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Assets and capabilities poverty in South Africa Sandile Simelane Statistics South Africa. Census. STATS SA. Education level of the labour force, 2011. Census. Outline. STATS SA. Background Research questions Data & methods Results Discussion, conclusions & policy implications. - PowerPoint PPT PresentationTRANSCRIPT
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 1
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Assets and capabilities poverty in South Africa
Sandile Simelane
Statistics South Africa
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 2
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Outline• Background
• Research questions
• Data & methods
• Results
• Discussion, conclusions & policy implications
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 3
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Background• This paper uses a composite index of household assets and
capabilities data to examine levels and trends of poverty for provinces, district councils (DCs) and local municipalities of South Africa.
• The resulting index: the assets and capabilities poverty (ACP)– Calculated at household level– demonstrates that poverty can be measured in the absence of income or
expenditure data. – The index of ACP is a complement not replacement of income & expenditure
measures of poverty
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 4
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Background contd….Motivations for the approach
1. The paper conceptualizes poverty as a multidimensional phenomenon that can be proxied by the socioeconomic variables that are commonly collected in pop. censuses and household surveys.
2. South Africa’s current socio-economic policy (RDP) states that meeting basic needs for all in the country’s population is the top priority for government.
3. Income data are poorly measured in LDCs (Bollen, Glanville and Stecklov 2001; World Bank 1995), including South Africa (Statistics South Africa 2000).
4. The index used has been found to be a good measure of wealth in other developing countries (Filmer and Pritchett 2001).
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 5
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Research questions
• Who are assets and capabilities poor in South Africa and what has been the trend in poverty levels between 1996 and 2007?
• How are the are the assets and capabilities poor (hholds/ individuals) distributed, spatially, in the country?
• What are the defining characteristics of the poor?
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 6
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methodsData
Pooled dataset comprising the 2007 CS and the 1996 & 2001 censuses of South Africa
Revisions to this work will include census 2011
Q. Why pooled data?A. To control for the cross-dataset differences in the distribution of the variables used
& derive estimate that are comparable across the datasets.
CS 2007 – Large sample survey– Representative at municipality level
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 7
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…
Methods
Identification of Assets & Capabilities poor households
Step 1: Computation of the index
Index of ACP computed using Principal Components Analysis (PCA) by combining information on 8 categories of household assets/characteristics and 2 measures of household functional capabilities.
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 8
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…About PCA• PCA is a statistical procedure that reduces the dimensionality of multiple
variables by transforming them into few linear components that are uncorrelated.
• For each component, PCA assigns to each observation (household) a scoring factor based on the household’s possession (or lack) of the variables included in the computation, after taking into account the covariation of these variables in the population being studied.
– These scores can be used to sort the observations (households) from the poorest to the wealthiest.
• The 1st component accounts for the largest proportion of the total variation in the set of variables used. For this reason, the 1st component is used as the index of ACP.
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 9
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…
Hhold asset/characteristics1. Telephone/ cell phone [yes/no]
2. Type of dwelling structure [modern; traditional/informal; other/caravan/tent]
3. Type of toilet [flush/chemical; pit latrine/bucket; no toilet/other e.g. open land] 4. Source of water [piped water inside; piped water outside; public tap; other source] 5. Refuse removal [local auth/private co.; communal/own dump; no disposal facility] 6. Energy for cooking [electricity/gas, paraffin, wood/coal/animal dung/other ]7. Energy for lighting [electricity/gas, paraffin, wood/coal/animal dung/other ]8. Energy for heating [electricity/gas, paraffin, candles/other ]
Capabilities1. Adult employment ratio
2. Proportion of adults with high school education & above
Variables used in PCA
23 binary variables & 2 continuous variables
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 10
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…
Assessment of the index
• The scoring factors are all in the expected direction. – All the variables associated with higher SES—e.g. having piped water inside the
dwelling—have bigger (and +ve) scoring factors than those that are perceived to measure lower levels of SES
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 11
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…
Identification of Assets & Capabilities poor households
Step 2: Calculation of the poverty line
Poverty line = ½ median value of index of ACP.– based on theory of Justice as Fairness (Rawls 1971)
Thus estimate of poverty level in a give geographic unit =
%100*
i
acpi
i H
HACP
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 12
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…Analysis of characteristics of poor households
Logistic regression model is employed
pTi = probability that household (i) is classified as poor in year T
Explanatory variables include: sex of the head of household; rural/ urban residence; province; age of household head; tenure status of dwelling unit; type of residence; crowding; etc.
