DP2018-02

Spatial Poverty and Inequality in South
Africa: A Municipality Level Analysis
An d a D A VI D
Na t ha li e GU IL B E RT
N ob ua k i H A M A GU CH I
Yu da i H I G A SH I
Hi r oy uk i HI N O
Mu r ra y L E IB BR A N DT
Mun a S HIF A

January 22, 2018

Spatial poverty and inequality in South Africa:
A municipality level analysis

Anda David, Nathalie Guilbert, Nobuaki Hamaguchi, Yudai Higashi, Hiroyuki Hino, Murray
Leibbrandt and Muna Shifa

Authors Information:
Anda David (davida@afd.fr) is a Research Officer at Agence Française de Développement in charge of
research projects on “inequality, poverty and migration".
Nathalie Guilbert (nathalie_guilbert@hotmail.com) is an independant consultant.
Nobuaki Hamaguchi (hamaguchi@rieb.kobe-u.ac.jp) is a Professor in the Research Institute for
Economics and Business Administration, Kobe University.
Yudai Higashi (hgs.ydi@gmail.com) is a PhD student in the Graduate School of Economics, Kobe
University.
Hiroyuki Hino (hinoinhawaii@gmail.com) is a Visiting Professor in the Southern Africa Labour andDevelopment Research Unit (SALDRU) working with the Poverty and Inequality Initiative of the Universityof Cape Town.
Murray Leibbrandt (murray.leibbrandt@uct.ac.za) is the Pro-Vice Chancellor, Poverty and Inequality of
the University of Cape Town, the Director of SALDRU and the DST/NRF Research Chair on Poverty andInequality.
Muna Shifa (muna.shifa@uct.ac.za) is a post-doctoral fellow in SALDRU.

Acknowledgments:
This paper is written as part of the “Social Cohesion, Inequality and Inclusive Development” partnershipagreement between the French Development Agency, South African Office and the University of Cape
Town. It is funded by the French Development Agency (AFD). We acknowledge very useful commentsfrom AFD seminars in Pretoria and in Paris as well as from participants in the 4th DIAL Conference on
Development Economics.

Abstract

Using the 2011 South African population census, we provide income and multidimensional
poverty and inequality estimates at the municipal level. We go on to estimate a spatial
econometric model to identify the correlates of poverty across municipalities in South Africa.
Our results show that both income and multidimensional poverty and inequality varysignificantly across municipalities in South Africa. In general, areas that are historicallycharacterized by low economic and welfare outcomes still experience significantly higher poverty
and deprivation levels. Using both global and local spatial autocorrelation measures we findsignificant and positive spatial dependence and clustering of regional development indicators.
The situation of poverty is both spatially unequal and autocorrelated.
Results from our spatial econometric analysis indicate negative and significant relations between
the municipal poverty levels and local levels of education and economic activity (GDP per
capita). Significant and positive relations are found between municipal poverty levels and local
inequality levels, suggesting that municipalities with higher levels of inequality also have higher
incidences of poverty. In contrast, natural geographic factors such as rainfall and temperature are
not significantly related to municipal poverty. Accounting for both direct, intra-municipality
effects as well as spillover effects of neighbouring municipalities is important. These spillover
effects notably reduce the coefficient sizes suggested by non-spatial, OLS regressions. Most
striking, the large negative coefficient that OLS attributes to residing within a historicalhomeland area is greatly reduced and even loses statistical significance in some spatial models.
Clearly municipalities in homeland areas are particularly likely to be surrounded by very poor
municipal neighbours and therefore subject to strong negative spillovers. That said, wheninteractions between this historical geographical variable and contemporary socio-economic
deprivations are included, then homeland becomes statistically significant once more. This makes
the important point that while, it is these socio-economic deprivations that are particularly
important in explaining contemporary income poverty across the county, those who reside inthese homeland areas remain especially badly off in terms of these deprivations.

11. Introduction

Spatial inequality as a key dimension of inequity has drawn renewed interest from scholars andpolicymakers (Kanbur & Venables, 2005; Ezcurra & Rodríguez-Pose, 2017). The spatial
distribution of economic activities has significant implications for the performance of a given
economy, as well as, on the welfare of individuals (Martin, 1999). Although the clustering of
economic activities in some regions is not a problem if lagging regions are catching up, large andpersistence spatial inequalities in economic activities may result in self-reinforcing disparities in
welfare outcomes. Recent evidence suggests that spatial inequalities in economic and welfare
outcomes are high and rising in most developing and transition economies (Kanbur & Venables,2005).
In this paper, we examine the spatial distribution of poverty and inequality in contemporary
South Africa. Both the pre- and post-1994 periods in the South African economy arecharacterized by high regional disparities in terms of economic activities, as well as welfare
outcomes (Fedderke & Wollnik, 2007; Wilson, 2011; NDP, 2012). Significant progress has been
made since 1994 in reducing overall poverty and deprivation levels in the country. Public
spending on social grants and the provision of basic goods and services played an important role
in reducing poverty and vulnerability in historically disadvantaged areas. However, high andpersistent inequalities, including spatial inequalities, are still among the main challenges thatneed to be addressed in order to create an inclusive society in South Africa (NDP, 2012).
With some exceptions (Rossouw & Naudé, 2008; Noble & Wright, 2013; Nobel et al., 2013;
Krugell, 2014; Statistics South Africa, 2014; Frame et al., 2016; Sartorius & Sartorius, 2016),
spatial analysis of poverty and inequality in South Africa is limited to national, province, andrural/urban levels. Research is limited in analysing the extent of inequality at disaggregate
geographical units (Sartorius & Sartorius, 2016), and especially in linking local-level inequality,
poverty, and other factors. Yet, understanding the extent of inequalities at local levels is
important for poverty reduction and other social policies. For instance, previous international
research finds that a high level of inequality in a given region is associated with worse antipoverty targeting performance (see Galasso & Ravallion, 2005; Araujo et al., 2008), higher crime
rates (Demombynes & Özler, 2005), lower income growth of the poor (Van der Weide &Milanovic, 2014), and lower intergenerational mobility (Chetty et al., 2014).
In this paper, we provide estimates of both poverty and inequality at the municipal level. We
consider estimates of both income and non-income dimensions of well-being. Using the two
approaches together provides a better picture of the spatial distribution of well-being across South
Africa. Thereafter we use some spatial correlation indices to explore the extent to which poorer
and more unequal municipalities cluster together. We then employ a spatial econometric model toidentify the correlates of poverty and explain why poverty levels in some municipalities in South2

Africa are higher than others? Using municipal-level data from the 2011 South African census
and other data sources we give particular attention to exploring the importance of levels of
economic activity and inequality within municipalities in driving municipal poverty levels. These
municipalities are relevant units of local area analysis because, following the decentralization of
fiscal power to local administrative units in 2000, local municipalities are expected to play a keyrole in delivery of social services and in promoting local economic development in South Africa(Edoun & Jahed, 2009). South Africa is divided into nine provinces which are divided into 52
districts. At the time of the 2011 census, the 52 districts were divided into 234 municipalities.
The remainder of the paper is organized as follows. Section 2 provides an overview of theliterature on the factors that are associated with the spatial distribution of development, anddiscusses the approach used to estimate our model. Section 3 provides a description of the dataset
used, approaches used in measuring poverty and inequality, and results from exploratory spatial
data analysis. In section 4, we present the results of our empirical analysis. The final section
offers the main conclusions of the paper.

1. Background and conceptual framework

Across a range of theoretical perspectives, the existence and persistence of spatial inequalities in
economic development have been explained by a combination of various factors including, initialconditions, history, endowments, institutions, trade, increasing returns to scale, imperfect
information, and transaction costs (Krugman, 1991; Martin & Sunley, 1996; Boschma & Frenken,
2006). For instance, once firms and people find it advantageous to cluster in a particular location,
this leads to a reduction in transport costs and an increase in profits, and further agglomeration.
However, there are differences among these approaches regarding the role each factor plays infacilitating a particular spatial pattern of economic development (Martin & Sunley, 1996). For
example, according to the new economic geography model, increasing returns to scale andreduced transaction costs drive agglomeration and the concentration of firms in some locations(Krugman, 1991). In contrast, an institutional approach stresses the role played by place-specificfactors such as culture and institutions in facilitating or constraining the process of local
economic development (Boschma & Frenken, 2006). The role of history is one common factor
stressed by the different approaches in explaining regional development patterns. History matters,“both in terms of the initial conditions and accidental events that set-in motion particular patternsof industrial development over time and space and in terms of the subsequent “locking in” ofthose patterns via self-reinforcing effects” (Martin & Sunley, 1996: P.286). Such cumulative
causations mean that shocks or adjustments are expensive and difficult to reverse and may havelong-term effects, and thus, economic activities and population tend to concentrate more in few
locations. (Martin & Sunley, 1996).
3

The South African economy is characterised by high and persistence spatial inequalities in
economic activities. As Figure 1 illustrates economic activities have been historicallyconcentrated in few regions of the country. In both 1996 and 2011, three of the nine provinces,
Gauteng, Kwazulu-Natal, and Western Cape, contributed about 65% to the South African
economy, with the contribution of Gauteng increasing slightly over time. Out of 234
municipalities, only three of them (Johannesburg, Ethekwini, and Cape Town) contributed about
35% to the South African economy in 2011. 1 Population settlement patterns also tend toconcentrate more in these locations. For instance, the Western Cape and Gauteng experienced
high net in-migration after 1996, while other provinces experienced negative net migration (e.g.
Eastern Cape) or very small positive net migration (i.e North West) (Harrion, 2013). By 2011,
Western Cape and Gauteng provinces constituted about 35% of the country’s population (StatsSA). Such patterns of concertation of the population and economic activities in these locations
are expected to continue in the near future NDP (2012).
Figure 1: Provincial and municipal contributions to GDP in South Africa (1996 and 2011)
Provincial contribution

W
es
t

8.3
5.3
6
33.6
34.5
15.7
16.8
5.6
7.1
6.9
7
6.4
6.5
2.1
2.2

N
el
so
n

K
N
E
w
or
M
as
aZ
N
th
pu
Fr
te
o
er
er
u
m
L
G
e
r
rn
lu
th
im
n
n
e
a
a
-N
ut
la
C
C
C
S
W
p
ta
ap
ng
ap
op
en
ap
at
es
te
al
o
g
e
a
e
e
t

7.5

14.2
14.3

1996

E
Jo
Th
m
G
M
ha
al
R
e
C
o
B
a
P
M
E
M
e
ah Mb vaununn T ap ku T d s ol a ff E M
aes he e T rh sh ela ten ok ng alo mf sun tlo len om n Mw
w
bu kw o ul
b
b
a B b
a au C ul d sa irg ini wn en ne ay urg ne ng ity en uz na MP ela ekiiii

Municipal cont(at least 1% of GDP)

2011

1.1
1.1
1.4
1.4
1.4
1.5
1.8
2
2.1
2.1
3.8
8.2
8.7
9.9
10.7
14

Source: Own calculations using data from Stats SA.

