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All content, including opinion, analysis and forecasts, has been based on information and sources believed to be ac-
curate and reliable at the time of publishing. The Department of Economic Development, Environmental Affairs and
Tourism makes no representation of warranty of any kind as to the accuracy or completeness of any information
provided, and accepts no liability whatsoever to any loss or damage resulting from the opinion, error, inaccuracies
or omissions affecting any part of the report.
3
This is the eighth edition of the Eastern Cape Socio-Economic Review and Outlook (SERO) since its auspicious introduction
in 2010. The SERO continues to provide valuable economic intelligence to municipalities and provincial departments
in the Eastern Cape and South Africa, in support of enhanced planning for economic growth, job creation and socio-
economic upliftment.
The current, global and domestic economic environment is particularly turbulent amidst a less than desirable economic
growth outlook. It is against this backdrop that the 2017 SERO is presented. Persistent unemployment, coupled with
changing population dynamics remain key challenges in the Eastern Cape. Other domestic constraints impacting on the
economic outlook relate to political uncertainties, energy, infrastructure, and skills shortages; while outcomes related to
education, health and broader social ills continue to impact on and are affected by the levels of economic development.
The National Treasury expects the economy to grow by 1.3% in 2017, with growth forecasted to reach 2.2% by 2019. In
the 2016 State of the Nation Address the President, recognised this and stated that the slow pace of growth impedes
government’s progress towards creating jobs and reducing inequality and poverty.
In response to these national and international economic challenges, the national government has sought to implement
measures that increase growth while at the same time pursing interventions that achieve sound management of the
fiscus through the policy of fiscal consolidation. Through strengthened efforts to curb wasteful spending and reprioritise
expenditure towards socio-economic interventions, greater impact from government spending can be achieved.
These economic pressures highlight the fundamental need for thorough integrated development planning, considered
decision making, active economic transformation and appropriate policy responses, which will in turn, stimulate economic
development. The SERO’s research findings are therefore, intended to complement provincial and municipal planning
in the Eastern Cape to ensure the effective use of resources, improve service delivery, attract additional investment,
strengthen democratic values, and to promote inter-governmental cooperation.
This publication also aims to improve our understanding of, and insight into the Eastern Cape economy at a sub-
regional level as part of an evidence-based approach towards provincial and municipal policy formulation, planning and
budgeting. More importantly, the SERO provides a detailed analysis and overview of the unique comparative advantages
and opposing challenges faced by the Eastern Cape.
In conclusion, I wish to express my sincere appreciation to the team who have contributed to this year’s research and
publication, and also those who will carry it through into the government’s policy, planning and implementation.
Yours sincerely,
The Honourable Sakhumzi Somyo
MEC Economic Development, Environmental Affairs and Tourism
foreword
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II
FOREWORD
EXECUTIVE SUMMARY
1. INTRODUCTION
i. Global Economic Outlook
ii. National Economic Outlook
iii. Eastern Cape Economic Outlook
iv. Automotive Industry in The Eastern Cape
2. GLOBAL ECONOMIC OUTLOOK
2.1 Global Growth
2.2 Economic Prospects in Key Markets
2.3 Outlook for 2017
3.4 Summary
3. NATIONAL ECONOMIC PERFORMANCE
3.1 National Economic Performance
3.2 Domestic Expenditure
3.3 Total Fixed Capital Formation
3.4 Labour Market Conditions
3.5 Prices and Inflation
3.6 Balance of Payments
3.7 Exchange Rates
3.8 Credit Rating
3.9 National Revenue and Expenditure
3.10 Summary
4 EASTERN CAPE ECONOMIC PROFILE
4.1 Demographic Dynamics
4.1.1 Population
4.2 Education
4.2.1 Learner Enrolment Trends
4.2.2 Educational Attainment
4.2.3 National Senior Certificate (NSC) Results
4.2.4 Educational Quality
4.3 Poverty and Grant Dependency
table of contents
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4.3.1 Poverty Headcount
4.3.2 Income Distribution
4.3.3 Gini Coefficient
4.3.4 Grant Dependency
4.4 Labour Market
4.4.1 Overview of the Labour Market
4.4.2 Composition of the Labour Force
4.4.3 Sectoral Employment
4.4.4 Unemployment in the Eastern Cape
4.5 Economic Performance
4.5.1 GDP-R and Projections
4.5.2 Sector Analysis of the Eastern Cape Economy
4.6 Trade
4.6.1 Exports
4.6.2 Imports
4.6.3 Trade Balance
4.6.4 Major Trading Partners
4.7 Fiscal Framework
4.7.1 Provincial Receipts
4.7.2 Equitable Share Allocations
4.7.3 Provincial Payments
4.8 Summary
5. EASTERN CAPE AUTOMOTIVE INDUSTRY
5.1 International Automotive Industry
5.1.1 International Trends
5.1.2 Global Production and Sales Performance
5.2 South African Automotive Industry
5.2.1 National Performance in 2016
5.2.2 Domestic Motor Vehicle Sales
5.2.3 Motor Industry Exports
5.2.3.1 Motor Vehicle Exports
5.2.3.2 Component Exports
5.2.4 Investment and Employment
5.3 Eastern Cape Automotive Industry
5.3.1 Eastern Cape Support Institutions
5.3.2 Notable Developments for the Eastern Cape
5.4 National Automotive Policy and Programmes
5.4.1 Automotive Production and Development Programme (APDP)
5.4.2 Transformation
5.4.3 Competitiveness and Supplier Development
5.5 Conclusion
6. STRATEGIC INITIATIVES
6.1 Coega Industrial Development Zone
6.2 East London IDZ (ELIDZ)
6.3 Industrial Parks
6.4 Urban Development
6.5 Infrastructure Development
6.6 Agriculture
6.7 Ocean Economy
6.8 Waste Economy
6.9 Tourism
6.10 Sports, Heritage, Arts and Culture
6.11 Energy Sector
6.12 Private Sector Initiatives
6.13 Summary
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APPENDiCES 1. Socio-Economic Profile of Sarah Baartman District Municipality
2. Socio-Economic Profile of Amathole District Municipality
3. Socio-Economic Profile of Chris Hani District Municipality
4. Socio-Economic Profile of Joe Gqabi District Municipality
5. Socio-Economic Profile of O.R. Tambo District Municipality
6. Socio-Economic Profile of Alfred Nzo District Municipality
7. Socio-Economic Profile of Nelson Mandela Bay Metro
8. Socio-Economic Profile of Buffalo City Metro
List of Definitions
REFErences
FIGURES
CHAPTER 2
Figure 2.1 Growth in GDP in Key Economies
Figure 2.2 Crude Oil prices: Brent
Figure 2.3 US Civilian Unemployment Rate
Figure 2.5 Growth in GDP of Key Sub-Saharan Africa Economies
Figure 2.6 Inflation Average Consumer Prices
CHAPTER
Figure 3.1 Composite Leading and Coincident Business Cycle Indicators
Figure 3.2 South African Real GDP Growth – Percentage Change
Figure 3.3 Real Value Added of the Mining and Agriculture Sectors
Figure 3.4 Real Value Added of the Manufacturing, Utility and Construction Sectors
Figure 3.5 Real Value Added of the Trade, Catering and Accommodation and Finance, Real Estate
and Business Services Sectors
Figure 3.6 Price of Gold and Platinum in US dollars, 2009-2016
Figure 3.7 FNB/BER Consumer Confidence Index 2010-2016
Figure 3.8 Sectoral Distribution of Employment in South Africa, 3rd Quarter 2016
Figure 3.9 Change in Employment Quarter-on-Quarter
Figure 3.10 Net Changes in Employment per Sector Between 2016Q3 and 2015Q4, Thousands
Figure 3.11 CPI and PPI
Figure 3.12 Comparison of Headline CPI to Food and Petrol inflation
Figure 3.13 Agricultural PPI Compared to Final Manufactured Food PPI and CPI Food
Figure 3.14 Consumer Price Inflation by Specific Food Category
Figure 3.15 Current Account as a Percentage of Gross Domestic Product
Figure 3.16 Trade Account – R Billions
Figure 3.17 Nominal and Real Effective Exchange Rates of the Rand
Figure 3.18 Selected Exchange Rates against the Rand
CHAPTER 4
Figure 4.1 District Population as a Share of the Eastern Cape’s Population, 2015
Figure 4.2 Eastern Cape Population Distribution, 2016
Figure 4.3 Number of Learners Enrolled and Public Ordinary Schools, 2014
Figure 4.4 District Educational Attainment Levels with Reference to Matric Education,
Ages 20+ (2015)
Figure 4.5 2015 Matric Pass Rate in South Africa
Figure 4.6 Average Eastern Cape Mathematics Pass Rates for Grades 3, 6 and 9 for
Public Ordinary Schools, 2012 – 2014
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Figure 4.7 Average Eastern Cape Home Language Pass Rates for Grades 3, 6 and 9
for Public Ordinary Schools, 2012 – 2014
Figure 4.8 Poverty Headcount by District Municipality in 2011 and 2016
Figure 4.9 Household Income Distribution per District, 2011
Figure 4.10 Gini Coefficients per Eastern Cape District in 2010 and 2015
Figure 4.11 Provincial Population Share Relative to Provinces Share of Total Social Grants
Figure 4.12 Quarterly Labour Force Participation Rate
Figure 4.13 Provincial and National Unemployment Rates, 2016Q3
Figure 4.14 Eastern Cape Unemployment Rates
Figure 4.15 Eastern Cape District’s Contribution to GVA-R In 2015
Figure 4.16 Eastern Cape GDP-R Performance Between 2009 and 2019 (Constant 2010 Prices)
Figure 4.17 Eastern Capes Historical Trade Position with World Markets
Figure 4.18 Total Eastern Cape Merchandise Exports (Rand, Million) Between 2005 and 2015
Figure 4.19 Export Composition by Value by Source Market Regions
Figure 4.20 Eastern Cape Import Composition by Value and Source Market Regions in 2015
Figure 4.21 Eastern Cape’s Trade Balance by Region in 2015
Figure 4.22 National Equitable Share Allocation, 2016/17 – 2019/20
CHAPTER 5
Figure 5.1 Composition of Global Production Over Time
Figure 5.2 Composition of International Motor Vehicle Sales, 2016
Figure 5.3 South African Total Production, Local Sales and Export Volumes
Figure 5.4 Growth in South African Total Production, Local Sales and Export Volumes
Figure 5.5 Composition of Domestic Vehicle Sales by Vehicle Type, 2015
Figure 5.6 Growth in Domestic sales by Vehicle Type
Figure 5.7 Composition of Domestic Market – Locally Produced vs Imported
Passenger Motor Vehicles
Figure 5.8 Total Number of Motor Vehicles and LCVs Imported into South Africa
Figure 5.9 Overall New Vehicle Market Share by Manufacturer, 2015
Figure 5.10 Composition of National Total Domestic Vehicles Sales by Province, 2015
Figure 5.11 Eastern Cape Total Domestic New Vehicle Sales Year on Year Growth
Figure 5.12 Automotive Industry Capital Investment
Figure 5.13 Employment in Vehicle Manufacturers
Figure 5.14 Eastern Cape and its Automotive Clusters
Figure 5.16 APDP Strategic Pillars
TABLES
EXECUTIVE SUMMARY
Table i Advanced Market Indicators
Table ii Emerging Market Indicators
Table iii South Africa Macro-Economic Indicators and Projections, 2014 – 2018
Table iv Key Variables of South African Labour Market
Table v Eastern Cape Macro-Economic Indicators and Projections, 2014 – 2018
Table vi Eastern Cape District Municipality Regional Gross Value Added (GVA-R)
Table vii Labour Market Historic Performance in the Eastern Cape
CHAPTER 2
Table 2.1 International Economic Indicators
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CHAPTER 3
Table 3.1 Gross Domestic Product
Table 3.2 Real Gross Domestic Product- Percentage Change
Table 3.3 Percentage Change in Domestic Expenditure Elements
Table 3.4 Contribution of Expenditure Elements to Growth in Real GDP
Table 3.5 Growth in Real Final Consumption Expenditure by Households
Table 3.6 Share Change in Gross Fixed Capital Formation
Table 3.7 Key Variables of South African Labour Market
Table 3.8 Labour Market Trends by Demographics
Table 3.9 Highest Level of Education of the Unemployed
Table 3.10 Headline Consumer Inflation in COICOP Categories
Table 3.11 BER Inflation Expectations Survey, 3rd Quarter 2016
Table 3.12 Balance of Payments: Current Account
Table 3.13 Net Financial Transactions Capital Account
Table 3.14 South African Credit Rating History 2009 – 2016
Table 3.15 South African Revenue and Expenditure 2015/16-2019/20
Table 3.16 Consolidated Government Expenditure, 2015/16-2019/20
Table 3.17 Additional Funding to Support Universities and Students, 2016/17-2019/20
CHAPTER 4
Table 4.1 Levels of Educational Attainment by Province in 2015
Table 4.2 National Senior Certificate Results, Eastern Cape 2010 – 2015
Table 4.3 2015 Matric Results Performance per Eastern Cape School District
Table 4.4 Poverty Headcount in 2011 and 2016
Table 4.5 Average Weighted Monthly Household Income per Province in 2001 and 2011
Table 4.6 Eastern Cape Social Grant Composition in 2015 and 2016
Table 4.7 Labour Market Historic Performance in the Eastern Cape
Table 4.8 Eastern Cape Labour Market Overview
Table 4.9 Industrial Composition of Formal Sector Employment, 2016Q3
Table 4.10 Overview of the Eastern Cape Labour Market by District, 2015
Table 4.11 Gross Domestic Product (Rand, Millions at Constant 2010 Prices)
Table 4.12 Sectoral and Sub-Sectoral Contribution to Provincial GVA-R 2015
(Constant 2010 Prices)
Table 4.13 Provincial Contribution to National Imports and Exports in 2015
Table 4.14 Eastern Cape Exports to Regional Source Markets by Value
Table 4.15 Eastern Cape’s Top Ten Export Commodities by Value in 2014 and 2015
Table 4.16 Eastern Cape Imports from Regional Source Markets by Value
Table 4.17 Eastern Cape’s Top Ten Imported Commodities in 2014 and 2015
Table 4.18 Profile of Top 5 Destination for Eastern Cape Exports
Table 4.19 Provincial Receipts (Rand, Billions)
Table 4.20 Historic Expenditure Against 2015 MTEF Allocations by Cluster (Rand, Billions)
CHAPTER 5
Table 5.1 Ranking of Global Motor Vehicle Producers, 2016
Table 5.2 Exports as Percentage of Production – Light vehicles
Table 5.3 Exports as Percentage of Production – Medium and Heavy Commercial Vehicles
Table 5.4 South Africa’s Top 5 Export Countries by Value, 2014 and 2015
Table 5.5 South Africa’s Top 5 Export Automotive Component Countries by Value, 2014 and 2015
Table 5.6 Growth of India and China as export destinations for automotive component countries by
value, 2014 and 2015
Table 5.7 Main Component Exports to Germany and US
Table 5.8 Eastern Cape Automotive Industry
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MAPS
CHAPTER 4
Map 4.1 Provincial Population
TEXT BOXES
CHAPTER 4
Text Box 3.1 Poultry Industry and Concerns over Imported Products
Text Box 3.2 The Mining Sector and Commodity Prices
Text Box 3.3 Funding Tertiary Education
CHAPTER 5
Text Box 5.1 The Countdown to Disruption Starts Now
Text Box 5.2 Eastern Cape leading South Africa’s Development of E-Mobility
Text Box 5.3 R11 billion Investment by BAIC in the Eastern Cape Automotive Industry
Text Box 5.4 The Specifics of the APDP
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VIII
LIST OF ACRONYMSACSA
ADZ
AGOA
AIDC-EC
AIEC
AIS
ANA
ANC
APDP
ASCCI
BAIC
BBBEE
BESDP
BC
BCM
BER
BKCOB
CBD
CDC
CEC
CEO
COICOP
CPI
CSIR
DEDEAT
Airports Company South Africa
Aquaculture Development Zone
African Growth and Opportunity Act
Automotive Industrial Development Corporation - Eastern Cape
Automotive Industry Export Council
Automotive Investment Scheme
Annual National Assessments
African National Congress
Automotive Production and Development Programme
Automotive Supply Chain Competitiveness Initiative
Beijing Automotive International Corporation
Broad Based Black Economic Empowerment
BBlack Business Empowerment Supplier Development Programme
Border Cricket
Buffalo City Municipality
Bureau for Economic Research
Border Kei Chamber of Business
Central Business District
Coega Development Corporation
Circular Economy Conference
Chief Executive Officer
Classification of Individual Consumption by Purpose
Consumer Price Index
Council for Scientific and Industrial Research
Department of Economic Development, Environmental Affairs and Tourism
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Eastern Cape Department of Education
Department of Rural Development and Agrarian Reform
Department of Rural Development and Land Reform
Department of Trade and Industry
Department of Water Affairs
Eastern Cape
Eastern Cape Automotive Industry Forum
Eastern Cape Development Corporation
Eastern Cape Socio Economic Consultative Council
East London Industrial Development Zone
e-Mobility Technology Innovation Programme
European Union
Electric Vehicle
Electric Vehicle Infrastructure Alliance
Gross Domestic Product
Regional Gross Domestic Product
Gross Fixed Capital Formation
Deutsche Gesellschaft für Internationale Zusammenarbeit
Gross Value Added Regional
Human Immunodeficiency Virus
Industrial Development Corporation
Industrial Development Zone
International Labour Organization
International Monetary Fund
Industrial Policy Action Plan
International Organisation for Standardisation
International Trade Administration Commission
Institute of Waste Management
Liquefied Natural Gas
Mandela Bay Development Agency
Mercedes-Benz South Africa
Manufacturing Competitiveness Enhancement Programme
Member of the Executive Council
DOE
DRDAR
DRDLR
DTI
DWA
EC
ECAIF
ECDC
ECSECC
EIA
EMTIP
EU
EV
EVIA
GDP
GDP-R
GFCF
GIZ
GVA-R
HIV
IDC
IDZ
ILO
IMF
IPAP
ISO
ITAC
IWMSA
LNG
MBDA
MBSA
MCEP
MEC
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Motor Industry Development programme
Medium Term Budget Policy Statement
Medium Term Expenditure Framework
Motor Vehicle
Manufacturing Value Add
Megawatt
National Association of Automotive Component Manufacturers
National Association of Automobile Manufacturers of South Africa
North American Free Trade Agreement
National Development Plan
National Energy Regulator of South Africa
National Front
Nelson Mandela Bay Business Chamber
Nelson Mandela Bay Municipality
Nelson Mandela Metropolitan University
National Senior Certificate
National Student Financial Aid Scheme
Original Equipment Manufacturer
Occupational Health and Safety Assessment Series
International Organisation of Automobile Manufacturers
Organisation of the Petroleum Exporting Countries
Provincial Equitable Share
Port Elizabeth Technikon Materials Resource Centre
Primary Health Care
Production Incentive
Producer Price Index
Quarterly Labour Force Survey
Reconstruction and Development Programme
Recycling and Economic Development Initiative of South Africa
South Africa
South African Breweries
South African Bureau of Standards
South African Chamber of Commerce and Industry
MIDP
MTBPS
MTEF
MV
MVA
MW
NAACAM
NAAMSA
NAFTA
NDP
NERSA
NF
NMBBC
NMBM
NMMU
NSC
NSFAS
OEMs
OHSAS
OICA
OPEC
PES
PETMRC
PHC
PI
PPI
QLFS
RDP
REDISA
SA
SAB
SABS
SACCI
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SADC
SAMPI
SANAS
SANRAL
SARB
SASSA
SBDM
SERO
SEZ
SMMEs
StatsSA
SUV
TB
TIA
TPM
TPP
TTIP
UK
US
VAA
VAT
VIP
VWSA
WCM
WEF
Southern African Development Community
South African Multidimensional Poverty Index
South African National Accreditation System
South African National Roads Agency Limited
South African Reserve Bank
South Africa Social Security Agency
Sarah Baartman District Municipality
Socio-Economic Review and Outlook
Special Economic Zone
Small, Medium and Micro Enterprises
Statistics South Africa
Sports Utility Vehicle
Tuberculosis
Technology Innovation Agency
Total Productive Maintenance
Trans-Pacific Partnership
Transatlantic Trade and Investment Partnership
United Kingdom
United States
Vehicle Assembly Allowance
Valued-added Tax
Ventilated Improved Pit
Volkswagen South Africa
World Class Manufacturing
World Economic Forum
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XIV
i. GLOBAL ECONOMIC OUTLOOK
The global recovery from the 2009 financial crisis remains precarious, as evidenced by muted growth forecasts
of 3.1% in 2016 and 3.4% in 2017 (IMF, 2016). In advanced economies, the combination of weak economic growth,
low or negative interest rates, and elevated asset prices has increased the likelihood of renewed financial volatility.
Furthermore, world trade growth forecasts for 2016 have been revised down from 3.1% in April, to 2.3% in October.
Major risks facing the global economy, include lower Chinese growth, continued declines in key commodity prices,
excessive debt levels, coupled with political uncertainty in several major economies. Over the short-term, the
United Kingdom’s exit from the European Union (EU) will remain a source of uncertainty. The long-term effects
of Brexit, particularly on South Africa, will depend on the timing and nature of trade and investment treaties
negotiated between the United Kingdom and the EU.
In contrast, the outlook for developing economies is mixed. GDP growth is anticipated to remain resilient in
both India and China, while a return to moderate growth in Brazil and Russia is projected in 2017, following two
consecutive years of economic contraction.
TABLE i: ADVANCED MARKET INDICATORS
Real GDP Growth Rates 2015 2016 2017
Japan 0.5 0.5 0.6
United Kingdom 2.2 1.8 1.1
United States 2.6 1.6 2.2
Euro Zone 2.0 1.7 1.5
European Union 2.3 1.9 1.7
Percentage Change in Consumer Price Index
United Kingdom 0.1 0.7 2.5
United States 0.1 1.2 2.3
Unemployment Rate
United Kingdom 5.4 5.0 5.2
United States 5.3 4.9 4.8
Euro Zone 10.9 10.0 9.7
Source: IMF, 2016
executive summary
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Capital inflows into developing economies have been supported by the low interest rate environment in several
developed economies including the United States, Europe and Japan. This stimulus has not however, translated
into greater investment, more economic activity or more positive expectations about GDP growth. Both advanced
and emerging economies have also experienced slower productivity growth. Countries that are highly reliant on
foreign portfolio investment, such as South Africa, will remain vulnerable to global financial volatility and rapid
capital movements.
The outlook for sub-Saharan Africa is marked by low commodity prices and falling export volumes, which have
resulted in foreign-currency shortages. The 2016 growth forecast for the region has subsequently been revised
downward, from 3.0% in April to 1.4% in October. Large economies, such as Nigeria and Angola, have been
particularly hard hit by low oil prices and disruptions in production. In contrast, Ethiopia, Kenya and Senegal are
expected to record growth rates of over 5.0%.
Slower growth in sub-Saharan Africa and global trade weakness are likely to limit South Africa’s future export
potential. Greater economic integration with the rest of Africa would enable those South African firms that cater
to the export market to capitalise on those countries with higher growth rates while at the same time increasing
their share of African trade.
TABLE ii: EMERGING MARKET INDICATORS
Real GDP Growth Rates 2015 2016 2017
Brazil -3.8 -3.3 0.5
China 6.9 6.6 6.2
India 7.6 7.6 7.6
Russia -3.7 -0.8 1.1
South Africa 1.3 0.5 1.3
Source: IMF, 2016; National Treasury, 2016. Note: South African growth rate is based on National Treasury growth estimates.
The National Treasury estimates that the average growth for 15 of South Africa’s major trading partners will be
4.1% in 2017. This is based on the International Monetary Fund’s (IMF) 2016 World Economic Outlook (IMF, 2016).
A trend that will be prevalent in 2017 and which will influence global economics is the rise in nationalism and trade
protectionism.
ii. National Economic Outlook
South Africa’s GDP growth for 2016, was forecast at 0.9% in the 2016 National Budget, but was revised downwards
to 0.5% by October of that year. Despite the downward revision, GDP growth is forecasted to increase to 2.2% by
2019, supported by a more reliable electricity supply, a correction in drought related inflation, modest improvements
in business and consumer confidence, stabilising labour relations, a global uptick in commodity prices, and modest
global growth.
Growth in the real output remained low in the first six months of 2016 compared with the same period in 2015.
Over this period mining growth declined and agricultural output deteriorated as a result of the drought. Likewise,
growth in the transport and telecommunications, and in electricity, gas and water sectors, fell on the back of weak
demand. In contrast, manufacturing output strengthened and the finance, real estate and business services sector
remained buoyant.
TABLE iii: SOUTH AFRICA MACRO-ECONOMIC INDICATORS AND PROJECTIONS, 2014 - 2018
2014 2015 2016 2017 2018
GDP at current prices (R billion) 3 796.5 4 013.6 4 300.0 4616.9 4 981.8
Real GDP growth (%) 5.8 1.3 0.5 1.3 2.0
CPI inflation (%) 6.1 4.6 6.4 6.1 5.9
Current account balance (%of GDP) -5.4 -4.3 -3.9 -3.9 -3.8
Source: National Treasury, 2016a; 2016b
XVI
Real gross domestic expenditure increased in the third quarter 2016, after two quarters of negative growth.
Nevertheless, real gross domestic expenditure contracted by 0.6% for the first three quarters of 2016 compared to
the same period 2015.
Consumer confidence remains low and higher inflation has reduced household purchasing power. Spending
on durable goods has declined since the first quarter of 2015 and remained negative throughout the first three
quarters of 2016. Credit extension to households remains subdued, as higher interest rates and tighter lending
conditions discourage borrowing. In the third quarter of 2016, applications for credit to the domestic private sector
fell to 5.6%. The ratio of household debt to disposable income has eased to 74.0% in the third quarter from 75.1%
in the second quarter of 2016. This is positive in terms of reducing household indebtedness but also points to low
domestic demand.