NB: No causality implied
TTi
Ti Xp
p
1
log
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 13
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…Statistical analysis of spatial distribution of poor households
• Moran’s I = global test for clustering/ autocorrelation– Operates like correlation coefficient
n
i
n
ijijx
n
i
n
ijjiij
wS
xxxxw
I2
))((
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 14
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Data & methods contd…Statistical analysis of spatial distribution of poor households• Local indicator of spatial association (LISA) statistics
Interpretation of LISA• positive Ii means either a high value is surrounded by high values
(high-high) or a low value is surrounded by low values (low-low). • A negative score of Ii means either a high value is surrounded by low
values (high-low) or vice versa (low-high).
n
ijjij
x
ii xxw
S
xxI )]([
)(2
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 15
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results: levels & trend Cumulative Distribution Functions (CDFs) of index of ACP, 1996-2007
• Huge but declining, levels of inequality in living stds during the period 1996-2007• Decline in national level of ACP driven by improvements among the poor
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 16
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results: levels & trend Levels and trend in household assets and capabilities poverty by province, 1996-2007
ProvinceCensus 1996
Census 2001
CS 2007
Western Cape 16.6 15.8 6.3Gauteng 23.8 23 14.5Northern Cape 41.0 33.5 27.5Free State 55.1 49.2 27.2KwaZulu-Natal 55.4 52.5 48.2North West 65.4 54 44.6Mpumalanga 65.1 59.3 60.1Eastern Cape 73 66.6 58.3Limpopo 82.7 75.8 79.5South Africa 49.1 45.1 38.2
3rd 3rd 3rd
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 17
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results contd…spatial distribution of ACP
Map showing the proportion (%) of assets & capabilities poor households by localMunicipality, South Africa 1996.
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 18
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results contd…spatial distribution of ACP
Lisa Cluster map for ACP, 1996Moran’s I = 0.4088, p<0.05
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 19
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results contd…spatial distribution of ACP
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 20
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results contd…spatial distribution of ACP
Lisa Cluster map for ACP, 2007Moran’s I = 0.5090, p<0.05
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 21
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results contd…spatial distribution of ACP
While there was general improvement in poverty levels nationally and in FS b/t 1996 & 2007 clustering of households according to poverty status worsened during the period.
Lisa Cluster map for ACP, 1996Moran’s I = 0.4088, p<0.05
Lisa Cluster map for ACP, 2007Moran’s I = 0.5090, p<0.05
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 22
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results contd… characteristics of assets & capabilities poor households
Odds ratios from logistic regression model of probability of hhold asset & capabilities poverty on selected variables, 2007
Effect Point Est.95% Wald Confidence Iimits
Province
Western Cape (RC) 1.000
Eastern Cape 4.445 4.172 4.736
Northern Cape 3.844 3.582 4.125
Free State 3.207 2.986 3.445
KwaZulu-Natal 3.575 3.354 3.810
North West 1.761 1.642 1.890
Gauteng 2.362 2.219 2.516
Mpumalanga 5.185 4.824 5.572
Limpopo 4.527 4.215 4.862RC= reference category
Likelihood of ACP highest in MP, EC, LP compared to RC
A hhold in FS was 3.2 times more likely to be poor than one in WC in 2007
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 23
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Results: characteristics of assets & capabilities poor households
Other findings
• Assets & capabilities poverty highest in rural areas
• Household headed by females more likely to be ACP
• Households headed by Black Africans more likely to be poor than other groups
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 24
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Discussion• SA experienced huge but declining, levels of inequality in living stds
during the period 1996-2007.
• Proportion of households that are assets & capabilities poor decreased nationally from 49.1% in 1996 to 38.2% in 2007.
• The same applies to Free State
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 25
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Discussion….. contd
• Assets & capabilities poverty highest in rural areas
• Household headed by females more likely to be ACP
• Households headed by Black Africans more likely to be poor than other groups
THE SOUTH AFRICA I KNOW, THE HOME I UNDERSTAND 26
STATS SA
Census
MoqhakaNgwathe
SetsotoDihlabeng
MasilonyanaMetsimaholo
NketoanaMafube
Maluti a PhofungTokologo
TswelopeleLetsemengPhumelelaMantsopaKopanong
MatjhabengMohokare
NalediNala
54.8
56.5
57.9
58.6
59.2
62.7
62.7
63.1
64.9
67.2
68.1
68.2
69.0
70.3
70.4
71.3
72.3
72.3
74.1
44.8
43.3
41.8
41.1
40.5
37.0
37.1
36.6
34.9
32.6
31.7
31.5
30.8
29.5
29.4
28.4
27.5
27.5
25.7
Below matric Matric and higher Unsp
Education level of the labour force, 2011
Criticism of the index• The index is not a good indicator of performance of individual
households because it is based on community variables.
Reflection• It is true that most of the variables included in the index are
community based but in a setting like in RSA where policy is clear that ALL households should enjoy the assets/variables it important to profile the hholds that lag behind….
Conclusion