The dominance of few locations in terms of economic activities can be attributed to a
combination of various factors. The role of history and initial conditions are vital in explaining
the spatial distribution of population, economic activities, and well-being in South Africa (Wilson,2011). The initial development of economically advanced cities such as Cape Town and Durban
1

We do not have comparable municipality level estimates for 1996.

4was associated with their function as trading ports, while the discovery of minerals (diamonds
and gold) was related to the initial development of inner cities such as Johannesburg andPretoria (Krugell & Naudé, 2005; Wilson, 2011). Continued urbanization together with
globalization played a role in driving the post-apartheid agglomeration around initially welldeveloped cities with little convergence (Naudé & Krugell, 2003; Krugell &Naudé, 2005; Krugell; 2014).
Although factor mobility is expected to promote convergence and reduce spatial inequalities in
economic and social developments, the initial divergent development patterns in South Africa
were further exacerbated and sustained by various Colonial and Apartheid segregation policies(Wilson, 2011; NDP, 2012; Todes & Turok, 2017). For instance, the Native Land Act of 1913 led
to the deprivation of black Africans’ right to own land and they were confined only to 13% of theland (Todes & Turok, 2017). Furthermore, following the Group Area Act of 1950, which maderesidential segregation compulsory, individuals were segregated based on their racial identity andBlacks were forcibly moved to mainly rural areas known as ‘homelands’, and to townships
within urban centres. Although the pass laws were repealed in 1987 a century of such policies
and restrictions on migration has led to dense settlements in rural areas, mainly homelands, andcircular migration between urban and rural areas that endures as a legacy to the present day (NDP,
2012). In effect, the majority of Blacks have been and still are restricted to living in areas which
are far from urban centres and jobs. 2
The removal of restrictions on labour movements from the late 1980s onwards and especiallyafter 1994 increased migration of people to areas where there are more jobs, which led to thebetter alignment of jobs and people (Harrison, 2013). In addition, public spending on theprovision of basic goods and services, and social grants have been intensified in an effort to
reduce poverty and vulnerability in historically disadvantage areas. Such policies and migration
to urban areas led to significant declines in overall poverty and deprivation levels in recent years 3.
However, high and persistence spatial disparity in economic activities and welfare outcomes arestill significant challenges in South Africa (NDP, 2012). Despite the increased urbanisation, an
estimated 40% of the South African population still lives in rural areas where economic activities
remain limited and many households experienced persistent deprivations (NDP, 2012).

2

Although the Apartheid regime had started to implement an industrial decentralization policy as a regional strategy
aiming to disperse industries towards Bantustan (homeland) areas since the late 1960s, such policy “had very little
impact on the “development” of the Bantustan sub-economies either in terms of the creation of employment
opportunities or through local multiplier effects”, (Wellings & Black, 1986). Todes and Turok (2017) provide a
detailed review of the various spatial initiatives and policies implemented in the past in South Africa.
3
A number of studies find that social spending reduced poverty levels in South Africa (See e.g. Leibbrandt et al.,2010; Leibbrandt et al., 2012; Gomersall, 2013; Coetzee, 2013; Bhorat. et al, 2014). Social spending is also
associated with reduction in income inequality (Bhorat et al, 2014; Hundenborn, Woolard & Leibbrandt, 2016). In
contrast, the study by Leibbrandt et al (2012) uses static income decomposition techniques and finds no significant
impact of social grants in reducing overall inequality levels.

5

In this paper, we examine the spatial distribution of poverty and inequality, and the link between
poverty, inequality, and other factors in South Africa. There is a fair amount of research in South
Africa that uses national survey data to analyse poverty and inequality at the national, provincial,
and rural/urban levels (see e.g. le R Booysen et al., 2003; Leibbrandt et al., 2010; Van der Berg etal., 2008; Ozler, 2007; Van der Berg, 2014; Finn et al., 2013; Adams et al., 2015; Burger et al.,2017). The findings from these studies suggest significant progress has been made since 1994 in
reducing overall poverty and deprivation levels in the country. With few exceptions (Rossouw &Naudé, 2008; Noble & Wright, 2013; Nobel et al., 2013; Krugell, 2014; Statistics South Africa,
2014; Frame et al., 2016; Sartorius & Sartorius, 2016) the analysis has not been at the local level.
These studies indicate that the more disaggregated analysis of spatial poverty indicate that there
are significant variations in the level of poverty and deprivation levels across space in South
Africa. For instance, using province level estimates, Burger et al (2017) find that a significant fallin deprivation levels across all provinces and evidence of catching up by some areas. However,provinces with high initial deprivation levels remain still the most deprived areas. Likewise,using indicators of multiple deprivations, Noble and Wright (2013) find that the most deprived
areas in 2001 in the country were located in the rural former homeland areas and these areas
remain among the most deprived. However, they also find large variations in deprivation levels
across the former homelands and variation within these areas. Such a fine-grained analysis ofpoverty at lower geographical units is expected to be helpful for policymakers to identify pocketsof poverty.
However, there is limited work analysing the extent of inequality at disaggregate geographicalunits (Sartorius & Sartorius, 2016), and the link between inequality, poverty, and other factors.
Sartorius and Sartorius (2016) use the 2007 South African Community Survey to estimate service
delivery inequality across districts and municipalities in South. They consider an individual’s
education status, and access to water, sanitation, electricity, and refuse removal. Using these
indicators, they constructed an index to measure the extent of service delivery for each district
and local municipality. The index measures service non-delivery, with the estimate varying
between 0 indicating the best level of service delivery and 1 reflecting the poorest level of service
delivery. They also use a Theil inequality index to estimate service delivery inequality levels.
They find a large disparity in the extent of service delivery and the level of service delivery
inequality across regions. Comparing the two measures at a district level, they find an inverted U
shape relationship. This suggests that the level of service delivery inequality is higher both inpoor and well-performing districts in terms of absolute service delivery, and districts with anintermediate level of service delivery sore have the highest level of service delivery inequality.
Understanding the extent of inequalities at local (or neighbourhood) levels are important forpoverty reduction and other social policies. Previous research indicates that the extent of local
level inequality is related to the effectiveness of anti-poverty targeting policies in developing
countries. For example, Galasso and Ravallion (2005: p.727) find that intra-village inequality inland distribution in Bangladesh is associated with poor targeting of anti-poverty programs in that6

country. One potential channel through which local level inequality affects anti-poverty targetingis that inequality affects the relative power of the poor in the local decision-making process(Galasso & Ravallion, 2005; Araujo et al., 2008). Higher inequality at the local level can lead tothe exclusion of the poor and minority groups. This exclusion can be consequential. A recent
literature suggests that high level of inequality is associated with lower income growth rates
among the poor (Van der Weide & Milanovic, 2014). Chetty, et al. (2014) find that areas
characterised by high racial and income segregations, and high-income inequality levels areassociated with lower- intergenerational income mobility in the United States.
It is not only income well-being that is impacted. There is a large literature showing thatpersistently high levels of inequality can be associated with high crime rates, and may lead toconflict and less political and social stability (Alesina, Baqir & Easterly,1999; Stewart, 2000;
Demombynes & Özler, 2005). Using disaggregated data from South Africa, Demombynes andÖzler (2005) find that high local level inequality is significantly correlated with high crime rates.
Such findings suggest that high levels of initial inequality at the local level may undermine
efforts to reduce poverty and other social developments. Such issues are important in the context
of South Africa given that historically disparities in economic development and welfare outcomesare aligned with both race and space. Thus, policies aiming to reduce poverty also should focus
on reducing local level inequalities.
This paper aims to add such a contribution to the existing literate. First, we provide estimates ofboth poverty and inequality at the municipal level. We provide income and non-income
dimensions of well-being. Using these two approaches together provides a more nuanced pictureof the distribution of well-being across regions as a ranking of places based on an economic andnon-economic dimension of quality of life is not necessarily the same in South Africa (Rossouw& Naudé, 2008). Importantly, it allows our analysis to be more sensitive to the impact of postapartheid social policies. Second, we test whether there are significant spatial correlations ofregional development patterns in South Africa using national (global) and local spatial correlation
measures (Moran’s I statistics). These statistics allow us to test whether or not municipalities
with high or low levels of poverty and inequality are clustered spatially. Third, we further explore
this descriptive picture by estimating a spatial econometric model to identify the factors that areassociated with local level poverty in South Africa and with due consideration of spatial
autocorrelation of poverty rates and spatial spillover effects on poverty rates across municipalities.
There is limited analysis of the correlates of poverty across space in South Africa (Naudé &Krugell, 2003; Naudé et al, 2009; Gnade et al., 2017). Using data from 1996–2005 Naudé et al(2009) constructed a local level vulnerability index for 354 magisterial districts from South
Africa. 4 The results of their analysis show that vulnerability is higher in areas which are remote
4

The local vulnerability index is constructed using various indicators: the size of the local economy, structure of thelocal economy, international trade capacity, remoteness, human development (poverty, unemployment humandevelopment index), governance etc.

7and isolated from economic hubs. Likewise, Naude´ and Krugell (2003) show that cities with
better human capital, and areas closer to large urban centres grow faster than others.
There are some technical issues that need to be confronted in such spatial estimations.
Conventional estimation methods such as OLS assume geographical units are independent, whichis unrealistic. This is especially problematic for us as the presence or absence of thesedependencies is part of what we are exploring. Spatial econometric approaches allow us toaccount for interdependences among spatial units. We can estimate spatial spillover effects,
which arise when an outcome of a given location is affected/related to characteristics of otherlocations. In our context, for example, change in economic development in a neighbouring or
nearby municipality may affect the extent of poverty in a given municipality. On channel could
be through migration. People migrate from one location to another in search of jobs and this may
reduce poverty levels in the migrants’ origin, other things held constant. We therefore use spatial
econometrics models in order to understand the relationship between local-level poverty,
inequality and other factors.

2. Data and Descriptive Analyses

2.1. Data description

The data for this paper is derived from various sources. Information on individual income andother socioeconomic characteristics such as, education level, and population size by race for eachmunicipality are obtained from the 2011 South African census. To measure economic activities
at municipality level, we use GDP per capita estimates at municipality levels from Stats SA’s
recent report (Morudu, 2016). Other variables such as information on climate and topography(mean temperature and mean rainfall and ground slope) are obtained from Aid watch. Google
maps is used to calculate distance in time and kilometers by road between municipal capitals. In
the next sub-sections, we discuss the approaches used to measure poverty and inequality atmunicipal levels and provide results from exploratory spatial data analysis of poverty andinequality.