Capital investment declined by 1.0% in the third quarter of 2016, compared with a 4.6% increase in the corresponding
period of 2015. The contraction was due to a decline in private investment, in a climate of weak business confidence.
Investment by public corporations and general government also fell, with the slowdown reflecting general delays,
declining revenue growth and deteriorating balance sheets at some state-owned enterprises.
Job creation remains the most pressing concern for the South African economy. The net change in employment
between the fourth quarter 2015 and the third quarter 2016 was a net loss of 186 340 jobs. A number of sectors
shed jobs over this period; including community and government services, trade and manufacturing. This change
also reflects 50 000 temporary employees associated with the general election. The unemployment rate stood at
a historic high of 27.1% in the third quarter of 2016, with the number of South Africans categorised as unemployed,
2.0% higher than in the same quarter in 2011.
TABLE iv: KEY VARIABLES OF SOUTH AFRICAN LABOUR MARKET
Thousands (000) 2011 Q3 2015 Q3 2016 ChangeQ3
2015-2016
Change 2011-2016
Working Age Population 33 640 36 114 36 750 1.8% 3 110
Labour Force 18 818 21 246 21 706 2.2% 2 888
Employed 14 118 15 828 15 833 0.0% 1 715
Unemployed 4 699 5 418 5 873 8.4% 1 174
Not Economicaly Active 14 822 14 867 15 044 1.2% 221
Discouraged Work-seekers 2 213 2 226 2 291 2.9% 78
Percentage Percentage Point Change
Unemployment Rate 25.0% 25.5% 27.1% 1.6 2.1
Unemployment Rate (expanded definition) 36.6% 34.4% 36.3% 1.9 0.3
Labour Force Participation Rate 55.9% 58.8% 59.1% 0.3 3.2
Labour Absorption Rate 42.0% 43.8% 43.1% 0.7 1.1
Source: Quantec, 2016. Forecasts from 2015 onwards
Consumer price index (CPI) inflation breached the upper limit of the target band in the first half of 2016, peaking at
7.0% in February, before moderating to 5.9% in August. This breach was attributed to higher food prices. Despite
rising inflation and higher inflation expectations, the South African Reserve Bank (SARB) only raised interest rates
by 0.75 basis points in the year. This was due to concerns of low economic growth and that inflation was largely
driven by drought effects. A moderation in inflation is expected in 2017 as drought related price impacts ease.
National exports grew by 4.5% in the third quarter of 2016 compared with the same period in 2015, supported by
manufacturing and recovery of mining exports, particularly platinum group metals. A surplus of the current account
was experienced in the second quarter of 2016, although the current account is expected to be in deficit in 2017
with anticipated dollar appreciation and increasing oil prices.
III. Eastern Cape Economic Outlook
The Eastern Cape contributed 7.5% to national GDP in 2015 and 9.1% to total South African employment in the
third quarter of 2016. Despite possessing a significant share of the country’s manufacturing sector, estimated
at approximately 7.5%, primarily centred on the automotive industry in the two metros, the regional economy
continues to be dominated by the non-tradable sectors (trade, finance and general government services).
XVII
TABLE v: EASTERN CAPE MACRO-ECONOMIC INDICATORS AND PROJECTIONS, 2014 - 2018
2014 2015 2016 2017 2018
GDP at current prices (R billion) 228.9 230.3 231.3 233.6 237.3
Real GDP growth (%) 1.0 0.6 0.4 1.0 1.6
Source: Urban-Econ calculations based on StatsSA, 2016b
The GDP growth rate of the Eastern Cape economy has declined sharply over the last decade from a high of 5.3%
per annum in 2007 to 0.6% in 2015. This decline in the province’s GDP growth rate however, is in line with the
national trend.
The two largest economies in the Eastern Cape, Nelson Mandela Bay and Buffalo City, experienced low GVA-R
growth rates in 2015 of 0.9% year-on-year. Alfred Nzo exhibited the highest growth in the province at 2.9%, but it
should be noted that this growth is occurring off a low base and is more difficult to estimate given the relatively
small size of the district’s population.
TABLE vi: EASTERN CAPE DISTRICT MUNICIPALITY REGIONAL GROSS VALUE ADDED (GVA-R)
GVA-R (R billions), 2015 GVA-R Growth Rate (%), 2015
Sarah Baartman 18.8 1.6
Amathole 14.5 1.6
Chris Hani 16.3 1.9
Joe Gqabi 7.2 2.3
O.R. Tambo 20.9 2.0
Alfred Nzo 9.8 2.9
Nelson Mandela Bay 81.2 0.9
Buffalo City 41.2 0.9
Source: Urban-Econ calculations based on Quantec, 2017b
The low economic growth in the province has impacted on job creation, as key sectors have shed jobs. Manufacturing
has shed 22 000 jobs since 2011. Despite these job losses, the Eastern Cape economy added 71 000 jobs in the third
quarter of 2016, representing a quarter-on-quarter increase of 5.1%. These additional jobs had a positive impact on the
overall unemployment rate which despite remaining high at 28.2%, decreased from 29.2% in the third quarter 2015.
Both the labour absorption rate and the labour force participation rate also exhibited slight increases to 34.7% and
48.4% respectively in the third quarter of 2016.
TABLE vii: LABOUR MARKET HISTORIC PERFORMANCE IN THE EASTERN CAPE
Period Absolute Change
Indicator Q3 2011 Q3 2015 Q3 2016 2015 - 2016 2011 - 2016
Working Age Population 3 969 4 115 4 153 184 38
Labour Force 1 724 1 937 2 008 284 71
Employed 1 263 1 372 1 443 180 71
Unemployed 461 565 565 104 0
Not economicaly Active 2 245 2 177 2 154 -100 -32
Discouraged Work-seekers 371 426 385 14 -41
Percentage Percentage Point Change
Unemployment Rate 26.8% 29.2% 28.2% 1.4 -1.0
Unemployment Rate (expanded definition) 41.5% 42.5% 41.3% -0.2 -1.2
Labour Force Participation Rate 43.4% 47.1% 48.4% 5.0 1.3
Labour Absorption Rate 31.8% 33.3% 34.7% 2.9 1.4
Source: StatsSA, 2016a
The majority of the Eastern Cape’s unemployed – those without jobs who wish to and are able to work – are in the
Nelson Mandela Bay Metro, Chris Hani and Amathole. Chris Hani had an unemployment rate of 45.2% in 2015 (104
570 people) making it the highest in the Province and 18.3% above the provincial average. The Buffalo City Metro
in contrast had the lowest unemployment rate in 2015 at 22.4%.
XVIII
The Eastern Cape contributed 4.5% of the total South African exports and 5.2% of the country’s total imported
merchandise in 2015. Between 2014 and 2015, total mechanise imports grew by 18.4% compared to 12.4% for
exported merchandise. This high growth rate resulted in the Eastern Cape’s import and export growth rates
outperforming the national averages of 0.5% and 4.2% respectively. Despite this strong performance, the Eastern
Cape’s trade balance deteriorated in 2015, increasing by R3.7 billion to R11.0 billion. This deterioration was driven
by higher imports relative to exports.
v. AUTOMOTIVE INDUSTRY IN THE EASTERN CAPE
The South African automotive industry outlook in 2017 sees good export growth and a moderate recovery in
domestic sales after a poor performance in 2016.
Local competitiveness remains below optimal levels however, greater policy certainty has seen increased capital
investment by new and existing automotive assemblers. Notable investments include the R11 billion investment of
the Chinese assembler Beijing Automotive International Corporation and the investment of FAW trucks into the
Coega IDZ. As well as Volkswagen’s R4.5 billion investment into their Uitenhage plant. Domestic production levels
are expected to rise in the future due to both increased exports and the Automotive Production and Development
Programme (APDP) incentives. E-mobility and autonomous vehicles however are considered to be global industry
disruptors. The Eastern Cape however is home to the national e-mobility technology innovation programme, uYilo
based at NMMU.
Component manufacturers in the province have established an automotive forum, called the Eastern Cape
Automotive Industry Forum (ECAIF). The ECAIF’s objective is to facilitate competitiveness of the Eastern Cape’s
automotive component manufacturers by driving initiatives to enable this competitiveness. To this end, the forum
has recently made representation to the Competition Commission to secure approval for combined tendering of
manufacturers for logistic and purchasing services.
From a policy perspective, the review of the APDP in 2015 has seen the programme shifting focus and the phasing
out of the one million vehicle production target. In 2016 the dti commenced work on the extension of the APDP,
which will extend the programme’s life span past 2020. The success of the APDP programme is clearly evident
via increased exports, in terms of both units and value, as well as greater capital investment into South Africa. The
programme however, faces criticism from component manufacturers who argue that it does not provide enough
support to the industry nor does it deepen localisation.
The poor national growth rate, combined with low consumer confidence is anticipated to adversely impact
domestic vehicle sales, which are only anticipated to grow by 3.3% to 565 000 units in 2017 (NAAMSA, 2016).
Vehicle exports, while positive, need to remain cognisant of the high levels of volatility present in international
markets. As work on the extension of the APDP gains momentum within the dti, it is anticipated that there will be
greater debate on how the APDP will be extended to meet the needs of the South African automotive industry.
XIX
The Eastern Cape Socio-Economic Review and Outlook (SERO) is an important economic intelligence report.
It provides an objective review and analysis of past and forecasted future economic growth, key labour market
dynamics, and socio-economic development of the province. This annual publication, which is compiled by the
Eastern Cape Department of Economic Development, Environmental Affairs and Tourism (DEDEAT), provides
an analytical base from which provincial economic development policies, strategies and interventions can be
developed, utilising an evidence-based platform to guide provincial planning.
Despite improvements in the global economy, economic growth continues to fall short of expectations in both
advanced and emerging economies. Increased uncertainty also pervades international markets, linked to the
unknown impact of Brexit as well greater trade protectionism by the United States of America.
The Eastern Cape, much like the rest of South Africa, is undergoing a difficult economic transition as evident by the
long-term GDP growth, which has fallen from 4% a decade ago to 2% today. This is partly attributable to global
realties, including a protracted slowdown in trade, lower commodity prices and a high risk of external volatility. It
is also a product of continued structural constraints coupled with low levels of investor confidence in the domestic
economy, which have led to rising unemployment. Perceptions of elevated political risk, and concerns about the
ability of public institutions to make decisions on difficult trade-offs and manage change, have further undermined
confidence.
Positive indicators however, have begun to emerge as several factors that have been limiting GDP growth recede,
creating an opportunity for a new growth cycle. New sources of renewable energy have come online and driven
the localisation of a renewable energy manufacturing sector. Agriculture is expected to recover from the worst
effects of the drought, although large parts of the Eastern Cape are still in the midst of a drought. Exports and
tourism receipts are growing again. A real, sustained depreciation in the exchange rate over a period of years has
created opportunities for increased export growth. Inflation is likely to moderate in the coming year from cost push
inflation due to drought spill over effects. The working days lost to strikes have fallen symbolising an improvement
in labour relations. All these factors suggest positive growth going forward.
Despite these positive prospects, South Africa’s economic performance exhibited some of the lowest levels of
business and consumer confidence since 1994. Private sector fixed investment, a key driver of economic growth,
also dropped off substantially in 2016. Accordingly, the GDP forecasts for South Africa have been revised down
to 0.5% in 2016, before climbing to 1.3% in 2017 (National Treasury, 2016a). The Eastern Cape, unlike the rest of
South Africa, has seen a moderate increase in consumer confidence. While this is a positive sign, this increase is
not anticipated to significantly impact the province’s GDP performance, which is anticipated to continue to mirror
the national economy. The GDP growth for the Eastern Cape is forecasted to be 0.4% in 2016, and 1.0% in 2017.
The improved provincial business confidence however has positively influenced employment growth in the Eastern
Cape, as is evident by the 180 000 jobs added in the twelve months ended September 2016. In the context of more
favourable economic conditions in the province and an improved investment climate, the provincial unemployment
rate fell from 29.2% to 28.2% (StatsSA, 2016). According to the third quarter 2016 Labour Force Survey, all sectors
in the Eastern Cape, apart from mining, utilities and community services gained jobs in the third quarter of 2016.
Ongoing increases in employment however, will continue to require higher economic growth and greater private-
sector investment in the Eastern Cape.
INTRODUCTION
1
Eastern Cape exports grew by 12.4% in 2015, compared with the same period in 2014, supported by automotive and
related component exports (Quantec, 2017). Over the 2010 to 2015 period, there was a marked increase in exports
to African markets which increased by 74.5%; reflecting positive economic conditions in the region.
In recent years, despite the large and sustained depreciation in the value of the rand, which since 2010 has fallen by
20.9%, Eastern Cape exports have not grown significantly. This is due to slow global demand leading to moderate
growth in Eastern Cape and South African goods.
Stronger Eastern Cape domestic demand was reflected in the increased volume of imports, which rose by 18.4% in
2015 compared to 2014. Over the medium-term, improved demand by Eastern Cape consumers is likely to support
further import growth, but the weaker currency will limit the expansion of volumes. National imports are expected
to contract in the current year and grow by 2.7% in 2017.
Despite global and national growth challenges, the Eastern Cape has a number of significant opportunities that can
unlock development and competitiveness in 2017:
CHANGING AUTOMOTIVE POLICY
The Automotive Production and Development Programme (APDP) has seen notably growth in vehicle
manufacturing and investment, with South African automobile manufacturers seeing growth in export markets.
The review of the APDP in 2015 has seen a gradual shift away from numerical targets towards greater focus on job
creation, localisation support and transformation. Work has commenced on the extension of APDP, which would
extend the APDP benefits past 2020. It is hoped that the extension of the programme will help to grow vehicle
and component exports from the Eastern Cape and encourage further investment by vehicle and component
manufacturers. The fruits of this are already evident in the recent R11 billion investment by the Beijing Automobile
International Corporation (BAIC).
WORKING TOGETHER FOR ECONOMIC GROWTH
As highlighted in the 2016 Medium Term Budget Policy Statement citizens have registered their concerns and
“want government to talk less and achieve more, act decisively against corruption and waste, contribute to growth
and job creation and speed up inclusive transformation and social justice” (National Treasury, 2016b:1). National
Treasury (2016b) also notes that it is necessary to ensure joint action and collaboration that restores confidence
and mobilises private investment to avoid a low-growth trap.
To promote faster growth and a more inclusive economy, government has strengthened its active collaboration
with business, trade unions and civil society so as to restore confidence and reduce constraints on economic
growth. This was most recently evident in the averting of the ratings downgrade, as well as the establishment of
the Presidential Business Working Group and the CEO Initiative (National Treasury, 2016b). Collaboration at a local
and regional level between organised business and government is seeing results. Examples of these successful
collaborations are highlighted in Chapter 6. Going forward, this collaboration is likely to continue strengthening the
prospects of coordinated action that promotes economic growth.
REVIVING BUSINESS AND CONSUMER CONFIDENCE
The past year saw business and consumer confidence drop sharply. Confidence has since recovered modestly and
government is seeking to ensure that more confidence grows. The critical ingredient in higher growth is greater
investment, driven in part by the private sector.
Steps that can assist in building confidence to attract investment include:
• Reducing political conflict and tension which is creating political risks for investors as well as undermining
needed reforms of state owned entities.
• Addressing the structurally high current account deficit which makes South Africa reliant on volatile capital
flows for financing.
• Addressing the country’s longstanding skills shortage, adverse terms of trade, and the slowdown in the private
sector’s capital investment programmes (National Treasury, 2016c, 2016d, 2016e).
2
• Going forward, the government will continue to work in partnership with the private sector and labour to
attract more investment, particularly in the Eastern Cape’s Special Economic Zones (SEZ), as well as promote
SMME development.
EDUCATION AS AN IMPERATIVE TO FUTURE ECONOMIC GROWTH
The quality and extent of education has a direct bearing on the performance of the economy. This is particularly
pertinent for the Eastern Cape, which consistently underperforms when compared to other provinces in terms of
educational outcomes. When assessing provincial educational infrastructure in areas such as reliable electricity
and water supply, access to adequate ablution facilities, science laboratories, libraries and computer facilities, the
province is severely lacking. In order to address these issues, the provincial government has allocated R104.4 billion
to education over the 2017 MTEF, representing 44.0% of the total provincial budget.
These interventions are intended to improve educational attainment and quality levels in the province, thereby
improving the prospects of learners and facilitating future economic growth in the Eastern Cape.
3
4
2016 saw global economies grapple with low inflation, low
commodity prices, capital flows into emerging markets and
the continuation of accommodative monetary policy. Global
growth remains below pre-financial crisis levels and persistently
underperforms forecasts. Thus begging the question: ‘when
will global growth recover?’
This chapter looks at some of the determinants of low global
growth. This includes low trade growth, due to the rise in anti-
trade liberalisation, which is set to increase as a risk factor.
Other growth inhibitors identified include the low interest rate
environment, low population growth in advanced economies
and the modest rise in labour productivity (IMF, 2016a).
Whilst lower growth in China, as the world’s largest importer
and exporter, means that even small changes in Chinese
demand have major impacts on countries, especially emerging
economy, commodity exporters.
A brief analysis of economic and political events in key trading
regions and countries is provided. The outlook for the coming
year is summarised around key themes that are likely to influence
politics and economics. This includes the US trade protection
policy stance and growth in a disinflation environment and
the risk of deflation trap for advanced economies. A national
identity often binds countries together, it rallies citizens to
causes greater than the individual. The rise in nationalism
and specifically-ethnic nationalism in many advanced nations
is a growing cause for concern as it disenfranchises groups
considered the ‘other’ and causes nations to look inward.
The potential of an inward looking US means that many
international cooperation issues such as climate change, trade
in wildlife, terrorism, as well as economic issues such as kick-
starting global growth and increasing growth in trade, may
start to lose momentum from other countries.
The chapter finally looks at the repercussions for South Africa.
These will be based on changes in monetary policy by the
US Federal Reserve Bank, an appreciating dollar, increased
US inflation and greater market uncertainty created by new
political and trade positions.
GLOBAL ECONOMICOUTLOOK
Global Growth in output is forecasted at 3.4% up marginally on 2016 (3.1%). However global growth is still below pre-financial crisis levels.
Rise in nationalism. With US election of Donald Trump and UK Brexit vote, countries start to look inward.
Trade protectionism is on the rise. US pulls out of TPP and TTIP and threatens to renegotiate NAFTA.
US dollar likely to appreciate based on increased fiscal spending programme.
Global labour productivity levels are persistently low, thus jump-starting global economic growth is a challenge.
General elections in April for France, holds the sustainability of the EU in the balance.
Refugees made up 50% of global migration flows in 2014/15. With unprecedented migration into Europe, Jordan, Turkey and Lebanon.
$
KEEPOUT
5
Figure 2.1: Growth in GDP in Key Economies
Figure 2.2: Crude Oil prices: Brent
Figure 2.3: US Civilian Unemployment Rate
-6
-4
-2
0
2
4
6
8
10
12
2011 2012 2013 2014 2015 (e) 2016 (e) 2017 (e)GDP
Gro
wth
Rat
e (%
)
Brazil China United States
European Union Sub-Saharan Africa World
128.14
26.01
53.93
0
20
40
60
80
100
120
140
2011-01-03 2012-01-03 2013-01-03 2014-01-03 2015-01-03 2016-01-03
)$( lerraB rep sralloD SU
3
4
5
6
7
8
9
10
11
Perc
enta
ge (%
)
Civilian Unemployment Rate
2.1 Global Growth
Global growth estimates for 2016 sit at 3.1% for 2016 and moderately higher at 3.4% for 2017 (IMF, 2016b). Figure 2.1
provides a perspective on global growth over the last 6 years, forecast to 2017. Whilst comparing global growth to
that of key advanced or developing countries and regions. As can be seen global growth has remained persistently
below 4% over this period. This being below pre-financial crisis levels when global growth was at 5.6% in 2007
(IMF, 2016b).
Global growth has been falling short of expectations in both advanced and emerging economies. As time passes
since the financial crisis of 2009, the reasons for low growth are becoming increasingly complex. These complex
forces include demographic changes, declines in productivity growth, lower commodity prices and regional shocks
(IMF, 2016a). Reasons also differs between advanced and the developing and emerging economies.
Figure 2.1 Growth in GDP in Key Economies
Advanced economies were at the epicentre of the 2009 financial collapse. The nature and the interaction of the
forces impeding growth in advanced economies includes low population growth levels, weak productivity growth
and low interest rates. These are discussed in more detail below.
In terms of demographic trends, low fertility rates and population growth in advanced economies are set to decline
further in future years. With an aging population and a larger share of workers aged 55-64 years old. An aging
population is set to place increasing pressure on pension and health care systems. Migration from emerging and
developing markets to advanced nations over last few decades has assisted to alleviate the impact of an aging
workforce in advanced nations. “The share of migrants in the advanced-economy population almost doubled
from 6 to 11 percent between 1990 and 2015. As the majority of migrants tend to be of working age, migration
contributed about half of the increase in the working-age population between 1990 and 2010” (IMF, 2016a:13).
Receiving migrants however creates its own challenges for advanced economies. Resident populations often fear
being displaced in the job market, losing their national identity, the lowering of wages and that governments will
incur additional costs. These concerns on migration have incurred political backlashes in the United States and in
the United Kingdom with the Brexit vote.
The civil war in Syria and conflict in the Middle East has created an unprecedented flow of refugees into Europe.
Most migrant flows are made up of economic migrants usually around 95% of global migration. Yet refugee flows
are a small but highly volatile group of migrants, who in 2014-15 made up 50% of migration flows globally (IMF,
2016a). During 2014-15 over 4.5 million people were dispersed due to conflicts in Syria, North Africa and the Middle
East. Jordan, Lebanon and Turkey have received 2.2 million refugees and Europe an unprecedented number for a
year at 1.25 million.
There are positive long term benefits for advanced nations who encourage immigration, these include per capita
income and productivity increases, with limited impact on employment rates and wage rates of resident populations.
A few studies have however identified negative effects on low income workers and that the state incurs additional
Source: IMF, 2016b. Note 2015 to 2017 data is based on IMF estimates.
6
costs in the short term. To harness the economic benefits of new migrants into these advanced economies,
governments would need to swiftly unlock economic opportunities for migrants and this means diverting fiscal
resources to these groups in the short term (IMF, 2016a).
Productivity growth is weak, and continues to remain below pre-crisis levels. This is presumed by the IMF to be
due to a continuation of the legacy of the financial crisis, weak investment and an exhaustion of the gains from
information and communication technology advancements. In advanced economies, GDP and investment have
grown slower than expected, yet employment has grown faster than expected, and this points to weaker labour
productivity growth. Thus weak labour productivity has pulled down total factor productivity.
Low interest rates and expectations of low long-term interest rates have also impacted on growth expectations.
Whilst persistent declines in productivity growth have also impacted on interest rate expectations as it reduces the
rate of return on capital and results in a lower real interest rate. There is also a greater risk aversion in the market,
following the financial crash this has created greater demand for low risk assets. This has contributed to historically
low yields on government bonds.
In emerging and developing economies growth rates have varied but are generally below pre-financial crisis
levels. Varied economic performance includes that of China and India forging ahead with growth of 6.6% and
7.6% respectively (IMF, 2016b). Whilst countries such as Brazil, experiences deep recessions. Key factors affecting
economic activity across this diverse grouping includes China’s rebalancing policy, adjustments to lower commodity
prices and demographic trends and export convergence.
China’s transition to focus more on a consumption- and service-based economy continues to influence emerging
markets, and most notably commodity producers and countries exposed to China’s manufacturing sector. The size
of the Chinese economy in GDP at market prices is equal to the total GDP of 12 of the next largest emerging and
developing economies in the world (IMF, 2016a). The size of the Chinese economy means even small changes in
its growth rate have large impacts on it demand for goods and especially commodities. Chinese economic activity
now affects more countries globally and to a greater extent than ever before. The outlook for emerging economies
is going to continue to be shaped by the performance and expectations around China’s rebalancing.
Commodity exporting nations have continued to adjust to low commodity prices. Oil prices crashed in 2015 causing
the incomes of oil-exporting, developing nations to decline. Figure 2.2 indicates the price of Brent Crude Oil over
this period. Although the ‘acute’ phase of the shock has passed, low commodity prices are passed through in terms
of constrained fiscal spending which impacts on domestic demand and government infrastructure programmes. It
also means subdued private investment in these countries, due to the high capital cost of extractive production.
The Organization of the Petroleum Exporting Countries (OPEC) agreed in 2016 to cutbacks in oil production, which
saw oil prices increase in late 2016 and should see these prices rise into 2017 (SARB, 2016). With less dramatic
changes in commodity prices recently compared to 2015, the real exchange rate of developing economies has
been less volatile.
Source: US Energy Information Administration, 2017. Note: Europe Brent Spot Price.
Figure 2.2 Crude Oil prices: Brent
Figure 2.1: Growth in GDP in Key Economies
Figure 2.2: Crude Oil prices: Brent
Figure 2.3: US Civilian Unemployment Rate
-6
-4
-2
0
2
4
6
8
10
12
2011 2012 2013 2014 2015 (e) 2016 (e) 2017 (e)GDP
Gro
wth
Rat
e (%
)
Brazil China United States
European Union Sub-Saharan Africa World
128.14
26.01
53.93
0
20
40
60
80
100
120
140
2011-01-03 2012-01-03 2013-01-03 2014-01-03 2015-01-03 2016-01-03
)$( lerraB rep sralloD SU
3
4
5
6
7
8
9
10
11
Perc
enta
ge (%
)
Civilian Unemployment Rate
7
Demographic changes are also affecting future growth forecasts in developing and emerging economies. This
includes lower population growth rates and declining working age populations in China where the population
growth rate for the next 5 years is 0.25% down from 0.5% (IMF, 2016a). In low-income, developing nations however
the trend is still higher population growth rates than other regions. These demographics need to be considered
when comparing per capita income levels or progress towards advanced economy per capita income levels. In
low-income, developing nations, it is expected that these nations will experience lower growth rates in the coming
years, due to low commodity prices and constrained fiscus, but also due to a higher population growth rates. High
population growth, will see the gap in their GDP per capita income between them and advanced economies reduce
only by 0.5% (IMF, 2016a).
