both 2011 and 2013. In 2011, 26.3 % of Westonaria residents were multidimensionally poor and this fell by 4.4 percentage points to 21.9 % in 2013. Mogale City moved from having the second highest headcount ratio in 2011 (16.0 %) to having the third lowest in 2013 (11.9 %). In fact, Mogale City recorded the fastest decline of 7.7 percentage points in headcount multidimensional poverty rate between 2011 and 2013. At the aggregate MPI level, Westonaria consistently has the highest MPI during the period under analysis. In 2011, this was driven by relatively high headcount ratios while in 2013 the high MPI was driven by high intensity of poverty among the multidimensionally poor.
Table 4

Headcount, intensity and MPI by municipality

Municipality

2011

2013

Headcount

Intensity

MPI (adjusted headcount ratio)

Headcount

Intensity

MPI (adjusted headcount ratio)

Ekurhuleni

19.1

51.6

0.098

14.5

50.6

0.090

Emfuleni

16.8

51.0

0.086

18.0

50.0

0.090

Johannesburg

15.1

49.7

0.075

10.4

48.1

0.056

Lesedi

19.3

52.4

0.101

11.3

49.7

0.059

Merafong

17.7

51.7

0.092

15.7

48.9

0.081

Midvaal

17.2

51.4

0.088

18.9

47.6

0.112

Mogale City

19.6

51.0

0.100

11.9

49.3

0.077

Randfontein

16.0

49.7

0.080

16.1

50.0

0.073

Tshwane

14.3

49.8

0.071

11.0

47.0

0.050

Westonaria

26.3

48.6

0.128

21.9

50.9

0.052

Total

16.5

50.5

0.083

12.4

48.9

0.061

Source Authors’ calculations based on GCRO’s 2013 Quality of Life survey

6.4 Multidimensional Poverty Index (MPI), Headcount and Intensity: Spatial Variation by Ward

A more detailed view of poverty is provided at Ward level for 2013 in Figs. 2, 3 and 4. The figures show that multidimensional poverty is more prevalent in peripheral areas. Most municipalities particularly in the West Rand relied heavily on mining activities which has since declined following the exhaustion of gold deposits in the area. Industrial activity in the south is also low given the preference by industry to locate in either Johannesburg or Ekurhuleni (two of Gauteng’s metro areas), for markets and logistical reasons. Areas to the north such as Soshanguve served mostly as dormitory towns with very little economic opportunities existing there. However, it is interesting to note that even in better performing municipalities of Johannesburg, Ekurhuleni, and Tshwane, pockets of multidimensional poverty do prevail such as areas Alex, Thembisa, Mamelodi, and Daveyton.
Fig. 2

Multidimensional poverty headcount ratio by ward, 2013, Source: Authors’ calculations based on GCRO’s 2013 Quality of Life survey

Fig. 3

Multidimensional poverty intensity by ward, 2013, Source: Authors’ calculations based on GCRO’s 2013 Quality of Life survey

Fig. 4

MPI by ward, 2013, Source: Authors’ calculations based on GCRO’s 2013 Quality of Life survey

Figures 5, 6 and 7 in the “Appendix” contain a mapping of wards that fell in the two worst categories in each of the three measures. The maps clearly demonstrate that wards that have high headcount are located on the edges of the province, mainly to the west, south and north western parts of the province. Although certain wards have high headcount, the intensity is not as high. Other wards had lower headcount but very high intensity meaning that for the few MPI poor households that exist in those wards, the extent of poverty is very high i.e. they are deprived in more indicators, on average. Such wards are concentrated along the gold reef running centrally from east to west and some are in the northern part of the province. In terms of the overall MPI, the worst areas are to the west and south west of the province and pockets in Johannesburg and Ekurhuleni. Overall, this mapping shows that (1) being located further away from the three metro regions (i.e. City of Johannesburg, Tshwane and Ekurhuleni) where economic activities are concentrated clearly presents disadvantages to these outlying areas and (2) pockets of poverty exist within the metro areas.