2.1.1. Income poverty and inequality
We use income data from the 2011 South African Census to estimate income poverty andinequality at the local municipality level. In the 2011 South African census, information on
personal income (annual gross income from all sources) was collected for all individuals living in8

a household (excluding those living in institutions). 5 There are some challenges in using income
data from the Census for analysing inequality and poverty. First, a large proportion of households
have zero income (14 %) and a large proportion of individuals have missing information (7.6 %).
Households with zero incomes could truly have zero income. However, this figure is expected to
be low given that income includes all income sources. The zero income could be due to the factthat some income types are not included (e.g. may be in-kind income is not included). To
minimize the fraction of households with zero income, we replaced individuals who were
employed but received zero income into missing. Then, we used imputation procedures to imputeincome levels for those with missing incomes. Although this procedure minimizes the fraction ofhousehold with zero income, still 10.5 % the households have zero income. 6
Second, the income data were collected only in bands and the top bin has no upper bound. The
common approach to deal with such bands is to use Pareto midpoint imputation (PME).
Following this procedure, we imputed the midpoint of each income band to each individual in theband, except for the top bin (which is unbounded). Assuming the top two bins follow a Pareto
distribution with a shape parameter of 2, the midpoint for the top bin is twice the value of the top
bin’s lower bound. Then, we summed incomes for each individual in a household and divided itby household size to get per capita income levels. We use Stats SA’s upper and lower poverty
lines in our income poverty analysis. The estimated upper poverty line is R779 per month per
capita, while the lower poverty line is R501 per month per capita.

2.1.2. Multidimensional poverty estimates

The 2011 South African census is also used to calculate a multidimensional poverty index (MPI)
for each municipality. The MPI is calculated using four dimensions and 10 indicators. The
selection of dimensions and indicators is in line with the recent literature in measuringmultidimensional poverty in South Africa (Statistics South Africa, 2014; Frame et al., 2016). We
use the Alkire and Foster’s (2011) counting approach to aggregate the various dimensions into asingle MPI. Each dimension is equally weighted and each indicator within a dimension is equallyweighted. Table 1A in the appendix presents dimensions, indicators, deprivation thresholds, andweights used to calculate the MPI. The four dimensions considered are education, health, living
standards, and employment. We use individuals as the unit of analysis to determine deprivation
cut-offs for education and health indicators. This is in line with the approach used by Frame et al(2016) in calculating MPI for youth in South Africa.
5

The income question was also asked for small children since they could have an income in the form of child
maintenance grants (census metadata, 2011).
6
Given the ordered nature of the income variable, we impute the missing individual level income values usingordered logistic regression imputation method. The following variables are used in the regression: education level,
employment status (for pay), province, rural/ urban dummy, race, age categories, sex, and access to basic services
and assets (dwelling type, water, sanitation, electricity, car, and TV). In the case of estimating Gini coefficient forincome inequality, we replaced households with zero income with a very small number(one).

9

Regarding inequality estimates, we use Gini coefficients to measure income inequality at amunicipal level. Following Seth and Alkire’s (2014) approach, we use the variance of multiple
deprivations as our measure of deprivation inequality. Appendix A provides the formulas used toestimate multidimensional poverty and deprivation inequality measures.
Table 1 provides income and poverty estimates for South Africa by region, gender andpopulation groups. The percentage of individuals considered income poor and multidimensional
poor in South Africa are 55% and 25% respectively. These estimates are significantly higher inrural areas compared to urban areas. Comparing poverty across provinces, the incidence of bothincome and multidimensional poverty are the highest in the Eastern Cape and Limpopo provinces,while it is relatively lower in the Western Cape and Gauteng provinces. There is a gender
poverty gap with households headed by females having relatively higher levels of income andmultidimensional poverty compared to their male counterparts. Also, the poverty estimates inTable 1 show significant disparities in poverty levels by race. Both income and multidimensional
poverty estimates are significantly higher for Black and Coloured population groups compared to
Whites and Indians/Asians. While the incidence of income poverty is 63% and 40% for the Blackand Coloured population groups respectively, the corresponding estimates for Whites and Indians
are about 6% and 16% respectively.

10

Table 1: Income and multidimensional poverty estimates for South Africa (2011)
Income poverty (PL=R779)
P0
P1
P2
54.5 0.34
0.25South Africa
Rural/urban
Rural
Urban
Province
Western capeEastern capeNorthern capeFree state
Kwazulu-NatalNorth west
Gauteng
Mpumalanga
LimpopoGenderFemaleMaleHousehold head gender
Male
FemalePopulation group
Black African
ColouredIndian or AsianWhite

Multidimensional poverty (Poverty
cut-off=1/3 of the weighted deprivation
scores)
MPI
H
A
0.129
25.2
51.2

73.8 0.47
42.7 0.26

0.34
0.20

0.197
0.088

39.1
16.8

50.3
52.3

37.3
68.7
55.3
59.4
62.3
58.5
37.2
58.8
69.9

0.21
0.43
0.32
0.35
0.39
0.36
0.23
0.37
0.45

0.15
0.32
0.22
0.25
0.29
0.27
0.18
0.27
0.34

0.074
0.196
0.134
0.126
0.151
0.146
0.080
0.124
0.162

14.4
37.6
25.7
23.7
29.2
28.2
15.2
24.7
34.2

51
52.3
52.1
53.3
51.6
51.8
52.4
50.0
47.3

56.4 0.35
52.4 0.33

0.26
0.25

0.138
0.119

26.7
23.6

51.6
50.5

44.5 0.27
66.9 0.43

0.20
0.32

0.118
0.143

23.0
27.9

51.1
51.2

62.8
40.4
15.6
6.4

0.30
0.14
0.06
0.04

0.148
0.073
0.048
0.027

28.9
14.2
9.5
5.3

51.1
51.3
50.8
51.2

0.40
0.21
0.09
0.04

Source: Own estimates using data from the South African Census, 2011.

11

3.2. Exploratory spatial data analysis

In this section, we present the distribution of poverty and inequality levels across municipalities
using exploratory spatial data analysis. Then, we test for global and local spatial autocorrelation
using Moran’s I statistic (Moran, 1950; Cliff and Ord, 1981; Anselin, 1995).

3.2.1. Patterns of inequality and poverty across municipalities

Figure 2 presents the spatial distribution of income poverty 7 and inequality in South Africa. As
found in other studies (Noble et al, 2013;), the level of income poverty is much higher formunicipalities found in the Eastern Cape and KwaZulu-Natal provinces. In contrast, poverty isrelatively lower for municipalities located in the Western Cape and near Johannesburg
metropolitan areas in Gauteng province. The level of income poverty is lower than 35 % in therichest nine municipalities (Gamagara, Thabazimbi, Steve Tshwete, Bergrivier, Midvaal,
Richtersveld, Stellenbosch, Cape Agulhas, and Saldanha Bay), while the figure ranges between
83%-87% in the poorest 12 municipalities (Mbhashe, Ngquza Hill, Nyandeni, Mbizana,
Maphumulo, Ratlou, Nkandla, Ntabankulu, Indaka, Nqutu, Msinga, and Port St Johns). The
figure also indicates that most of the former homeland areas are among the poorest locations.

Figure 2: Patterns of income inequality and poverty across municipalities

Source: Own estimates using data from the South African Census, 2011.

7

We present results based only on the upper income poverty line (PL=R779).

12

Among the largest metropolitan cities (Ekurhuleni, City of Johannesburg, City of Tshwane,
eThekwini, Mangaung, Nelson Mandela Bay, City of Cape Town, and Buffalo City), the level ofincome poverty is relatively lower in the City of Cape Town, City of Tshwane, and City of
Johannesburg (about 35%-36%), while the figure is about 52%-54% in Buffalo city and Nelson
Mandela Bay.
The Gini coefficient estimates indicates that the level of income inequality is at least 0.57 in allmunicipalities. Thus, there is significant inequality in all municipalities. Against this picture,
there are significant variations across municipalities. The large metropolitan areas arecharacterised by high-income inequality with the Gini coefficient ranging between 0.73-0.76.
The Gini coefficient for income inequality ranges between 0.80-0.83 in five municipalities(Abaqulusi, Maquassi Hills, Renosterberg, Kheis, Pixley Ka Seme). The level of income poverty
in these municipalities ranges from 63% in Renosterberg to 72% in Abaqulusi. The Gini
coefficient of income inequality is relatively lower in seven municipalities with the figure ranging
between 0.57-0.65 (Laingsburg, Richtersveld, Vulamehlo, Bergrivier, Cederberg, Nqutu,
Hessequa). The poverty estimates in these municipalities, however, vary significantly. While theincidence of poverty reaches 81% and 86% in Vulamehlo and Nqutu respectively, the figure is
40% and less in other municipalities. Income inequality in the poorest 10 municipalities ranges
from 0.65 in Nqutu and 0.68 Maphumulo to 0.77 in Mbizana and Ngquza Hill. These figuressuggest that income inequality is high both in poor and rich municipalities.
The patterns of regional disparities in income poverty described above are also reflected in Figure
3, which maps the values of the MPI and variance of deprivation scores across municipalities.
The MPI estimates range between 0.058 and 0.074 in the ten riches municipalities (Swartland,
Drakenstein, Saldanha Bay, Bergrivier, City of Johannesburg, Langeberg, Witzenberg, City ofCape Town, Stellenbosch, Mossel Bay, City of Tshwane) and between 0.266-0.289 in the poorest
ten municipalities (Matatiele, Vulamehlo, Msinga, Ngqushwa, Umzimvubu, Mbhashe, Engcobo,
Elundini, Intsika Yethu, Ntabankulu). The proportion of people who are consideredmultidimensional poor is 14 % and less the riches ten municipalities, the figure ranges between
50%-54% in the poorest ten municipalities. Deprivation level inequality estimates are higher inmunicipalities with higher MPI estimates, while these figures are relatively lower in richer
municipalities. Among the largest metropolitan cities inequalities in deprivation levels are
relatively lower in City of Cape Town and Johannesburg, while the figure is higher in Buffalo
and Mangaung Cities.

13

Figure 3: Patterns of multidimensional poverty and inequality across municipalities

Source: Own estimates using data from the South African Census, 2011.

Figure 4 shows a strong positive relationship between the level of income poverty andmultidimensional poverty estimates, with Spearman’s rank correlation coefficient being 0.88.
This indicates that areas that are deprived of access to education and other basic services are alsocharacterized by lack of income generating opportunities. Overall, the figures above indicate thatboth income and multidimensional poverty and inequality estimates vary significantly acrossmunicipalities in South Africa. We also find that within municipality inequalities in
multidimensional deprivation scores seems higher in poor municipalities. These results are in linewith a recent finding by Sartorius and Sartorius (2016) who show that although there are large
disparities in service delivery between richer and poorer districts, within district inequality ishigher in both richer urban districts and poorer rural districts.

14

40
30
10

20

Multidimensional poverty(%)

50

60

Figure 4: Income and multidimensional poverty estimates by municipality

30

40

50

60
Income poverty(%)

95% CI7080

90

Fitted values

Source: Own estimates using data from the South African Census, 2011.