2.2 Economic Prospects in Key Markets
In this section the main economic trends affecting key markets are discussed including China, the United States, the
Euro Zone and Brazil. Table 2.1 provides key indicators on these markets.
TABLE 2.1 INTERNATIONAL ECONOMIC INDICATORS
GDP GrowthRate 2016
GDP GrowthRate 2017
UnemploymentRate 2017
InflationRate 2017
United States 1.6% 2.2% 4.8% 2.3%
European Union 1.9% 1.7% 1.3%
China 6.6% 6.2% 4.1% 2.3%
Brazil -3.3% 0.5% 5.4%
Sub-Saharan Africa 1.4% 2.9% 11.5% 10.8%
South Africa 0.1% 0.8% 27.0% 6.0%
Source: IMF, 2016b
United States of America
The US Federal Reserve Bank left interest rates untouched in 2016, even though the US job market had strengthened
as can be seen Figure 2.3. The election of Donald Trump in the US has ushered in an era of greater protectionism
and isolationism for the US. Trump’s planned protectionist trade policies and infrastructure spending programme
may see a resurgence of inflation and the normalisation of bond markets. A programme of new infrastructure
projects however is also likely to raise fiscal spending and hence the debt ceiling.
Figure 2.3 US Civilian Unemployment Rate
Source: Federal Reserve Bank of St. Louis, 2017. Note: seasonally adjusted civilian unemployment rate.
Figure 2.1: Growth in GDP in Key Economies
Figure 2.2: Crude Oil prices: Brent
Figure 2.3: US Civilian Unemployment Rate
-6
-4
-2
0
2
4
6
8
10
12
2011 2012 2013 2014 2015 (e) 2016 (e) 2017 (e)GDP
Gro
wth
Rat
e (%
)
Brazil China United States
European Union Sub-Saharan Africa World
128.14
26.01
53.93
0
20
40
60
80
100
120
140
2011-01-03 2012-01-03 2013-01-03 2014-01-03 2015-01-03 2016-01-03
)$( lerraB rep sralloD SU
3
4
5
6
7
8
9
10
11
Perc
enta
ge (%
)
Civilian Unemployment Rate
The US has seen inflation improve from 0.1% in 2015 to 1.2% in 2016. The October 2016 projections for 2017 are
that consumer price inflation in the United States, inflation would increase to 2.3% in 2017 (IMF, 2016b). Thus
expectations are of an easing of deflationary forces however President Trump’s policies could see a refocus on the
use of fiscal spending and this could spur US inflation even higher.
8
A move away from monetary policy to fiscal policy stimulus, should normalise bond markets. Quantitative easing
saw forced bond buying which drove down bond yields. Rising US interest rates in a protectionist trade environment,
may not be in the best interest of emerging economies. If rates normalise in advanced economies offering less
risk, the capital outflow from emerging economies could be volatile for many emerging market exchange rates
(Finweek, 2017).
President Trump favours direct intervention in the US manufacturing sector to bring manufacturing jobs back
to the states. Global economies however are moving away from goods-based economies to service economies.
Technology advances, have also minimised the number of jobs in manufacturing whilst global value chains have led
to increasing labour specialisation of the work force. Trump’s interventions into the trading relationships is focused
on trade partners with a large manufacturing export base. Whilst Mexico has received most of the attention others
include: South Korea, Japan, Germany, Turkey and of course China. These interventions are expected to be highly
disruptive to trade and international relations between these countries.
The commitment of the new administration in the US, to-lead-as-they-have-campaigned and to go through with
protectionist trade policies and increased deficit spending, remains to be seen. The US election, however, has
marked a powerful shift in international relations which is expected to increase market volatility.
The European Union and the Euro Zone Growth expectations for 2017 for the European Union have declined from October 2015 (IMF, 2015) when growth
was estimated at 2%. Output growth is now estimated at 1.7% for 2017 (IMF, 2016b). There is a growing rise in
nationalism and public sentiment moving against membership of the European Union.
The first round of the French presidential election will be held on the 23rd of April 2017 and could raise the stakes
for the sustainability of the European Union. The National Front (NF) has gained ground in the primaries, led by
Marine Le Pen who has campaigned on leaving the European Union. Whilst other contenders are campaigning on
a more pro-European, liberal outlook that looks to help workers adapt to a technological age. The election will be
an important one for the future of the European Union (Economist, 2016d).
On the 23rd of June 2016, the United Kingdom voted in favour of leaving the European Union in a momentous
referendum result. The nature and extent of how the UK will leave the European Union is still highly contentious
and may take many years for an exit strategy to be developed and decided upon.
CHINA
China remains the leader in global growth even if growth is far below its 2011 levels when it grew at 9.5% per
annum. Growth is expected for 2017 to be at 6.2% per annum. The Chinese economy's rebalancing is seeing robust
consumer demand growth and a growth in service industries (IMF, 2016a). China is the world’s largest economy in
terms of purchasing power and is the world’s largest exporter and second largest importer of merchandise goods
(WEF, 2017). The Chinese currency the renminbi is managed to ensure a stable trade weighted exchange rate using
a crawling peg against the US dollar. An appreciating dollar has meant that Chinese reserves of dollars are being
used to prop up the currency and a credit crisis could develop unless the currency is allowed to depreciate.
BRAZIL
Latin America’s powerhouse, Brazil, continued into a recession in 2016 with growth at -3.3%. The coming year
is expected to see the economy recovering from the bottom of the recession with growth expected at 0.5%.
The Brazilian economy has been particularly affected by the past shocks in 2015, which included the decline in
commodity prices, administered price adjustments and political tumult.
Sub-Saharan Africa
Sub-Saharan Africa has been particularly affected by low commodity prices and a drop in trade with China.
Exceptionally high growth in Sub-Saharan Africa was experienced in 2007 when it stood at 7.1% p.a. is now at
much lower levels, with growth in 2016 expected at 1.4%. This is expected to increase in 2017 to 2.9%. Economies
that are export-commodity reliant were worse affected in 2016 than others. Angola experienced zero growth in
2016, due to a drop in export earnings and is only expected to grow 1.5% in 2017. Nigeria, Africa’s largest economy,
saw economic activity contract, by 1.7% in 2016. This was due to low oil prices, limited investor confidence, foreign
currency shortages and electricity supply shortages. Based on IMF data the South African economic growth rate
was far below the average for sub-Saharan Africa but the country fared slightly better than Nigeria in 2016 with an
estimated growth rate of 0.12% (IMF, 2016).
9
Figure 2.4 Growth in GDP of Key Sub-Saharan Africa Economies
2.3 Outlook for 2017
Key trends that are expected to be driving the economic agenda in 2017, include slow global trade and the rise of
protectionism. Advanced economies will continue to grapple with low inflationary environment and policy reactions
to it. The spill overs from migration are seeing a rise of nationalism and China’s refocused growth will continue to
change trade and economic patterns with its traditional trading partners.
Global trade growth has slowed since 2012, relative both to historic trade growth and to global economic growth.
Trade is a highly contentious issue and thus the question that has been asked increasingly by politicians and the
public is: do markets open up more or do countries close in and protect their industries? The key is to consider why
trade is declining. Is it simply due to generally weak global economic environment, or is it a consequence of a rise
in constricting policies?
The role of private investment in stimulating trade is critical and thus the decline in private investment is attributed
to three quarters of the slowdown in the economy according to the IMF (2016a). Low growth in commodity prices
has resulted in many exporters reducing their capital investment.
Other factors attributed to slow trade growth, is waning trade liberalisation. Trade costs declined historically
between 1985 and 2007, due to policy-driven reductions and this strengthened trade growth. As trade costs
declined and transportation and communication technology improved global value chains spread across the world
and production processes became globally dispersed, and the rise of the global value chain was created. Findings
have shown that the slowdown in trade growth is a symptom of the low growth environment and that policies need
to look at constraints to growth, removal of trade barriers and the rise of protectionism.
The rise of protection is expected to be a gathering force in 2017. US President Donald Trump has made it clear that
the US, will not ratify the Trans-Pacific Partnership (TPP). A free trade agreement between 12 major trading nations
of the Pacific Rim. The trade agreement would have provided reduced tariffs and increased trade between these
nations and was expected to increase economic growth. The US withdrawing, calls the future of this agreement into
question. Another agreement that is unlikely to find support with the Trump administration was the Transatlantic
Trade and Investment Partnership (TTIP) between the European Union and the US. This trade agreement however
was in a much early stage of development than the TTP. Trump has also called for a renegotiation of the North
American Free Trade Agreement (NAFTA) between the US, Canada and Mexico (BBC, 2017).
Inflation has declined globally and especially in advanced economies to historically low values, see Figure 2.6 of
consumer price levels in three world regions. Disinflation which is the slow-down in the rate of inflation, has become
an international phenomenon. Whilst deflation is the fall in the price level. Disinflation is not necessarily bad, it can
be due to a fall in a price of a commodity. The concern is if persistently low inflation, leads firms and households to
revise down their beliefs about the future path of inflation. If medium term expectations continue to drift down, a
deflationary cycle can arise and this may lead to a deflation trap. This is a state of persistent deflation that prevents
real interest rates from decreasing (IMF, 2016a).
Figure 2.5: Growth in GDP of Key Sub-Saharan Africa Economies
Figure 2.6: Inflation Average Consumer Prices
0
1
2
3
4
5
6
7
8
9
10
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Year
-on-
egnahC egatnecreP raeY
World Advanced Economies Emerging Market and Developing Economies
-2-10123456789
10
2014 2015 2016 2017 (F) 2018 (F) 2019 (F)
)%( ETAR HT
WORG PDG
Democratic Republic of the Congo Mozambique
Nigeria South Africa
Sub-Saharan Africa AngolaSource: IMF, 2016b. Note 2015 to 2017 data is based on IMF estimates.
10
Inflation has declined across many countries and regions, in both headline and core measures, but most markedly
in tradable goods sectors as opposed to services. The recent decline in inflation has been associated with drops in
oil and other commodity prices. Core inflation which excludes energy and food, highly volatile commodities — has
also remained below central bank targets for several consecutive years in most of the major advanced economies.
Low interest rates are undesirable in that they leave little room to ease monetary policy if needed and given sticky
wages, with weak demand, the net effect can be large scale, job losses. Often domestic monetary policy can do
little to ward off international shocks. Economic slack and changes in commodity prices are the main drivers of
disinflation. The implications are that in economies that miss inflation targets, then the credibility of monetary policy
begins to be eroded. Thus policy makers need to boost demand and create firm expectations on the movement of
interest rates and inflation expectations (IMF, 2016a).
Figure 2.5 Inflation Average Consumer Prices
Source: IMF, 2016b. Note 2015 to 2017 data is based on IMF estimates.
The year 2017 will continue to see spill overs from other regions' economics and politics, and these will have
heightened effects on other nations. China’s share of global imports and exports means that bumps in its economy
have ripple effects across other nations. The widespread migration into Europe from conflicts in the Middle East,
will continue to have spill over effects. It has already changed political sentiment within Europe towards regional
integration and in the US towards migration (IMF, 2016a).
There is a growing movement towards nationalism in many nations. This however is not the civic nationalism,
which is associated with uniting a country around common values and rallies citizens to a common cause, to
accomplish something bigger than the individual. Examples of civic nationalism include US support for Team USA
in the Olympics; South Africa hosting the FIFA World Cup or the inclusive nationalism that countries like Canada
and Sweden, pride themselves on.
The move is towards ethnic nationalism, which draws on race and history to set a nation apart. An immediate
example is of President Trump’s “Make America Great Again" campaign that centred on a pessimistic world view
in which the interests of global partners compete with the interest of the nation. There are however growing
examples around the world of ethnic nationalism, such as Russia’s Vladimir Putin, who has shunned liberal values,
to support nationalist, traditional values. This has led a modern Russia, to decriminalise domestic violence. Turkey
under President Recep Tayyip Erdogan, has moved away from a secular state, from efforts to join the European
Union, and peace talks with Kurdish minorities; to more strident Islamic nationalism. Chinese nationalism focuses
on the Han cultural identity, as being the national identity of China, which runs the risk of creating conflicts with
minority groups. The major concern is however with an increasingly, inward looking US, that other countries will
take a cue from its politics and global challenges will become harder to solve (Economist; 2016a, 2016b, 2016c and
2016d). These challenges include support for the international criminal court, global warming, low global economic
growth, environmental protection and terrorism.
Figure 2.5: Growth in GDP of Key Sub-Saharan Africa Economies
Figure 2.6: Inflation Average Consumer Prices
0
1
2
3
4
5
6
7
8
9
10
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Year
-on-
egnahC egatnecreP raeY
World Advanced Economies Emerging Market and Developing Economies
-2-10123456789
10
2014 2015 2016 2017 (F) 2018 (F) 2019 (F)
)%( ETAR HT
WORG PDG
Democratic Republic of the Congo Mozambique
Nigeria South Africa
Sub-Saharan Africa Angola
11
2.4 Summary
The impact of US President Trump’s policies on South Africa is still rather uncertain, but fiscal led expansions are
likely to strength the US dollar. This would lead to higher financial burdens on dollar denominated debt, although
it would support commodity exports from South Africa and increase export earnings. An appreciating dollar
however would see a realignment of global exchange rates and this would provide conditions for capital flight from
emerging markets. Improved US growth would also provide the space for the US Federal Reserve Bank to raise
interest rates and there could be a greater normalisation on bond markets. This would in turn impact on capital
flows to emerging markets.
Greater trade protectionist policies between the US and its trading partners would limit the potential global trade
growth and thwart trade liberalisation efforts. It is however unclear to what extent South Africa will be negatively
or positively affected by changing trading relationships and if South Africa would be directly focused on.
Low global growth will continue, into the medium term, due to limited trade growth, low factor productivity
and limited private investment. Low global demand will impact on South Africa’s ability to export and grow its
industries. Commodity prices have been low but are recovering from the shock in 2015 and should start to tick up
in 2017. This should be positive for commodity exporters in South Africa although increasing oil prices will place
increased strain on the current account.
In 2017, South Africa will have to respond to much uncertainty. With the potential of an appreciating dollar,
normalisation in US monetary policy, increasing commodity prices and low global demand. It is however expected
to be a year of much volatility as new trading and international partnerships are formed and advanced economy
interest rates may normalise leading to capital flight and exchange rate volatility.
ˇ
12
14
4
Fixed investment declines in 2016, due to a drop in private sector investment. The decline in investment reduced GDP by 0.2% by the 3rd quarter.
The year that was 2016 saw an erratic pattern of economic
activity for the country. Going into 2016, a very low bar for
economic performance was set with a dramatically depreciated
rand by the end of 2015 and a prolonged drought having a
debilitating effect on the economy and spill over effects into
the price of food staples. Manufacturing came under pressure
in the 3rd quarter, and the year saw weak manufacturing
performance. Mining growth improved, as global commodity
prices began to rally; whilst households cut back on purchases,
especially in durable good purchases and motor vehicles.
2016 was a particularly bad year for the domestic retail
motor industry. Consumer confidence was down for the 8th
consecutive quarter this resulted in households reducing
expenditure and holding off on large purchases. This was
good in terms of debt consolidation trend for households but
resulted in volatility in trade sector.
Real gross fixed capital formation registered its fourth
consecutive quarterly decline in the 3rd quarter of 2016. This
was due to a significant drop in the capital investment by the
private sector due to low consumer and business confidence
levels.
Employment creation was subdued in 2016 with the
unemployment rate increasing to a record high of 27.1%
(SARB, 2016a). Youth unemployment remains one of the most
pressing social and economic issues facing the country with
38.2% of all persons aged 15-34 years old out of employment.
The twelve-month rate of consumer inflation was above
the upper limit of the target for most of the year, with food
price inflation being the main driver. Producer price inflation
increased significantly in agriculture and food processing due
to the cost of imported grains and drought induced impacts.
Consumers felt the pinch in 2016, with sugar costing 28% more
than the previous year and milled grain being 15.7% more
expensive. Sugar-sweetened-beverages will increase further in
2017, as from April, when a new tax comes into effect.
There was a noticeable recovery in the exchange rate from a
very low base in the 4th quarter of 2015. The rand strengthened
on the back of surprise global events and investors’ improved
sentiment of the rand. This saw the terms of trade improve as
costs of imports declined and demand for commodities picked
up. The investment flows into the country financed the current
account deficit, which improved by the 3rd quarter, compared
to the same quarter in the previous year.
The Monetary Policy Committee of the Reserve Bank raised
interest rates in January and March by a quarter basis point, so
as to ensure that economic growth was not stifled but also to
Electricity supply stabilises- no load shedding in 2016. Due to a mild winter, new supply coming online but also low economic activity.
Balance on trade account turns positive in 2nd quarter 2016 and only slightly negative in 3rd quarter.
Consumer and Business Confidence reach all-time lows. 8th consecutive quarter of negative consumer confidence.
0.5% estimated Annual GDP growth for 2016. With the drought seeing devastating impact on agriculture. Agriculture has had 7 quarters of negative growth.
Unemployment rate increases further to 27.1% up from 25.5% the previous year, its highest level yet. Youth unemployment (15-34 years) now stands at 38.2%.
SARB confirms SA is in the downward phase of the business cycle.
NATIONAL ECONOMICPERFORMANCE
JOBLESS
15
Forecast
2015Actual
2016Estimate
2017 2018 2019
Annual Growth Rate 1.3% 0.5% 1.3% 2.0% 2.2%
GDP (R Billions) at market prices 4 013.6 4 300.0 4 616.9 4 981.1 5 385.3
Source: StatsSA, 2016a; National Treasury, 2016b
The expected moderate growth in GDP in 2017 is aligned to growing momentum in the leading and coincident
indicators (Figure 3.1). The SARB’s composite leading business cycle indicator started to rise in May 2016, and it
continued to climb through August and September. Movements in this indicator typically lead the general business
cycle by between 6 and 12 months. Thus, indications are that moderate growth is to be expected in the short term
and that a turning point in the current downward business cycle has been reached (SARB, 2016a; The World Bank,
2017).
ward against inflationary pressures and expected rate hikes in the United States. As inflationary pressures abated
by the middle of 2016, the South African Reserve Bank kept the Repo Rate at 7% from March 2016.
The fiscal consolidation policy came under pressure, with increased allocations to fund higher education institutions
and the National Student Financial Aid Scheme (NSFAS) taken from the contingency reserve. Debt financing has
increased by 10% year-on-year, with financing of external government debt becoming a growing expenditure item
on the national budget.
Coordinated efforts by the National Treasury, government, and businesses were able to stave off a ratings
downgrade to junk status, which would impact on the cost of government’s borrowing. The risk of downgrade still
exists in 2017, unless concrete steps are made to reduce political infighting, boost economic activity, undertake
structural reforms and implement business friendly policies.
The South African Reserve Bank has officially confirmed that the economy is in a downward business cycle with
the turning point being November 2013. The last upward business cycle lasted for 51 months between September
2009 and November 2013.
This chapter looks at these economic indicators in more detail and expectations for economic performance in 2017.
3.1 NATIONAL ECONOMIC PERFORMANCE
The past year was characterised by lower than expected growth in real Gross Domestic Product (GDP) the lowest
economic growth rate recorded in the last six years. Table 3.1 provides the actual, estimated and forecasted
real GDP growth rates and value of GDP for South Africa. GDP for 2016 was revised downwards in the Medium
Term Budget Policy Statement (MTBPS) to 0.5%, down from the previous estimate of 0.9% in the February 2016
National Budget. The IMF also lowered its projection, estimating domestic growth at only 0.1% over the same
period (National Treasury, 2016a). This was due to low business and consumer confidence and falling private sector
fixed investment.
The growth expectations for 2017 however, have been raised; bolstering GDP growth to 1.3%, driven by recoveries
expected in the agricultural sector as well as a depreciated rand which should assist with manufacturing exports.
Other factors that should improve economic performance in 2017 include the expected moderate recovery in
commodity prices, the new electricity generation capacity that has been brought online, and a moderation in
inflation. Risks to growth forecasts are ever present and include renewed pressure from ratings agencies, the
recurrence of the public political spats that were seen at the end of 2016, and a volatile global economic market.
Projections are that growth rates are only expected above 2% in 2019.
TABLE 3.1 Gross Domestic Product
16
Figure 3.2 indicates the quarter-on-quarter and year-on-year change in real economic growth in South Africa.
Performance in real economic growth has been volatile since 2013, with three periods of negative growth in the 1st
quarter 2014, the 2nd quarter 2015, and the 1st quarter 2016. The year-on-year growth trend in gross value added
shows a downward trend over time, with South Africa only managing a 0.7% growth by the 3rd quarter 2016 on
the same period in 2015 (SARB, 2016a).
Table 3.2 provides the percentage change per quarter of real value added by sector. The primary sector experienced
a slower growth rate by the 3rd quarter 2016, down from 12.0% in the 2nd quarter. The primary sector experienced
an annualised growth rate of 3.8% in the 3rd quarter due in part to a deceleration in mining growth. Mining activity
was volatile in 2016, declining in the 1st quarter and accelerating in the 3rd quarter as changes in the value of the
rand and international commodity prices impacted on the value created by the sector. The mining sector has seen
four consecutive quarters of low or negative growth over 2015 and 2016. A discussion of the mining sector and commodity prices is provided in Text Box 3.2.
The agricultural sector recorded its 7th consecutive quarter decline in real output by the 3rd quarter 2016.
Devastating impacts were experienced on field crop and livestock production. Agricultural real output declined
-6.5% in the 1st quarter 2016; the rate of decline moderated in the 2nd and 3rd quarters to grow at -0.8% and
-0.3%, respectively. Another impact that agricultural producers- in the poultry sub-sector – have experienced; is
the increase in volume of frozen chicken imports. Text Box 3.1 below discusses the content and impact of increased poultry imports into South Africa.
Figure 3.1 Composite Leading and Coincident Business Cycle Indicators
Figure 3.2 South African Real GDP Growth – Percentage Change
Figure 3.1 Composite Leading and Coincident Business Cycle Indicators
Figure 3.3 Real Value Added of the Mining and Agriculture Sectors
Figure 3.2 South African Real GDP Growth – Percentage Change
100
104
108
112
116
120
90
92
94
96
98
100
102
104
Coin
cide
nt In
dica
tor I
ndexxednI rotacidnI gnidaeL
Leading indicator Coincident indicator
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
6%
Perc
enta
ge C
hang
e
Quarter to Quarter % Change Year on Year % Change
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
Mining and quarrying Agriculture
Figure 3.1 Composite Leading and Coincident Business Cycle Indicators
Figure 3.3 Real Value Added of the Mining and Agriculture Sectors
Figure 3.2 South African Real GDP Growth – Percentage Change
100
104
108
112
116
120
90
92
94
96
98
100
102
104
Coin
cide
nt In
dica
tor I
ndexxednI rotacidnI gnidaeL
Leading indicator Coincident indicator
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
6%
Perc
enta
ge C
hang
e
Quarter to Quarter % Change Year on Year % Change
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
Mining and quarrying Agriculture
Source: SARB, 2016a
Source: StatsSA, 2016a. Note: Seasonally adjusted annualised rate
17
Text Box 3.1: Poultry Industry and Concerns over Imported Products
The South African poultry industry contributes 16% to agricultural GDP and provides employment to 108 000
people through the value chain. It is an economic driver of many rural towns and communities. The poultry
industry however is facing a crisis with large scale job losses and factory closures imminent. Company
earnings have dropped amid increased supply of imported products, historically high grain prices and lower
consumer demand.
One of South Africa’s largest poultry producers, RCL Foods- previously Rainbow Chickens - posted a decline
in earnings before tax of 62% compared to the same period last year for its poultry division (The Poultry Site,
2016a). The firm will retrench 1 350 staff and sell 15 of its 25 poultry farms in Hammersdale, KwaZulu-Natal.
Whilst another top South African producer, Country Bird, will retrench 1 500 workers (IOL, 2017).
The increase in US access to South African markets was widely attributed to the pressure on the poultry
industry. A condition as part of the extension of the African Growth and Opportunity Act (AGOA) was
that the US could export 65 000 tons of bone-in chicken portions per annum to South Africa without anti-
dumping duties of R9.40 per kg (The Herald, 2016; The Poultry Site, 2016b). The rebate came into effect
on the 18th of December 2015 and the quota is divided into equal portions over the year. US imports have
however subsequently been found, not to be the main factor in the increased pressure on the domestic
industry.
Between January 2016 and September 2016, 230 643 tons of bone-in poultry portions were imported into
South Africa. US imports between January and September 2016 made up only 8%, or 18 000 tons, of total
imports into the country (The Poultry Site, 2016b).
Figure 3.3 Real Value Added of the Mining and Agriculture Sectors
Figure 3.1 Composite Leading and Coincident Business Cycle Indicators
Figure 3.3 Real Value Added of the Mining and Agriculture Sectors
Figure 3.2 South African Real GDP Growth – Percentage Change
100
104
108
112
116
120
90
92
94
96
98
100
102
104
Coin
cide
nt In
dica
tor I
ndexxednI rotacidnI gnidaeL
Leading indicator Coincident indicator
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
6%
Perc
enta
ge C
hang
e
Quarter to Quarter % Change Year on Year % Change
-25%
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
Mining and quarrying Agriculture
Source: StatsSA, 2016a. Seasonally adjusted annualised rate
table 3.2 real gross domestic product - percentage change
Percentage Change2015 2016
1st Q 2nd Q 3rd Q 4th Q Year 1st Q 2nd Q 3rd Q
Primary Sector 6.3 -10.9 -10.8 -0.5 0.9 -15.1 12 3.8
Agriculture -11.3 -20.4 -11.8 -6.7 -5.9 -6.5 -0.8 -0.3
Mining 12.7 -7.8 -10.5 1.4 3.2 -17.5 16.1 5.1
Secondary Sector -0.4 -4.9 2.5 -1.3 0.0 0.1 5.2 -2.5
Manufacturing -2.1 -6.3 4.7 -2.5 -0.3 0.6 8.1 -3.2
Tertiary Sector 1.7 0.8 1.5 1.4 1.6 0.8 1.9 0.5
Non-primary Sector
1.2 -0.5 1.7 0.8 1.3 0.7 2.7 -0.2
Total 2 -2 0.3 0.4 1.3 -1.2 3.5 0.2
Source: StatsSA, 2016. Note: Seasonally adjusted annualised rates.
