Statistics South Africa and its contribution towards...
Transcript of Statistics South Africa and its contribution towards...
Statistics South Africa and itscontribution towards national
planning and policy making
Statistics South Africa
Role players in the Statistical Environment
President
Minister
Statistician-General
Government
Statistics Council
• Appoints SG• Removes SG
• Approves WorkProgramme on adviceof the SG & Council
• Manages performanceof the SG
• Appoints the Council
• Execute the Statistics Act• Exercises independence• Defends statistical methods & best practice• Responsible for official and other statistics
• Safeguards official statistics• Advises both Minister
and the SG• Independent of Stats SA
Serv
ing
th
e:P
ub
licB
usi
nes
sO
rgan
s o
f st
ate
Triple scourge of • Poverty• Unemployment • Inequality
Economic Growth, Investment and Redistribution
Policy (What?)
Interventionfor example• EPWP• Social Security• Jobs Fund
Policy Makers
How to measure• Methods• Standards• Certification
Statistics SALorem IpsumNDP
Statistics Informing Policy
Development planning should be unapologetic…
Triple challenge• Unemployment• Poverty • Inequality
Statistics play a critical role in illuminating policy choices and program design, monitoring policy and program implementation as well as
evaluating the impact of policies.
There can be no credible planning,
monitoring and evaluation without
statistics
INTEGRATED INDICATOR FRAMEWORK
Agenda 2030 and SDGs
National Development Policies, Plans and Programmes
Integrated into
Building National Data Systems to measure National Development
Regional Frameworks/Action
Plans
SDG global indicators
Thematic indicators
NDP indicators
National Indicator Framework
Developing National Indicator Framework and Indicators
Develop National
Indicators
Develop Strategies and Plans for
Strengthening of National Statistical
System (Transformative)
Assess Capacity of
National Statistical
System (NSS)
Systematically implement
Strategies and Plans
Cape Town Global Action Plan
Building National Data Systems to measure SDGs
Why Who How Outputs
SDG BaselineReport 2017
Newtown Declaration & other NGOs
(CSO demands)
MDGR 2010 Cabinet Decisions
Implementation of Stats Act (no.
6 1999) for International
reporting
Satisfying international
requirements for SDG reporting
Organised Labour
Organised Business
CSOs (including faith-based
organisations
Organs of State
Civil Society Organisations
International partners (e.g. UN
agencies )
SA Human Rights Commission
Expanded RDT
NCC
SWG Economic
SWG Social
SWG Environment
SWG Governance
Decide
TWG
SDG Secretariat SDG
Secretariat
SDG Coordination StructuresCoordination
Inputs
Who to Coordination
Coordination Mechanisms
Coordination Outputs
SDG Thematic Reports
SDG Country Report
Goal Reports
Report Drafting
Team (RDT)
Key Milestones 2017-2018
2018 June
SWG programme
Sep 2018
Nov 2018
Dec 2018 Feb 2019Mar 2019
Apr 2019
Jul 2019
May 2019
Methodology workshop
SWG submit data and metadata to SDG
secretariat
1st draft of the thematic reports
1st draft of the SDG country report
Final draft of the country report
Validation workshop
Ratification workshop Jun 2019Sensitisation process
Voluntary National
Reporting
Sep 2019
President hand over the SA report
Final draft of the thematic reports
Jan 2019
2019 SDG Country report
12
3
International• UNSC• BRICS• City/Expert
Groups• Study tours: in-
and outbound
Continental• DGs Committee• StatCOM• SHaSA• ASSD• African Peer review
Mechanism• Support & sharing:
• Technical• Strategy• Governance
Regional: SADC• Statistics Committee• Support & sharing:
• Technical• Strategy• Governance
International footprint
Government
Economy
SocietyPopulat ion
Cr ime and Just ice
AgricultureEcosystemsEmissions
National, P r o v i n c i a land Local Spending. Service Delivery by Municipalities, Tertiary Institutions
GDP, CPI ,PPIPrimary Secondary and Tertiary Sectors
Employment/UnemploymentPoverty and InequalityService DeliveryLiving ConditionsEducation
Population EstimatesMigration and Tourism
Births, DeathsMortality and Causes of
DeathHealth and Nutrition
Key Indicators
KEY INDICATORSStats SA Publishes More
Than 250 Releases Annually
Governance, Public Safety and Justice (GPSJS)
52
53
54
55
56
57
58
59
60
Milli
on
s
Impact of births, deaths and
migration on population, 2019
BIRTHS
NETMIGRATION DEATHS+1,2M
+0,2M
-0,5M
57,9M*
58,8M
SA 2018 SA 2019
*data based 2019 series.