Municipalities characterized with a high level of poverty estimates have very low level of percapita GDP estimates. Figure 5 maps the distribution of GDP per capita for 2011 (in Rands). The
figure shows large disparities in the level of economic activities across municipalities in South
Africa. For example, GDP per capita in the richest municipality was close to 100% of that in thepoorest. The figure shows that the top 10 richest municipalities include (Tlokwe city council,
Overstrand, Bela-Bela, Mookgopong, Steve Tshwete, Rustenburg, Modimolle, Knysna, Govan
Mbeki, and the city of Matlosana). The City of Johannesburg and eThekwini rank 18 and 19
respectively in per capita terms, while Cape Town ranks 37. The poorest municipalities include
Nongom, Ndwedwe, Umhlabuyalingana, Ezingoleni, Vulamehlo, Msinga, Umzumbe, Joe
Morolong, Ntambanana, Indaka.

15

Figure 5: Patterns of GDP per capita across municipalities

Source: Own calculations using data from Morudu (2016)

3.2.2. Spatial autocorrelation

Next, we test whether there are significant spatial autocorrelations of regional development inSouth Africa using both global and local spatial autocorrelation measures. Global spatial
autocorrelation measures such as the global Moran’s I test whether there is an overall spatial
dependency in regional development (Moran, 1950; Cliff & Ord, 1981). The estimated value
takes positive (negative) if there is positive (negative) spatial autocorrelation in spatial
dependency in regional development; namely, the trend of regional development between
neighbouring municipalities is similar (different). In addition, a value of zero indicates random
spatial pattern. The formula for calculating the global Moran’s I statistics is given as follows:
𝑁𝑁
∑𝑁𝑁
𝑁𝑁
𝑖𝑖 ∑𝑗𝑗 𝑤𝑤𝑖𝑖,𝑗𝑗 �𝑥𝑥𝑖𝑖 − 𝑋𝑋��𝑥𝑥𝑗𝑗 − 𝑋𝑋�
𝐼𝐼 = 𝑁𝑁 𝑁𝑁
2∑𝑖𝑖 ∑𝑗𝑗 𝑤𝑤𝑖𝑖,𝑗𝑗∑𝑁𝑁�𝑥𝑥 − 𝑋𝑋�
𝑖𝑖

𝑖𝑖

16

Where 𝑥𝑥𝑖𝑖 is an attribute for municipality i, 𝑥𝑥𝑗𝑗 is an attribute for municipality j, 𝑋𝑋 is the mean of
the corresponding attribute, 𝑤𝑤𝑖𝑖,𝑗𝑗 is the spatial weight between municipality i and j. For statisticalsignificance test, we utilize standarlized form of global Moran’s I, which is assumed to follow
asymptotically standard normal distribution. Then, we conduct a Z-test, for which the null
hypothesis is that of a random distribution in regional development. Table 2 presents global
Moran’s I estimate for the various indicators of development in South Africa. The coefficientestimates are all positive and statistically significant. These results suggest there is significant andpositive spatial dependence in the distribution of regional development.

Table 2: Measures of global spatial autocorrelation, Moran's I
Variables
GDP_per capita
Income Poverty (LPL)
Income Poverty _(UPL)
Income Gini
Multidimensional poverty
Deprivation inequalities

I
0.318
0.643
0.591
0.150
0.614
0.594

E(I)
-0.004-0.004-0.004-0.004-0.004-0.004

sd(I)
0.041
0.041
0.041
0.041
0.041
0.041

Z
7.881
15.78414.5293.785
15.07714.605p-value*
0.000
0.000
0.000
0.000
0.000
0.000

Source: Own estimates using data from the South African Census, 2011 and GDP from Stats SA.
1-tail test

Although the global Moran’s I suggests significant positive spatial autocorrelation, the approach
does not tell us whether there are regional heterogeneities in patterns of developments. For thispurpose, we use the local Moran’s I statistics, which allow us to identify whether high or low
values are clustered spatially (Anselin, 1995). The formula of local Moran’s I is given by:
𝐼𝐼𝑖𝑖 =

𝑥𝑥𝑖𝑖 − 𝑋𝑋

𝑁𝑁 −1 ∑𝑁𝑁𝑖𝑖 �𝑥𝑥𝑖𝑖 − 𝑋𝑋�𝑁𝑁

2 � 𝑤𝑤𝑖𝑖,𝑗𝑗 �𝑥𝑥𝑗𝑗 − 𝑋𝑋�𝑗𝑗

A positive value for the local Moran I statistic indicates that a given municipality has
neighbouring municipalities with similarly high or low poverty or inequality estimates(identifying clustering), while a negative value indicates that a municipality has neighbouringmunicipalities with dissimilar poverty or inequality estimates (identifying an outlier). The Zscore of the local Moran's I index, and its p-value are used to test whether either the clustering or
the outlier features are statistically significant.
Figure 6 maps the local Moran’s I estimates for poverty and inequality indicators. Figure 1B and
2B in the appendix present the corresponding scatter plots for the local Moran’s I estimates. We
find that there are significant clusters of high-income poverty and multidimensional poverty
measures (hotspots) mainly around KwaZulu-Natal and Eastern Cape provinces. In contrast,17

coldspots are found in municipalities located in Western Cape (for all measures) as well as forpoverty around Gauteng’s agglomerated areas. In the case of multidimensional estimates, we findsome areas with clusters of Low-High poverty estimates. Three municipalities with relatively
lower multidimensional estimates (Greater Kokstad, The Msunduzi, eThekwini) are surroundedby municipalities with higher poverty levels.

Figure 6: Mapping local Moran’s I

Source: Own estimates using data from the South African Census, 2011.

18

3. Estimation results for the correlates of poverty

Our analysis in the previous section indicates that both income and multidimensional poverty andinequality estimates vary significantly across municipalities in South Africa. In addition, using
both global and local spatial autocorrelation measures we find significant, positive spatial
dependence and clustering in the distribution of regional development indicators. Theseobservations show that the situation of poverty is both spatially unequal and autocorrelated. In
this section, using the municipality-level data from the 2011 Census and other data sources, we
conduct a spatial econometric analysis in order to understand the relationship between local-level
poverty, inequality and their determinants. In this section of the paper, we will restrict ouranalysis to income poverty in order to avoid the endogeneity problems that arise when dealing
with the MPI and with inequality as outcomes. In order to estimate these relationships, weestimate the following spatial econometric models:The spatial autoregressive model, SAR:y = ρ𝐖𝐖y + 𝐗𝐗𝐗𝐗 + ε

and the spatial Durbin model, SDM
y = ρ𝐖𝐖y + 𝐗𝐗𝐗𝐗 + 𝐖𝐖𝐖𝐖γ + ε

Here y is a measure of poverty (income) for each municipality and X includes inequality and theother control variables. The parameter ρ captures the magnitude of the spatial autoregressive
process in the level of poverty; namely, how poverty rates between neighbouring regions
mutually affect each other. If ρ is positive, the level of poverty in a particular municipality has asimilar trend to municipalities in its neighbourhood. The parameter γ captures effects ofneighbouring municipalities’ independent variables on the poverty levels of each municipality. W
is a spatial weight matrix, which is row-normalized form, i.e. the sum of each row equals one.
We use both a contiguity spatial weight matrix and an inverse-distance spatial weight matrix. A
contiguity spatial weight matrix captures only the effects from adjacent municipalities (whichshare the common border), while an inverse-distance spatial weight matrix captures the effects
from all other municipalities weighted by the nearness (inverse-distance). ε is a vector of error
terms, which are assumed to be i.i.d. across municipalities and follow a standard distribution withzero mean and constant variance 𝜎𝜎 2 . In order to deal with endogenous bias stemming from aspatially lagged dependent variable, 𝐖𝐖y, we estimate the SAR and SDM using a maximum
likelihood approach (LeSage and Pace, 2009). The SAR model incorporates spatial
autoregressive process in the dependent variable y. In addition to the spatial autoregressive
process in the dependent variable, the SDM captures spillovers of the effects of explanatory
variables from neighbouring municipalities.
With the aim of disentangling the complex nature of the spatial poverty, we employ three
categories of independent variables: historical, geographical, and socio-economic factors in order19

to take into account the fact that, in the case of South Africa, poverty and inequality are shapedby the country’s geography and history.
Only 16.7 million hectares of South Africa’s surface areas (13.7%) are arable and only 1.3
million hectares of arable land (10%) are irrigated. The rest of rural land is arid or semi-arid,
suitable only for livestock farming and pastoralism, where returns from farming are extremely
low. Since the colonial administration in 1900s until the country’s independence in 1994, richagricultural lands were reserved for the whites. The colonial administration established “native
reserves” and subsequently “home lands” across the country, all in inhospitable and least fertile
areas, and forcibly moved South Africans of African ethnicity to their respective homeland. They
were deprived of education and other basic public services, and were governed under the
traditional regime. In urban areas, only several metropoles have been developed, each adjacent toa major port or big mines; consequently, they are spatially separated. Each metropolis was
spatially segregated by race, and hence, largely by income class. For non-Whites mobility was
restricted to work reasons.
These systems of spatial segregation by race were abolished from the mid-1980s to 1994, but
spatial separation in effect still remains to a large extent, and hence spatial inequality in income.
Similarly, the Constitution (adopted in 1997) intended to redress the large disparities in theprovisions of basic public services across the country by directly allocating certain proportions ofnational revenues to local jurisdictions – and earmarked to each category of public services –under fiscal federalism. However, large disparities in public services still remain – and hence,spatial inequality in the multiple deprivation index – owing in part to the capacity constraints ofmunicipal government as well as differing geographical difficulties in spreading public service
facilities uniformly across a municipality.
Given the history discussed above, homeland is expected to be a strongly significant determinantof poverty headcount. In our model, however, several important characteristics of homeland such as its geography, educational attainment and homogeneity of its population - are separately
accounted for. Therefore, in our model, the homeland variable captures the effects of only: (a) thetraditional governance regime; and (b) ethnicity – i.e., residents in homeland are of African
ethnicity. The regression results will reveal whether or not these factors are significant indetermining the extent of poverty. In our model, homeland is a dummy variable taking the value
1 if the data is from a municipality in former Bantustans (or Bantu homelands).
The models further postulate that vertical inequality within a municipality raises poverty. Thispostulate is motivated by a historical observation – in both South Africa and elsewhere – that
inequality within a community lowers trust in that community and hence hinders economic andsocial development over time. Although inequality generally impacts poverty with a considerable
lag, the model assumes contemporaneous correlation due to data limitations. As a result, in thecase where the poverty headcount is based on income in the dependent variable and a Gini
coefficient of income distribution is adopted to represent inequality, the correlation between
poverty and inequality captures two aspects of inequality of the same density function. To the20extent that the functional forms of density functions are similar and because the mean income is
not too different in most municipalities, we expect a strong positive correlation between poverty
headcount and Gini coefficient. In other words, the impact of inequality on poverty via trust of acommunity may not be shown by regression in this case. When the Gini of the multidimensional
poverty index is used as the independent variable, the model is free of this complication.
Furthermore, the models adopt an interaction between the share of the white population and theGini, and an interaction between education and homeland. The former combines horizontal
inequality (as measured by the share of white population) and vertical inequality (as measured bya Gini). This captures the extent of overall inequality in a community, and allows us to abstractfrom influences of other factors discussed above. The latter captures a combined impact of
having been a homeland and the extent of educational attainment of residents in that municipality.
Given the history and its traditional governance regime, we expect that having been a homeland
has a unique impact on the effectiveness of education to reduce poverty in a municipality.
However, it is not immediately clear whether such impact should be positive or negative, in part
because having been educated, most men and many women leave former homelands and migrate
to mining and urban areas; for others who remain, economic opportunities to raise income are
generally very limited.
Geographical variables include the land slopes, the average air temperature 2001-2011, theaverage annual rainfall 2001-2011, the variance from the average air temperature 2001-2011, andthe variance from the average annual rainfall 2001-2011. Among the socio-economic variables,
we also control for the share of the adult population at least high-school education, the share ofwhite population, , and the disparity of access to basic public services measured by thedeprivation inequality index. We further control for the degree of development of eachmunicipality by adding the urban household ratio variable.
As a baseline estimation, we run OLS regression without spatial terms which we only present inTable 2B in the appendix because, technically, it is inferior to our spatial models. The positiveand significant correlation of the former homeland dummy in the both estimations shows theinfluence of historical factors on current poverty. Natural geographical factors suggest that
greater rainfall is associated with higher poverty, while more land inclinations and variabletemperatures will lower poverty.
Next, we turn to estimation results for the spatial models of equations 1 and 2. Table 3 and 4
respectively provide estimated coefficients using the SAR and SDM estimation models for theincome poverty measure. The estimation results in Table 3 and 4 are based on using the rownormalized binary spatial weight matrix (both contiguity and inverse-distance spatial weights). 8
In both the SAR and SDM model estimates, we include all the variables used in the OLS model.