18
The main source of increased imports into the market is the EU, which saw imports by volume increase by
19.5% on a monthly basis and 48.1 % on a year-on-year basis by November 2016 (SA Poultry, 2016). The
poultry industry faces a number of significant challenges to its competitiveness apart from increased import
penetration. These include the rising cost of feed, rising electricity tariffs, exchange rate fluctuations and
limited access to finance and markets (The Supermarket and Retailer, 2015)
The dti has responded to the crisis by establishing a national committee of industry and government
representatives, to address the difficulties that the poultry industry is facing. The dti has also identified a
number of interventions to boost the competitiveness of domestic production including ‘value-addition and
technology upgrading; trade measures; export support to assist the domestic industry to access foreign
markets; industrial finance and incentives’ (dti, 2017:1).
Poultry producers have also been encouraged to apply to the International Trade Administration Commission
(Itac) for further relief (The Herald, 2016). Itac has instituted an investigation as regards the surge of imports of
bone-in pieces from the EU market to South Africa. The dti is looking at other remedies including opening up
new markets for producers into the Middle East (The Poultry Site, 2016c). Whilst Gwede Mantashe, Secretary
General of the ANC, has suggested that government should buy up poultry farms that have been placed
in financial difficulty (The Poultry Site, 2017). Poultry firms and trade unions have called on government to
impose higher import tariffs on EU and Brazilian imports (IOL, 2017).
Figure 3.4 Real Value Added of the Manufacturing, Utility and Construction Sectors Figure 3.4 Real Value Added of the Manufacturing, Utility and Construction Sectors
Figure 3.5 Real Value Added of the Trade, Catering and Accommodation and Finance, Real Estate and Business Services Sectors
Figure 3.6 Price of Gold and Platinum in US dollars, 2009-2016
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
ot retrauQ
morf egnahC egatnecrePQ
uart
er
Manufacturing Electricity, gas and water Construction
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
ot retrauQ
morf egnahC egatnecrePQ
uart
er
Trade, catering and accommodation Finance, real estate and business services
600
800
1000
1200
1400
1600
1800
US
dolla
r Pric
e
Platinum Gold
Figure 3.5 Real Value Added of the Trade, Catering, Accommodation & Finance, Real Estate & Business Services Sectors
Figure 3.4 Real Value Added of the Manufacturing, Utility and Construction Sectors
Figure 3.5 Real Value Added of the Trade, Catering and Accommodation and Finance, Real Estate and Business Services Sectors
Figure 3.6 Price of Gold and Platinum in US dollars, 2009-2016
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
ot retrauQ
morf egnahC egatnecrePQ
uart
er
Manufacturing Electricity, gas and water Construction
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
ot retrauQ
morf egnahC egatnecrePQ
uart
er
Trade, catering and accommodation Finance, real estate and business services
600
800
1000
1200
1400
1600
1800
US
dolla
r Pric
e
Platinum Gold
Source: StatsSA, 2016a. Seasonally adjusted annualised rate
Source: StatsSA, 2016a. Seasonally adjusted annualised rate
19
The secondary sector experienced an annualised growth rate of 0% in 2015. In 2016, the sector rallied in the 2nd
quarter with 2.5% growth in real output but this contracted in the third quarter to -2.5%. The sub-sectors, which
influenced the decline were manufacturing and utilities. The manufacturing sector contracted in the 3rd quarter to
an annualised rate of -3.2%, after positive growth in the second quarter of 8.1%. This was due to lower production
figures of durable and non-durable manufactured goods. Production volume declines were experienced across
8 of the 10 manufacturing sub-sectors. The highest declines were in the petroleum and chemical products, iron
and steel, and motor vehicles sub-sectors. The contraction in the manufacturing sector decreased total economic
growth by 0.4% for the quarter. The utilities sector saw three quarters of negative growth in real output in 2016,
due in part to lower demand for electricity. The decline in electricity demand was due to a decline in manufacturing
activity, but especially the decline in electricity-intensive, metal processing sector activity. Electricity demand from
households also reduced due to higher electricity prices and a mild winter. Growth in the construction sector was
limited in 2016, with growth in real value added up by 0.4% in the 1st quarter, dropping to -0.2% in the 2nd quarter,
and rising to 0.3% in the 3rd quarter.
The tertiary sector saw volatile growth in the first three quarters of the year, growing by an annualised rate of
0.8% in the 1st quarter, increasing to 1.9%; and then declining to 0.5% in the 3rd quarter. The trade, catering
and accommodation sector saw a contraction in real economic output in the 3rd quarter, declining by -2.1%.
There was an acceleration in government sector output to an annualised rate of 1.8% in the 3rd quarter, but this
was counteracted by the decline in trade activity and a moderation in the finance sector output at 1.2%. Trade
performance was impacted on by a reduction in retail and motor vehicle sales as well as a contraction in wholesale
trade. Domestic demand for commercial and passenger motor vehicles slackened off sharply. See Chapter 5 for
a discussion on the domestic sales environment for motor vehicles. The transport, storage and communication
sector saw negative growth for two quarters; that being the 4th quarter 2015 and the 1st quarter 2016 at -0.3% and
-2.7%, respectively. Low domestic economic activity and weak international trade meant there was a contraction
in passenger and freight railway transport and a decrease in shipping container trade. Growth in the finance and
other business services sector was buoyant in 2016, with growth of 1.9% and 2.9% in the first two quarters of the
year, whilst growth in real output of this sector decelerated in the third quarter to 1.2%. Growth in the government
sector’s real value added was positive throughout the three quarters of 2016, accelerating to 1.8% by the 3rd
quarter. The increase in output in the third quarter was due to expenditure on the local government elections.
Text Box 3.2: The Mining Sector and Commodity Prices
The mining sector has been the driver of the South African economy ever since the first discovery of gold
on the Vaal. Key mineral exports include gold, diamonds, platinum and coal. Other mineral exports include
chrome, vanadium, manganese and titanium. Gold and platinum exports have been, for many years, key
contributors to the nation’s export earnings. The mining sector saw 16% growth in real value added in the
2nd quarter of 2016 after a number of quarters of negative growth. Mining shed a cumulative 79 400 jobs
between its employment peak in the 2nd quarter 2012 and the 1st quarter 2016 (SARB, 2016a).
Since 2011, world commodity prices have been in decline after a steady rise throughout late 2000’s as part
of the commodities super-cycle. It is estimated that the decline in commodity prices since 2012 has cost
South Africa, 4 percentage points off GDP (World Bank, 2017). Figure 3.6 indicates the US dollar price of
gold reached US$ 1,776.25 per ounce in September 2011, whilst the platinum price reached US$ 1,827.43
per ounce in February 2011. These prices have since declined due to a slowdown in demand globally, and
the rebalancing of the world’s largest market for commodities - China. Prices have also stayed low due to
high levels of excess capacity. Prices in commodities are not only the result of supply and demand factors
but are highly speculative. Export earnings and profitability of South African mining companies are also
highly dependent on the rand dollar exchange rate, as the depreciated rand has cushioned mines from
commodity price declines to some extent. Mining in South Africa has been grappling in recent years with
lower profitability, higher production costs and labour disputes.
In December 2015, the international gold price dropped to US$ 1,068 per ounce, its lowest price since 2009.
Platinum saw a low in January of 2016 at US$ 851 per ounce, its lowest level since 2008. Commodity prices
however, rallied from the low levels in early 2016 to end the year higher, with gold trading at US$1,238 and
platinum at US$ 951.
The dollar denominated gold price rose between June and July on the back of the surprise Brexit vote.
Although the international gold price rose, the average realised rand price of net gold exports decreased
with a rand appreciation and a contraction of 13.8% in the volume of gold exports. As a result, the value of net
gold exports decreased by 14.6% in the 3rd quarter 2016 compared to an increase of 5% in preceding quarter.
The US dollar price of a basket of South African-produced non-gold commodities, saw two consecutive
quarters of growth in 2016, rising by 5.7% in the third quarter of 2016. There were significant increases in
the prices of nickel, coal and platinum. The appreciation of the rand however, meant the rand price of South
African-produced non-gold commodities declined by 0.9 % over the period.
20
Percentage change2015 2016
1st Q 2nd Q 3rd Q 4th Q Year 1st Q 2nd Q 3rd Q
Final ConsumptionExpenditure
Households 2.0 0.3 2.4 2.1 1.7 -1.7 1.4 2.6
General Government
-1.7 1.6 0.8 2.6 0.2 1.2 1.4 2.1
Gross Fixed CapitalFormation
2.6 -0.9 4.6 -2.8 2.5 -10.0 -6.8 -1.0
Domestic Final Demand 1.4 0.3 2.5 1.2 1.6 -2.8 -0.3 1.8
Change in inventories(R billions)
68.4 -10.6 -16.9 -4.2 9.2 1.2 -28.3 20.0
Gross DomesticExpenditure
9.2 -8.5 1.7 1.9 1.7 -2.1 -4.2 8.1
Source: SARB, 2016a. Note: Constant 2010 prices. Seasonally adjusted annualised rates.
Gross fixed capital formation (GFCF) has been on a decline since a high in mid-2015 when it grew 4.6% in the 3rd
quarter, dropping to -2.8% in the 4th quarter. Growth for the year 2015 was at 2.5%; but by 2016, it exhibited three
consecutive quarters of negative growth.
Table 3.4 provides the relative importance of each expenditure item to GDP. GFCF contributed 5% to the growth
in GDP in 2015; but by the 3rd quarter of 2016, the growth in contribution was negative at -0.2% (SARB, 2016a).
Change in inventories made no contribution to growth in GDP in 2015 at 0%. This contribution improved in 2016,
and by the 3rd quarter was at 6.3%. Real inventories declined by R28.3 billion in the 2nd quarter 2016, and increased
by R20 billion by the 3rd quarter. This was due to an accumulation of inventories in the mining sector.
Figure 3.6 Price of Gold and Platinum in US dollars, 2009-2016
Source: SARB, 2016b. Note: London gold price in US dollars.
3.2 DOMESTIC EXPENDITURE
Real gross domestic expenditure increased in the 3rd quarter 2016 after two quarters of negative growth (Table 3.3).
Nevertheless, real gross domestic expenditure contracted by 0.6% for the first three quarters of 2016 compared to
the same period 2015 (SARB, 2016a).
Real final consumption expenditure by government increased at an annualised rate of 2.1% in the 3rd quarter 2016
after a lacklustre 2015, when it grew 0.2%. Government spending in the first three quarters of 2016 as compared to
the same period in 2015, was 1.6% higher. This is associated with government’s spending on the local government
elections in August 2016.
Table 3.3 Percentage Change in Domestic Expenditure Elements
Figure 3.4 Real Value Added of the Manufacturing, Utility and Construction Sectors
Figure 3.5 Real Value Added of the Trade, Catering and Accommodation and Finance, Real Estate and Business Services Sectors
Figure 3.6 Price of Gold and Platinum in US dollars, 2009-2016
-8%
-6%
-4%
-2%
0%
2%
4%
6%
8%
10%
ot retrauQ
morf egnahC egatnecrePQ
uart
er
Manufacturing Electricity, gas and water Construction
-3%
-2%
-1%
0%
1%
2%
3%
4%
5%
ot retrauQ
morf egnahC egatnecrePQ
uart
er
Trade, catering and accommodation Finance, real estate and business services
600
800
1000
1200
1400
1600
1800U
S do
llar P
rice
Platinum Gold
21
Table 3.4 Contribution of Expenditure Elements to Growth in Real GDP
Percentage Points2015 2016
1st Q 2nd Q 3rd Q 4th Q Year 1st Q 2nd Q 3rd Q
Final Consumption Expenditure
Households 1.2 0.2 1.4 1.3 1.0 -1.0 0.8 1.6
General Government -0.4 0.3 0.2 0.5 0.0 0.2 0.3 0.4
Gross Fixed Capital Formation
0.5 -0.2 1.0 -0.6 0.5 -2.2 -1.4 -0.2
Change in Inventories 7.0 -10.2 -0.8 1.7 0.0 0.7 -3.9 6.3
Net exports -7.0 6.9 -1.5 -1.5 -0.4 1.0 7.9 -7.6
Residual 0.6 1.0 0.0 -0.9 0.1 0.1 -0.2 -0.3
Gross Domestic Product 2.0 -2.0 0.3 0.4 1.3 -1.2 3.5 0.2
Source: SARB, 2016a.
Table 3.5 provides the growth in real final consumption expenditure and its components. Real finalconsumption
expenditure was subdued in 2015, averaging 1.7% and then contracting in the 1st quarter to -1.7%. In the 2nd and
3rd quarters of 2016, it picked up pace at 1.4% and 2.6%, respectively.
Growth in durable goods for 2015 was negative at -2.1%, with this trend continuing into 2016. Durable goods
experienced six successive quarters of negative growth, with -12.5% growth in the 1st quarter moderating to -3.8% in
the third quarter. This expenditure item includes households’ expenditure on transport equipment and computers.
The low household expenditure growth on motor vehicles has contributed to 2016 being a particularly poor year
for domestic car sales. The lowdomestic demand for durable goods has been due to poor economic activity in the
period, limited growth in credit extensions, and low consumer confidence levels.
Growth in household spending on semi-durable goods rose in the 2nd quarter of 2016 to 2.1%, but moderated again
in the third quarter to an annualised rate of 0.6%. This follows a good year in expenditure on semi-durable items
in2015, when the annualised growth rate was 4%.
Households’ expenditure on non-durable goods grew at an annualised rate of 2.2% in 2015, but was lower in the
first two quarters of 2016. By the third quarter expenditure on this item had grown to 2.3%, due to an acceleration
in spending on food, beverages and medical products.
Services saw 1.8% annualised growth in 2015. Growth dropped in the 1st quarter to 0.1% but then accelerated
through 2016 to 3.8% and 4.7% in the 2nd and 3rd quarters, respectively.
Table 3.5 Growth in Real Final Consumption Expenditure by Households
Percentage Change2015 2016
1st Q 2nd Q 3rd Q 4th Q Year 1st Q 2nd Q 3rd Q
Durable Goods -1.1 -11 -3.6 -5.5 -2.1 -12.5 -6.4 -3.8
Semi-Durable Goods 8.0 -0.5 8.9 11.1 4.0 -1.2 2.1 0.6
Non-Durable Goods 1.5 1.3 1.1 3.1 2.2 -0.9 0.4 2.3
Services 1.9 2.4 3.6 1.1 1.8 0.1 3.8 4.7
Total 2.0 0.3 2.4 2.1 1.7 -1.7 1.4 2.6
Source: SARB, 2016a. Note: Percentage change at seasonally adjusted annualised rates
Consumers were reluctant to incur more debt in 2016 with quarter-to-quarter declines in the use of instalment
sales, credit and leasing finance in the 3rd quarter (SARB, 2016a). Consumer confidence was down considerably
in 2016, with the index negative, for eight consecutive quarters. Consumer confidence started plummeting in 2015,
with the index turning from slightly negative at -4 points in the 1st quarter, to significantly negative in the 2nd
quarter at -15 points; this being the largest quarter-on-quarter change in consumer sentiment in 14 years. This
value was far lower than consumer sentiment during the 2009 economic crisis, and it was only the second time
since 1994, that the index has dropped below -12 index points. The index recovered somewhat in the 3rd quarter of
2015, only to plummet again below the benchmark to -14 index points. Between the 1st and 3rd quarter, consumer
sentiment has remained persistently negative and is a great concern for improving domestic expenditure.
22
Percentage Change2015 2016
1st Q 2nd Q 3rd Q 4th Q Year 1st Q 2nd Q 3rd Q
Private Sector Business Investment -3.4 -9 0 -4.4 -0.6 -13.3 -4.1 -1.6
Public Corporations Investment 4.4 0.1 -2.9 4.1 3.5 3.5 -10.5 -1.3
Government Investment 29.5 36.3 33.5 -4.4 14.6 -12.3 -11.2 1.5
Total GFCF Investment 2.6 -0.9 4.6 -2.8 2.5 -10.0 -6.8 -1.0
Source: SARB, 2016a. Seasonally adjusted annualised rates.
Figure 3.6 SHARE of Gross fixed capital formation
3.3 Total Fixed Capital Formation
Total fixed capital formation is used as a measure of investment within an economy, and is comprised of government
capital formation, private capital formation, and public corporation or state entities’ capital formation. Total real
GFCF declined for four consecutive quarters, up to the 3rd quarter 2016. Real capital outlays decreased by -10% in
the 1st quarter, -6.8% in the 2nd quarter, the decline moderated to -1% by the 3rd quarter due to increased outlay
on machinery and construction equipment. GFCF declined as a ratio to GDP by the 3rd quarter of 2016 to 19.4%,
down from 20.5% in 2015 (SARB, 2016a).
The decline in GFCF was driven by the continued decline in private sector investment. Private sector investment
makes up almost two thirds of total capital investment but declined to -13.3% in the 1st quarter of 2016.
The rate of decline in the growth of capital investment by the private sector stabilised by the 3rd quarter of 2016 at
-1.6%, due in part to increased capital investment in the manufacturing sector, especially by motor manufacturers.
Capital investment by state owned corporations saw positive growth in 2015 at 3.5%, but has been declining in 2016.
Capital investment by government was the main contributor to growth in GFCF, growing 14.6% for the year. This
growth dropped in the first two quarters of 2016 at -12.3% and -11.2%, until moderating at 1.5% in the 3rd quarter.
This improvement in capital investment is likely due to increased spending on road infrastructure during the period.
Figure 3.7 FNB/BER Consumer Confidence Index 2010-2016 Figure 3.7 FNB/BER Consumer Confidence Index 2010-2016
Figure 3.8 Sectoral Distribution of Employment in South Africa, 3rd Quarter 2016
-20
-15
-10
-5
0
5
10
15
20
Inde
x
CCI
Agriculture5.6% Mining
2.8%
Manufacturing10.6%
Utilities0.7%
Construction9.4%
Trade20.2%
Transport5.8%
Finance14.7%
Community & Social Services
22.1%
Private Households
8.1%
Source: Bureau for Economic Research, 2016
The low levels of growth in private sector capital investment have been cited by National Treasury and international
rating agencies as an aspect of concern in improving the growth and employment outlook for the country.
3.4 Labour Market Conditions
In terms of the structure of the South African labour market, as of the 3rd quarter 2016 the Quarterly Labour
Force Survey reported, there were 15.8 million people employed within South Africa, and 5.8 million defined as
unemployed by the narrow definition of unemployment. With a working age population of 36 million, there are 15
million persons who are not economically active. Table 3.7 provides the key variables in the South African labour
market.
23
1 International Labour Organisation (ILO) definition of youth unemployment.
The narrow definition of unemployment stood at 27.1% in the 3rd quarter 2016, up 1.6 percentage points from the
3rd quarter 2015. This was a new record high for the unemployment rate. The expanded definition of unemployment
was 36.3% in the 3rd quarter of 2016 up from 34.4% in the same quarter of the previous year.
The labour force participation rate measures the proportion of the working-age population that is either employed
or unemployed. This stood at 59.1% in the 3rd quarter 2016, up by 0.3 percentage points from 58.8% in the same
quarter 2015.
The labour absorption rate indicates the proportion of the working-age population that is employed. This ratio
declined in the 3rd quarter 2016 to 43.1% down 0.7 percentage points from the same period in 2015. This ratio has
however, improved since 2011 when it stood at 42%.
Table 3.7 Key Variables of South African Labour Market
Thousands (000) 2011 Q3 2015 Q3 2016
Change
Q3 2015 -
Q3 20162011 - 2016
Working age population 33 640 36 114 36 750 1.8% 3110
Labour force 18 818 21 246 21 706 2.2% 2 888
Employed 14 118 15 828 15 833 0.0% 1715
Unemployed 4 699 5 418 5 873 8.4% 1147
Not economically active 14 822 14 867 15 044 1.2% 221
Discouraged work-seekers 2 213 2 226 2 291 2.9% 78
Percentage Percetage Point Change
Unemployment rate - Narrow definition 25.0% 25.5% 27.1% 1.6 2.1
Unemployment rate - Expanded definition 36% 34.4% 36.3% 1.9 0.3
Labour force participation rate 55.9% 58.8% 59.1% 0.3 3.2
Labour absorption rate 42.0% 43.8% 43.1% -0.7 1.1
Source: StatsSA, 2016b
Table 3.8 provides national trends in labour market dynamics by race, gender and age. Youth unemployment
of those aged 15 – 24 years old1 stands at 54.2%, an increase on the same quarter of the previous year of 4.3
percentage points. Youth unemployment using the expanded age group of 15 – 34 years old, was at 38.2% a 2.4
percentage point increase on the same quarter in the previous year. With unemployment in this age group so
high, it is not surprising that youth unemployment has been identified as one of the most serious issues facing the
economy in 2017.
The unemployment rate increased across all race groups between the 3rd quarter 2015 and the 3rd quarter 2016.
The highest unemployment rate by race is in the Black African population group, which stands at 30.5%.
Unemployment by gender indicates that the male unemployment rate stands at 25.2% and female unemployment
rate at 29.3% in the 3rd quarter 2016. This is a 1.7 and 1.4 percentage point increase, respectively, in male and female
unemployment rates.
Table 3.8 Labour Market Trends by Demographics
Percentage 2011Q3
2015
Q3
2016
Percentage Point Change
Q3 2015 -
Q3 20162011 - 2016
Unemployment Rate by Race
Black/African 28.7% 28.8% 30.5% 1.7 2.8
Coloured 23.9% 22.8% 22.9% 0.1 -1
Indian/Asian 10.8% 12.5% 13.2% 0.7 2.4
White 5.6% 5.9% 7.3% 1.4 1.7
Unemployment by Gender
Female 27.5% 27.9% 29.3% 1.4 1.8
Male 22.9% 23.5% 25.2% 1.7 2.3
Youth Unemployment
15-24 years (ILO definition) 51.0% 49.9% 54.2% 4.3 3.2
15-34 years (SA definition) 35.9% 35.8% 38.2% 2.4 2.3
Source: StatsSA, 2016b
24
Table 3.9 indicates the education levels of the unemployed. As can be seen, the majority of South Africa’s unemployed
have secondary education but have not completed Matric (48.5%); followed by those with a completed secondary
education or the equivalent of a Matric (31.8%). The proportion of the unemployed with a tertiary level of education,
has been increasing from 5.9% in 2011 to 8.0% by the 3rd quarter 2016. An increase of 36% over this period. This is
a concerning trend, pointing to higher educational attainment as no longer guaranteeing employment.
Table 3.9 Highest Level of Education of the Unemployed
Figure 3.7 FNB/BER Consumer Confidence Index 2010-2016
Figure 3.8 Sectoral Distribution of Employment in South Africa, 3rd Quarter 2016
-20
-15
-10
-5
0
5
10
15
20
Inde
x
CCI
Agriculture5.6% Mining
2.8%
Manufacturing10.6%
Utilities0.7%
Construction9.4%
Trade20.2%
Transport5.8%
Finance14.7%
Community & Social Services
22.1%
Private Households
8.1%
Source: StatsSA, 2016b
The informal non-agricultural sector employment accounted for 2.64 million jobs in the 3rd quarter 2016 compared
to 11.03 million formal non-agricultural sector jobs. Informal sector jobs make up 19.3% of non-agricultural
employment compared to 80.7% of formal sector jobs. Since 2011, informal sector non-agricultural employment has
grown 18% from 2.23 million in 2011, whilst formal employment has grown by 8% from 10.2 million in 2011. Informal
sector employment thus grew faster than formal employment. Informal sector jobs did grow off a smaller base but
also the informal sector is growing to accommodate a work force who cannot find formal employment.
In 2016, employment dropped quarter-on-quarter dramatically in the 1st quarter and 2nd quarter by 344 000 and
129 000 jobs, respectively. This was due to job losses in the trade, manufacturing and construction sectors. This
was in the 1st quarter of 2016 when overall GDP growth was negative at -1.2%, with many sectors affected by low to
negative growth. There was a positive change in employment by the third quarter 2016, with 287 000 jobs created.
It should however, be noted that this increase in employment also accounts for the 51 000 temporary jobs created
for the local government elections by the IEC, which were terminated after August 2016.
2011 Q3 2015 Q3 2016
Change
Q3 2015 -
Q3 20162011 - 2016
Highest Level of Education of the Unemployed
No schooling 1.9% 1.7% 1.4% -16% -27%
Less than primary completed 6.9% 5.7% 6.1% 7% -12%
Primary completed 4.7% 4.4% 3.7% -17% -22%
Secondary not completed 46.8% 46.3% 48.5% 5% 4%
Secondary completed 33.0% 34.0% 31.8% -6% -3%
Tertiary 5.9% 7.5% 8.0% 6% 36%
Other 0.8% 0.4% 0.5% 30% -31%
Source: StatsSA, 2016b
In Figure 3.8, the sectoral distribution of employment in South Africa is provided, including the private household
sector. Thus, the largest employer is the community and social services sector with 22.1%, or 3.4 million people,
employed. Agriculture contributed 5.6%, mining 2.8%, manufacturing 10.6%, construction 9.4%, trade 20.2%, transport
5.8%, finance and business services 14.7%, private households 8.1%, and community and social services 22.6%.