6.5 Multidimensional Poverty Index (MPI), Headcount and Intensity: Variation Across Income Groups

Table 5 below suggests a correlation between income and multidimensional poverty: not only are low income households more likely to be multidimensionally poor, they also suffer higher intensities of poverty. However, the good news is that although the headcount ratio and intensity of poverty decreased for all income groups between 2011 and 2013, the decrease was faster amongst low income groups. As expected, the MPI falls as income increases.
Table 5

Headcount, intensity and MPI by decile

Municipality

2011

2013

Headcount

Intensity

MPI (adjusted headcount ratio)

Headcount

Intensity

MPI (adjusted headcount ratio)

No income

43.9

53.7

0.236

36.8

50.9

0.188

R1—R1600

31.5

51.4

0.162

27.3

49.6

0.136

R1601—R 3200

21.6

49.4

0.107

12.9

48.3

0.063

R3201—R6400

8.6

46.7

0.040

5.5

45.5

0.025

R6401—R12800

4.0

46.2

0.018

2.0

44.4

0.009

R12801 or more

1.6

49.9

0.008

0.8

47.4

0.004

Total

16.5

50.5

0.083

12.4

48.9

0.061

Source Authors’ calculations based on GCRO’s 2011 and 2013 Quality of Life surveys

7 Discussion

Gauteng province is the richest province in South Africa, and judging from the per capita GDP, it has a wealth base capable of sustaining its population on decent standards of living. However the distribution of income is highly skewed spatially, and given the high Gini, wealth is concentrated in the hands of a few individuals. Spatially, wealth is concentrated in the three metropolitan areas of Johannesburg, Tshwane and Ekurhuleni. The metropolitan areas also offer better opportunities of employment due to proximity and the existence of very large formal and informal economies compared to outlying areas.

Since 1994, South Africa has made efforts to address poverty and inequality through various policies and programmes. The main aim of these policies has been redressing the imbalances and injustices created by apartheid. The local sphere of government in South Africa is constitutionally mandated to providing and maintaining basic services especially water, sanitation and electricity. A national policy on indigency was introduced to serve as a safety net to cushion those families that are too poor to afford the cost of basic services. The indigent policy came into being after government realised that levels of deprivation are too high for particular households and in most cases households lack a source of income. Local municipalities are required to raise funds to support this particular group of people in order to avoid extreme deprivation. However, some municipalities, especially those in outlying areas do not have a wide tax base to raise sufficient revenue to support indigent households. Hence access to free basic services varies across municipalities. Although social grants offer complementary support to such households, these are often inadequate to lift indigent households out of poverty.

Despite these bold measures during the last 20 years, the results of this study show that challenges remain. By combining the dimensions upon which a household is deprived, the MPI method used in this paper does not reveal a consistent improvement between 2011 and 2013. Some dimensions improved while others worsened. For example, the number of households living in shack dwellings rose between 2011 and 2013, along with a slight increase of 2.1 percentage points in the people with no access to piped water; the same applies to households’ access to flush toilets. However, the number of households with no members working decreased significantly from 38.0 % in 2011 to 27.4 % in 2013. Education has done quite well as the proportion of those with <5 years of schooling is declining. As more people gain access to education, future prospects for better standards of living are positive as long as the throughput rate is kept high and the economy generates enough jobs to accommodate new entrants to the job market. This is so because our data shows that municipalities with low economic activity such as Westonaria, Randfontein, Midvaal and Lesedi have high MPI values.