8

We report here only results using the contiguity spatial weights. The corresponding estimations based on the
inverse-distance spatial weights are provided in Table 3B and Table 4B in the Appendix.

21

The spatial lag coefficient ρ is positive and highly significant, suggesting that the spatial
correlation is important and the spatial models are superior to the OLS model. Importantly, thesign and significance of this coefficient makes robust our observation of section 3 that poverty inSouth Africa shows a spatial correlation: i.e., poor municipalities are located next to each other.
The validity of spatial models is also supported by the fact that the variances of error terms of thedependent variable are statistically significant.

Table 3: Correlates of income poverty (headcount): SAR models
W: contiguity
0.046*
0.0187-0.004-0.006*0.003
0.001
0.001*
0.001*-0.000-0.000-0.022-0.111**-0.989***-1.064***-1.720-0.0300.208
0.325***
0.618**
0.793***-0.027***-0.125***0.024-0.1430.588***
0.341***
0.228***
0.208***
0.004***
0.003***
234
234

Homeland
0.030**
0.057**
Slope-0.004-0.006*
Temperature (mean)
0.003
0.001
Rain(mean)
0.001*
0.001*Rain(var)-0.000-0.000Temperature (var)-0.025-0.010**
Education (>high school)-0.998***-0.984***White population (%)-0.033-2.169*Beta
Income Gini
0.384***
0.115
Deprivation inequality
0.606**
0.828***Log GDP-0.030***Urban households (%)-0.119***White population x Income _Gini
0.030*
Education x Homeland-0.337*_cons
0.486***
0.467***
Rho
W*headc_i_hi
0.221***
0.219***
var(e.headc_i_hi)
0.003***
0.003***
Sample size
234
234
Source: Own estimates. * p<0.05 ** p<0.01 *** p<0.001
Note: The results presented were obtained using the inverse distance spatial weights in the appendix.

22

Table 4: Correlates of income poverty (headcount): SDM models

Beta

Rho

homeland
Slope
Temperature (mean)
Rain(mean)
Rain(variance)
Temperature(variance)
Education (> high school)
White population (%)
Income _Gini
Deprivation inequality
Log GDP pc
Urban households (%)
White population x Income _Gini
Education x Homeland_cons

0.030**-0.0030.004
0.000-0.000-0.018-0.932***-0.0140.355***
0.762**-0.033***-0.028W*headc_i_hi
0.472***
homeland
0.006
slope
-0.000Temperature (mean)-0.001Rain(mean)
0.001
Rain(variance)-0.000Temperature (variance-0.059
Education (> high school)
0.280
Gamma
White population (%)
0.106
Income _Gini
0.215
Deprivation inequality-0.274
Log GDP pc
0.0221**
Urban households (%)
White population x Income _Gini
Education x Homelandvar(e.headc_i_hi)
0.003***
Sample size
234
Source: Own estimates. * p<0.05 ** p<0.01 *** p<0.001

W: contiguity
0.040
0.0128-0.002-0.003
0.003
0.003
0.000
0.000
-0.0000.000-0.025-0.098*-0.913*** -0.951***-1.5690.005
0.194
0.287***
0.793**
0.602*-0.032***-0.179***0.022-0.1220.094-0.204
0.466***-0.018-0.002-0.0010.001-0.000-0.050
0.225
0.347
0.277
-0.3910.019*-0.0040.206
0.002***
234

0.030-0.002
0.002
0.000
0.000
-0.104*-0.914***-2.367**0.050
0.628*-0.175***0.0332**-0.161-0.0630.437***
0.003-0.001-0.0020.001-0.000-0.0300.295-0.0240.509**
0.045

0.433***
0.007-0.003-0.0020.001-0.000-0.0150.267
0.416
0.580*-0.0480.106**

0.103**-0.007-0.0380.002***
234

0.002***
234

In econometric terms, the existence of this autocorrelation may have caused biases in the OLS
estimates of β in table 2B in the Appendix. We can expect that such biases will be corrected byincluding spatial lag terms. However, estimated β in tables 3 and 4 are not directly comparable
23

with those from the OLS estimates because of cumulative nature of spatial correlation. We cansee this point by transforming our two earlier equations into the following data generatingprocess (DGP) form:
𝑦𝑦 = (𝑰𝑰 − 𝜌𝜌𝑾𝑾)−1 (𝑿𝑿𝑿𝑿 + 𝑾𝑾𝑾𝑾𝛾𝛾 + 𝜀𝜀)

where 𝑰𝑰 is an N×N unit matrix. This equation represents the DGP of SAR if 𝛾𝛾 = 0 is assumed,
while it is the DGP of SDM if 𝛾𝛾 ≠ 0 is assumed. According to the DGP, the coefficients in SAR
and SDM do not denote marginal effects owing to (𝑰𝑰 − 𝜌𝜌𝑾𝑾)−1.

Based on the DGP, we can consider the following spatial dependence structure in regional
development in South Africa. First, the characteristics of municipality i are associated with thepoverty rate of own municipality i. This pass is labeled the direct effect. This includes feedback
effects via other regions. Second, the endogenous variable, i.e. the poverty rate, is affected byown regional factors affecting poverty rates of its neighboring municipalities. This effect is called
indirect impact.
The coefficient estimates of the kth variable are provided under three heading: direct effects,
indirect effects, and total effects as proposed by LeSage and Pace (2009):
𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 =𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 =1
𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡�(𝑰𝑰 − 𝜌𝜌𝑾𝑾)−1 (𝛽𝛽𝑘𝑘 𝑰𝑰 + 𝛾𝛾𝑘𝑘 𝑾𝑾)�,𝑁𝑁
1 ′𝜄𝜄 �(𝑰𝑰 − 𝜌𝜌𝑾𝑾)−1 (𝛽𝛽𝑘𝑘 𝑰𝑰 + 𝛾𝛾𝑘𝑘 𝑾𝑾)�𝜄𝜄𝑁𝑁 ,𝑁𝑁 𝑁𝑁

𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 = 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 − 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸,

where 𝜄𝜄 is an N×1 vector of ones. According to the form of Direct, Indirect, and Total effects,
𝑾𝑾𝑾𝑾 in the SDM boosts the indirect effect owing to 𝛾𝛾𝑘𝑘 𝑾𝑾. The SDM explicitly models the indirect
impact, i.e. spatial spillovers, by containing the spatially lagged explanatory variable 𝑾𝑾𝑾𝑾, which
represents the characteristics of the neighbouring municipality j.
Looking at the income poverty estimates shown in Table 5, the “Direct effect” panels of SAR
indicates that coefficient estimates, and significance levels are similar when we use either thecontiguity or inverse-distance spatial weight matrix. It is worth observing that the inclusion ofinteraction terms between the share of white population and income inequality variables slightly
changes the results. Without the interaction term, the share of white population is not significant
and the coefficient on the income Gini estimate is significant with a positive sign, while with theinteraction term the coefficient on the share of white population is significant with a negative sign
and income inequality becomes insignificant. In the case of SDM, with an inverse-distancespatial weight and with the interaction term, the coefficient on the share of white population issignificant with a negative sign and income inequality is positive and marginally significant. Thissuggests that municipalities with higher white population tend to have lower income poverty
incidences, but “whiter” municipalities with higher inequalities tend to have more poverty. These
results imply that in municipalities where white population shares are insignificant, low levels of
income inequalities can be associated with higher poverty because everyone there is almost
24equally poor. However, where white population shares are higher poverty rates are found only inmunicipalities with higher income inequalities.
The “Direct effect” panels of SDM show that the influences of natural geography are notsignificant, except for the positive correlation with mean temperature. This suggests that poverty
is higher in hotter areas and is rather intuitive since areas with higher temperatures are more arid
and with lower productivity, leading to higher income poverty incidence. As mentioned in
section 3.2.2, poor municipalities are located in “hotspots” of poverty with poor municipalities
being located adjacent to each other. In the regression without the interaction term, we canclearly see that having belonged to former homelands leads to higher poverty headcounts at themunicipal level. When the interaction term between education and homeland dummy is included,
the homeland variable loses significance. As expected, education has a strong effect on poverty
reduction, with the variable having a highly significant negative coefficient. With theintroduction of the interaction term, the poverty reduction effect of education is even stronger.
The indirect effect coefficients that measure spatial spillovers are bigger in the SDM than in the
SAR. This is as expected. Also, they satisfy the expected sign conditions. However, in the SDM
most of those effects are not significant. Nonetheless, it is important to control for these indirect
effects because it is natural to expect such spatial spillovers across municipalities. Our results
show that direct impacts of neighbourhood independent variables are not quite strong. One
plausible explanation is that municipalities may be too large spatial unit to observe direct spatial
interactions. It is also possible that transportation infrastructure is not adequately provided tosupport interactions among municipalities.

