Statistics South Africa and its contribution towards ...pmg-assets.s3-website-eu-west-1.amazonaws.com/... · Why Who How Outputs SDG Baseline Report 2017 Newtown Declaration & other

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%


Intsika YethuHeadcount 27,7%


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


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


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



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


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.


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


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


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!

Documents

Statistics South Africa and its contribution towards ...pmg-assets.s3-website-eu-west-1.amazonaws.com/... · Why Who How Outputs SDG Baseline Report 2017 Newtown Declaration & other