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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-poornational datazone deciles
ExposIncxy
050
010
0015
0020
0025
00F
requ
ency
0 .2 .4 .6 .8Exposure score
Chart 23: National distribution of datazone Exposure scores- Income -
010
0020
0030
00
0 .2 .4 .6 .8 0 .2 .4 .6 .8
Metro Non-MetroF
requ
ency
Exposure scoreGraphs by metro_status
Chart 24: National distribution of datazone Exposure scores- Income -
By metro/non-metro status
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
Total in the 10% highest exposure decile nationally
2,216 100.0
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
Number of datazones in the
municipality
Number of datazones in the
10% highest ExposIncxy decile
nationally
Percentage of municipality
datazones in the 10% highest
ExposIncxy decile
nationally
Gamagara 9 7 77.8%
Stellenbosch 60 44 73.3%
City of Cape Town 1388 986 71.0%
Saldanha Bay 34 23 67.6%
City of Tshwane Metro 951 502 52.8%
Mossel Bay 37 10 27.0%
City of Johannesburg Metro 1599 290 18.1%
George 67 12 17.9%
Ekurhuleni Metro 1188 206 17.3%
Nokeng tsa Taemane 21 3 14.3%
Focus on the metropolitan municipalities(Exposure of ‘poor’ to ‘non-poor’)
0.5
1E
xpos
ure:
(aL
DP
xy*
Inco
me)
0 .5 1Income Deprivation score
Cape Town
Johannesburg
Tshwane
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
Mangaung
Chart 25: Datazone deprivation rate against exposure score- Income -
0.5
1E
xpos
ure:
(aL
DP
xy*
Em
ploy
men
t)
0 .5 1Employment Deprivation score
Cape Town
Johannesburg
Tshwane
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
Mangaung
Chart 26: Datazone deprivation rate against exposure score- Employment -
0.5
1E
xpos
ure:
(aL
DP
xy*
Edu
catio
n)
0 .5 1Education Deprivation score
Cape Town
Johannesburg
Tshwane
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
Mangaung
Chart 27: Datazone deprivation rate against exposure score- Education -
0.5
1E
xpos
ure:
(aL
DP
xy*
Liv
Env
)
0 .5 1Liv Env Deprivation score
Cape Town
Johannesburg
Tshwane
Ekurhuleni
Buffalo City
eThekwini
Nelson Mandela
Mangaung
Chart 28: Datazone deprivation rate against exposure score- Living Environment -
.2.3
.4.5
.6.7
Exp
osu
re
Cape
Town
Tshwan
e
Joha
nnes
burg
Ekurh
uleni
eThe
kwini
Nelson
Man
dela
Man
gaun
g
Buffa
lo City
Income
.4.5
.6.7
.8E
xpo
sure
Tshwan
e
Cape
Town
Joha
nnes
burg
Ekurh
uleni
Man
gaun
g
eThe
kwini
Nelson
Man
dela
Buffa
lo City
Employment
.65
.7.7
5.8
.85
Exp
osu
re
Tshwan
e
Joha
nnes
burg
Cape
Town
Ekurh
uleni
Nelson
Man
dela
eThe
kwini
Buffa
lo City
Man
gaun
g
Education
.4.5
.6.7
.8E
xpo
sure
Tshwan
e
Cape
Town
Ekurh
uleni
Joha
nnes
burg
Nelson
Man
dela
eThe
kwini
Man
gaun
g
Buffa
lo City
Living Environment
Chart 29: Exposure Scores - Metropolitan municipalities
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_Emp Expos_Edu Expos_Liv
Expos_Inc 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_Emp Expos_Edu Expos_Liv
Expos_Inc 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.