Births are the main driver of population growth in South Africa.
Source Stats Sa: Mid year estimates 2019
0
0,2
0,4
0,6
0,8
1
1,2
1,4
Age 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 72 75 77 79
Mil
lio
ns
Children 0-14 Youth 15-34 Elderly 60+
Population age structure by
single years, 2019
(17,0M) (20,6M) (5,3M)
Adult 35-59
(15,9M)
28,8% 35,1% 9,0%27,1%
Around 17 million are children (0-14 years), making up close to a third of SA’s total population.
Source Stats Sa: Mid year estimates 2019
Housing and Service Delivery
Number of households that lived in formal,
informal and traditional dwellings, 2018
To meet the increasing need for housing, the number of formal dwellings have
increased sharply over the past 17 years. The shortfall is, however, filled by an increase in
informal dwellings.
Increasing
number of formal
dwellings
13,5M
0
2
4
6
8
10
12
14
16
18
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Mill
ion
s
8M
FORMAL DWELLINGS
1,5M
2,1M
1,2M
0,8M
Other
0,01M
2002 2018
Source: GHS 2018
Percentage of households with
access to piped or tap water in their dwellings, off-site
or on-site
2002: 85%
2018: 89%
Source: GHS 2018
72,6%
92,7%
84,7%
2002 2004 2006 2008 2010 2012 2014 2016 2018
76,0%
90,7%
2002 2004 2006 2008 2010 2012 2014 2016 2018
88,5%
87,9%
2002 2004 2006 2008 2010 2012 2014 2016 2018
55,3%
87,4%
2002 2004 2006 2008 2010 2012 2014 2016 2018
68,6%
83,5%
2002 2004 2006 2008 2010 2012 2014 2016 2018
87,2%
77,7%
2002 2004 2006 2008 2010 2012 2014 2016 2018
81,6%
91,7%
2002 2004 2006 2008 2010 2012 2014 2016 2018
85,1%
91,2%
2002 2004 2006 2008 2010 2012 2014 2016 2018
Declines in connection to electricity in Gauteng can be associated with the rapid in-
migration experienced by the province. Household growth in Gauteng can be attributed to in-
migration.
LP NC FS
MP WC EC
KZN GP
SA
82,0%
83,7%
2002 2004 2006 2008 2010 2012 2014 2016 2018
Percentage of households connected to the mains
electricity supply by province, 2002─2018
NW
92,2%
93,8%
83,0%
2002200420062008201020122014201620172018
88,9%91,8%
2002 2004 2006 2008 2010 2012 2014 2016 2017 2018
75,5%
90,0%
2002 2004 2006 2008 2010 2012 2014 2016 2017 2018
33,4%
88,0%
2002 2004 2006 2008 2010 2012 2014 2016 2017 2018
64,7%85,5%
2002 2004 2006 2008 2010 2012 2014 2016 2017 2018
50,9%
81,4%
2002 2004 2006 2008 2010 2012 2014 2016 2017 2018
54,1%
70,6%
2002200420062008201020122014201620172018
50,7%
68,1%
2002 2004 2006 2008 2010 2012 2014 2016 2017 2018
26,9%
58,9%
2002 2004 2006 2008 2010 2012 2014 2016 2017 2018
WP GP NC
EC FS KZN
NW MP LP
Despite nearly doubling access to improved sanitation since 2002, access remains the
most limited in Limpopo. EC had large improvements in ventilated toilets
Percentage of households that have access to
improved sanitation per province, 2002–2018
SA
Source: GHS 2018
29,6%
31,4%
35,3% 35,1%
20%
25%
30%
35%
40%
Q1:2018 Q1:2019
APPROXIMATELY 3,4 MILLION (33,2%) OUT OF 10,3 MILLION YOUNG PEOPLE AGED
15-24 YEARS WERE NOT IN EMPLOYMENT, EDUCATION OR TRAINING (NEET). The
overall NEET rate increased by 0,8 of a percentage point in Q1:2019 compared to Q1:2018.