A focus on poverty dynamics at much localised levels using the 2013 data showed that, as expected, Headcount MPI is generally high in previously disadvantaged south and south-western high-density locations: Diepsloot, Alexandra, Tembisa and parts of Ekurhuleni. Where multidimensional poverty exists, the intensity is high. Intensity of poverty is highest in Ekurhuleni (3 wards), Johannesburg (2 wards), Mogale City, Merafong City and Emfuleni (1 ward each). Most of these areas are associated with informal settlements, overcrowding and backyard buildings. Where housing conditions are poor, service delivery is also difficult to provide and opportunities for employment are limited.

8 Test for Robustness

The results of this analysis were subjected to a number of tests in order to check if the MPI is robust across a range of weights that deviate from the initial equal weighting across the four dimensions. Each of the four dimensions had a weighting of 25 % which in turn was distributed equally among the indicators of that dimension. In order to check for robustness the MPI was re-estimated using four alternative weighting structures that gave, on a rotational basis, a 50 % weighting to one dimension and equal weight to the remainder i.e. 16.7 % each. Applying new weights has two immediate effects. First, changing the MPI estimates for each municipality or ward and second, re-ranking of municipalities based on the new MPI values. The robustness tests check for the stability of these rankings to prove whether the rankings are sensitive or not to the choice of weights. Alkire et al. (2010) suggest three types of tests that can be used to test for this sensitivity namely, the correlation coefficient analysis, the concordance index, pairwise comparisons, and large rank changes.

The correlation analysis gives the pairwise correlation coefficients between the rankings under the equal weight structure against each of the four alternative weighting structures, for all municipalities and wards. The concordance tests check for consistency between the rankings criteria. When the ranking criteria are independent of each other, the indices will be equal to zero and one if and only if the ranking criteria are perfectly consistent. Pairwise comparisons compare the MPI estimates for all possible pairs of municipalities and wards across all five different weighting structures. The idea is to check if a higher poverty municipality/ward does remain so when the weighting changes. Lastly the large rank changes focus on municipalities/wards whose ranks change quite substantially when the different weighting scenarios are applied.

Alkire and Santos (2010), however argue that it is not only weights that are important but also the dimensional cut-offs and the indicators included. If these are changed and results are found to be consistent, it is possible to confirm the robustness of the results and state them as independent from the choice of the poverty line and choice of indicators.

Correlation coefficients between rankings were estimated using three methods: Spearman’s rho, Pearson’s r, and Kendall’s Tau. See the “Appendix” for details. The estimated correlation coefficients in Table 7 in the “Appendix” show that the municipal rankings are stable with respect to all dimensions except for the dimension on economic activity.

Furthermore, the analysis was also done to test the sensitivity of the choice of k i.e. the number of deprivations that a household must experience in order for them to be considered multi-dimensionally poor. The Pearson and Kendall coefficients indicate stability with respect to choice of k, even though the Spearman coefficient is weak (see Table 8 in the “Appendix”).

9 Conclusion

This paper contributes to deepened understanding of poverty in subnational South Africa by exploring changes in non-money-metric measures of poverty and well-being between 2011 and 2013 using the Gauteng province as a case study. It uses two recent Quality of Life survey datasets collected by the Gauteng City Region Observatory (GCRO) to (1) expand the analysis of poverty by computing a Multidimensional Poverty Index (MPI) for Gauteng and (2) examine the spatial configuration of multidimensional poverty within the province.

Multidimensional poverty is found to be correlated with income poverty: not only are households that are low income households are more likely to be multidimensionally poor, they suffer from higher intensities of poverty. Further, the study highlights the interconnectedness between infrastructural development and socio-economic indicators. Specifically, being deprived in one poverty indicator is associated with a higher likelihood of being deprived in other indicators.