25

Table 5: Cumulative (direct and indirect) spatial models of income poverty (headcount)
SAR/Wcontig
Direct effectHomeland
Slope
Temperature (mean)
Rain(mean)
Rain(variance)
Temperature(variance)
Education (> high school)
White population (%)
Income _Gini
Deprivation inequality
Urban ratio
White pop x Income Gini
Education x Homeland

0,019*-0,006**0,001
0,001**
0,000-0,112***-1,073***-0,0300,328***
0,800***-0,127***

0,058***-0,006**0,001
0,001**
0,000-0,101***-0,994***-2,191**
0,116
0,837***-0,120***0,030**-0,341**

Indirect effectHomeland
0,005*
0,016**
Slope-0,001**-0,002**Temperature (mean)
0,000
0,000
Rain(mean)
0,000**
0,000**Rain(variance)
0,000
0,000
Temperature(variance)-0,028**-0,027**
Education (> high school)-0,270***-0,267***White population (%)-0,008-0,588**
Income _Gini
0,083***
0,031
Deprivation inequality
0,201***
0,224***
Urban ratio-0,032***-0,032***White pop x Income Gini
0,008*
Education x Homeland-0,091**
Total effectHomeland
0,024*
0,073***
Slope-0,007**-0,007**
Temperature (mean)
0,002
0,001
Rain(mean)
0,001
0,001**Rain(variance)
0,000
0,000
Temperature(variance)-0,140***-0,128***
Education (> high school)-1,344***-1,261***White population (%)-0,038-2,779**
Income _Gini
0,410***
0,147
Deprivation inequality
1,002***
1,061***
Urban ratio-0,158***-0,152***White pop x Income Gini
0,038**
Education x Homeland-0,432**
Source: Own estimates. * p<0.05 ** p<0.01 *** p<0.001

SDM/Wcontig
0,014-0,003
0,003
0,000
0,000
-0,106**-0,964***0,003
0,354***
0,635***-0,176***

0,032-0,002
0,002
0,000
0,000
-0,110***-0,928***-2,4310,113
0,652***-0,172***0,034-0,1720,014-0,004-0,0010,001*
0,000-0,120-0,201-0,035
1,059***
0,516
0,047

0,033
-0,006-0,0010,001
0,000-0,100-0,214-1,0100,997**
0,372
0,045
0,013
-0,1790,027-0,007
0,002
0,002**
0,000-0,226**-1,165***-0,032
1,413***
1,151**-0,128**0,065-0,0080,001
0,002*
0,000-0,210*-1,142***-3,442
1,110**
1,023*-0,127**0,047-0,35126

As mentioned, looking at ‘Total effects’ allows us to understand the determinants of poverty
headcount at the municipal level, taking into account the direct effects as well as the spillover
effects of the neighbouring municipalities. Our results show that once these spillovers are
considered, having been part of a homeland is not correlated with current income poverty rates.
Rather, these rates are mainly explained by education, urban ratio and the average rain volume.
It is now possible to compare the coefficients of the direct effects and the OLS estimates in table
2. We can see that the impacts of each of the variables are systematically smaller in the spatialmodels than in the OLS case. This suggests that the coefficients are overestimated in the latter.
Notably, the coefficients of homeland dummy becomes smaller and even statisticallyinsignificant in some spatial model estimations. The latter results imply that althoughcontemporary spatial poverty and inequality in South Africa are often attributed to the direct
historical reasons for being the former Bantustans, the clustering of poverty within thesemunicipalities is not statistically different from that of non-Bantustan municipalities when we
control for other geographic and socio-economic determinants of poverty. This does not meanthat the historical roots of poverty should be discarded. Clearly, residing in a homeland area iscorrelated with socio-economic deprivation. That is why the estimation results of the homelandcoefficient in the SDM (with inverse-distance spatial weight matrix) suggest an insignificant
coefficient that then turns significant after adding the interaction term of homeland with the shareof population having higher education. Our spatial models prove to be very useful in unpacking
the large impact of the homeland dummy variable on poverty that we see in the non-spatial
estimations of municipality-level data as well as in our earlier mapping. Some of this effect isseen to be due to spillovers attendant upon being surrounded by very poor municipalities. Someof this effect is due to fact that those who reside within these apartheid boundaries are still
especially prone to the socio-economic deprivations that are driving contemporary poverty.

27

4. Conclusions

In this paper, we provide estimates of poverty and inequality at municipal level using the 2011
South African population census. We show that both income and multidimensional poverty andinequality levels vary greatly across municipalities. The poorest municipalities in both measures
are located mainly in the Eastern Cape and KwaZulu-Natal provinces, where some of the largest
former homeland areas are located. Using Moran’s I statistics we find that there is a significant
level of spatial dependency in regional development in South Africa. High poverty and inequality
estimates are spatially clustered mainly in the Eastern Cape and KwaZulu-Natal provinces. Those
residing within areas that were homelands under apartheid seem particularly badly off.
The determinants of the poverty headcount at the municipal level are explored further through aseries of OLS and spatial regression models. Results from this analysis indicate that there is anegative and significant relation between the municipal poverty levels and lower urbanisation
rates, local levels of education and economic activity (GDP per capita). A significant, positive
relationship is found between municipal poverty levels and local inequality levels, suggestingthat municipalities with higher levels of inequality also have higher incidences of poverty. In
contrast, natural geographic factors such as rainfall and temperature are not significantly related
to municipal poverty.
The spatial models allow for the estimation of a total effect, that includes both direct intramunicipality effects as well as spillover effects of neighbouring municipalities. Accounting forspillover effects is important. It reduces the coefficient sizes as estimated by non-spatial, OLS
regressions. Now poverty rates are mainly explained by local education levels, urban ratios andthe average rain volume. Most striking, the large negative coefficient that OLS attributes toresiding within a historical homeland area is greatly reduced and even not statistically significant
in some spatial models. However, when interactions between this historical geographical variable
and contemporary socio-economic deprivations are included, then homeland becomes statisticallysignificant once more. This makes the important point that while, across the county, it is thesesocio-economic deprivations that are particularly important in explaining contemporary income
poverty, those who still reside in these homeland areas remain especially badly off in terms ofthese deprivations.
Although our analysis provides suggestive evidence linking poverty with inequality and otherfactors at the municipality level, the results from these estimates cannot be interpreted as causal
relationships. First, our analysis of poverty and inequality, and the relationship between the two
is based on a single census and, as such is a static analysis. Change in most of the variables
including inequality and GDP per capita takes time and, as such, these static correlations are not a
substitute for longer-term estimations of how changes in these variables impact on poverty andinequality. Second, the processes generating local level poverty are more complicated than ourestimations can capture. There are other variables that can potentially affect poverty that we do
not include in our estimations. These include the extent of public spending, local level capacity28

and the efficiency of local level spending and even corruption. These limitations make it clearthat there is plenty of scope for further research.

29

References

Adams, C., Gallant, R., Jansen, A., & Yu, D. (2015). Public assets and services delivery in South
Africa: Is it really a success? Development Southern Africa, 32(6), 697-710.
Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical Analysis, 27(2),93-115.
Araujo, M. C., Ferreira, F. H., Lanjouw, P., & Özler, B. (2008). Local inequality and projectchoice: Theory and evidence from Ecuador. Journal of Public Economics, 92(5), 10221046.
Ayala, L., Jurado, A., & Pérez‐M ayo, J. (2011). Income poverty and multidimensional
deprivation: Lessons from cross‐regional a nalysis. Review of Income and Wealth, 57(1),40-60.
Alesina, A., Baqir, R., & Easterly, W. (1999). Public goods and ethnic divisions. The Quarterly
Journal of Economics, 114(4), 1243-1284.
Boschma, R. A., & Frenken, K. (2006). Why is economic geography not an evolutionary
science? Towards an evolutionary economic geography. Journal of Economic
Geography, 6(3), 273-302.
Bhorat, H., Tseng, D., & Stanwix, B. (2014). Pro-poor growth and social protection in South
Africa: Exploring the interactions. Development Southern Africa, 31(2), 219–240.
Burger, R., Van der Berg, S., van der Walt, S., & Yu, D. (2017). The long walk: Considering theenduring spatial and racial dimensions of deprivation two decades after the fall ofapartheid. Social Indicators Research, 130(3), 1101-1123.
Chetty, R., Hendren, N., Kline, P., & Saez, E. (2014). Where is the land of opportunity? The
geography of intergenerational mobility in the United States. The Quarterly Journal of
Economics, 129(4), 1553-1623.
Cliff, A. D., & Ord, J. K. (1981). Spatial processes: models & applications. Taylor & Francis.
Coetzee, M. (2013). Finding the benefits: Estimating the impact of the South African childsupport grant. South African Journal of Economics, 81(3), 427–450.
Demombynes, G., & Özler, B. (2005). Crime and local inequality in South Africa. Journal of
Development Economics, 76(2), 265-292.
Edoun, E. I., & Jahed, M. (2009). The merits of decentralisation and local economic development
in South Africa. Pretoria: Global Action for Africa’s Development.
Ezcurra, R., & Rodríguez-Pose, A. (2017). Does ethnic segregation matter for spatial
inequality? Journal of Economic Geography, 17(2), 1149–1178.
Frame, E., De Lannoy, A., & Leibbrandt, M. (2016). Measuring multidimensional poverty among
youth in South Africa at the sub-national level. SALDRU Working Paper, No.169.
Southern Africa Labour and Development Research.

30

Fedderke, J., & Wollnik, A. (2007). The Spatial Distribution of Manufacturing in South Africa
1970-1996, its Determinants and Policy Implications (No. 53). Available at
SSRN: https://ssrn.com/abstract=1812143 or http://dx.doi.org/10.2139/ssrn.1812143

Fernandez, R., & Rogerson, R. (1996). Income distribution, communities, and the quality of
public education. The Quarterly Journal of Economics, 111(1), 135-164.
Fernández, R., & Levy, G. (2008). Diversity and redistribution. Journal of Public
Economics, 92(5), 925-943.
Finn, A., Leibbrandt, M., & Woolard, I. (2013). What happened to multidimensional poverty inSouth Africa between 1993 and 2010? SALDRU Working Paper, No.99. Southern Africa
Labour and Development Research.
Galasso, E., & Ravallion, M. (2005). Decentralized targeting of an antipoverty program. Journalof Public Economics, 89(4), 705-727.
Gnade, H., Blaauw, P. F., & Greyling, T. (2017). The impact of basic and social infrastructure
investment on South African economic growth and development. Development Southern
Africa, 34(3), 347-364.
Gomersall, J. (2013). The performance of the child support grant: Review and research priorities.
Development Southern Africa, 30(4–5), 525–544.
Harrison, P. (2013). South Africa’s spatial development: The journey from 1994, Working
document, input into 20-year review process. Pretoria: Presidency.
Hundenborn, J., Woolard, I., & Leibbrandt, M. (2016). Drivers of inequality in South Africa.
SALDRU Working Paper, No.194. Southern Africa Labour and Development Research.
le R Booysen, F. (2003). Urban–rural inequalities in health care delivery in South
Africa. Development Southern Africa, 20(5), 659-673.
Kanbur, R., & Venables, A. J. (2005). Rising spatial disparities and development. Available at:https://www.wider.unu.edu/sites/default/files/PB2005-003.pdf
Kanbur, R., Venables, A. J., & Wan, G. (2005). Introduction to the special issue: spatial
inequality and development in Asia. Review of Development Economics, 9(1), 1-4.
Krugman, P. (1991). Increasing returns and economic geography. Journal of Political
Economy, 99(3), 483-499.
Krugman, P. (1999). The role of geography in development. International Regional Science
Review, 22(2), 142-161.
LeSage, James, and R. Kelley Pace. 2009. Introduction to Spatial Econometrics. Boca Raton, FL:
CRC Press.
Leibbrandt, M., Finn, A., & Woolard, I. (2012). Describing and decomposing post-apartheid
income inequality in South Africa. Development Southern Africa, 29(1), 19–34.
Leibbrandt, M., Woolard, I., Finn, A., & Argent, J. (2010). Trend in South African income
distribution and poverty since the fall of apartheid. OECD Social, Employment andMigration Working Papers, No. 101. Paris: Organisation for Economic Co-operation andDevelopment.
31