Figure 3.8 Sectoral Distribution of Employment in South Africa, 3rd Quarter 2016
25
The net change in employment for the year between the 4th quarter 2015, and the 3rd quarter 2016 was a loss of
186 349 jobs. This net loss was caused by a number of sectors that shed jobs over this period. The largest shedder
of jobs was the community and social services sector with a net decrease of 125 400, followed by the trade sector
with 82 500 jobs, manufacturing with 54 970 jobs, and closely followed by mining with 45 470 jobs. The utilities
sector shed 5 000 jobs. Sectors which saw the largest net increase in jobs were construction with 52 870 jobs and
finance with 49 940 jobs.
Figure 3.10 Net Changes in Employment per Sector between 2016Q3 and 2015Q4, Thousands
3.5 Prices and Inflation
Headline consumer inflation struggled to keep within the target band of between 3%-6% in 2016, with consumer
price inflation (CPI) peaking at 7% in February, before moderating to 5.9% in August. This was due to relief offered
by petrol price decreases and a slowdown in consumer services inflation. By the end of the 3rd quarter, petrol price
increases and durable goods inflation pushed up CPI to breach the upper limit of the band at 6.1%.
Producer price inflation (PPI) on final manufactured goods peaked at 8.1% in February 2016. It subsequently
moderated, ending the 3rd quarter at 6.6%. Figure 3.11 indicates movements in headline consumer price index and
producer price index of final manufactured goods.
Source: StatsSA, 2016c and 2016d
Figure 3.9 Change in Employment Quarter-on-Quarter
Figure 3.10 Net Changes in Employment per Sector between 2016Q3 and 2015Q4, Thousands
Figure 3.11 CPI and PPI
-400
-300
-200
-100
0
100
200
300
400
Change in Employment quarter on quarter (numbers)
-45.5
-55.0
- 5.1
52.9
-82.5
15.4
49.9
-125.4
21.1
Mining
Manufacturing
Utilities
Construction
Trade
Transport
Finance
Community and social services
Agriculture
0%1%2%3%4%5%6%7%8%9%
10%
PPI CPI
Figure 3.9 Change in Employment Quarter-on-Quarter
Source: StatsSA, 2016b
Figure 3.9 Change in Employment Quarter-on-Quarter
Figure 3.10 Net Changes in Employment per Sector between 2016Q3 and 2015Q4, Thousands
Figure 3.11 CPI and PPI
-400
-300
-200
-100
0
100
200
300
400
Change in Employment quarter on quarter (numbers)
-45.5
-55.0
- 5.1
52.9
-82.5
15.4
49.9
-125.4
21.1
Mining
Manufacturing
Utilities
Construction
Trade
Transport
Finance
Community and social services
Agriculture
0%1%2%3%4%5%6%7%8%9%
10%
PPI CPI
26
Figure 3.9 Change in Employment Quarter-on-Quarter
Figure 3.10 Net Changes in Employment per Sector between 2016Q3 and 2015Q4, Thousands
Figure 3.11 CPI and PPI
-400
-300
-200
-100
0
100
200
300
400
Change in Employment quarter on quarter (numbers)
-45.5
-55.0
- 5.1
52.9
-82.5
15.4
49.9
-125.4
21.1
Mining
Manufacturing
Utilities
Construction
Trade
Transport
Finance
Community and social services
Agriculture
0%1%2%3%4%5%6%7%8%9%
10%
PPI CPISource: StatsSA, 2016c
Figure 3.11 CPI and PPI
The consumer price index of petrol fluctuated throughout 2016, spiking in February of 2016 to 20.6%; thereafter
decelerating to negative and zero values in the latter part of the year. On the back of one of the most disastrous
droughts in South Africa’s history, consumer price inflation on food outstripped headline inflation peaking at 13.2%
at the end of October 2016. Higher food prices pushed up the headline CPI over the period beyond its target band.
Figure 3.12 Comparison of Headline CPI to Food and Petrol inflation Figure 3.12 Comparison of Headline CPI to Food and Petrol inflation
Figure 3.13 Agricultural PPI compared to Final Manufactured Food PPI and CPI Food
Figure 3.15 Current Account as a Percentage of Gross Domestic Product
-30%
-20%
-10%
0%
10%
20%
30%
-12%
-7%
-2%
3%
8%
13%
Perc
enta
ge C
hang
e ov
er T
wel
we
Mon
ths
-Pet
rol
shtnom e
wlewt revo egnahC egatnecreP
-Fo
od a
nd C
PI
Headline CPI CPI Food CPI: Petrol
-0.06
-0.01
0.04
0.09
0.14
0.19
0.24
0.29
PPI: Agriculture PPI: Final Manufactured Food CPI: Food
-8
-7
-6
-5
-4
-3
-2
-1
0
2011
/01
2011
/02
2011
/03
2011
/04
2012
/01
2012
/02
2012
/03
2012
/04
2013
/01
2013
/02
2013
/03
2013
/04
2014
/01
2014
/02
2014
/03
2014
/04
2015
/01
2015
/02
2015
/03
2015
/04
2016
/01
2016
/02
2016
/03
Perc
enta
ge o
f GDP
Current Account as a Percentage of GDP
Source: StatsSA, 2016c
An analysis of the price changes within CPI based on the classification of individual consumption by purpose
(COICOP) categories in Table 3.10 suggests acceleration of price changes in key categories. The table is listed
in order of highest percentage change. Commodities with the highest increase in price levels over the one-
year period were food and non-alcoholic beverages at 12.9%; these also have a third highest weighting in the
consumer basket. This was followed by recreation and culture at 7.2% and restaurants and hotels at 6.5%.
These three categories are significantly higher than the upper limit of the inflation target.
Most measures of inflation accelerated in 2016, but if food, non-alcoholic beverages and petrol prices are
removed from the calculation, then inflation moderated to 5.8% by October 2016. Thus, food price pressures
were the dominant driver in the acceleration of inflation in 2016.
27
Producer price inflation for agriculture (PPI: Agriculture) was elevated for the first 6 months of the year, peaking
at 27% in February 2016 due to the prolonged drought conditions in the country. In the latter part of 2016, PPI:
Agriculture moderated, dropping to 10.5% by October 2016. This was due to most of the sub-categories recording
slower price movements. Producer prices for final manufactured food (PPI: Final Manufactured Food) also increased
over the period, peaking at 8.1% in February 2016.
The effect of marked increases in PPI: Agriculture and PPI: Manufactured Food was to increase consumer food
prices significantly in 2016, as indicated in Figure 3.13. CPI: Food more than doubled rising to 13.2% by October
2016 compared to the same period 2015 when food price inflation stood at 5%. CPI: food continued to escalate
throughout the year and averaged around 11.8%.
Figure 3.13 Agricultural PPI compared to Final Manufactured Food PPI and CPI Food
Figure 3.12 Comparison of Headline CPI to Food and Petrol inflation
Figure 3.13 Agricultural PPI compared to Final Manufactured Food PPI and CPI Food
Figure 3.15 Current Account as a Percentage of Gross Domestic Product
-30%
-20%
-10%
0%
10%
20%
30%
-12%
-7%
-2%
3%
8%
13%
Perc
enta
ge C
hang
e ov
er T
wel
we
Mon
ths
-Pet
rol
shtnom e
wlewt revo egnahC egatnecreP
-Fo
od a
nd C
PI
Headline CPI CPI Food CPI: Petrol
-0.06
-0.01
0.04
0.09
0.14
0.19
0.24
0.29
PPI: Agriculture PPI: Final Manufactured Food CPI: Food
-8
-7
-6
-5
-4
-3
-2
-1
0
2011
/01
2011
/02
2011
/03
2011
/04
2012
/01
2012
/02
2012
/03
2012
/04
2013
/01
2013
/02
2013
/03
2013
/04
2014
/01
2014
/02
2014
/03
2014
/04
2015
/01
2015
/02
2015
/03
2015
/04
2016
/01
2016
/02
2016
/03
Perc
enta
ge o
f GDP
Current Account as a Percentage of GDP
Source: StatsSA, 2016c
WeightIndexDec2014
IndexDec2015
PercentageChange
Oct 2016 -Oct 2015
Food and Non-Alcoholic Beverages 15.4 116.6 131.6 12.9%
Recreation and Culture 4.0 108.0 115.8 7.2%
Restaurants and Hotels 3.5 120.9 128.8 6.5%
Clothing and Footwear 4.0 114.5 120.8 5.5%
Housing and Utilities 24.5 117.7 124.0 5.4%
Health 1.4 117.4 123.5 5.2%
Miscellaneous Goods and Services 14.7 123.2 129.5 5.1%
Alcoholic Beverages and Tobacco 5.4 123.2 129.5 5.1%
Education 2.9 129.7 135.7 4.6%
Transport 16.4 110.9 115.4 4.1%
Household Content, Maintenance and Equipment 4.7 108.1 112.5 4.1%
Communication 2.6 99.5 99.3 -0.2%
All Items - Headline Consumer Price Index 100 116.2 124 6.7%
Source: StatsSA, 2016c
Table 3.10 Headline Consumer Inflation in COICOP Categories
28
Source: StatsSA, 2016c. Note: percentage change in price level between October 2015 and October 2016.
Most food sub-categories experienced double digit inflation; with the exception of meat prices, which recorded the
lowest consumer inflation at 5.9%. This was due to culling of herds due to lack of fodder and water for stock. It is
expected that it will take time to build up stock levels, accelerating meat price inflation. Due to its large weighting
in the basket of goods, this could push up food price inflation. Figure 3.14 provides the change in the price level of
common foodstuffs between October 2015 and October 2016.
Figure 3.14 CONSUMER PRICE
Sugar 23%Vegetables
15.9%
Bread 14.8%
Fruit25.3%
MeatProducts
5.9%
Average inflation expectations measured in the third quarter 2016 by the BER Inflation Expectations Survey shifted
lower to 6.2%, compared to expectations recorded in the 2nd quarter, which averaged 6.3%. Inflation expectations
have also lowered for 2017, from 6.2% to 6.0%. This moderation is due to expectations that most of the drought
related price impacts would ease in 2017. Inflation expectations for 2017 are close to or above the upper target
range (SARB, 2016a).
Table 3.11 BER Inflation Expectations Survey, 3rd Quarter 2016 Average InflationExpected For
FinancialAnalysts
BusinessRepresentatives
Trade UnionRepresentatives
All SurveyedParticipants
2016 6.4 6.0 6.3 6.2
2017 5.7 6.1 6.3 6.0
2018 5.3 6.1 6.2 5.9
The next five years 5.5 6.0 6.2 5.9
Source: StatsSA, 2016c
3.6 Balance of Payments
The current account deficit as a percentage of GDP, narrowed between 2015 and the 3rd quarter 2016, due in part
to a surplus on the trade account in the 2nd quarter of 2016 and a smaller current account deficit in the 3rd quarter.
Figure 3.15 indicates the movement of the current account as a percentage of GDP. The current account has
remained persistently negative, but has improved from its low in 2013 when it was -6.7% of GDP. The balance on
the current account improved in 2016, with the current account deficit as a percentage of GDP at 4.1% by the end
of the 3rd quarter 2016.
29
Figure 3.16 Trade Account- R Billions Figure 3.16 Trade Account- R Billions
Figure 3.17 Nominal and Real Effective Exchange Rates of the rand
Figure 3.18 Selected Exchange Rates against the rand
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2011
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2011
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2012
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R Bi
llion
s
Trade Balance
50
60
70
80
90
100
110
Real Nominal
Stronger rand = higher
80
90
100
110
120
130
140
Real US Dollar Pound Euro Yen
Rand appreciation
Rand depreciation
Table 3.12 provides a breakdown of quarterly developments on the current account between 2015 and 2016. The
current account deficit in the 2nd quarter of 2016 was R123 billion and by the 3rd quarter, this had increased to R176
billion. Merchandise exports were R974 billion for the year ending 2015. Improvements were seen in 2016 as exports
grew to R1.005 trillion in the first quarter, increasing to R1.112 trillion in the 2nd quarter, and moderating down in the
3rd quarter to R1.033 trillion. There is a widening shortfall evident in the net services, income and transfer payments
accounts, which rose in 2016 and ended the 3rd quarter at R172 billion.
Source: SARB, 2016a
Figure 3.16 illustrates the trade account value in billions. In the first three quarters of 2016, fluctuations on the trade
balance were evident due to fluctuations in the merchandise and gold exports and merchandise imports. The 2nd
quarter of 2016 saw the first surplus on the current account in a year, and the highest surplus since 2011 at R48
billion. The surplus on the trade account was reversed in the 3rd quarter of 2016 due to a contraction in export
volumes. Export earnings also contracted as the rand gained strength against leading currencies. This meant that
the positive effect of international price movements in commodities was lost out on as the rand strengthened.
Figure 3.12 Comparison of Headline CPI to Food and Petrol inflation
Figure 3.13 Agricultural PPI compared to Final Manufactured Food PPI and CPI Food
Figure 3.15 Current Account as a Percentage of Gross Domestic Product
-30%
-20%
-10%
0%
10%
20%
30%
-12%
-7%
-2%
3%
8%
13%
Perc
enta
ge C
hang
e ov
er T
wel
we
Mon
ths
-Pet
rol
shtnom e
wlewt revo egnahC egatnecreP
-Fo
od a
nd C
PI
Headline CPI CPI Food CPI: Petrol
-0.06
-0.01
0.04
0.09
0.14
0.19
0.24
0.29
PPI: Agriculture PPI: Final Manufactured Food CPI: Food
-8
-7
-6
-5
-4
-3
-2
-1
0
2011
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2011
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2011
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2011
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2012
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2012
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2012
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2013
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2013
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2015
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2015
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2015
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2015
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2016
/01
2016
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2016
/03
Perc
enta
ge o
f GDP
Current Account as a Percentage of GDP
Source: SARB, 2016b
Figure 3.15 Current Account as a Percentage of Gross Domestic Product
30
Table 3.12 Balance of Payments: Current Account
Source: SARB, 2016a. Note: Seasonally adjusted annualised data.
Table 3.13 below indicates the net financial transactions on the capital account. Change in liabilities refers to foreign
holdings of South African assets whilst change in assets refers to South African holdings of foreign assets. A
negative symbol indicates an outflow of capital on the financial account, a positive number indicates an inflow.
Movements on the capital account have been affected by the Brexit vote, the decision by the European Central Bank
to retain their monetary policy stance and the reduced concerns about Chinese growth. Emerging market assets
benefited from these developments, with South Africa experiencing increased inflows of capital in 2016, especially
in the second and third quarters. Inward portfolio investment into South Africa reflected foreign purchases of
domestic debt securities. This increase in 2016 was due to international investors seeking higher yielding assets.
Portfolio investment increased between the 1st quarter 2016 when it stood at R18.1 billion, to the 2nd quarter when
it increased to R33 billion and by the 3rd quarter when it stood at R38.8 billion.
There was a net inflow of capital into South Africa for the year to date on the financial account of the balance of
payments. Net capital inflows were recorded for direct investment, portfolio investment and other investment. In
total, financial account inflows amounted to 3.5% of GDP as of 3rd quarter.
Table 3.13 Net Financial Transactions Capital Account
R Billions2015 2016
1st Q 2nd Q 3rd Q 4th Q Year 1st Q 2nd Q 3rd Q
Merchandise exports 940 984 984 988 974 1 005 1 112 1 033
Net gold exports 63 71 65 72 68 52 55 47
Merchandise imports -1 070 -1 050 -1 082 -1 101 -1 076 -1 105 -1 119 -1 083
Trade Balance -68 5 -34 -41 -34 -48 48 -4
Net service, income and current transfer payments
-134 -128 -148 -150 -140 -174 -171 -172
Balance on Current Account
-202 -123 -182 -191 -174 -221 -123 -176
As percentage of gross domestic product
-5.1 -3.1 -4.5 -4.6 -4.3 -5.3 -2.9 -4.1
R Billions2015 2016
2nd Q 3rd Q 4th Q Year 1st Q 2nd Q 3rd Q
Change in Liabilities
Direct Investment 5.5 16.1 14.3 22.0 11.4 8.6 7.0
Portfolio Investment 55.0 11.9 -0.3 106.0 18.1 33.0 38.8
Financial Investment -74.3 -71.0 -103.0 -320.9 -149 -103.8 -116.1
Other Investment -20 8.1 44.2 72.3 -6.9 -13.8 26.3
Change in Assets
Direct Investment -9.6 -16.2 -38.5 -73.2 -22.3 -6.5 -1.4
Portfolio Investment -9.7 -11.2 -10.9 -36.5 8.9 -2.1 -16.4
Financial Investment 70.9 78.3 103.3 325.8 148.5 94.4 115.0
Other Investment -22.0 42.2 47.9 46.8 14.9 2.9 -22.5
Reserve Assets 1.4 0.5 -5.1 9.1 4.2 1.2 7.8
Total Identified Financial Transactions -2.9 58.8 51.9 151.2 27.4 13.8 38.4
Percentage of GDP (%) -0.3 5.8 5.1 3.8 2.6 1.3 3.5
Source: SARB, 2016a. Note – values indicate an outflow and + values an inflow.
31
Figure 3.18 illustrates that the rand strengthened against several currencies, with the most significant being the
British pound. Other currencies it strengthened against were the US dollar and the euro. Currencies that the rand
weakened against included the Brazilian real and Japanese yen, with the yen being one of the best performing
currencies internationally in 2016.
3.7 Exchange Rates
The rand strengthened in 2016 off a low base. The trade weighted average of the rand against a basket of 20
currencies appreciated in 2016 with an appreciation of 2.5% in the 1st quarter, 2.1% in the second quarter and 5.6%
in the third quarter.
Key reasons for the strengthening of the rand include the continued low to negative interest rates in developed
markets. Thus, investors were encouraged to search for yields in emerging markets. The surprise Brexit vote to
leave the European Union led to a depreciation in the British pound, with the rand gaining 38% to the pound
between January 2016 and October 2016. There were also the capital inflows surrounding the Anheuser-Busch
InBev SABMiller buy-out and better than expected current account deficit figures.
Figure 3.17 indicates the historic trend in nominal and effective exchange rates, with the rand weakening steadily
since 2011. In the last year, the rand strengthened in its value, coming off a low base it entered at the end of 2015.
The real effective exchange rate improved from its recent low in January 2016 to end the year 17.2% higher. The
appreciation in the rand did reduce the competitiveness of local exports, but is still only 9% below of its 15-year
average (SARB, 2016a).
Domestic political events have affected financial markets, which caused the nominal effective exchange rate to
weaken in August 2016. By early November, the unexpected US election outcome and associated expectations of
higher US interest rates caused the rand to depreciate.
Figure 3.17 Nominal and Real Effective Exchange Rates of the rand
Figure 3.16 Trade Account- R Billions
Figure 3.17 Nominal and Real Effective Exchange Rates of the rand
Figure 3.18 Selected Exchange Rates against the rand
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2011
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R Bi
llion
s
Trade Balance
50
60
70
80
90
100
110
Real Nominal
Stronger rand = higher
80
90
100
110
120
130
140
Real US Dollar Pound Euro Yen
Rand appreciation
Rand depreciation
Source: SARB, 2016b. Note: 2010 = 100.
32
The rand was one of the most volatile currencies in 2016 and is major challenge for fixed foreign investment
into the country (World Bank, 2017). The rand’s volatility is driven by commodity price shocks, which affects the
profitability of many South African companies and thus financial flows. As well as global market volatility, which
shifts global finances between riskier and less risky assets, and naturally the domestic environment especially
domestic policy uncertainty (World Bank, 2017).
The rand remains vulnerable to changes in international monetary policy, domestic political events, weak economic
performance, and the current account deficit. Volatility of the currency will be a key element of 2017; however, the
risk that the exchange rate posed to inflation has moderated somewhat since 2016.
3.8 Credit Rating
In 2016, the decisions of international rating agencies Moody’s, Fitch and Standard and Poor’s were widely reported
in the media due to the expectations of a downgrade to below investment grade or ‘junk status’. The ramifications
of such a downgrade would be to deter investment and increase government borrowing costs. The most highly
awaited response was the Moody’s decision on the 25th of November. The decision by Moody’s to leave South
Africa’s government bond long and short term ratings at Baa2 /P-2 unchanged with a negative outlook, was
welcomed by National Treasury. This being two levels above sub-investment grade. Table 3.14 indicates the current
rating of South African sovereign debt and past changes to the ratings by the leading credit rating agencies.
Table 3.14 South African Credit Rating History 2009 - 2016
By close of 2016, all three ratings agencies had retained South Africa’s investment grade on long term government
debt, but with a negative outlook. Fitch kept its rating at BBB-, but changed the outlook from stable to negative
on the 25th of November 2016. S&P retained South Africa at BBB- an investment grade but with a negative outlook.
S&P however, saw that rising risks necessitated the agency lowering the debt rating on long term local currency
debt from ‘BBB+’ to ‘BBB’. This rating is however, still two notches above sub-investment grade.
Date Moody’s Fitch Standard and Poor’s
2011 A3 (negative) BBB+ (stable) BBB+ (stable)
2012 Baa1 (negative) BBB (negative) BBB (negative)
2013 Baa1 (negative) BBB BBB (negative)
2014 Baa2 (stable) BBB (negative) BBB+ (stable)
2015 Baa2 (negative) BBB+ (stable) BBB (negative)
2016 Baa2 (negative) BBB (negative) BBB (negative)
Source: Trading Economics, 2016
Figure 3.16 Trade Account- R Billions
Figure 3.17 Nominal and Real Effective Exchange Rates of the rand
Figure 3.18 Selected Exchange Rates against the rand
-110-90-70-50-30-101030507090
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R Bi
llion
s
Trade Balance
50
60
70
80
90
100
110
Real Nominal
Stronger rand = higher
80
90
100
110
120
130
140
Real US Dollar Pound Euro Yen
Rand appreciation
Rand depreciation
SourcSource: SARB, 2016b. Note: 2016-01-04 = 100.
Figure 3.18 Selected Exchange Rates against the rand
33
In response to the positive news, National Treasury released a statement on the 2nd of December 2016 that the
department welcomed the news and the decision was as a result of ‘working together as South Africans to ensure
that the country remains an investment grade.’ (National Treasury, 2016c:1).
The strengths South Africa can draw on in future and which persuaded ratings agencies to keep the investment
grade rating, included:
• The country has a large and active local currency fixed-income market.
• The South African Reserve Bank has ensured an independent monetary policy. South Africa’s inflation
targeting policy has anchored inflation expectations and reduced interest rate volatility.
• South Africa has a strong democracy with independent media and reporting.
• The country has a great deal of institutional strength, particularly regarding the judiciary which provides
checks and balances and accountability.
• Government has low foreign currency-denominated debt.
• The South African Revenue Service (SARS) has consistently improved the efficiency of the tax system
and has generally exceeded revenue collection targets.
• Government has shown expenditure restraint and maintained the expenditure ceiling.
• The country’s banking sector’s performance has remained reasonably strong
• South African exports are increasing, particularly to Asia and Europe (National Treasury, 2016c, 2016d,
2016e)
The key weaknesses however, identified by these agencies to maintaining the country’s investment grade included:
• South Africa is highly dependent on resident and non-resident purchases of rand denominated currency
debt to finance the fiscal and external deficit.
• Low growth is a major challenge.
• S&P and Fitch cite increased political squabbles as distracting the country from necessary economic
reforms and creating political risk for investors. These political tensions may undermine needed reforms
to state owned entities.
• The student protests and consequently, the zero percentage point increase and increased bursaries,
have shown that government can be forced to concede to social pressure to increase spending. Which
is concerning when a spending ceiling is in place.
• South Africa continues to have a structurally high current account deficit reliant on volatile flows for
financing.
• S&P stated that the country’s longstanding skills shortage, adverse terms of trade, and the slowdown
in the private sector’s capital investment programmes are adding to the negative growth outlook
(National Treasury, 2016c, 2016d, 2016e).
Rating agencies will continue to keep a close eye on the performance of South Africa in 2017, indicating that issues
such as the continuation of political infighting, the absence of fundamental structural reforms supporting higher
and sustainable medium-term growth, and the continued low business and consumer confidence could further
damage the rating status of government debt.
3.9 National Revenue and Expenditure
The 2016 budget, delivered in February 2016, focused on strengthened measures to narrow the fiscal deficit more
rapidly in the next three years and to stabilise government gross loan debt. South Africa continued with this
programme of fiscal consolidation in 2016. The national budget commits R2 trillion of public funding per year.
The major concern highlighted by National Treasury is the quality of the spending with too much spending being
wasteful, ineffectively targeted, or offering limited value for money. Since 2009, a growing percentage of spending
has been funded by borrowing. Government debt now exceeds R2 trillion and the cost of servicing the debt is
escalating. Increasing costs of debt servicing means that an increasing proportion of funding is diverted away from
other priorities such as infrastructure or services towards debt payments.
A key concern is avoiding the low growth trap in the medium term. This is a scenario whereby low growth
produces limited tax revenue. Fiscal policy becomes more aggressive to consolidate debt and curb spending – this
undermines the economy, although it can also be seen as positive by investors. If fiscal policy continues to increase
spending, this is seen negative by ratings agencies, as there is a capital flight risk, rand depreciation and risk of
interest rate increases. To avoid the low growth trap, the national treasury is undertaking a balanced consolidation.
This includes a combination of tax policy to raise an additional R43 billion over two years and a reduction in
expenditure of R26 billion.
34
Table 3.16 indicates the expenditure by function over financial years 2015/16 to 2019/20. The expenditure item,
which will experience the largest average annual growth over the period 2016/17-2019/20 is debt servicing costs;
this expenditure item will see a 10.1% average increase. This is followed by post-school education and training, which
will grow at 9.2% over the period. Other expenditure items expected to grow over the period are health and social
protection at 8.2%, and human settlements at 8%. The contingency reserve has been reduced to accommodate
increased university subsidies and to narrow the budget deficit. The projected reserves of R10 billion and R15
billion, have been reduced to R6 billion and R10 billion for 2017/18 and 2018/19.
