Spatial measures of socio-economic
inequality in South Africa
Spatial exposure to inequality: Results
David McLennan, University of Oxford
Michael Noble, Southern African Social Policy Research Institute
Benjamin J. Roberts, Human Sciences Research Council
Exposure of ‘poor’ to ‘non-poor’
Exposure of poor to non-poor
national datazone deciles
ExposIncxy
1500
1000
0
500
Frequency
2000
2500
Chart 23: National distribution of datazone Exposure scores
- Income -
0
.2
.4
Exposure score
.6
.8
Chart 24: National distribution of datazone Exposure scores
- Income By metro/non-metro status
Non-Metro
0
1000
Frequency
2000
3000
Metro
0
.2
.4
.6
.8
0
Exposure score
Graphs by metro_status
.2
.4
.6
.8
Table 1: Exposure of poor to non-poor: location of
datazones in the 10% highest ExposIncxy decile nationally
Municipality
Number
Percentage
City of Cape Town
986
44.5
City of Tshwane Metro
502
22.7
City of Johannesburg Metro
290
13.1
Ekurhuleni Metro
206
9.3
Others (23 municipalities)
232
10.5
2,216
100.0
Total in the 10% highest
exposure decile nationally
Table 2: Exposure of poor to non-poor: location of the ten
municipalities with the largest proportions of datazones in the
highest ExposIncxy decile nationally
Municipality
Gamagara
Stellenbosch
City of Cape Town
Saldanha Bay
City of Tshwane Metro
Mossel Bay
City of Johannesburg Metro
George
Ekurhuleni Metro
Nokeng tsa Taemane
Number of
datazones in the
municipality
9
60
1388
34
951
37
1599
67
1188
21
Number of
datazones in the
10% highest
ExposIncxy decile
nationally
7
44
986
23
502
10
290
12
206
3
Percentage of
municipality
datazones in the
10% highest
ExposIncxy decile
nationally
77.8%
73.3%
71.0%
67.6%
52.8%
27.0%
18.1%
17.9%
17.3%
14.3%
Focus on the metropolitan municipalities
(Exposure of ‘poor’ to ‘non-poor’)
1
Chart 25: Datazone deprivation rate against exposure score
- Income -
Cape Town
Johannesburg
Tshwane
.5
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
0
Mangaung
0
.5
Income Deprivation score
1
1
Chart 26: Datazone deprivation rate against exposure score
- Employment -
Cape Town
Johannesburg
Tshwane
.5
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
0
Mangaung
0
.5
Employment Deprivation score
1
1
Chart 27: Datazone deprivation rate against exposure score
- Education -
Cape Town
Johannesburg
Tshwane
.5
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
0
Mangaung
0
.5
Education Deprivation score
1
1
Chart 28: Datazone deprivation rate against exposure score
- Living Environment -
Cape Town
Johannesburg
Tshwane
.5
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
0
Mangaung
0
.5
Liv Env Deprivation score
1
ap
e
ity
M
in
i
o
C
ity
an
ga
un
g
Bu
ffa
l
M
ek
w
a
g
an
de
l
eT
h
on
el
s
ha
nn
es
bu
r
hu
le
ni
n
an
e
To
w
hw
Ts
Ek
ur
C
C
in
i
a
an
ga
un
g
o
ek
w
an
de
l
Bu
ffa
l
M
n
hu
le
ni
To
w
g
.4
.5
.6
.8
.7
Exposure
.75
.8
.85
Education
Jo
M
eT
h
on
el
s
ap
e
Ek
ur
C
ha
nn
es
bu
r
an
e
.7
N
N
ity
M