Generally, the urban space economy of Gauteng derives from apartheid geography and the history of mining activity. Our findings support that there is path dependency and show that the legacy of apartheid, in terms of infrastructural imbalances still prevails. Spatially, multidimensional poverty tends to be highest in areas that have low economic activity and happen to be located at the edges of the province. These include, among others, Westonaria and Merafong City. There appears to be a disadvantage of being further away from the three metro regions (i.e. City of Johannesburg, Tshwane and Ekurhuleni) where economic activities are concentrated. This is a policy challenge given the finding that the unemployment indicator is the largest contributor to the overall MPI. Although the incidence of households with none of its members working recorded the fastest decline between 2011 and 2013, the relative contribution of this indicator to the overall MPI increased during this period. This raises questions about the ability of current investment patterns to create jobs and subsequently foster socio-economic development in outlying areas.

Multidimensional poverty is, however, not restricted to areas at the edges of the province: even in the highest performing three metro regions, pockets of severe multidimensional poverty prevail. Clear examples include Alexandra, Diepsloot and Tembisa. This is indicative of high infrastructural inequalities within these metro regions suggesting the need for local municipalities to devise policies that channel investments into lagging areas. Revitalising township economies can unlock the potential of these areas to contribute to multidimensional poverty reduction in the province.

The study also highlights that the role of mining on socio-economic development is not clear-cut. For example, Westonaria has high multidimensional poverty rates despite its heavy reliance on mining activities. It is, therefore, not apparent that mining contributes to socio-economic development in Westonaria.

The tests for robustness using the Pearson and Kendall coefficients indicate stability with respect to choice of weights as well as the number of deprivations that a household must experience in order for them to be considered multi-dimensionally poor. This means that the observed pattern of multidimensional poverty is confirmed.

In sum, the foregoing analysis underscores the heterogeneity of communities and suggests that more in-depth analyses of developmental challenges at much localised levels are needed to improve the effectiveness of evidence-based planning. This way, government is able to customise interventions that take into account these heterogeneities and continually improve targeting of policy interventions. In addition, given that the different indicators of multidimensional poverty are related to services whose provision falls under the mandate of different spheres of government, an integrated approach to service delivery is key to reducing multidimensional poverty in Gauteng.

Footnotes

  1. 1.

    For a concise history and use of unidimensional measures see Alkire and Foster (2011).

  2. 2.

    The Premier for Gauteng province, Mr. David Makhura stressed during the State of Province Address, that the province was adopting evidence-based planning.

  3. 3.

    Social wage refers to monetary and in-kind support given to vulnerable households. Four components make up the social wage in South Africa (i) housing and community amenities, (ii) health, (iii) education, and (iv) social protection. The first three replace or subsidise day-to-day expenses for housing, education and health hence reducing the cost of living. The fourth is income paid directly to vulnerable groups.

  4. 4.

    For example, for the Housing-adjustedstandard of living dimension, the weight is calculated as: \(\frac{1}{4}\times\frac{1}{6} = \frac{1}{24}\).

  5. 5.

    The uncensored headcount of an indicator refers to the proportion of total households deprived in that indicator. The censored headcount, on the other hand, refers to proportion of all household that are multidimensionally poor (i.e. households that fall within the cut-off point of k = 33 percent) and deprived in that indicator at the same time.

  6. 6.

    Table 6 in the “Appendix” presents the results for 2011.

Notes

Acknowledgments

The authors would like to acknowledge the invaluable review comments by Prof. Ingrid Woolard. Ingrid is a Professor in the School of Economics and a Research Associate of SALDRU at the University of Cape Town (UCT). She holds a Ph.D. in economics from UCT and is currently one of the Principal Investigators of the National Income Dynamics Study. A special thanks to participants of the 2015 Oxford Poverty & Human Development Initiative (OPHI) Summer School held at Georgetown University, Washington D.C. USA, who also gave us invaluable comments and suggestions for deepening our analysis. Special thanks to Samy Katumba, a Junior Researcher at GCRO for assisting with the GIS mapping.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Gauteng City-Region Observatory (GCRO)JohannesburgSouth Africa
  2. 2.Poverty Programme of the World BankPretoriaSouth Africa
  3. 3.Macroeconomics and Fiscal ManagementWorld BankPretoriaSouth Africa

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