Table 3.16 Consolidated Government Expenditure, 2015/16-2019/202015/16 2016/17 2017/18 2018/19 2019/20 Average
AnnualGrowth
R billion Outcome Revised Medium-Term Estimates 2016/17 -2019/20
Basic Education 212.5 228.4 244.8 261.9 280.6 7.1%
Health 159.8 169.3 184.4 198.9 214.2 8.2%
Defence, Public Order and Safety 179.1 189.5 197.9 210.7 224.6 5.8%
Post School Education and Training 64.5 68.6 76.6 81.1 89.3 9.2%
Economic Affairs 187.2 207.6 216.4 225.8 239.6 4.9%
Human Settlement & MunicipalInfrastructure
174.5 181.1 197.6 212.1 228.3 8.0%
Agriculture, Rural Development & LR 25.2 26.3 26.9 28.4 30.3 4.8%
General Public Services 88.5 67.8 69.8 73.0 76.4 4.1%
Social Protection 153.0 165.1 180.0 193.3 208.9 8.2%
Allocation by Function 1 244.3 1 303.8 1 394.3 485.2 1 592.2 6.9%
Debt Service Costs 128.8 147.7 163.6 180.8 197.2 10.1%
Contingency Reserve 6.0 10.00 20.0
Consolidated Expenditure 1 373.1 1451.5 1 564.0 1 676.0 1 809.4 7.6%
Source: Trading Economics, 2016
2015/16 2016/17 2017/18 2018/19 2019/20
R Billions/ Percentage of GDP Outcome Revised Medium-term Budget Estimates
Revenue 1 220.9 1 301 1 416.9 1 537.9 1 670.4
Percentage of GDP 29.9 29.7 30.1 30.3 30.4
Expenditure 1 373.1 1 451.5 1 564 1 676 1 809.4
Percentage of GDP 33.6 33.1 33.3 33 33
Budget Balance -152.2 -150.5 -147.1 -138.2 -139
Percentage of GDP -3.7 -3.4 -3.1 -2.7 -2.5
Total Net Loan 1 804.6 2 004.4 2 209.4 2 417.1 2 632.4
Percentage of GDP 44.2 45.8 47 47.6 47.9
Source: Trading Economics, 2016
In the long term, if the economy continues to grow at below 2% for an extended period of time, increasing pressure
will be placed on the fiscus to continue to support certain government commitments. The fiscal resources available
and limited room to increase taxation, mean that difficult trade-offs will need to be made. The National Treasury
has indicated that long term policy aspirations far exceed available resources.
Table 3.15 shows the revenue and expenditure over the 2015/16 to 2019/20 period. The ratio of government debt
to GDP for 2015/16 was at 44.2%, higher than what was expected in the previous Medium Term Budget Policy
Statement (MTBPS) when it was expected to be revised to 43.9%. The expectation for 2016/17 is of net loan
commitments at 45.8% of GDP. The expectations are that this will rise, but stabilise around 47% of GDP over the
next three years. Government debt is now in excess of R2 trillion. In order to increase revenue taxation on wealth
and income is likely to be targeted as well as the usual sin taxes. Another tax that will be introduced from April 2017
is the tax on sugar-sweetened-beverages, which has been imposed to try reduce obesity and diabetes levels in the
country. The tax is an effective 20% tax on sugar in drinks based on the nutritional labelling of beverages (National
Treasury, 2016f, TIPS, 2016).
The budget balance was in deficit by October 2016, at -3.4% of GDP; but with the expectation of declining to -3.1%
in 2017/18, and to -2.7% by 2018/19. Revenue is expected to increase from R1.3 trillion in 2016/17 to R1.416 trillion in
2017/18. Revenue growth will assist in narrowing the budget deficit; this is to be undertaken by increased taxes to
raise an additional R43 billion over the next two years, in conjunction with a reduction in the expenditure ceiling of
R26 billion (National Treasury, 2016b).
Table 3.15 South African Revenue and Expenditure 2015/16-2019/20
35
Text Box 3.3: Funding Tertiary Education
The ‘Fees Must Fall’ campaign placed the funding and cost of higher education at the forefront of policy
debates in 2016. Key challenges were identified as regards the access and efficiency of education funding.
The major problems identified, which triggered the campaign, include firstly the amount of funding in place
to serve students. The National Student Financial Aid Scheme (NSFAS) has seen an increase in the number
of deserving students from poor backgrounds enrolling for aid; however, the available funding has not kept
pace with the demand. Secondly there are students who fall into a funding gap, in which they do not qualify
for NSFAS funding but do not have enough finance to attend university and pay for the other resultant
expenses.
Although the Department of Basic Education has the largest slice of the national budget, the education
system is not achieving the desired results. Getting the basics right will be the focus of budget spending
on education in future, with a focus on early childhood development centres, overcoming institutional
weaknesses, improving vocational and technical skills and improving the impact of resources to skills
training. There is a need to progressively expand post-school education within available resources. Focusing
on improving the results and quality of vocational and technical colleges, should also improve the education
system (National Treasury, 2016b).
The percentage of GDP dedicated to post-school training and education has grown since 2008, from 1%
to 1.5% of GDP. This budget however, was mainly focused on the development of vocational colleges,
sector education and training and the National Skills Fund, rather than universities. In the 2016 MTBPS, an
acceleration in the spending on post-school education is proposed (National Treasury, 2016b).
In the 2016 budget, R5.6 billion was added to university subsidies to fund the zero percent fee increase for
the 2016 academic year. NSFAS received additional funding of R10.6 billion over the 2016 Medium Term
Expenditure framework period. Of this, R2.5 billion was allocated in the current year for debt relief for 71 753
unfunded or inadequately funded students who were at universities in 2013, 2014 and 2015. The remaining
R8 billion was set aside for new and continuing students for the 2016 academic year and beyond (National
Treasury, 2016b)
In 2017 MTEF government will fund the increase in fees at higher learning institutions for the 2017 academic
year up to a maximum of 8% for students from households earning up to R600 000 per year (National
Treasury, 2016b).
Table 3.17 Additional Funding to Support Universities and Students, 2016/17-2019/20
R millions 2016/17 2017/18 2018/19 2019/20 Total
2016 Additions 4 882 5 555 5 832 16 269
NSFAS historical debt relief 2 543 2 543
NSFAS extension 2 039 2 992 3 013 8 044
Zero Fee Increase 300 2 563 2 819 5 682
2017 Additions 1 543 4 988 5 346 5 717 17 594
NSFAS Extension 1 543 2 370 2 560 2 764 9 237
Universities: Fee Increase Subsidy 2 460 2 618 2 775 7 853
TVET Colleges: Fee Increase Subsidy 158 168 178 504
Total 6 425 10 543 11 178 5 717 33 863
3.10 Summary
South Africa is expected to see moderate GDP growth of between 1.3% and 1.1% in 2017, as projected by National
Treasury and The World Bank respectively (National Treasury, 2016b; World Bank, 2017). Key factors that will
drive this mild acceleration in growth are the modest rise in commodity prices, the easing of inflationary pressures,
and a pickup in household consumption demand. It is expected that growth in government spending will not be a
significant driver of the recovery as the policy of fiscal consolidation will continue to be followed. The economic
landscape in 2017 will be one of increased global volatility and uncertainty, conditions that could impact on
domestic economic activity, and financial markets.
36
Unemployment, especially amongst South Africa’s youth, is one of the greatest challenges facing the country.
Youth unemployment is growing, with a 4.3 percentage point increase in unemployment in the 15-24 year old
age group between October of 2015 and October of 2016. The pace of job creation has fallen behind the National
Development Plan target of creating 600 000 jobs per annum, with a net loss of 186 349 jobs between the 3rd
quarter 2016 and the 4th quarter 2015 (StatsSA, 2016b). Improving job creation may need to look beyond current
interventions and as the World Bank (2017) has advised, refocus incentives on industrial sector support.
Another barrier to growth in South Africa is the low rate of private sector investment, improving the growth in
GFCF will be critical in 2017. Fixed investment is projected to continue to decline in 2017, but the rate of decline
should moderate, from -5.9% estimated in 2016 to -2.8% projected for 2017 (World Bank, 2017).
Exports are expected to grow in 2017 by 3.7% (World Bank, 2017) as the economic recovery takes effect, whilst
imports are also expected to increase 2%. Imports will increase as consumer demand for imported products picks
up from its low base in 2016 and improved economic activity will increase demand for imported machinery and
inputs.
The rand has recovered from a historically weak value in early 2016. The currency’s strengthening was due in part
to speculative flows due to surprise international events in the US and Britain, but also the rise in commodity prices
of South Africa’s major exports. The currency is likely to be volatile in the coming year and will be particularly
susceptible to domestic political shocks.
South Africa’s economy has been profoundly affected by the end of the commodities super-cycle. It is estimated
that the decline in commodity prices since 2012 has cost South Africa 4 percentage points of GDP growth (World
Bank, 2017). A projected growth rate of 1.3% in 2017 will not be able to make a dent in poverty and inequality. The
level of inequality is expected to have risen by 1.3% between 2010/11 and 2017/18 (World Bank, 2017). Efforts to
restructure the economy, improve the investment environment, and create policy certainty will be needed in order
to improve consumer and business sentiment.
37
38
4
2015 Gini coefficient was 0.72 this represented an improvement from 2010 when it was 0.74.
1.2%, decrease in the expanded unemployment rate.
The final outcome of economic growth should be an
improvement in the overall quality of life as measured by
various socio-economic development indicators, if this growth
is to be inclusive. Socio-economic improvements however,
can also have positive impacts on growth. Government
policies aimed at these improvements thus become agents of
economic growth and thereby creating favourable conditions
for economic development.
The review of socio-economic conditions and developments
in the Eastern Cape focuses on population dynamics, income,
poverty, education, access to services, labour market
conditions and the nature of the economy in terms of GDP-R
and trade. These areas provide a perspective of the context
in which the Eastern Cape economy operates, including key
current and emerging trends.
The provincial economy generally grew at a faster rate than
the population between 2010 and 2015. This resulted in an
increase in average income as measured by GDP-R per capita
in the Eastern Cape during 2015. Although there is a noticeable
variation in GDP-R per capita across the province, the figure
has risen across all districts including the two metros, albeit
only marginally in the Buffalo City Metro.
Levels of poverty in the Eastern Cape, as measured by the
South African Multidimensional Poverty Index (SAMPI), are
the highest in the country. This, coupled with low household
incomes, places considerable pressure on the government
to provide basic services that are required to offset some
of the more severe effects of poverty. Poverty levels in the
Eastern Cape are highest in the former homeland areas due to
migration and income patterns, as well the presence of large
social backlogs.
The rise in per capita income in the Eastern Cape, combined
with small improvements in the poverty headcount, suggests
that these indicators are moving in the right direction, albeit at
a very slow pace. The extent of these improvements however,
vary across the province.
The pace of population growth in the Eastern Cape impacts
significantly on the delivery of education in the province. The
Eastern Cape Education Department experienced a decrease
in overall learner enrolment between 2010 and 2014. A marginal
increase in learner numbers however, was registered between
2013 and 2014.
The matric pass rate fell to 56.8% in 2015, in absolute terms however the number of candidates that qualified for a bachelor’s degree increased.
0.6% growth in GDP-R, the second lowest economic growth rate in South Africa after the Northern Cape between 2014 and 2015
180 000 increase in employment between the third quarter of 2015 and third quarter of 2016.
74.5% increase in Eastern Cape exports to Africa, Africa was the fastest growing source market for Eastern Cape exports between 2010 and 2015.
49.1% of households in the Eastern Cape are female headed households, significantly higher than the national average.
EASTERN CAPEECONOMIC PROFILE
JOBLESS
39
This report details aspects as to the quality of education, based on the Annual National Assessments (ANA)
conducted in 2014 which reflect varying results across all grades in the province. Average scores in Mathematics
and Languages were however, exceptionally poor and in most cases, lower than the national average.
This chapter will consider these variables in greater detail, focusing on the past year and the trend over time.
This review looks at demographic and development indicators, labour market conditions, and provincial fiscal
framework.
4.1 DEMOGRAPHIC DYNAMICS
The demographic profile of the Eastern Cape over time, provides essential data on patterns of population change.
Demographic figures are critical to decision makers in the province during the policy formulation process as it
provides them with an indication of how many people reside within the Eastern Cape as well as the characteristics
of the provincial population. Demographic figures are also important in advancing economic development in the
province through the role that demographics play in the consumption of goods and services as well as human
capital development. Information on demographic trends is also integral in planning for service delivery.
4.1.1 POPULATION
Population growth can have several benefits for a region, such as a growing labour force that allows for the
expansion of production. Rapid population growth however, can create difficulties for governments as it endeavours
to provide basic services to an ever-increasing number of citizens.
MAP 4.1 PROVINCIAL POPULATION
As of 2016, South Africa had a total population of 55.9 million people, of which 7 061 700 (12.6%) resided in the
Eastern Cape. The Eastern Cape Province has experienced a notable increase in population. In 2010, the province
was home to 6 743 800 of South Africa’s population. This represented an increase of just under 320 000 people
between the period. The Eastern Cape had the third highest population share after Gauteng and KwaZulu-Natal,
although the provincial share dropped from 12.9% in 2010, to 12.6% in 2016.
Key determinants of the population growth rate are fertility and migration patterns. The total fertility rate has been
declining over the last few years in all provinces. The Eastern Cape has seen a marked decline in the total fertility
rate: down from 3.55 (between 2001 and 2006) to an estimated 3.06 (between 2011 and 2016). The Western Cape
has the lowest estimated fertility rate (2.21) and KwaZulu-Natal has the highest (3.08).
The low level of fertility in the Eastern Cape can be offset by net migration flows. Based on the mid-year population
estimates, however, the Eastern Cape is expected to have a net migration outflow of 52 930 between 2011 and
2016 (StatsSA, 2016a).
Over the 2011 to 2016 period, the Eastern Cape’s population contracted by 52 930 people due to the net effects of
migration. Despite the net loss of individuals, the province experienced in-migration of 194 500 people, of which
30 840 were individuals from outside of South Africa. The majority of estimated domestic in-migrants are from the
Western Cape (60 042) and Gauteng (59 561). Out-migration over the same period is estimated at 247 437, with
most people migrating to the Western Cape (81 399) and Gauteng (71 964).
MAP 4.1 PROVINCIAL POPULATION
FIGURE 4.1 DISTRICT POPULATION AS A SHARE OF THE EASTERN CAPE’S POPULATION, 2015
Sarah Baartman462 937
6.7%
Amathole942 61213.6%
Chris Hani837 40412.1%
Joe Gqabi370 329
5.4%
O.R.Tambo1 447 364
20.9%
Alfred Nzo849 21712.3%
Nelson Mandela Bay1 194 106
17.3%
Buffalo City805 88511.7%
Source: Urban-Econ calculations based on StatsSA, 2016a
40
FIGURE 4.1 DISTRICT POPULATION AS A SHARE OF THE EASTERN CAPE’S POPULATION, 20151
According to the most recent estimates, the majority (20.9%) of the Eastern Cape’s population is located in O.R.
Tambo. The population share of the province’s districts remained relatively stable compared to 2014. The Nelson
Mandela Bay Metro is home to the second largest population in the Eastern Cape, followed by the Amathole
District. The Joe Gqabi District remains the least populated district, with only 5.4% of the province’s population.
FIGURE 4.2 EASTERN CAPE POPULATION DISTribution, 2016
Figure 4.2 provides a breakdown of the Eastern Cape population by age group and gender for 2016. The population
age structure of the Eastern Cape mirrors that of South Africa in that it has a large proportion of young people. It
is evident in the figure that minors (ages 0-14) account for approximately 34.8% of the provincial population. This
is higher than the national average where minor children only account for 30.0% of the total population, and the
second highest in the country after KwaZulu-Natal (34.9%). The high proportion of minors has a direct impact on
the demand for healthcare and educational services in the province as well as other social infrastructure such as
recreational facilities.
Figure 4.2 also highlights the skewed gender mix in the Eastern Cape. In 2016, females over the age of 30 years old
accounted for 56.8% of the total provincial population. The comparable national figure was 53.0%. The difference in
the male-to-female ratio for the Eastern Cape begins to diverge from the national trend at the 30 to 34 age cohorts
and persist thereafter.This, coupled with high levels of male outward migration, results in the province having almost
half of its households (49.1%) headed by women compared to a national average of 41.3%. According to Statistics
South Africa (2011), female-headed households in South Africa are, on average, 47.0% poorer than households
headed by men. In addition to lower household incomes, female-headed households, in general, also experience
lower educational attainment and poorer health. The nature of poverty amongst Eastern Cape households can thus
be linked to migration flows out of the province.
Source: Urban-Econ calculations based on Quantec, 2017a
MAP 4.1 PROVINCIAL POPULATION
FIGURE 4.1 DISTRICT POPULATION AS A SHARE OF THE EASTERN CAPE’S POPULATION, 2015
Sarah Baartman462 937
6.7%
Amathole 942 61213.6%
Chris Hani 837 40412.1%
Joe Gqabi 370 329
5.4%
O.R.Tambo1 447 364
20.9%
Alfred Nzo 849 21712.3%
Nelson Mandela Bay 1 194 106
17.3%
Buffalo City805 88511.7%
FIGURE 4.2 EASTERN CAPE POPULATION DISTRIBUTION, 2016
FIGURE 4.3 NUMBER OF LEARNERS ENROLLED AND PUBLIC ORDINARY SCHOOLS, 2014
FIGURE 4.4 DISTRICT EDUCATIONAL ATTAINMENT LEVELS WITH REFERENCE TO MATRIC EDUCATION, AGES 20+ (2015)
8 6 4 2 0 2 4 6 8
0–4 years
10–14 years
20–24 years
30–34 years
40–44 years
50–54 years
60–64 years
70–74 years
80+ years
Percentage
Female Male
0
100000
200000
300000
400000
500000
600000
700000
800000
0200400600800
1 0001 2001 4001 6001 800
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R. Tambo Alfred Nzo NelsonMandela
Bay Metro
Buffalo CityMetro
Num
ber o
f sch
ools
Num
ber o
f lea
rner
s
Schools Learners
8%
13%
14%
15%
17%
14%
3%
5%
11%
61%
64%
62%
63%
59%
67%
53%
51%
59%
27%
19%
22%
20%
22%
18%
41%
40%
28%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Sarah Baartman
Amathole
Chris Hani
Joe Gqabi
O.R. Tambo
Alfred Nzo
Nelson Mandela Bay
Buffalo City
Eastern Cape
No Education Less than Matric Matric or Higher Other
Source: StatsSA, 2016a
1 At the time of publishing, 2016 population data per district had not yet been published. The values presented in this section are therefore the revised 2015 figures.
41
There is a noticeable reduction in the proportional contribution to the total provincial population of the older age
cohorts (20-24, 25-29 and 30-34 year olds). These age cohorts also exhibit a statistically significant difference
between the male and female population distributions relative to the national mean distribution. This suggests
that a disproportionate number of the Eastern Cape’s outward migrants are men leaving the province to seek job
opportunities in other parts of the country. As a result, women account for 52.1% of the total provincial population.
4.2 EDUCATION
Education plays a critical role in the development of a region and in its labour market prospects. Low education
attainment and a poor quality of education perpetuates poverty by excluding people from the labour market.
Generally, low levels of educational attainment and a skills mismatch can impede the achievement of desired
economic goals. Moreover, poor educational attainment makes it harder for the national and provincial economy
to absorb new entrants into the labour market. Due to these structural challenges and the relationship between
education and the overall well-being of an individual, education plays a critical role in economic and social
transformation.
The level and quality of educational attainment as well as the acquisition of skills, relevant to the needs of the
economy therefore, remains a key priority for not only the Eastern Cape but South Africa as a whole. This is evident
in the National Development Plan (NDP) that seeks to, by 2030, have an education system with the following
attributes:
• High quality early childhood education, with access rates exceeding 90.0%;
• Quality school education, with globally competitive literacy and numeracy standards;
• Further and higher education and training that enables all people to fulfil their potential;
• An expanding higher education sector that is able to contribute towards rising incomes, higher productivity
and the shift to a more knowledge-intensive economy; and
• A wider system of innovation that links key public institutions with areas of the economy consistent with
our economic priorities
4.2.1 LEARNER ENROLMENT TRENDS
The pace of population growth in the Eastern Cape has had a significant impact on the delivery of education in the
province. Despite the reduction in learner numbers in recent years, the under-resourced nature of the Eastern Cape
education system means that it still faces significant challenges in terms of learner retention. The Eastern Cape
Education Department experienced a decrease in overall learner enrolment of 105 501 learners (5.1%) between 2010
and 2014, with a decrease of 13 445 enrolments (0.7%) between 2012 and 2013 alone. In 2014, learner numbers
increased marginally by approximately 8 800. Learner numbers are expected to decline marginally in 2015. This
is largely due to a downward trend in Grade 1 enrolment levels, and the lower retention levels between Grades 10
and 12.
FIGURE 4.3 NUMBER OF LEARNERS ENROLLED AND PUBLIC, ORDINARY SCHOOLS, 20142
When disaggregating at schooling level, 58.6% (out of a total of 1 946 800 learners) that are enrolled at public
ordinary schools are at a primary level (i.e. Grades 1 to 7). Positively, the Eastern Cape has attained near perfect
parity in terms of gender representative across all levels of basic education.
FIGURE 4.2 EASTERN CAPE POPULATION DISTRIBUTION, 2016
FIGURE 4.3 NUMBER OF LEARNERS ENROLLED AND PUBLIC ORDINARY SCHOOLS, 2014
FIGURE 4.4 DISTRICT EDUCATIONAL ATTAINMENT LEVELS WITH REFERENCE TO MATRIC EDUCATION, AGES 20+ (2015)
8 6 4 2 0 2 4 6 8
0–4 years
10–14 years
20–24 years
30–34 years
40–44 years
50–54 years
60–64 years
70–74 years
80+ years
Percentage
Female Male
0
100000
200000
300000
400000
500000
600000
700000
800000
0200400600800
1 0001 2001 4001 6001 800
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R. Tambo Alfred Nzo NelsonMandela
Bay Metro
Buffalo CityMetro
Num
ber o
f sch
ools
Num
ber o
f lea
rner
s
Schools Learners
8%
13%
14%
15%
17%
14%
3%
5%
11%
61%
64%
62%
63%
59%
67%
53%
51%
59%
27%
19%
22%
20%
22%
18%
41%
40%
28%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Sarah Baartman
Amathole
Chris Hani
Joe Gqabi
O.R. Tambo
Alfred Nzo
Nelson Mandela Bay
Buffalo City
Eastern Cape
No Education Less than Matric Matric or Higher Other
Source: Department of Basic Education, 2016
2 Under Department of Basic Education statistics, the Buffalo City Metro (BCM) is not shown as a separate district but rather included as part of the Amathole District Municipality. For the purpose of this table, the East London school district has been used as a proxy for the BCM. It should however, be noted that this excludes the King Williams Town schooldistrict (which forms part of the BCM). This school district has been excluded as it covers other settlements not part of the BCM such as Peddie, Sutterheim and Keiskammahoek.
42
4.2.2 EDUCATIONAL ATTAINMENT
An estimated 28.0% of the Eastern Cape population over the age of 20 years old have either a matric qualification
or some form of tertiary education in 2015. This is the lowest in the country and also notably less than the national
average (39.0%). Positively, the Eastern Cape has the fourth lowest proportion of individuals with no schooling
after the Western Cape (3.2%), Gauteng (4.2%) and the Free State (7.5%). This suggests that the Eastern Cape,
while successful at expanding access to basic education, has had limited success in converting this to post-matric
education.
The overall educational attainment level of the Eastern Cape population over the age of 20 years old relative to the
rest of South Africa is illustrated in Table 4.1.
TABLE 4.1 LEVELS OF EDUCATIONAL ATTAINMENT BY PROVINCE IN 2015Province No
SchoolingSome
PrimaryPrimary School
Some Secondary
Matric Higher Education
Other
Western Cape 3.2% 10.4% 3.3% 60.1% 8.6% 13.4% 1.0%
Eastern Cape 10.7% 17.1% 6.0% 36.0% 19.4% 8.6% 2.2%
Northern Cape 11.4% 16.3% 6.2% 34.1% 22.3% 7.6% 2.2%
Free State 7.5% 15.5% 5.1% 33.5% 25.4% 9.2% 3.7%
KwaZulu-Natal 11.1% 12.8% 4.1% 30.8% 30.4% 9.2% 1.7%
North West 12.1% 16.3% 5.2% 32.4% 24.3% 7.4% 2.3%
Gauteng 4.2% 7.7% 3.4% 32.4% 32.8% 17.1% 2.4%
Mpumalanga 14.7% 11.7% 4.2% 31.1% 27.7% 9.0% 1.6%
Limpopo 17.1% 11.2% 4.3% 34.6% 21.7% 9.3% 1.8%
South Africa 9.0% 11.9% 4.5% 33.3% 27.4% 11.6% 2.3%
Source: Urban-Econ calculations based on Quantec, 2017a
Figure 4.4 illustrates the average educational attainment for those over the age of 20 years old across the various
districts in the Eastern Cape. As evident from the figure the Nelson Mandela Bay and Buffalo City Metros are
the two districts with the lowest percentage of the population with no schooling at 3.4% and 4.9%, respectively.
In contrast, the O.R. Tambo District has the highest proportion of individuals with no schooling (17.1%). This is
followed closely by Joe Gqabi, where 14.8% of the population over the age of 20 years old in 2015 had no schooling.
FIGURE 4.4 DISTRICT EDUCATIONAL ATTAINMENT LEVELS WITH REFERENCE TO MATRIC EDUCATION, AGES 20+ (2015)
O.R. Tambo was one of the poorest performing districts in 2015 based on educational attainment as is evident by
the fact that three of the five worst performing school districts in terms of their matric pass rate were located in the
district. These districts and their associated matric pass rates were: Libode (48.6%), Qumbu (47.9%) and Lusikisiki
(47.2%). Conversely, the Eastern Cape metropolitan areas have higher levels of educational attainment as well as
the third and sixth highest matric pass rates in the Eastern Cape during 2015.