Martin, R. (1999). The new ‘geographical turn' in Economics: Some criticalreflections. Cambridge Journal of Economics, 23(1), 65-91.
Martin, R., & Sunley, P. (1996). Paul Krugman's geographical economics and its implications forregional development theory: a critical assessment. Economic Geography, 72(3), 259-292.
Moran, P. A. (1950). Notes on continuous stochastic phenomena. Biometrika, 37(1/2), 17-23.
Morudu, H. D. (2016). Developing annual population and gross domestic product estimates for
local municipality development models in South Africa: applications of Zipf's rule from2001 to 2013. South African Geographical Journal, 98(2), 367-385.
National Planning Commission. 2012. National Development Plan 2030. Our future - make itwork. Available at: http://www.gov.za/issues/national-development-plan-2030.
Noble, M., Zembe, W., Wright, G., & Avenell, D. (2013). Multiple deprivation and income
poverty at small area level in South Africa in 2011. Cape Town: SASPRI.
Noble, M., & Wright, G. (2013). Using indicators of multiple deprivation to demonstrate thespatial legacy of apartheid in South Africa. Social Indicators Research, 112(1), 187-201.
Naudé, W., McGillivray, M., & Rossouw, S. (2009). Measuring the vulnerability of subnational
regions in South Africa. Oxford Development Studies, 37(3), 249-276.
Naude´, W. A. & Krugell, W. F. (2003). An enquiry into cities and their role in subnational
economic growth in South Africa. Journal of African Economies, 12(4), pp. 476–499.
Naudé, W. A., & Krugell, W. F. (2006). Economic geography and growth in Africa: The case of
sub-national convergence and divergence in South-Africa. Papers in Regional
Science, 85(3), 443-57.
Özler, B. (2007). Not separate, not equal: Poverty and inequality in post-apartheid South Africa.
Economic development and cultural change, 55(3), 487-529.
Rossouw, S., & Naudé, W. (2008). The non-economic quality of life on a sub-national level inSouth Africa. Social Indicators Research, 86(3), 433-452.
Sartorius, K., & Sartorius, B. (2016). Service delivery inequality in South African municipal
areas: A new way to account for inter-jurisdictional differences. Urban Studies, 53(15),3336-3355.
Statistics South Africa. (2014). The South African MPI: Creating a multidimensional poverty
index using census data. Pretoria: Statistics South Africa.
Stewart, F. (2000). Crisis prevention: Tackling horizontal inequalities. Oxford Development
Studies, 28(3), 245‐ 262.
Todes, A., & Turok, I. (2017). Spatial inequalities and policies in South Africa: Place-based or
people-centred? Progress in Planning. https://doi.org/10.1016/j.progress.2017.03.001
Van der Berg, S. (2014). Inequality, poverty and prospects for redistribution. Development
Southern Africa, 31(2), 197-218.
Van der Berg, S., Louw, M., & Yu, D. (2008). Post‐transition poverty trends based on analternative data source. South African Journal of Economics, 76(1), 58-76.

32

Van der Weide, R., & Milanovic, B. (2014). Inequality is Bad for Growth of the Poor (But Not
for that of the Rich). World Bank Policy Research Working Paper No. 6963. Available at
SSRN: https://ssrn.com/abstract=2461941
Wellings, P., & Black, A. (1986). Industrial decentralisation in South Africa: Tool of Apartheidor spontaneous restructuring? GeoJournal, 12(2), 137-149.
Wellings, P., & Black, A. (1986). Industrial decentralization under apartheid: The relocation ofindustry to the South African periphery. World Development, 14(1), 1-38.

33

Appendix A: Multidimensional Poverty and Inequality Estimates

In order to estimate the extent of multidimensional poverty and inequality estimates, we consider
four dimensions: education, health, living standards, and employment. Table 1A presents the listof indicators used to measure these dimensions, weights, and deprivation cut-offs. We use thecounting methodology proposed by Alkire and Foster (2011) for aggregation. We use induvial as
the unit of analysis to determine deprivation cut-offs for the health and education indicator, which
is line with the approach used by Frame et al (2016) in calculating MPI for youth in South Africa.
In the case of the Stata SA and Alkire and Foster (2011) approaches, deprivations are determinedat a household level for all indicators and all individuals in a household assigned household-level
deprivation scores. For example, irrespective of an individual’s level of education, individuals are
considered not deprived in years of schooling if at least one member of a household has six yearsof education. One key problem in using this approach is that it does not allow one to analyse
intra-household inequalities (e.g. by age structure or gender).
Using the deprivation cut-offs and weights proved in Table 1A, we calculated a weighted
deprivation score for each individual (the sum of weighted deprivation scores). Then we use apoverty cut-off k=33.3% (1/3 the weighted indicators) to identify poor and non-poor individuals.
Thus, an individual is considered multidimensionally poor, if he or she is deprived in 1/3 theweighted deprivation scores. The incidence of poverty is measured as the multidimensional
headcount ratio (H):H=

qn

Where q is the number of people who are multidimensionally poor and n is the total population.
The average proportion of deprivations poor people experience, intensity of poverty (A) is
calculated using the following formal:∑ C (k )iA=qWhere Ci (k ) is deprivation score of the poor (the censored deprivation score of individual i), k
is a poverty cut-off (=33% of the weighted deprivation scores). The MPI value is given as the
product of the multidimensional poverty headcount ratio (H) and the intensity of poverty (A).
The following formula to estimate inequality in deprivation levels given as follows:𝑛𝑛

4
𝐼𝐼(𝐶𝐶) = �[𝑐𝑐𝑖𝑖 − 𝜇𝜇(𝑐𝑐𝑖𝑖 )]2
𝑛𝑛
𝑖𝑖=1

Where Ci is a measure of weighted deprivation levels for each individual, and 𝜇𝜇(𝑐𝑐𝑖𝑖 ) is average
deprivation levels for each municipality.
34

Table 1A: Dimensions, indicators, deprivation thresholds, and weights of the MPI
Dimensions

Indicators

HealthGeneral healthandfunctioning

Deprivation cut-off(deprived if)
Individual experiences difficulty in one or morefunctions: hearing, vision, communication, mobility(walking or climbing stairs), or cognition(remembering or concentrating)

Weights

1/4

Children aged <7 -not deprived in schooling andchildren age 7 & 8 -not deprived if currently attending
school (even though completed zero schooling).

Education

Years of
SchoolingNot deprived if:
Age 9-16 completed grades from 1 to 7
Age 17-20 completed at least grade 9
Age 21>above completed at least grade 12
1/4

Living standard
Dwelling type is informal shack /traditional
dwelling/caravan/tent /other

1/28

Water

No piped water in a dwelling or on stand

1/28

Sanitation
Fuel forcocking
Fuel forlighting
Fuel forheatingAssetownership

No flush toilet
Household is using paraffin/ wood/ coal animal dung/other/ none

1/28

Household is using paraffin/candle/ otherHousehold is using paraffin /wood /coal /animal dung/other/ none
Household does not own more than one of television,
radio, telephone or refrigerator and does not own a car
All adults (aged 15 to 64) in a household are
unemployed

1/28

Dwelling type

Economic
Activity

Unemployment

1/28

1/28
1/28
1/4

35

Appendix B: Tables and Figures
Table 1B: Summary Statistics
Variable
Dependent variables
Income poverty(headcount)
MPI
Independent variables
HistoryHomelandNatural geography
slope
Mean_temp
Mean_rain
Rain(variance)
Temperature(variance)
Socio-economic conditions
Education (> high school)
White population (%)
Income _Gini
Deprivation inequality
Log GDPUrban households (%)
Source: Own estimates

Obs

Mean

Std.
Dev.

Min

Max

234
234

0.671
0.192

0.144
0.083

0.371
0.061

0.897
0.398

234

0.333

0.472

0

1

234

234
234

2.892
17.42268.564
0.192
0.024

1.905
2.162
58.613
0.063
0.007

0.266
12.937
1.222
0.068
0.008

8.331
23.066465.040
0.475
0.041

234
234
234
234
234
234

0.137
0.073
0.719
0.186
9.866
0.529

0.062
0.069
0.042
0.023
1.498
0.349

0.044
0
0.594
0.124
4.937
0

0.361
0.386
0.831
0.236
11.8240.998

36

Table 2B. Ordinary least squares non-spatial model estimation results
Homeland
Slope
Temperature (mean)
Rain(mean)
Rain(variance)
Temperature(variance)
Education (> high school)
White population (%)
Income _Gini
Deprivation inequality
Urban households (%)
Education x Homeland
White population x Income _Gini
Constant

OLS
0.030**
0.063***(0.011)(0.022)-0.006-0.006**(0.004)(0.003)0.002
0.002(0.002)(0.002)0.001***
0.001***(0.0003)(0.0003)-3.49E-05-1.33E-05(5.63E-05)(8.53E-05)-0.119**-0.110(0.045)(0.041)-1.131***-1.068***(0.107)(0.105)-0.0424-2.049**(0.055)(1.025)0.452***
0.260*(0.118)(0.139)1.087***
1.127***(0.226)(0.224)-0.136***-0.131***(0.022)(0.019)-0.288*(0.155)0.028*(0.014)0.319***
0.439***(0.093)(0.118)
0.832
0.882
234
234

adj. R-sq
Sample size
Source: Own estimates. Standard errors in parentheses* p<0.05 ** p<0.01 *** p<0.001

37

Table 3B: Correlates of income poverty estimates (headcount): SAR models

Beta

Rho

homelandslope
Temperature (mean)
Rain(mean)
Rain(variance)
Temperature(variance)
Education (> high school)
White population (%)
Income _Gini
Deprivation inequality
Log GDP pc
Urban households (%)
White population x Income _Gini
Education x Homeland_cons
W*headc_i_hi
var(e.headc_i_hi)
Sample size

0.032***-0.0040.003
0.001*-0.000-0.029-1.037***-0.0240.430***
0.571*-0.029***0.211*
0.594***
0.003***
234

W: inverse distance
0.052*
0.021*-0.004-0.006*
0.003
0.001
0.001
0.001*-0.000-0.000-0.026-0.113**-1.022***-1.096***-1.615-0.021
0.263
0.369***
0.586**
0.754***-0.027***-0.124***0.022-0.1650.290*
0.082
0.619***
0.564***
0.003***
0.00310***234
234