13,
900
7,80
0
Ran
k of
Exp
osur
e F
acto
r 20
01 w
ithin
Met
ros
[whe
re 1
= g
reat
est e
xpos
ure]
Tsh
wan
e
Cap
e T
own
Joha
nnes
burg
Eku
rhul
eni
eThe
kwin
i
Nel
son
Man
dela
Man
gaun
g
Buf
falo
City
Interquartile Range ranked WITHIN Metropolitan MunicipalitiesChart 30: Datazone Exposure Factor Ranks by Municipality
13,
900
7,80
0
Ran
k of
Exp
osur
e F
acto
r 20
01 w
ithin
Met
ros
[whe
re r
ank
1 =
hig
hest
exp
osur
e]
Blo
uber
gM
elkb
osst
rand
Miln
erto
nD
urba
nvill
eG
oodw
ood
Bra
cken
fell
Bel
lvill
eH
out B
ayF
ish
Hoe
kC
ape
Tow
nS
imon
's T
own
Kui
ls R
iver
Epp
ing
Indu
stria
Mam
reA
tlant
isM
uize
nber
gS
omer
set W
est
Par
owG
ordo
ns B
ayG
rass
y P
ark
Kom
met
jieP
ella
Cap
e M
etro
Bel
har
Kra
aifo
ntei
nA
thlo
neE
erst
e R
ivie
rE
lsie
s R
ivie
rB
lue
Dow
nsM
itche
lls P
lain
Imiz
amo
Yet
huS
tran
d
City
of C
ape
Tow
n N
UM
atro
osfo
ntei
nM
acas
sar
Lang
aG
ugul
ethu
Noo
rdho
ekF
ista
ntek
raal
Bla
ckhe
ath
Del
ftN
yang
aC
ross
road
sP
hilip
piM
fule
niK
haye
litsh
aN
omza
mo
Chart 31: Datazone Exposure Factor ranks by Cape Town MainPlaceInterquartile Range ranked WITHIN Metropolitan Municipalities
1,50
02,
000
2,50
03,
000
3,50
0Ran
k of
Exp
osur
e F
acto
r 20
01 w
ithin
Met
ros
[whe
re r
ank
1 =
hig
hest
exp
osur
e]
Tem
bani
Eku
phum
leni
Vill
age
V1
Sou
thM
ande
la P
ark
Vill
age
V2
Nor
thV
illag
e V
1 N
orth
Tre
vor
Vila
kazi
Grif
fiths
Mxe
nge
Kha
yelit
sha
T3-
V3
Kha
yelit
sha
T3-
V4
Har
are/
Hol
imis
aV
illag
e V
4 N
orth
Kha
yelit
sha
T3-
V5
Tow
n 3
Vill
age
V3
Nor
thV
icto
ria M
erge
Ikw
ezi P
ark
Kha
yelit
sha
SP
Mon
wab
isi
Silv
er T
own
Kha
yelit
sha
T2-
V2b
Sol
omon
Mah
lang
uB
onga
ni T
R S
ectio
nR
R S
ectio
n
Chart 32: Datazone Exposure Factor ranks by Khayelitsha SubPlaceInterquartile 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.
0.2
.4.6
.8aL
DP
xyi*
0 .2 .4 .6 .8 1dz_rate_inc
lowest_percentile_intensity highest_percentile_intensity
Exp
osIn
c xy
scor
e
Proportion of population income deprived
050
010
0015
0020
00
0 .2 .4 .6 0 .2 .4 .6
Metro Non-MetroF
requ
ency
Intensity scoreGraphs by metro_status
Chart 33: National distribution of datazone Intensity scores- Income -
By metro/non-metro status
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
intensity_inc 1
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 onlyJust Cape Town intensity_inc intensity_emp intensity_edu intensity_liv
intensity_inc 1
intensity_emp 0.9329 1
intensity_edu 0.8666 0.8122 1
intensity_liv 0.9320 0.8780 0.8402 1
13,
900
7,80
0
Ran
k of
Inte
nsity
Fac
tor
2001
with
in M
etro
s[w
here
1 =
gre
ates
t int
ensi
ty]
Eku
rhul
eni
eThe
kwin
i
Man
gaun
g
Nel
son
Man
dela
Joha
nnes
burg
Buf
falo
City
Tsh
wan
e
Cap
e T
own
Interquartile Range ranked WITHIN Metropolitan MunicipalitiesChart 34: Datazone Intensity Factor Ranks by Municipality
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 socio-economic inequality.
High correlations exist between the four dimension-specific 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 [email protected]