o
C
ity
a
in
i
an
de
l
Bu
ffa
l
on
ek
w
an
ga
un
g
rh
ul
en
i
eT
h
M
Ek
u
n
g
To
w
an
e
C
hw
ap
e
Ts
o
an
ga
un
g
a
in
i
an
de
l
ek
w
ha
nn
es
bu
r
C
g
rh
ul
en
i
Bu
ffa
l
M
M
n
an
e
To
w
hw
eT
h
Ek
u
on
el
s
Jo
Ts
ap
e
ha
nn
es
bu
r
el
s
Jo
C
.4
.2
.5
.3
.5
.6
Exposure
.4
Exposure
.7
.6
.8
.7
Income
N
N
Jo
hw
Ts
.65
Exposure
Chart 29: Exposure Scores - Metropolitan municipalities
Employment
Living Environment
Tables 3 & 4: Spearman rank correlation coefficients
between the four dimension-specific exposure measures
Table 3: All metropolitan datazones (n=7,800)
Expos_Inc
Expos_Inc
Expos_Emp
Expos_Edu
Expos_Liv
1
Expos_Emp
0.9171
1
Expos_Edu
0.8104
0.8281
1
Expos_Liv
0.8947
0.7821
0.7225
1
Table 4: City of Cape Town datazones only (n=1,388)
Expos_Inc
Expos_Inc
Expos_Emp
Expos_Edu
Expos_Liv
1
Expos_Emp
0.9344
1
Expos_Edu
0.8752
0.8861
1
Expos_Liv
0.9339
0.8592
0.8116
1
Creating ExposFacxy
1.
Each of the four separate dimension-specific exposure scores at
Datazone level was ranked and transformed to a normal
distribution.
2.
The four normalised rank variables were entered into a
maximum likelihood factor analysis.
3.
Weights derived from the factor analysis were used to combine
the four normalised rank variables to form a single composite
measure at Datazone level: ‘ExposFacxy’.
4.
The 7,800 metropolitan Datazones were re-ranked on the
ExposFacxy measure.
Chart 30: Datazone Exposure Factor Ranks by Municipality
falo
City
Buf
Man
gau
ng
and
ela
Nel
son
M
i
kwin
eTh
e
eni
Eku
rhul
Joh
ann
esb
urg
Tow
n
Cap
e
Tsh
wan
e
7,800
3,900
1
Interquartile Range ranked WITHIN Metropolitan Municipalities
Mel Blou
kbo ber
ss
g
Milntrand
Dur erto
Goobanvilln
Bra dwooe
cke d
B nfell
Houellville
Fi t Ba
Capsh Hoey
S im e T k
on's own
Epp Kuils Town
ing
InduRiver
s
Mamtria
re
A
SomMuizetlantis
erse nber
tW g
Gor Parest
Gradons Bow
ssy ay
Pa
Kom
met rk
Cap Peljie
e M la
e
Kra Belhtro
aifo ar
n
Eer Athlotein
ste
Elsi Rivne
B es R ier
Mitclue Doivier
Imiz hells wns
amo Plain
City
Ye
of C
ape Str thu
T
Mat ow and
roos n N
U
f
Macontein
ass
a
GugLangar
u
le
N
Fistoordhothu
ante ek
Blac kraa
khe l
at
Delh
N
Cro yang ft
ssro a
Philads
ip
Kha Mfulepi
yeli ni
Nom tsha
zam
o
7,800
3,900
1
Chart 31: Datazone Exposure Factor ranks by Cape Town MainPlace
Interquartile Range ranked WITHIN Metropolitan Municipalities
Tem
ban
Eku
i
phu
ml e
Villa
ni
ge V
1S
outh
Man
dela
Par
Villa
k
ge V
2N
orth
Villa
ge V
1N
orth
Tre
vor
V ila
kaz
Grif
i
fiths
Mxe
Kha
nge
yeli
tsha
T3Kha
V3
yeli
tsha
T3Har
V4
are/
Hol
imis
Villa
a
ge V
4N
Kha
orth
yeli
tsha
T3V5
Tow
Villa
n3
ge V
3N
orth
Vict
oria
Mer
ge
Ikw
ezi
Par
Kha
k
yeli
tsha
SP
Mon
wab
is i
Silv
e
Kha
r To
yeli
wn
tsha
T2Solo
V2b
mon
M
ahla
Bon
ngu
gan
i TR
Sec
tion
RR
Sec
tion
3,500
3,000
2,500
2,000
1,500
Chart 32: Datazone Exposure Factor ranks by Khayelitsha SubPlace
Interquartile Range ranked WITHIN Metropolitan Municipalities
Summary of Exposure results