FIGURE 4.2 EASTERN CAPE POPULATION DISTRIBUTION, 2016
FIGURE 4.3 NUMBER OF LEARNERS ENROLLED AND PUBLIC ORDINARY SCHOOLS, 2014
FIGURE 4.4 DISTRICT EDUCATIONAL ATTAINMENT LEVELS WITH REFERENCE TO MATRIC EDUCATION, AGES 20+ (2015)
8 6 4 2 0 2 4 6 8
0–4 years
10–14 years
20–24 years
30–34 years
40–44 years
50–54 years
60–64 years
70–74 years
80+ years
Percentage
Female Male
0
100000
200000
300000
400000
500000
600000
700000
800000
0200400600800
1 0001 2001 4001 6001 800
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R. Tambo Alfred Nzo NelsonMandela
Bay Metro
Buffalo CityMetro
Num
ber o
f sch
ools
Num
ber o
f lea
rner
s
Schools Learners
8%
13%
14%
15%
17%
14%
3%
5%
11%
61%
64%
62%
63%
59%
67%
53%
51%
59%
27%
19%
22%
20%
22%
18%
41%
40%
28%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Sarah Baartman
Amathole
Chris Hani
Joe Gqabi
O.R. Tambo
Alfred Nzo
Nelson Mandela Bay
Buffalo City
Eastern Cape
No Education Less than Matric Matric or Higher Other
Source: Urban-Econ calculations based on Quantec, 2017a
43
4.2.3 NATIONAL SENIOR CERTIFICATE (NSC) RESULTS
The matric pass rate serves as one of several indicators of the overall level of education within the Eastern Cape.
The measure however, has several limitations, particularly in its ability to measure the overall quality of these
passes. Despite these limitations, the matric pass rate (see Table 4.3) serves as an indicator of the opportunities
available to youth in the province to access tertiary education.
According to Eastern Cape Socio Economic Consultative Council (ECSECC) (2016a) the majority of the country’s
matriculants come from provinces with large rural populations. These provinces all attained matric pass rates lower
than the 2015 national average of 70.7%. The Western Cape attained the highest matric pass rate at 84.7%, followed
closely by Gauteng at 84.2%. The Eastern Cape had the lowest matric pass rate in the country, which was 27.9%
lower than that of the Western Cape.
FIGURE 4.5 2015 MATRIC PASS RATE IN SOUTH AFRICAFIGURE 4.5 2015 MATRIC PASS RATE IN SOUTH AFRICA
Figure 4.8 POVERTY HEADCOUNT BY DISTRICT MUNICIPALITY IN 2011 AND 2016
FIGURE 4.9 HOUSEHOLD INCOME DISTRIBUTION PER DISTRICT, 2011
56.8%
60.7%
65.9%69.4% 70.7%
78.6%81.5% 81.6%
84.2% 84.7%
50%
55%
60%
65%
70%
75%
80%
85%
90%
5.2%
18.7%
15.6%16.8%
21.1%
25.6%
9.3%
4.6%4.5%
18.7%16.4%
13.4%
19.2%
22.0%
7.3%
3.0%
0%
5%
10%
15%
20%
25%
30%
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R.Tambo Alfred Nzo Buffalo City NelsonMandela Bay
2011 2016
15% 13% 15% 14% 14% 16% 15% 16% 17%
60%67%
64% 63% 62% 58% 59%
57% 56%
0%10%20%30%40%50%60%70%80%90%
100%
EasternCape
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R. Tambo Alfred Nzo NelsonMandela
Bay
Buffalo City
No income R1 - R9 600 R9 601 - R153 600 R153 601 - R614 401 R614 401+
Source: ECSECC, 2016a
The three most rural provinces; i.e., KwaZulu-Natal, Limpopo and Eastern Cape, had approximately 428 752
registered matriculants, representing 53.6% of the total candidates for the NSC in 2015. Of these three provinces, the
Eastern Cape accounted for the fewest candidates (109 0523). Despite the comparably high number of candidates
in the Eastern Cape, only 87 0904, or 97.0%, wrote the NSC.
Of this total of 428 752 candidates that sat for the NSC in these three rural provinces, 215 182 candidates (50.2%)
from KwaZulu-Natal, Limpopo and Eastern Cape were able to obtain NSC passes. These figures were notably lower
than those recorded in 2014 and represented a 9.0%, 8.6% and 7.0% drop in the overall pass rate for KwaZulu-Natal,
Eastern Cape and Limpopo, respectively.
TABLE 4.2 NATIONAL SENIOR CERTIFICATE RESULTS, EASTERN CAPE 2010 - 2015
Wrote Passed Passed %Access toBachelor’s
Degree
% Access toBachelor’s
Degree
2010 64 081 37 363 58.3% 11 368 15.9%
2011 65 383 37 997 58.0% 10 265 15.7%
2012 63 989 39 443 61.6% 11 262 17.6%
2013 72 138 46 840 64.9% 13 686 18.9%
2014 64 519 42 370 65.7% 13 435 20.8%
2015 87 090 49 475 56.8% 15 291 17.5%
Source: ECSECC, 2016a
3 This figure includes both full-time enrolment (89 740) as well as part-time enrolment (19 312). 4 Only full-time enrolled students are considered.
44
The NSC results for the Eastern Cape between 2010 and 2015 are shown in Table 4.2. In 2015, 49 475 of the
Eastern Cape’s Grade 12 learners passed the NSC examination. As evident from Table 4.2, there has been a steady
increase in the overall matric pass rate. Between 2010 and 2014, the matric pass rate increased from 58.3% to a
high of 65.7%. Over this period there was also a corresponding rise in the number of learners that attained access
to a bachelor’s degree. There was however, a notable decline in 2015 when the pass rate fell to 56.8%, and the
percentage of candidates that attained entrance to a bachelor’s degree fell to 17.5%.
TABLE 4.3 2015 MATRIC RESULTS PERFORMANCE PER EASTERN CAPE SCHOOL DISTRICT
School District
Number of
Candidates
(2015)
Pass RatePercentage Change
(2014-2015)2014 2015
Cradock 1 004 82.9% 71.6% -11.3%
Uitenhage 3 459 75.5% 69.0% -6.5%
Port Elizabeth 9 349 74.3% 66.0% -8.3%
Mthatha 6 889 67.6% 63.7% -3.9%
Cofimvaba 1 871 66.7% 61.9% -4.8%
East London 7 470 74.9% 61.8% -13.1%
Grahamstown 1 099 71.9% 60.6% -11.3%
Graaff-Reinet 961 63.3% 60.1% -3.2%
King Williams Town 5 759 64.0% 59.0% -5.0%
Queenstown 3 161 58.0% 56.7% -1.3%
Maluti 2 522 61.7% 55.9% -5.8%
Mt Fletcher 2 282 64.9% 55.7% -9.2%
Mount Frere 4 837 55.1% 55.1% 0.0%
Butterworth 4 253 57.1% 54.9% -2.2%
Mbizana 4 078 60.1% 53.4% -6.7%
Dutywa 4 933 57.9% 52.2% -5.7%
Sterkspruit 2 939 60.5% 49.8% -10.7%
Fort Beaufort 2 046 56.9% 49.7% -7.2%
Libode 6 925 62.4% 48.6% 13.8%
Ngcobo 2 634 65.9% 48.1% -17.8%
Qumbu 2 842 75.0% 47.9% -27.1%
Lusikisiki 3 870 61.1% 47.2% -13.9%
Lady Frere 1 907 63.9% 46.3% -17.6%
Eastern Cape 87 090 65.7% 56.8% 8.9%%
Source: ECSECC, 2016a
The top performing Eastern Cape school districts in 2015 were Cradock, Uitenhage and Port Elizabeth, all of which
attained an overall matric pass rate of over 65%. The Qumbu school district, saw the greatest drop between 2014
and 2015. In 2014 this school district has the 3rd highest matric pass rate; but by 2015, it was ranked 21 out of the
23 school districts in the Eastern Cape. The three lowest performing school districts were Lady Frere, Lusikisiki and
Qumbu, which all attained pass rates between 46.3% and 47.9% (ECSECC, 2016a).
4.2.4 EDUCATIONAL QUALITY
Foundation level literacy and numeracy yield high returns to both individuals and the broader economy (ILO, 2010).
In order to establish whether South African learners have these prerequisite skills in numeracy and literacy, the
Department of Basic Education has begun conducting Annual National Assessments (ANA) for learners in Grades
1 to 6 as well as learners in Grade 9. The intention of this process is to identify potential risks to quality teaching
and learning as well as designing interventions that are data-driven and based on credible assessment measures.
45
FIGURE 4.6 AVERAGE EASTERN CAPE MATHEMATICS PASS RATES FOR GRADES 3, 6 & 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 - 2014
Source: Department of Basic Education, 2014
The mathematics test scores between 2012 and 2014 vary considerably across Grades. For example, in 2014, 143
829 learners wrote the Grade 6 systemic test, of which 22.0% (31 642) attained an acceptable level5,6, attaining an
average score of 38.1%. This was compared to a pass rate of 8.1% in 2012 and an average score of 24.9%. In contrast
2.2% of the Grade 9’s that wrote the systemic test in 2014 attained an acceptable level, compared to 2.6% in 2012.
The average market for Grade 9’s also declined from 14.6% in 2012 to 11.1% in 2014. Across all three selected grade
cohorts, the Eastern Cape underperformed by the national average in terms of both percentage of learners that
attained an acceptable level and average test scores.
Language7 pass rates for the ANA’s in the Eastern Cape were notably higher than those for mathematics. Both
Grade 6 and Grade 9 learners in the Eastern Cape exhibited positive growth in terms of the number that were able
to attain an acceptable level of achievement in their home language. Amongst Grade 6’s the average pass rate
remained almost consistently higher than that of Grades 3 and 9 over the years analysed. Approximately 471 850
Grade 3, 6 and 9’s wrote the systemic test in 2014, with between 37.6% (Grade 9) and 51.0% (Grade 3) passing. The
average marks for Grades 3, 6 and 9 were 48.2%, 47.7% and 43.8%, respectively in 2014. As with mathematics, these
average percentages were lower than the national averages.
FIGURE 4.7 AVERAGE EASTERN CAPE HOME LANGUAGE PASS RATES FOR GRADES 3, 6 AND 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 - 2014
5 Acceptable level is defined by the Department of Basic Education (DBE) as those learners who attained a Level 4 rating, that is they attained an average score of 50% or greater.6 The DBE contracted an independent agent to report on the reliability of ANA scores. In order to be able to report on the reliability of ANA results the independent agent had to verify that test administration and marking took place in line with acceptable standards. The values quoted in this section are all based on the verified figures. 7The figures discussed here focus on the marks attained for Home Language.
20132014
Grade 3 Grade 6 Grade 92012 50.3% 38.4% 42.6%2013 47.0% 44.8% 35.2%2014 48.2% 47.7% 43.8%
2010 2015Sarah Baartman 0.75 0.73Amathole 0.74 0.70Chris Hani 0.73 0.72Joe Gqabi 0.74 0.71O.R. Tambo 0.74 0.71Alfred Nzo 0.75 0.70Nelson Mandela Bay0.76 0.74Buffalo City 0.77 0.74Eastern Cape 0.74 0.72
FIGURE 4.6 AVERAGE EASTERN CAPE MATHEMATICS PASS RATES FOR GRADES 3, 6 AND 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 - 2014
FIGURE 4.7 AVERAGE EASTERN CAPE HOME LANGUAGE PASS RATES FOR GRADES 3, 6 AND 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 -2014
FIGURE 4.10 GINI COEFFICIENTS PER EASTERN CAPE DISTRICT IN 2010 AND 2015
Grade 3 Grade 6 Grade 92012 40.5% 24.9% 14.6%2013 50.6% 33.0% 15.8%2014 48.8% 38.1% 11.1%
0%
10%
20%
30%
40%
50%
60%
Grade 3 Grade 6 Grade 92012 50.3% 38.4% 42.6%2013 47.0% 44.8% 35.2%2014 48.2% 47.7% 43.8%
0%
10%
20%
30%
40%
50%
60%
SarahBaartman Amathole Chris Hani Joe Gqabi O.R.
Tambo Alfred NzoNelson
MandelaBay
BuffaloCity
EasternCape
2010 0.75 0.74 0.73 0.74 0.74 0.75 0.76 0.77 0.742015 0.73 0.70 0.72 0.71 0.71 0.70 0.74 0.74 0.72
0.66
0.68
0.70
0.72
0.74
0.76
0.78
20132014
Grade 3 Grade 6 Grade 92012 50.3% 38.4% 42.6%2013 47.0% 44.8% 35.2%2014 48.2% 47.7% 43.8%
2010 2015Sarah Baartman 0.75 0.73Amathole 0.74 0.70Chris Hani 0.73 0.72Joe Gqabi 0.74 0.71O.R. Tambo 0.74 0.71Alfred Nzo 0.75 0.70Nelson Mandela Bay0.76 0.74Buffalo City 0.77 0.74Eastern Cape 0.74 0.72
FIGURE 4.6 AVERAGE EASTERN CAPE MATHEMATICS PASS RATES FOR GRADES 3, 6 AND 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 - 2014
FIGURE 4.7 AVERAGE EASTERN CAPE HOME LANGUAGE PASS RATES FOR GRADES 3, 6 AND 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 -2014
FIGURE 4.10 GINI COEFFICIENTS PER EASTERN CAPE DISTRICT IN 2010 AND 2015
Grade 3 Grade 6 Grade 92012 40.5% 24.9% 14.6%2013 50.6% 33.0% 15.8%2014 48.8% 38.1% 11.1%
0%
10%
20%
30%
40%
50%
60%
Grade 3 Grade 6 Grade 92012 50.3% 38.4% 42.6%2013 47.0% 44.8% 35.2%2014 48.2% 47.7% 43.8%
0%
10%
20%
30%
40%
50%
60%
SarahBaartman Amathole Chris Hani Joe Gqabi O.R.
Tambo Alfred NzoNelson
MandelaBay
BuffaloCity
EasternCape
2010 0.75 0.74 0.73 0.74 0.74 0.75 0.76 0.77 0.742015 0.73 0.70 0.72 0.71 0.71 0.70 0.74 0.74 0.72
0.66
0.68
0.70
0.72
0.74
0.76
0.78
Source: Department of Basic Education, 2014
46
Noting the poor attainment levels across all grades assessed in the ANAs, the Department of Basic Education
(DBE) has implemented a number of interventions that are intended to enhance learner performance and support
the teaching of mathematics and languages. Some of these interventions include:
• Library provisioning
• Strengthening of English as the language of learning and teaching in Grade 4
• Promotion of African languages
• Teacher and subject advisor training to equip teachers with the prerequisite knowledge and skills to equip
them to undertake multi-grade teaching that will enable them to effectively and efficiently deliver the
curriculum.
• Reconfiguration of Dinaledi and Technical Schools Grants into the Maths, Science and Technology Schools
Improvement Grant.
4.3 POVERTY AND GRANT DEPENDENCY
Monetary measures in the form of income distribution are often the most widely quoted when assessing poverty
levels. These measures however, frequently conceal the extent of inequalities, especially those concerning quality
of life. The multi-dimensional approach, which draws on variables such as ownership of assets, access to basic
services, employment, education and health, is more frequently used. A range of development indictors are
therefore, considered including: poverty headcount and intensity, income distribution and average household
income, and grant dependency.
4.3.1 POVERTY HEADCOUNT
As part of the 2016 Community Survey, Statistics South Africa utilised the South African Multidimensional Poverty
Index (SAMPI) to measure the extent of poverty in the country. The SAMPI is an index that is constructed using
eleven indicators across four dimensions, namely: health, education, living standards and economic activity.
Poverty headcount figures were then determined based on the proportion of households that are considered to be
“multidimensional poor” in terms of the index. Table 4.4 illustrates the poverty headcount across South Africa in
2011 and 2016, while Figure 4.8 shows the comparable figures for each district in the Eastern Cape.
TABLE 4.4 POVERTY HEADCOUNT IN 2011 AND 2016
Province 2011 2016
Western Cape 3.6% 2.7%
Eastern Cape 14.4% 12.7%
Northern Cape 7.1% 6.6%
Free State 5.5% 5.5%
KwaZulu-Natal 10.9% 7.7%
North West 9.2% 8.8%
Gauteng 4.8% 4.6%
Mpumalanga 7.9% 7.8%
Limpopo 10.1% 11.5%
Source: StatsSA, 2016a
Most provinces reported a decline in the poverty headcount between 2011 and 2016. The lowest poverty headcounts
were reported in the more urban provinces such as the Western Cape (2.7%) and Gauteng (4.6%). These were
followed by the Free State (5.5%), Northern Cape (6.6%), KwaZulu-Natal (7.7%), North West (8.8%), Limpopo
(11.5%), and Eastern Cape (12.7%). Positively, the Eastern Cape’s poverty headcount has declined by 1.7% since 2011.
The Alfred Nzo District had the highest poverty head-count (22.0%) in the Eastern Cape during 2016, the Intsika
Yethu Local Municipality, in the Chris Hani District, recorded the highest proportion of people living under the
poverty line at 27.7 %. This represented an increase of almost 5.0% between 2011 and 2016. Based on the total
population of Intsika Yethu Municipality in 2016 (145 370 people), the 27.7% poverty headcount translates into 40
260 people in the municipality living below the poverty line.
47
FIGURE 4.8 POVERTY HEADCOUNT BY DISTRICT MUNICIPALITY IN 2011 AND 2016
Source: StatsSA, 2016b
The poverty headcount has dropped across all districts in the province except Chris Hani and Amathole. In
Amathole, the level of poverty, as measured by the poverty headcount, remained unchanged while in Chris Hani it
increased by 0.8% between 2011 and 2016.
Despite the reduction in the poverty headcount between 2011 and 2016, the 2016 Community Survey indicated
that the intensity of poverty in the Eastern Cape has increased from 41.9% in 2011 to 43.3% in 2016. While the
poverty headcount indicates the absolute number of people in an area that fall below the poverty line, the intensity
measure records the extent to which people fall below the poverty line (i.e. average proportion of indicators in
which multidimensional poor households are deprived). An increase in the intensity of poverty in the province is
of particular concern, as it suggests that those living in poverty are getting poorer. This is further highlighted by
the fact that 464 830 of households in Eastern Cape reported that they had run out of money to buy food in the
12 months before the 2016 Community Survey. Nearly a fifth (17.6% or 311 260) of households in the Eastern Cape
also indicated that they had missed a meal over the same period (StatsSA, 2016b).
4.3.2 INCOME DISTRIBUTION
Gauteng and the Western Cape have the highest average household income at R216 667 per annum (R18 056 per
month) and R199 231 per annum (R16 603 per month), respectively (see Table 4.5). This is attributed to the greater
number of job opportunities, higher levels of educational attainment, and greater concentration of economic
activity in these provinces. Average household incomes are lower in the more rural provinces, particularly those
that incorporate former homelands. For example, Limpopo, Eastern Cape, North West, and KwaZulu-Natal had an
average monthly household incomes of R6 571, R7 462, R8 093, and R9 606, respectively, in 2011.
TABLE 4.5 AVERAGE WEIGHTED MONTHLY HOUSEHOLD INCOME PER PROVINCE IN 2001 AND 20118
Province 2001 2011
Western Cape R15 755 R16 603
Eastern Cape R5 960 R7 462
Northern Cape R7 938 R9 970
Free State R6 233 R8 709
KwaZulu-Natal R7 748 R9 606
North West R6 082 R8 093
Gauteng R15 436 R18 056
Mpumalanga R6 246 R8 980
Limpopo R4 589 R6 571
South Africa R9 662 R11 934
Source: Urban-Econ calculations based on StatsSA, 2011
FIGURE 4.5 2015 MATRIC PASS RATE IN SOUTH AFRICA
Figure 4.8 POVERTY HEADCOUNT BY DISTRICT MUNICIPALITY IN 2011 AND 2016
FIGURE 4.9 HOUSEHOLD INCOME DISTRIBUTION PER DISTRICT, 2011
56.8%
60.7%
65.9%69.4% 70.7%
78.6%81.5% 81.6%
84.2% 84.7%
50%
55%
60%
65%
70%
75%
80%
85%
90%
5.2%
18.7%
15.6%16.8%
21.1%
25.6%
9.3%
4.6%4.5%
18.7%16.4%
13.4%
19.2%
22.0%
7.3%
3.0%
0%
5%
10%
15%
20%
25%
30%
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R.Tambo Alfred Nzo Buffalo City NelsonMandela Bay
2011 2016
15% 13% 15% 14% 14% 16% 15% 16% 17%
60%67%
64% 63% 62% 58% 59%
57% 56%
0%10%20%30%40%50%60%70%80%90%
100%
EasternCape
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R. Tambo Alfred Nzo NelsonMandela
Bay
Buffalo City
No income R1 - R9 600 R9 601 - R153 600 R153 601 - R614 401 R614 401+
8 The 2001 and 2011 figures are illustrated in 2016 prices.
48
The household income distribution for the Eastern Cape as well as its district municipalities in 2011, is illustrated in
Figure 4.9. As evident from the figure, over 13.0% of all households in each respective district recorded a household
income of zero. The only district where this was not the case was Sarah Baartman, where only 12.5% of households
indicated that they had no income. The overwhelming majority of households across the Eastern Cape have a
cumulative income of between R9 601 and R153 600 per annum. This income bracket however, is very large and
does not provide enough information to analyse individual household economic circumstances.
FIGURE 4.9 HOUSEHOLD INCOME DISTRIBUTION PER DISTRICT, 2011
Source: Urban-Econ calculations based on StatsSA, 2011
Both the Nelson Mandela Bay (16.9%) and Buffalo City Metros (15.2%) have the highest proportion of households
earning in the top two income brackets, i.e. where household’s income exceeds R153 600 per annum. In contrast,
Alfred Nzo (4.1%) and Amathole (4.3%) have the lowest proportion of top earning households.
4.3.3 GINI COEFFICIENT
Average household income provides a skewed representation of average income per households as incomes
are inequitably distributed amongst households in a region. The Gini coefficient, which is a measure of statistical
dispersion intended to represent the income distribution of a region’s residents, provides an indication of the levels
of income inequality in an area. This value varies between 0 (which represents complete equality) and 1 (which
represents complete inequality).
FIGURE 4.10 GINI COEFFICIENTS PER EASTERN CAPE DISTRICT IN 2010 AND 2015
Source: Urban-Econ calculations based on StatsSA, 2011
Figure 4.10 indicates that income inequality, as measured by the Gini coefficient, decreased marginally within the
Eastern Cape from 0.74 in 2001 to 0.72 in 2015. This suggests that the increases in per capita income may be less
concentrated amongst the higher income groups than in 2010. The two metros in the province have the highest
Gini coefficients at 0.74. This is to be expected given the higher income patterns in the two metros as well as the
in-migration from the more rural parts of the province.
FIGURE 4.5 2015 MATRIC PASS RATE IN SOUTH AFRICA
Figure 4.8 POVERTY HEADCOUNT BY DISTRICT MUNICIPALITY IN 2011 AND 2016
FIGURE 4.9 HOUSEHOLD INCOME DISTRIBUTION PER DISTRICT, 2011
56.8%
60.7%
65.9%69.4% 70.7%
78.6%81.5% 81.6%
84.2% 84.7%
50%
55%
60%
65%
70%
75%
80%
85%
90%
5.2%
18.7%
15.6%16.8%
21.1%
25.6%
9.3%
4.6%4.5%
18.7%16.4%
13.4%
19.2%
22.0%
7.3%
3.0%
0%
5%
10%
15%
20%
25%
30%
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R.Tambo Alfred Nzo Buffalo City NelsonMandela Bay
2011 2016
15% 13% 15% 14% 14% 16% 15% 16% 17%
60%67%
64% 63% 62% 58% 59%
57% 56%
0%10%20%30%40%50%60%70%80%90%
100%
EasternCape
SarahBaartman
Amathole Chris Hani Joe Gqabi O.R. Tambo Alfred Nzo NelsonMandela
Bay
Buffalo City
No income R1 - R9 600 R9 601 - R153 600 R153 601 - R614 401 R614 401+
20132014
Grade 3 Grade 6 Grade 92012 50.3% 38.4% 42.6%2013 47.0% 44.8% 35.2%2014 48.2% 47.7% 43.8%
2010 2015Sarah Baartman 0.75 0.73Amathole 0.74 0.70Chris Hani 0.73 0.72Joe Gqabi 0.74 0.71O.R. Tambo 0.74 0.71Alfred Nzo 0.75 0.70Nelson Mandela Bay0.76 0.74Buffalo City 0.77 0.74Eastern Cape 0.74 0.72
FIGURE 4.6 AVERAGE EASTERN CAPE MATHEMATICS PASS RATES FOR GRADES 3, 6 AND 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 - 2014
FIGURE 4.7 AVERAGE EASTERN CAPE HOME LANGUAGE PASS RATES FOR GRADES 3, 6 AND 9 FOR PUBLIC ORDINARY SCHOOLS, 2012 -2014
FIGURE 4.10 GINI COEFFICIENTS PER EASTERN CAPE DISTRICT IN 2010 AND 2015
Grade 3 Grade 6 Grade 92012 40.5% 24.9% 14.6%2013 50.6% 33.0% 15.8%2014 48.8% 38.1% 11.1%
0%
10%
20%
30%
40%
50%
60%
Grade 3 Grade 6 Grade 92012 50.3% 38.4% 42.6%2013 47.0% 44.8% 35.2%2014 48.2% 47.7% 43.8%
0%
10%
20%
30%
40%
50%
60%
SarahBaartman Amathole Chris Hani Joe Gqabi O.R.
Tambo Alfred NzoNelson
MandelaBay
BuffaloCity
EasternCape
2010 0.75 0.74 0.73 0.74 0.74 0.75 0.76 0.77 0.742015 0.73 0.70 0.72 0.71 0.71 0.70 0.74 0.74 0.72
0.66
0.68
0.70
0.72
0.74
0.76
0.78
49
In contrast, the Gini coefficients across the Eastern Cape’s various districts are notably lower than the two metros.