Income and Poverty Dynamics
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
Money-metric Poverty headcounts in 2015
In 2015, more than a quarter of the population
were living below the food poverty line
Source: Living Conditions Survey
Is the Rand value below which individuals are unable to purchase or consume enough food to supply them with minimum per-capita-per-day energy requirement for good health
Provides an austere threshold below which one has to choose between food and important non-food items
Provides an unambiguous threshold of relative deprivation below which people cannot afford the minimum lifestyle desired by most South Africans
51,0%
47,6%
36,4%
40,0%
0,0
10,0
20,0
30,0
40,0
50,0
60,0
2006 2009 2011 2015
Per
cen
tage
Money-metric poverty headcounts based on the LBPL
Source: Living Conditions Survey 2015
Msinga Headcount 59,8%
Msinga Headcount 24,5%
Intsika YethuHeadcount 27,7%
Msinga Headcount 37,2%
Source: CS 2016
Multidimensional Poverty By Municipalities 2001-2016
CS 2016
4052
33
10 Years
5 Years
Poverty Drivers
R444 446
R271 621
R172 765
R92 983
R350 937
R195 336
R124 445
R67 828
White
Indian/Asian
Coloured
Black African
R0 R50 000 R100 000 R150 000 R200 000 R250 000 R300 000 R350 000 R400 000 R450 000 R500 000
Average annual household consumption expenditure
and income by population group of household head
Average Expenditure Average Income
Source: Poverty Trends In South Africa
White-headed households (R350 937) spent five times more than black African-headed households (R67 828) and three times more than the national average
43%
43%
59%
73%
16%
12%
9%
3%
30%
35%
20%
10%
9%
6%
10%
10%
1,2
3,32
2,18
4,31
0 10 20 30 40 50 60 70 80 90 100
LP
EC
NC
MP
NW
FS
KZN
RSA
GP
WC
Salaries Remittances Other SourcesGrantsPensions
Percentage distribution of sources of
household income by province, 2018
GRANTS REMAIN A SIGNIFICANT SOURCE OF INCOME FOR SA HOUSEHOLDS, PARTICULARLY IN RURAL AREAS
Source: GHS 2018
Vulnerability to hunger at an individual and household level has been declining whilst access to grants has been increasing.
22,8%
9,7%
27,7%
11,3%
12,8%
31,0%30,8%
44,3%
2002 2004 2006 2008 2010 2012 2014 2016 2018
Grants and Vulnerability to hunger
2002 - 2018
Grant: persons
Grant: households
Vulnerability to hunger: persons
Vulnerability to hunger: HH
Source: GHS 2018
Employment Outcomes
27,6%
29,0%
0%
5%
10%
15%
20%
25%
30%
35%
40%
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Unemployment rate from
Q1:2008 to Q2:2019
South Africa’s unemployment rate increased by 1,4 percentage points to 29,0% in Q2 of 2019. The highest unemployment rate since Q1 of 2008
Source Stats Sa: QLFS Q2 2019
The working age population (15-64 years) in Q2:2019 was 38,4 million
UnemployedEmployed
16,3 6,72,7
12,7Other NEA
Not Economically Active
Dis
co
ura
ge
d
wo
rk s
ee
kers
15,5 million
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,
Labour force
23,0 million
38,4 millionPeople of working age in South Africa (15 – 64 year olds)
M
M
M
Employed Unemployed
South Africa’s
official
unemployment rate
stands at
29,0%
M
Source Stats Sa: QLFS Q2 2019
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Q2:2009 Q2:2019
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Q2:2019 Q2:2019
31.3%
27,1%
SA: 29,0%
42,5%
35,0%
SA: 38,5%
Official Unemployment Rate
29,0% (+1,4 % Point Change Q/Q)
Expanded Unemployment Rate
38,5% (+0,5 % Point Change Q/Q)
10,2 millionpeople were unemployed in Q2:2019
An increase of 232 000 q/q
Expanded Definition includes the following
- Official unemployment (searched and available) 6,7 M
- Available to work but are/or
• Discouraged work-seekers 2,7 M
• Have other reasons for not searching 0,8 M
6,7 millionpeople were unemployed in Q2:2019
An increase of 455 000 q/q
Source Stats Sa: QLFS Q2 2019
Official Expanded Provincial unemployment rate:
Official vs Expanded Q2:2019
41,1%
23,8%
42,1%
38,5%
44,8%
35,0%
46,6%
41,6%
43,5%
46,5%
20,3%
20,4%
26,1%
29,0%
29,4%
31,1%
33,0%
34,4%
34,7%
35,4%
LP
WC
KZN
RSA
NC
GP
NW
FS
MP
EC
20,8% points difference
15,4% points difference
16,0% points difference
Highest official unemployment rate recorded in EC and highest expanded
unemployment rate recorded in NW. LP, KZN and NC provinces all have more than 15 % points difference between their expanded and official unemployment rates
Source Stats Sa: QLFS Q2 2019
28,9%
30,5%
34,4% 34,1%
20%
22%
24%
26%
28%
30%
32%
34%
36%
Q2: 2018 Q2: 2019
NEET (15-24 years) by sex
FEMALE NEET
Down by 0,2 of a
percentage point
MALE NEET
Up by 1,6
percentage points
15-24 YEARS
Approximately 3,3 million (32,3%) out of 10,3 million young people aged 15-24 years were not in employment, education or training (NEET). The
overall NEET rate increased by 0,7 of a percentage point in Q2:2019 compared to Q2:2018.