Exposure to socio-economic inequality is typically highest in the
urban areas, particularly the metropolitan municipalities.

There are strong correlations at datazone level between the four
separate dimension-specific measures of exposure (income,
employment, education, living environment)

The composite ExposFacxy measure constructed across the 7,800
metropolitan datazones shows that exposure is typically highest in
Tshwane and Cape Town, but that there is far more variation
within Tshwane than within Cape Town.

The exposure results can be analysed at a detailed geographical
level to explore variations within municipalities.
Community ‘Intensity’ of exposure
(‘poor’ to ‘non-poor’):
National analyses
Neighbourhood ‘Intensity’ of exposure to
socio-economic inequality




The exposure measures represent the likelihood of a
given individual living in a given neighbourhood of
being exposed to socio-economic inequality.
Typically, a geographical area with low poverty rates
(e.g. Sandton) will be characterised by relatively high
levels of exposure amongst the poor population.
But some neighbourhoods (e.g. Alexandra) have high
poverty and high exposure to inequality.
In these areas, it may be argued there is a high
community-level ‘intensity’ of exposure to inequality.
.8
.6
.4
0
aLDPxyi*
ExposIncxy score
.2
0
.2
.4
.6
dz_rate_inc
Proportion of population income deprived
lowest_percentile_intensity
.8
highest_percentile_intensity
1
Chart 33: National distribution of datazone Intensity scores
- Income By metro/non-metro status
Non-Metro
1000
0
500
Frequency
1500
2000
Metro
0
.2
.4
.6
0
Intensity score
Graphs by metro_status
.2
.4
.6
Intensity of exposure
(‘poor’ to ‘non-poor’):
Focus on the metropolitan municipalities
Tables 5 & 6: Spearman rank correlation coefficients
between the four dimension-specific ‘intensity’ measures
Table 5: All metropolitan datazones
All Metros
intensity_inc
intensity_emp
intensity_edu
intensity_liv
1
intensity_inc
intensity_emp
0.8245
1
intensity_edu
0.7960
0.7068
1
intensity_liv
0.8810
0.7529
0.8399
1
Table 6: City of Cape Town datazones only
Just Cape
Town
intensity_inc
intensity_inc
intensity_emp
intensity_edu
intensity_liv
1
intensity_emp
0.9329
1
intensity_edu
0.8666
0.8122
1
intensity_liv
0.9320
0.8780
0.8402
1
Chart 34: Datazone Intensity Factor Ranks by Municipality
Tow
n
Cap
e
wan
e
Tsh
falo
City
Buf
Joh
ann
esb
urg
and
ela
Nel
son
M
Man
gau
ng
i
kwin
eTh
e
Eku
rhul
eni
7,800
3,900
1
Interquartile Range ranked WITHIN Metropolitan Municipalities
Summary of ‘Intensity’ results




‘Intensity’ can be regarded as a measure of the degree
to which neighbourhoods are characterised by the twin
stressors of high poverty and high exposure to socioeconomic inequality.
High correlations exist between the four dimensionspecific intensity measure, justifying the construction of
an ‘IntensityFacxy’ composite measure.
Datazone neighbourhoods with very high levels of
‘intensity’ are found in all metropolitan municipalities.
All eight metro municipalities exhibit a wide range of
datazone level intensity scores, i.e. heterogeneity.
Conclusions

Spatial inequality measures – particularly the P* Exposure indices
– offer a valuable contribution to the evidence base concerning
inequality in South Africa.

They provide a means to examine geographical patterns in
people’s lived experience of inequality.

They can be used as explanatory factors when analysing
attitudinal data (as is the focus of the ESRC/NRF-funded project).

They can also be used to identify geographical areas
characterised by both high levels of poverty and high levels of
exposure to inequality, which may be most at risk of social unrest
or high levels of crime (our ‘Safe and Inclusive Cities’ project).
David McLennan
david.mclennan@spi.ox.ac.uk