Unemployment and Poverty in South Africa A Provincial ... and Poverty in South Africa A Provincial...
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Unemployment and Poverty in South Africa A Provincial Perspective Risenga Maluleke Statistics South Africa
Transcript of Unemployment and Poverty in South Africa A Provincial ... and Poverty in South Africa A Provincial...
Unemployment and Poverty in South Africa
A Provincial Perspective
Risenga Maluleke
Statistics South Africa
NDP Success likely to be judged by 3 yardsticks: Employment creation, Poverty reduction, and Economic
growth.
Employment creation
Poverty reduction
Economic growth
These three yardsticks are intertwined.
The money metric poverty headcounts, provide a complementary overview of the current poverty landscape
28.4%
33.5%
21.4%
25.2%
51.0%
47.6%
36.4%
40.0%
66.6%
62.1%
53.2%
55.5%
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
2006 2009 2011 2015
Per
cen
tage
Approximately 13,8 million South Africans were living below the FPL in 2015, down from a peak of 16,7 million in 2009.
Poverty headcounts based on the FPL, LBPL and UBPL
Upper-Bound Poverty Line Lower-Bound Poverty Line
Non Poor -45.5%
Poor -55.5%
Non Poor- 60.0%
Poor -40.0%
Non Poor -74.8%
Poor -25.2%
Food Poverty Line
Poverty headcounts in 2015In 2015, more than a quarter of the
population were living below the food poverty line
8.0
18.0
28.0
38.0
48.0
58.0
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LPEC
KZN
MPNC
NW
FS
WC
GP
The poorest three provinces in the country have consistently been Limpopo, Eastern Cape & KwaZulu-Natal.
Gauteng & Western Cape remain the two provinces with the lowest poverty headcounts at 13,6 % & 12,8% respectively.
For Periods 2006 / 2009 / 2011 / 2015
Poverty headcounts by province (LBPL)
Source: Poverty Trends Report
KZN
South Africa has an urbanising, youthful population. This presents an opportunity to boost economic growth, increase employment and reduce poverty.
(NDP: pg30)
Source: Community Survey 2016
Age structure based on CS 2016
First demographic wave: Children of 1996
The life circumstances of first demographic wave have not achieved full potentialHigh Unemployment/Poor Educational outcomes
Second demographic wave
Need to invest in second demographic
wave to achieve outcomes not seen in
their parents generation
Source: Community Survey 2016
Age structure based on CS 2016
22,3 million(down by 150 000 q-q)
Labour force
16,1 million(down by 113 000 q-q)
Employed
6,2 million(down by 37 000 q-q)
Unemployed
14,9 million(up by 306 000 q-q)
Not economically active*
*Of which 2,4 million
were discouraged work
seekers
( up by 83 000 q-q)
37,2 million(up by 157 000 q-q)
people of working age in
South Africa(15 – 64 year olds)
ILO hierarchy – Employed first then
unemployed and the remainder is NEA
(including discouraged job-seekers).
3 mutually exclusive groups. Cannot be in two
groups at the same time
NDP target 2030
Employment:
24 million
The labour market Q2:2017
31.3%
23.6%
13.3%
5.7%
27.7%
0% 5% 10% 15% 20% 25% 30% 35%
Black/African
Coloured
Indian/Asian
White
Total
Unemployment Rate by Population Group
Significant variation in Unemployment by Population Group
Source QLFS Q2:2017
White
Indian/Asian
Coloured
Black African
Source: QLFS, Q2:2017
2017 Q216.1
10.0
15.0
Q12008
Q2
Mill
ion
Number of employed
2010 Q3
2017 Q2, 43.3
35%
40%
45%
50%
Q12008
Q2
Absorption rate
Number of employed people decreased from 16,2 million in Q1:2017 to
16,1 million in Q2:2017
Absorption rate was 43,3% in Q2:2017 and has not recovered to level of
45,8% in 2008
59.9%
50%
55%
60%
65%
Q12008
Q2
Labour force participation rate
Labour market dashboard
Labour force participation rate of 59,9% recorded in Q2:2017 after the highest
LFPR of 60,5% in Q1:2017.