Source Stats Sa: QLFS Q2 2019
INVESTING IN HUMAN POTENTIAL
Percentage of those aged 5 – 24
years who attend educational
institution, 2018
Source: GHS 2018
There is noticeable representation of learners who are older than the ideal graduation age in primary and secondary schools.
Main reasons given by persons
aged 7 to 18 years for not attending
an educational institution, 2018
3,5%
7,5%
7,9%
9,8%
10,8%
13,3%
22,9%
24,2%
Working at home
Education is useless
Family commitments
Completed education
Illness and disability
Other
Poor academic performance
No money for fees
M F
Vast gender
disparities in
family
commitment
14.4%0,2%
11,8%3,9%
Source: GHS 2018
Over a fifth of learners cited a lack of money as the main reason for not attending an educational institution. Some reasons for not attending an educational
institution are particularly affected by gender
In what economy do South Africans find themselves in?
-3,2%
in Q1: 2019(quarter-on-quarter)
Seasonally adjusted and annualised
0,0%
in Q1: 2019(year-on-year)
unadjusted
Real GDP Real GDP
Growth figures for Q1: 2019
Source GDP Q1 2019
0,2
0,2
0,1
-0,8
-0,3
GDP
-3,2%
LHS: growth rates RHS: contributions
-0,1
-1,1
Industry growth
rates in Q1: 2019Quarter-on-quarter, seasonally
adjusted and annualised
-0,1
-0,5
-0,4
Source GDP Q1 2019
GDP quarter-on-quarter percentage growth: seasonally adjusted and annualised
Source GDP Q1 2019
South Africa has experienced eight recessions since 1961The longest ran over two
years: 1991 and 1992
1994 2018
Source Stats SA: GDP
RANKING OF INDUSTRIES ACCORDING TO THEIR PERCENTAGE CONTRIBUTION TO GDP
Finance and
business
services
20%
Government
19%
Trade
15%
Manufacturing
13%
Transport
10%
Mining
8%
Personal
services
6% Electricity,
gas and
water
4%
Construction
4%
Agri-
culture
2%
Percentages have been rounded and may not sum to 100%
Which are the largest (and smallest) industries in South Africa?Percentage contribution to total nominal GDP in Q1: 2019
Source Stats SA: GDP
Strong Exposure to
Mining
Agriculture relatively
large contributor
MP-8%
Mining
ElectricityConstruction
Agriculture
Personal Services
Mining
Gauteng economy represents over 1/3rd of the national economy
Source Stats SA: GDP
Share of
Nominal
GDP
(Q1:2019)
24,7%
15,0%
20,3%
13,1%
3,9%
9,7%
1,9%
7,6%
3,8%
0,9%
2,3%
5,2%
6,0%
7,7%
8,4%
11,0%
15,3%
21,0%
22,2%
Utilities
Mining
Agriculture
Transport
Private Households
Construction
Manufacturing
Finance
Trade
Services*
Employment and GDP share
per industry
Employment shares, Q2: 2019
*Government and Personal services
Trade, construction and agriculture have higher employment shares relative to their GDP contribution.
Source Stats SA: GDP/QLFS
Power and influence of sectional interests
Unethical Behavior
ArbitrarinessAnecdote
Political Ideology
Perils of Avoiding Empirical Evidence
Policy formulation and decision-making that does not follow the culture of empirical evidence is at
the mercy of*
*As argued by Christopher Scott (Measuring Up to the Measurement Problem)
Benefits of evidence-based policy formulation
Analyse data to design and re-design policies
01
Draw on empirical research findings to shape policy options
02
Map the spatial and socio-economic landscape
03
Randomize the phase-in of policy implementation and give assurance to the
citizenry that their chance will come the next time
04
Plan for the future to sharpen policy options
and implementation alternatives
05
Monitor the success or failures of policies
06
Statistics that enjoy a high level of integrity
assist both government and its citizens to
collectively
Integrity
Accessibility Accuracy
Timeliness Relevance
Pre-requisitesInterpretability
Comparability & Coherence Methodological Soundness
South African Statistical Quality Assurance
Framework (SASQAF)9 Dimensions
Ndzi hela kwala!