0.062**-0.006*
0.001
0.001
0.000
-0.102**-1.013***-2.056*0.171
0.787***-0.117***0.029*-0.353*0.180
0.604***
0.00296***234

Source: Own estimates. Standard errors in parentheses* p<0.05 ** p<0.01 *** p<0.001

38

Table 4B: Correlates of income poverty estimates (headcount): SDM model

Homeland
Slope
Temperature (mean)
Rain(mean)
Rain(variance)
Temperature(variance)
Education (> high school)
White population (%)
Income _Gini
Deprivation inequality
Log GDP pc
Urban households (%)
White population x Income _Gini
Education x Homeland_cons

0.027**-0.0020.003
0.000-0.000-0.069-0.879***-0.0290.434***
0.809**-0.034***-2.477

W: inverse distance
0.050*
0.0154-0.001-0.003
0.001
0.002
0.000
0.000
0.000
0.000
-0.076-0.120**-0.854***-0.940***-2.190*-0.03360.232
0.383***
0.869***
0.527*-0.032***-0.179***0.030*-0.201-0.090-3.291*

W*headc_i_hi
Homeland
Slope
Temperature (mean)
Rain(mean)
Rain(variance)
Temperature(variance)
Education (> high school)
Gamma
White population (%)
Income _Gini
Deprivation inequality
Log GDP pc
Urban households (%)
White population x Income _Gini
Education x Homelandvar(e.headc_i_hi)
Sample size

0.695***
0.213-0.075*0.019
0.007-0.000-1.181*-1.609-0.7784.573**-6.468*0.061

0.655**
0.223-0.092*
0.020
0.005
0.001
-1.059*-1.973-29.01
2.415-8.542**
0.036

Beta

Rho

0.003***
234

0.396-0.6100.003***
234

0.044*-0.002
0.000
0.000
0.000
-0.121**-0.887***-2.434**0.149
0.582*-0.172***0.033*-0.266-2.4510.700***
0.143-0.0670.023
0.009*-0.001-1.151*-1.602-1.5925.654***-5.367*0.639**
0.567*-0.073
0.027
0.007
0.000
-0.916-1.625-15.845.179*-7.045**

0.422

0.402
0.190
-4.261*0.002***
234

0.003***
234

Source: Own estimates. Standard errors in parentheses* p<0.05 ** p<0.01 *** p<0.001

39

Figure 1B: Local Moran's I scatterplot for income poverty

Income_Poverty
3

2

Wz

1

0

-1

-2

Ngquza Hill
Port St Johns
Nyandeni
Umhlabuyalingana
Ntabankulu
Hibiscus Coast
Abaqulusi
JoziniKing Sabata
Dalindyebo
Endumeni
UlundiThe Big
5Nkandla
False Bay
Mhlontlo
NongomaHlabisaUphongolo
Mthonjaneni
Mtubatuba
Naledi-NW
Ubuhlebezwe
MusinaSakhisizwe
Ba-Phalaborwa Umtshezi
Thulamela
UmvotiMbhashe Nqutu
Elundini
Umuziwabantu
uMhlathuze
Umzimvubu
MnqumaEngcoboeDumbeFetakgomo
Ezingoleni
Kagisano/Molopo
Greater KokstadEmnambithi/Ladysmith
MakhadoEmadlangeni
MafikengNewcastle
MsingaMbizana
Makhuduthamaga
Kwa Sani
Intsika
Yethu
Imbabazane
Ntambanana
Polokwane
UmzumbeMolemole
Mkhondo
Maphumulo
Richmond
Greater
TubatseMpofana
MfoloziOkhahlamba
LukanjiGreater Tzaneen
Ephraim
MogaleGreater
LetabaAganang
KwaDukuza Lekwa-TeemaneGreater
GiyaniuMlalazi
Lepele-Nkumpi
IndakaBuffaloCity
UmdoniMatatiele Umzimkhulu
Senqu
RatlouPhokwane Mamusa
Maletswai
Thaba ChweuThe Msunduzi
Ingwe
Emalahleni-EC
Greater
Taung
Amahlathi
eThekwini
Vulamehlo
Maquassi
PixleyKa Hills
Seme
Dihlabeng
MutaleGreat
Keia Phofung
Blouberg
Dannhauser
Tswaing
Maluti
Maruleng
UmjindiMookgopongof Matlosana Ditsobotla
PhumelelauMngeniCityMatjhabeng
Mogalakwena Ndwedwe
Inkwanca
Tswelopele
Magareng
Mandeni
Mohokare
Mkhambathini
Mantsopa
Nkonkobe Bushbuckridge
Mangaung
Gamagara
MakanaNketoana
Kgetlengrivier
GariepTsolwana
Masilonyana
Lephalale
Sol Plaatjie
Nala Elias Motsoaledi
uMshwathi
Mbombela
Impendle
Dr Nkomazi
JS Moroka
Nxuba
LekwaYethemba
Dikgatlong
InxubaKopanong
MoqhakaNdlambeNaledi-FS
Tokologo
SetsotoMsukaligwa
Ngqushwa
Thabazimbi
Mafube
Ga-Segonyane
Blue Crane Route
Joe Morolong
Steve
TshweteKhara
Hais
Emakhazeni
TlokweCity Council
Kgatelopele
Renosterberg Umsobomvu
Ramotshere Moiloa
Tsantsabane
Siyathemba
Emthanjeni
Metsimaholo
LetsemengNgwatheCamdebooUbuntuThembelihle
Ikwezi
Rustenburg
SundaysRiver Valley
Modimolle
Bela-Bela
Albert Luthuli
Siyancuma
Kareeberg
Nelson Mandela
Bay Thembisile
City of Tshwane Baviaans
Ventersdorp
MosesKheis
KotaneKouga
Kai !Garib
Beaufort WestDipaleseng
Merafong
City
Govan
Mbeki
MidvaalLesedi
Emalahleni-MP
Madibeng
GeorgeEmfuleniMier
Kou-Kamma
Karoo
HooglandPrince
HantamKh�i-Ma
Laingsburg
Oudtshoorn Albert
KnysnaHessequa Randfontein
Moretele
Westonaria
Cityof Johannesburg
Ekurhuleni Bitou
Matzikama
Victor Khanye
MogaleCity
MosselKhoi
Bay
RichtersveldNama
Kamiesberg
Kannaland
Swellendam
Langeberg
Witzenberg
Cederberg
CapeTheewaterskloof
AgulhasStellenbosch
CityofValley
Cape Town
BergrivierDrakenstein
BreedeOverstrand
Swartland
Saldanha Bay

-2

-1

0

1

2

3

4

z
Source: Own estimates using data from South African Census, 2011.

40

Figure 2B: Local Moran's I scatterplot for multidimensional poverty

MD_Poverty
3

2King Sabata Dalindyebo
Sakhisizwe
MnqumaNgquza Hill
Ntabankulu
NyandeniMhlontlo
Port St Mbhashe
Johns
EngcoboElundini
Buffalo City
Ubuhlebezwe
Greater Kokstad
Hibiscus Coast
Umzimvubu
Umhlabuyalingana
Intsika Yethu
Matatiele
MbizanaUmuziwabantu
UmzumbeEzingoleni
Kwa Sani Thulamela
NkandlaThe
Umvoti
Big
5
False
Bay
LukanjiRichmond
Jozini
Kagisano/Molopo
Umzimkhulu
Amahlathi
MusinaGreat Kei
Mthonjaneni
UlundiEndumeni Naledi-NW
Maphumulo Ingwe
Senqu
Mpofana Umtshezi
The Msunduzi Ba-Phalaborwa
Umdoni
Hlabisa
Mtubatuba
Imbabazane
eThekwini
Abaqulusi
uMhlathuze
Mafikeng Uphongolo
Vulamehlo
Makhado Nqutu
MsingaNongoma
Greater Giyani Emalahleni-EC
KwaDukuza
FetakgomouMlalazi
NdlambeNewcastle
Molemole
Gamagara
MakanaMaletswai
RatlouMakhuduthamaga
Nkonkobe
MutaleGreater
TubatseEmnambithi/LadysmitheDumbeIndakaLepele-NkumpiGreater
Greater
LetabaNtambanana
Okhahlamba
Impendle
TzaneenNdwedweKhara uMngeni
Hais
InkwancaMfolozi
Mandeni MkhondoMkhambathini
Maluti a Phofung
Thaba
Chweu
Greater
Taung
MamusaPolokwane
Maruleng
Kgetlengrivier
PixleyKa Seme uMshwathi
PhokwaneNgqushwa
Emadlangeni
Ditsobotla
Blouberg
Joe Morolong
Ephraim
MogaleNxuba Aganang
Dihlabeng Ga-Segonyane
Tswaing
Dannhauser
Magareng
Lekwa-Teemane
Lephalale
Elias
Motsoaledi
Mohokare
Ramotshere Moiloa
Tsantsabane Mogalakwena
Cityof Matlosana
Bushbuckridge
Kgatelopele
Mookgopong
Siyathemba
Mbombela
Dikgatlong Phumelela
Kai
Lekwa
!GaribThabazimbi
Sol
Plaatjie
GariepRustenburg
Moses
Nketoana
KotaneUmjindiMafubeNama
Khoi
Msukaligwa
Steve Tshwete
Maquassi
Hills
Hantam
NelsonMandelaMangaung
Bay
Tswelopele
Siyancuma
KareebergAlbert Luthuli Tsolwana
Nkomazi
Emakhazeni
Matjhabeng
SetsotoMantsopa
Naledi-FS
Inxuba Yethemba
Masilonyana
Dr
JS
MorokaModimolle
LetsemengBlue
Kopanong
Crane
Kh�i-Ma
Route
Matzikama
IkweziMoqhaka
Kheis
Renosterberg
TlokweEmthanjeni
City
Council
Ventersdorp
Tokologo
Kouga
SundaysRiver Valley
Metsimaholo
Nala
Thembelihle
Beaufort
Govan
WestMbeki
Thembisile
Merafong
Bela-Bela
Madibeng
Kamiesberg
NgwatheCity
CamdebooRichtersveld
City of Tshwane
Umsobomvu
Randfontein
Lesedi Ubuntu
Mier
Emfuleni
Moretele
Baviaans Dipaleseng
Karoo Hoogland
GeorgeKou-Kamma
PrinceAlbertMogaleCity
Emalahleni-MP
Bitou
City of Johannesburg
Witzenberg
Laingsburg
MidvaalLangeberg
VictorKhanyeOudtshoorn
Hessequa
Westonaria
Kannaland
EkurhuleniKnysna
Cederberg
Mossel
Bay
Swellendam
Cape
AgulhasBreedeOverstrand
ValleyTheewaterskloof
Stellenbosch
Cityof Cape Town
Bergrivier
Drakenstein
Swartland
Saldanha Bay

Wz

1

0

-1

-2
-2

-1

0

1

2

3

4

z
Source: Own estimates using data from South African Census, 2011.

41