2017 2017 2017
South Africa
27,7%(0,0)
NC
30,5%(-0,2)
WC
20,7%(-0,8)
EC
34,4%(+2,2)
NW
27,2%(+0,7)
MP
32,3%(+0,8)
KZN
24,0%(-1,8)
FS
34,4%(-1,1)
LP
20,8%(-0,8)
GP
29,9%(+0,7)
Quarter-to-quarter changes
Provincial unemployment rate
Source: Quarterly Labour Force SurveyQuarter 2: 2017
EC saw the largest increase in unemployment between Q1 and Q2 2017 and has joint highest unemployment rate with the FS
Source GDP Q4 2016
0.720.70
0.690.68
0.640.66
0.65 0.65
0.600.57
0.59 0.580.56
0.53
0.50
0.560.56
0.470.45
0.51
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
0.75
0.80
2006 2009 2011 2015
Gin
i Co
eff
icie
nt
The population group with the highest level of inequality are black Africans
Black Africans had the highest level of income inequality
Both whites and Indian/Asians saw their Gini coefficients increase, with the white population rising to 0,51 and Indian/Asians returning to their 2006 level of 0,56
Gini Coefficient (Income per capita) by population group (2006, 2009, 2011 & 2015)
350 937
195 336
124 445
67 828
White-headed households (R350 937) spent five times more
than black African-headed households (R67 828) and three
times more than the national average
Black African
Coloured
Indian
White444 446
271 621
172 765
92 983
0 50000 100000 150000 200000 250000 300000 350000 400000 450000
Average Expenditure Average Income
Indians/Asian headed households
(R195 336) spent almost three times
more than black headed households
Average annual household consumption expenditure and income by population group of household head
Significant strides have been made by Government towards poverty and inequality reduction
Government interventions towards poverty and inequality reduction
About 3.6 million households are registered as indigent
households of which;
62,8% receive free
electricity
67,8%receive free
piped water
57,6%receive free
sanitation services
57,6%receive free refuse
removal services
To date more than 17 million social grants are
issued on monthly basis to people who qualify
the means test
About 4.3 million RDP houses and
subsidies have been delivered since 1994
About 76.2% of pupils in South Africa are
benefiting from school feeding schemes
More than 20 000 schools are declared as
no fee schools
Source: NFCM Source: NFCM
Source: SASSA and Department of Human Settlements Source: Department of Basic Education
71%
70%
58%
55%
54%
54%
52%
50%
44%
41%
11%
10%
21%
25%
22%
26%
26%
32%
36%
32%
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0
WC
GP
RSA
FS
MP
KZN
NW
NC
EC
LP
Salaries Remittances Pensions Grants Other sources None
Grants represent a significant source of income, in a number of provinces.
Percentage distribution of main source of income by province, 2016
Source: GHS 2016
• In 2001 wide dispersion of Poverty with Msinga having a poverty Headcount of around 60%
• Between 2001 and 2011 poverty generally declines for all municipalities
• However between 2011 and 2016 poverty trends diverge between municipalities
Multidimensional Poverty by Municipalities 2001-2016
Msinga Headcount 59,8%
Msinga Headcount 24,5%
Intsika YethuHeadcount 27,7%
Msinga Headcount 37,2%
CS 2016
Multidimensional Poverty headcounts by Province 2001/2011/2016
Source: Census 2001/2016/CS 2016
Education and Unemployment continue to drive Multidimensional Poverty
CS 2016
Multidimensional Poverty Drivers
4052
33
10 Years
5 Years
2%
3%
3%
4%
4%
5%
5%
8%
15%
16%
35%
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0
Energy for lighting
Dwelling type
Assets
Energy for cooking
Water
Energy for heating
Sanitation
General health and functioning
NEET
Adult unemployment
Educational attainment
%
The major contributor to the poverty situation of the youth in South Africa
is educational attainment.
Source CS 2016
Main contributors to Multi Dimensional poverty amongst Youth (15-24)
No Income by Level of Education and age
Source: Census 2011
significant progress is possible and is within our reach as we gain better handle on planning through the planning tools.
“Minister in the Presidency: Planning, Monitoring and Evaluation, Mr Jeffrey Thamsanqa Radebe
to plan we need 5 capabilities in our data systems,
namely descriptive, diagnostic, predictive,
prescriptive and adaptive capability.
• Description of phenomena – recognition of features of phenomena and highlighting their presence
• Analysis of phenomena – understanding relationships in the manifestation of phenomena
• Diagnosis of phenomena – paying attention to specifics emanating from analysis
• Prediction – knowing and understanding temporal and spatial conduct of phenomena
• Prescription – applying discriminant rules and practices to address and change spatio-temporal conduct of phenomena
• Adaptation to adjust and manage consequences of intervention
Levels of data interrogation and intelligence visibility
LEVEL 3: PROVISION OF ADVANCED
ANALYTICAL INTELLIGENCE
LEVEL 2: PROVISION OF BASIC
ANALYTICAL INTELLIGENCE
LEVEL 1:PROVISION OF
RAW DATA INTELLIGENCE
MULTI-SECTOR MACROECONOMIC MODEL
LINKED MACRO-EDUCATION MODEL
POVERTY-INEQUALITY MODEL
PUBLIC EMPLOYMENT MODEL
LINKED NATIONAL-PROVINCIAL MACROECONOMIC MODEL
Modelling
System-wide infusion and intelligenceDescription - Diagnosis - Prediction - Prescription - Adaptation
to plan we need 5 capabilities in our data systems, namely descriptive,
diagnostic, predictive, prescriptive and adaptive capability.