All districts in the Eastern Cape saw an improvement in their Gini coefficients between 2010 and 2015, with
coefficient values of between 0.70 and 0.73. Alfred Nzo and Amathole exhibited the greatest improvement in their
Gini coefficients over the period. Although improvements in the Gini coefficient have been observed across all
districts, high levels of inequality persist.
4.3.4 GRANT DEPENDENCY
For a developing country, South Africa has a well-established social welfare system, with a sizeable percentage of
social spending going towards social grants. As of 2016, just under 17 million South Africans received some form
of social assistance.
As of January 2016, the South African Social Security Agency (SASSA) (2016) estimated that there 16 893 570
grant recipients in South Africa. This represents a marginal 2.4% increase from the 16 494 520 registered in 2015.
Despite this increase, the percentage of the total South African population dependent on social grants remained
largely unchanged at 30.2% in 2016.
FIGURE 4.11 PROVINCIAL POPULATION SHARE RELATIVE TO PROVINCES SHARE OF TOTAL SOCIAL GRANTS
Source: Urban-Econ calculations based on StatsSA, 2011
Source: Urban-Econ calculations based on SASSA, 2016 and StatsSA, 2016a
From Figure 4.11 it is evident that the more rural provinces and those with lower average household incomes (see
Table 4.6) are more dependent on social assistance. For example, despite the Eastern Cape having only 12.6% of
the total South African population, it accounts for 16.2% of all social grants. In addition, the Eastern Cape has the
second highest number of social grant recipients in both absolute terms (2.7 million) as well as in proportional
terms (16.2%) after KwaZulu-Natal. Despite Gauteng and the Western Cape accounting for 35.4% of the total South
African population, they only account for 23.0% of all social grant recipients.
TABLE 4.6 EASTERN CAPE SOCIAL GRANT COMPOSITION IN 2015 AND 2016
FIGURE 4.11 PROVINCIAL POPULATION SHARE RELATIVE TO PROVINCES SHARE OF TOTAL SOCIAL GRANTS
FIGURE 4.12 QUARTERLY LABOUR FORCE PARTICIPATION RATE
16.2%
5.7%
14.2%
23.2%
14.0%
8.3%
2.7%
7.0%
8.7%
12.6%
5.1%
24.1%
19.8%
10.4%
7.7%
2.1%
6.8%
11.3%
0% 5% 10% 15% 20% 25% 30%
Eastern Cape
Free State
Gauteng
KwaZulu-Natal
Limpopo
Mpumalanage
Northern Cape
North West
Western Cape
Population % Share Grant % Share
40%
45%
50%
55%
60%
2011
Q3
2011
Q4
2012
Q1
2012
Q2
2012
Q3
2012
Q4
2013
Q1
2013
Q2
2013
Q3
2013
Q4
2014
Q1
2014
Q2
2014
Q3
2014
Q4
2015
Q1
2015
Q2
2015
Q3
2015
Q4
2016
Q1
2016
Q2
2016
Q3
South Africa Eastern Cape
Jan-15 Jan-16 Change % Grant composition
Old Age 526 261 534 553 8 292 19.6%
Disability 182 663 181 598 -1 065 6.7%
Foster Child 106 473 101 817 -4 656 3.7%
Child Support 1 845 098 1 874 748 29 650 68.7%
Other9 35 089 38 002 2 913 1.4%
Total 2 695 584 2 730 718 35 134
Source: Urban-Econ calculations based SASSA, 2015; 2016
9 Other grants include: Care Dependent (2015: 19 067; 2016: 19 630), Grant in Aid (2015: 15 939; 2016: 18 332), and War Veterans (2015: 53; 2016: 40).
50
Between 2015 and 2016, the number of social grant recipients increased by 1.3% compared to a national growth
rate of 2.4%. This was the second lowest growth rate of social grant recipients in the South Africa after KwaZulu-
Natal. Although, the number of social grant recipients grew at a lower rate than the national average, 38.7% of the
Eastern Cape’s population is dependent on some form of social grant. This is the second highest in the country and
contrasted to a national figure of 30.2%.
Grant composition in the Eastern Cape largely mirrors the national figures, with the overwhelming majority of
social grants being to support children (68.7%). The Eastern Cape has a slightly higher proportion of old age grant
recipients (19.6%) relative to the national average of 18.8%. This is in line with the age profile of the province.
4.4 LABOUR MARKET
The labour market is where the supply and demand for labour interact. Although the labour market brings together
these two elements, this does not necessarily mean that the mix of labour supplied by the working age population
will meet the requirements of potential employers. In this sense, there is no single labour market, but rather a
variety of markets that demand various types of types of labour at different locations. Given this characteristic, it
is therefore, possible for a skills shortage to exist alongside high levels of unemployment as seen in South Africa.
One of the challenges facing policy makers is to improve the balance between supply and demand across the
various labour markets. Interventions in the labour market can thus take a number of forms including growing
the supply of skills (through improving access education and training), encouraging the establishment of labour
intensive enterprises, or promoting a culture of lifelong learning amongst potential workers to better equip them
for the changing global environment.
The following section outlines the characteristics that define the Eastern Cape labour market. This is done in order
to frame the provincial economic structure and performance within the context of its impact on the population.
4.4.1 OVERVIEW OF THE LABOUR MARKET
Table 4.7 indicates that employment in the Eastern Cape increased by 180 000 people (from 1.2 million to 1.4
million) over the last five years. This implies an average annual rate of growth of 2.7% per annum, which is slightly
faster than the national rate of growth (2.3% per annum). Employment growth in the Eastern Cape was notably
higher than the growth in the working-age population, similar to the national trend. This should, place downward
pressure on both the Eastern Cape and national unemployment rate.
TABLE 4.7 LABOUR MARKET HISTORIC PERFORMANCE IN THE EASTERN CAPE
Period Absolute change
Thousands (000) Q3 2011 Q3 2015 Q3 2016 2011-2016 2015-2016
Working age population 3 969 4 115 4 153 184 38
Formal employment (non-agricultural) 841 874 896 55 22
Informal employment (non- agricultural) 249 294 333 84 39
Labour force 1 724 1 937 2 008 284 71
Employed 1 263 1 372 1 443 180 71
Unemployed 461 565 565 104 0
Not economically active 2 245 2 177 2 145 -100 -32
Discouraged workers 371 426 385 14 -41
Percentage Percentage Point Change
Unemployment rate 26.8% 29.2% 28.2% 1.4 -1.0
Unemployment rate (expanded definition) 41.5% 42.5% 41.3% -0.2 -1.2
Labour absorption rate 31.8% 33.3% 34.7% 2.9 1.4
Female unemployment rate 27.7% 29.5% 26.9% -0.8 -2.6
Youth unemployment rate 37.9% 39.0% 39.8% 1.9 0.8
Labour force participation rate 43.4% 47.1% 48.4% 5.0 1.3
Source: Urban-Econ calculations based on StatsSA, 2016c
51
Working-age individuals in the Eastern Cape are less likely to be employed than is the case in South Africa. The
labour absorption rate, which compares the number of employed individuals to the size of the working-age
population, was only 34.7% in the third quarter of 2016. This means that just over a third of the working-age
population in the province is employed. Nationally, though, the labour absorption rate was 43.1%. In 2016, the rate
for the Eastern Cape has improved by 2.9% compared to five years previously. This was higher than the national
improvement, where the rate in the third quarter of 2011 was 42.0%.
The provincial unemployment rate (26.8%) was lower than the national rate (27.1%) as of the end of the third
quarter 2016. The Eastern Cape saw a 1.4 percentage point increase in the unemployment rate between 2011 and
2016, compared to a national growth of 2.1 percentage points.
Men outnumber women within the labour force by over 355 000 in 2016, and account for 60.0% of the province’s
labour force. The gap in the share of men and women within the labour force however, has increased, from 3.9% in
2011 to 20.0% in 2016.
In the third quarter of 2016, almost 60.0% of the labour force was aged between 15 and 34 years, while 57.8% was
over the age of 34 years. The absolute size of the latter cohort has increased by 89 000 over the last five years
compared to the 195 000 for the former. The cohorts between 25 and 34 years and between 45 and 54 years,
account for 37.2% and 17.4% of the labour force respectively. As the labour force continues to age, labour force
growth has been most rapid amongst 55 to 65 year olds (7.3% per annum) and for those aged 35 to 44 years (5.8%
per annum).
FIGURE 4.12 QUARTERLY LABOUR FORCE PARTICIPATION RATE
Source: Urban-Econ calculations based on Quantec, 2017
FIGURE 4.11 PROVINCIAL POPULATION SHARE RELATIVE TO PROVINCES SHARE OF TOTAL SOCIAL GRANTS
FIGURE 4.12 QUARTERLY LABOUR FORCE PARTICIPATION RATE
16.2%
5.7%
14.2%
23.2%
14.0%
8.3%
2.7%
7.0%
8.7%
12.6%
5.1%
24.1%
19.8%
10.4%
7.7%
2.1%
6.8%
11.3%
0% 5% 10% 15% 20% 25% 30%
Eastern Cape
Free State
Gauteng
KwaZulu-Natal
Limpopo
Mpumalanage
Northern Cape
North West
Western Cape
Population % Share Grant % Share
40%
45%
50%
55%
60%
2011
Q3
2011
Q4
2012
Q1
2012
Q2
2012
Q3
2012
Q4
2013
Q1
2013
Q2
2013
Q3
2013
Q4
2014
Q1
2014
Q2
2014
Q3
2014
Q4
2015
Q1
2015
Q2
2015
Q3
2015
Q4
2016
Q1
2016
Q2
2016
Q3
South Africa Eastern Cape
52
2016Q3 Working Age
Population
Employed Unemployed Discouraged job
seekers
Labour Force
Participation
Rate
Unemploy-
ment Rate
Total
('000s)
Share
(%)
Total
('000s)
Share
(%)
Total
('000s)
Share
(%)
Total
('000s)
Share
(%) % %
Total 4 153 1 443 565 385 59.2% 28.2
By Race
Black African 3 952 86.5% 1 174 80.2% 505 89.2% 370 95.7% 42.5% 30.1%
Coloured 416 9.1% 177 12.1% 56 9.9% 15 3.9% 55.9% 24.0%
Indian/Asian 4 0.1% 4 0.3% - - - - 91.7% 0.0%
White 198 4.3% 109 7.5% 6 1.0% 1 0.4% 58.1% 4.8%
By Gender
Male 2 149 47.0% 755 51.6% 307 54.3% 197 51.0% 49.4% 28.9%
Female 2 421 53.0% 708 48.4% 259 45.7% 189 49.0% 39.9% 28.9%
By Age Group
Youth 15-34 2 484 37.4% 614 29.9% 374 39.8% 274 41.6% 39.8% 37.9%
15-24 1 366 20.6% 111 5.4% 129 13.8% 119 18.0% 17.6% 53.8%
25-34 1 118 16.8% 503 24.5% 245 26.1% 155 23.6% 66.9% 32.7%
35-44 724 10.9% 403 19.6% 117 12.5% 63 9.6% 71.9% 22.5%
45-54 525 7.9% 286 13.9% 63 6.7% 36 5.5% 66.4% 18.0%
55-64 420 6.3% 140 6.8% 12 1.2% 11 1.7% 36.0% 7.6%
By Education
No Education 236 5.2% 20 1.4% 7 1.2% 12 3.1% 11.4% 24.2%
Grade 0-7 236 22.3% 233 16.0% 91 16.2% 112 29.1% 32.0% 28.1%
Secondary
incomplete
2 138 47.0% 570 39.0% 289 51.2% 184 47.8% 40.2% 33.6%
Secondary
completed
784 17.2% 378 25.9% 134 23.8% 68 17.6% 65.3% 26.2%
Tertiary 378 8.3% 258 17.7% 43 7.7% 9 2.3% 79.8% 14.4%
Source: Urban-Econ calculations based on StatsSA, 2016c
4.4.3 SECTORAL EMPLOYMENT
In the third quarter of 2016, the three largest formal sector industries in terms of employment in the Eastern Cape
were community, social and personal services, which includes the government sector (28.6% of formal sector em-
ployment), wholesale and retail trade (23.3%) and manufacturing (10.7%). Together, these three industries account
for 62.6% of all formal sector employment in the Eastern Cape in 2016. Their share is similar nationally (57.7%),
although community, social and personal services sector was a proportionally smaller employer within the national
formal sector (4.5% smaller than provincial share).
4.4.2 COMPOSITION OF THE LABOUR FORCE
The provincial labour force numbered just over 2.0 million in the third quarter of 2016, up by approximately 284
000 since the third quarter of 2011 (see Table 4.8). Black Africans accounted for 82.7% of the labour force, followed
by Coloured (11.5%) and Whites (5.7%). Black Africans account for the overwhelming majority of the increase in
the size of the provincial labour force between 2011 and 2016; their numbers increasing by 368 000, while there
was negligible change over the period in the number of Coloureds and Indians/Asians. The White labour force in
contrast, contracted by 73 000 over the period.
TABLE 4.8 EASTERN CAPE LABOUR MARKET OVERVIEW
53
Between the third quarter of 2015 and the third quarter of 2016 the Eastern Cape economy added 77 000 formal
sector jobs. This equated to a formal sector employment growth rate of 6.1% quarter-to-quarter. In comparison, the
South African economy shed 9 000 formal sector jobs over the same period. The principle driver of this Eastern
Cape employment growth was the tertiary sector; specifically, the wholesale and retail trade (42 000) and the
transport (16 000) sectors, which jointly added 50 000 jobs over the period. The agricultural sector added the
third highest number of jobs over the period, adding 11 000 and growing at quarter-on-quarter rate of 12.4%. In
comparison, the community and social services sector shed 12 000 formal sector jobs.
TABLE 4.9 INDUSTRIAL COMPOSITION OF FORMAL SECTOR EMPLOYMENT, 2016Q3
South Africa Eastern Cape
Number (‘000) % Share Number (‘000) % Share
Primary Sector 1 335 9.1% 102 7.6%
Agriculture, forestry and fishing 897 6.1% 101 7.5
Mining and quarrying 438 3.0% 1 0.1%
Secondary Sector 3 338 22.7% 334 24.8%
Manufacturing 1 708 11.6% 144 10.7%
Utilities 120 0.8% 6 0.4%
Construction 1 510 10.3% 184 13.7%
Tertiary Sector 10 053 68.3% 912 67.7%
Wholesale and retail trade 3 235 22.0% 315 23.3%
Transport, storage and communication 927 6.3% 74 5.5%
Finance, insurance and business services 2 347 15.9% 137 10.2%
Community, social and personal services 3 544 24.1% 385 28.6%
Total Formal Sector Employment 14 726 1 347
Source: StatsSA, 2016c
4.4.4 UNEMPLOYMENT IN THE EASTERN CAPE
Figure 4.13 compares the unemployment rates in each province to the national average. In the third quarter of 2016,
the narrow unemployment rate for South Africa was estimated at 27.1%. Including discouraged work seekers, this
proportion rises to 36.3%. In the Eastern Cape, the unemployment rate is 28.1%, and the expanded rate is 41.3%. At
13.1%, this gap between the official and expanded unemployment rates is higher than five other provinces due to
the large number of discouraged work seekers in the province.
FIGURE 4.13 PROVINCIAL AND NATIONAL UNEMPLOYMENT RATES, 2016Q3
Source: StatsSA, 2016c
FIGURE 4.13 PROVINCIAL AND NATIONAL UNEMPLOYMENT RATES, 2016Q3
FIGURE 4.14 EASTERN CAPE UNEMPLOYMENT RATES
FIGURE 4.15 EASTERN CAPE DISTRICT’S CONTRIBUTION TO GVA-R IN 2015
27.1%21.7%
28.2% 29.6%34.2%
23.5%
30.5% 29.1% 30.4%
21.9%
36.3%
24.8%
41.3% 41.8% 40.4% 40.4%44.6%
32.8%
41.4%36.3%
0%5%
10%15%20%25%30%35%40%45%50%
Sout
h Af
rica
Wes
tern
Cap
e
East
ern
Cape
Nor
ther
n Ca
pe
Free
Sta
te
KwaZ
ulu-
Nat
al
Nor
th W
est
Gaut
eng
Mpu
mal
anga
Lim
popo
Offical Expanded
0%
10%
20%
30%
40%
50%
60%
Ove
rall
Blac
k Af
rican
Colo
ured
Indi
an/ A
sian
Whi
te
Mal
e
Fem
ale
Yout
h 15
-34
15-2
4
25-3
4
35-4
4
45-5
4
55-6
4
No
Educ
ation
Grad
e 0-
7
Seco
ndar
yin
com
plet
e
Seco
ndar
yco
mpl
eted
Terti
ary
2011 2016
Sarah Baartman9.0%
Amathole6.9%
Chris Hani7.8%
Joe Gqabi3.4%
O.R.Tambo10.0%
Alfred Nzo4.7%
Nelson Mandela Bay
38.7%
Buffalo City19.6%
54
In an environment characterised by high levels of unemployment, inter-provincial comparisons of the official
unemployment rate are not always clear due to the dynamics associated with actively seeking employment (e.g.
cross province job searches). High levels of unemployment may mean that individuals have given up actively
seeking employment as they feel that they are unlikely to find work; an effect that may vary according to local
perceptions of the labour market. Individuals may also be prevented from actively seeking employment due to
geographic constraints. For example, where an individual job seeker is located in poorly connected rural area,
far from economic opportunities, searching costs may represent barriers to employment. These are some of the
factors that explain the difference between the Eastern Cape and the rest of South Africa’s unemployment levels.
Between the third quarters of 2011 and 2016, changes in official unemployment rates varied only marginally across
the various demographic characteristics analysed (see Figure 4.14). The official unemployment rate for the province
in the third quarter of 2016 was 28.2%, only marginally higher than five years earlier (26.8%).
FIGURE 4.14 EASTERN CAPE UNEMPLOYMENT RATES
Source: StatsSA, 2016c
The key message from Figure 4.14 is the persistence of the historical patterns of labour market disadvantages that
characterise both the provincial and national labour markets. Firstly, there is a clear racial disparity in unemployment
rates in the Eastern Cape: highest for Black Africans (30.1%) followed by Coloureds (24.0%), and lowest for Whites
(4.8%), and Indians/Asians (0.0%). Importantly, these provincial unemployment rates are not vastly different from
those exhibited at a national level. Secondly, women remain relatively disadvantaged within the labour market, with
an unemployment rate of 36.6% compared to only 28.9% for men. Thirdly, there is a negative relationship between
age and the rate of unemployment. The provincial unemployment rate for 15 to 24 year olds is 53.8%, falling to
32.7% for 25 to 34 year olds, and to 7.6% for 55 to 65 year olds. Youth unemployment remains one of the key
challenges facing South African policymakers, due in part to the long-term consequences for future employability.
Lastly, unemployment is lower for individuals with higher levels of education. The unemployment rate for those
with post-secondary education in the Eastern Cape is around 14.4%, compared with 35.5% for matriculants and
33.6% for those with incomplete secondary education.
TABLE 4.10 OVERVIEW OF THE EASTERN CAPE LABOUR MARKET BY DISTRICT, 2015
District Labour
Force
Employed Unemployed Unemployment
Rate Formal Informal
Sarah Baartman 196 208 95 972 56 323 45 379 23.0%
Amathole 261 264 103 681 67 376 91 216 34.8%
Chris Hani 229 984 79 586 47 056 104 578 45.2%
Joe Gqabi 94 869 43 050 25 654 26 693 28.0%
O.R. Tambo 316 331 148 110 85 049 84 276 26.5%
Alfred Nzo 210 500 86 813 58 890 65 427 31.0%
Nelson Mandela Bay 548 743 242 633 149 766 159 877 28.9%
Buffalo City 367 600 177 352 109 322 82 838 22.4%
Source: Urban-Econ calculations based on Quantec, 2017a
FIGURE 4.13 PROVINCIAL AND NATIONAL UNEMPLOYMENT RATES, 2016Q3
FIGURE 4.14 EASTERN CAPE UNEMPLOYMENT RATES
FIGURE 4.15 EASTERN CAPE DISTRICT’S CONTRIBUTION TO GVA-R IN 2015
27.1%21.7%
28.2% 29.6%34.2%
23.5%
30.5% 29.1% 30.4%
21.9%
36.3%
24.8%
41.3% 41.8% 40.4% 40.4%44.6%
32.8%
41.4%36.3%
0%5%
10%15%20%25%30%35%40%45%50%
Sout
hAf
rica
Wes
tern
Cap
e
East
ern
Cape
Nor
ther
n Ca
pe
Free
Stat
e
KwaZ
ulu-
Nat
al
Nor
thW
est
Gaut
eng
Mpu
mal
anga
Lim
popo
Offical Expanded
0%
10%
20%
30%
40%
50%
60%
Ove
rall
Blac
k Af
rican
Colo
ured
Indi
an/ A
sian
Whi
te
Mal
e
Fem
ale
Yout
h 15
-34
15-2
4
25-3
4
35-4
4
45-5
4
55-6
4
No
Educ
ation
Grad
e 0-
7
Seco
ndar
y in
com
plet
e
Seco
ndar
y co
mpl
eted
Terti
ary
2011 2016
Sarah Baartman9.0%
Amathole6.9%
Chris Hani7.8%
Joe Gqabi3.4%
O.R.Tambo10.0%
Alfred Nzo4.7%
Nelson Mandela Bay
38.7%
Buffalo City19.6%
55
In absolute terms, the majority of the unemployed (i.e. those without jobs but which are willing to work) are located
in Nelson Mandela Bay, Chris Hani and Amathole Districts. Approximately 104 000 people (45.2%) were unemployed
in Chris Hani during 2015, using the official unemployment definition. This was the highest in the Eastern Cape, and
15.6% above the provincial average. Buffalo City, at 22.4% had the lowest unemployment rate in the province. The
unemployment rate remained fairly constant across the districts between 2014 and 2015, although no districts
exhibited a decline in the unemployment rate. O.R. Tambo and Alfred Nzo however, registered no change in their
unemployment rates over the period. The Chris Hani District saw the highest increase in its unemployment rate,
which rose by 1.2% between 2014 and 2015.
Employment in the formal sector predominates across the districts as evident in Table 4.10. The formal to informal
employment ratio for the Eastern Cape was 1: 0.61 in 2015. This translates to 0.61 informal sector jobs for every one
formal sector job. This is slightly higher than in 2014, when the figure was 1: 0.57, suggesting a growing informal
sector. District ratios vary from a low of 1: 0.57 in O.R. Tambo to a high of 1: 0.68 in Alfred Nzo.
4.5 ECONOMIC PERFORMANCE
The real GDP-R of the Eastern Cape increased by 0.6% year-on-year to an estimated R230.3 billion in 2015. This
made the province the fourth largest regional economy in South Africa ahead of Mpumalanga and Limpopo. The
Eastern Cape’s contribution to total national output declined by 0.1%, to 7.5%. In absolute terms, the Eastern Cape
economy added R1.4 billion in additional GDP-R over the 2014 to 2015 period. This was 37.1% lower than the R2.2
billion added by the Eastern Cape economy between 2013 and 2014. The decline in the Eastern Cape year-on-year
GDP-R growth in 2015 relative to 2014, was in line with the South African economy, whose GDP growth rate fell
from 1.6% in 2014 to 1.3% in 2015.
TABLE 4.11 GROSS DOMESTIC PRODUCT (RAND, MILLIONS AT CONSTANT 2010 PRICES)
Province 2014 GDP-R
(R millions)
Estimated 2015
GDP-R (R millions)
Year-on-year
change (R)
millions
Growth Rate
2014 – 2015 2010 - 2015
Western Cape 415 905 419 551 3 646 0.9% 2.1%
Eastern Cape 228 919 230 345 1 426 0.6% 1.2%
Northern Cape 66 452 66 838 386 0.6% 2.2%
Free State 160 357 162 133 1 776 1.1% 1.9%
KwaZulu-Natal 482 953 487 421 4 468 0.9% 2.0%
North West 178 897 186 554 7 657 4.3% 0.3%
Gauteng 1 045 616 1 054 552 8 936 0.9% 2.1%
Mpumalanga 221 649 224 049 2 400 1.1% 1.9%
Limpopo 216 289 220 304 4 015 1.9% 1.6%
South Africa 3 017 037 3 055 192 38 155 1.3% 1.9%
Source: Urban-Econ calculations based on StatsSA, 2016d
Despite its low GVA-R growth rate (0.9%), Nelson Mandela Bay had the largest economy in the Eastern Cape,
accounting for 38.7% of the province’s GVA-R in 2015. The Buffalo City Metro likewise has a low growth rate (0.9%),
which somewhat understates the metro’s importance to the provincial economy. In 2015, the Buffalo City Metro
contributed a further 19.6% to total provincial GVA-R. The low, 0.9% GVA-R growth rate for the Buffalo City also
understates, the importance of the metro’s economy, which contributes a further 20.8% to the Eastern Cape’s
GVA-R. In contrast, the relatively small districts, which are rapidly catching up in terms of growth (e.g. Alfred Nzo:
2.9%; Joe Gqabi: 2.3%), have smaller economies as reflected by their contribution to total provincial GVA-R.
56
FIGURE 4.15 EASTERN CAPE DISTRICT’S CONTRIBUTION TO GVA-R IN 2015
Source: Urban-Econ calculations based on Quantec, 2017a
4.5.1 GDP-R AND PROJECTIONS
The GDP-R growth rate of the Eastern Cape economy has declined sharply over the last decade, from a high
of 5.3% per annum in 2007 to 0.6% in 2015 (see Figure 4.16). This decline in the province’s GDP-R growth rate
however, is in line with the national trend which; over the same period, has fallen from 5.4% to 1.6%, respectively.
Since 2010, economic growth in the Eastern Cape has been, on average, 0.5 percentage points lower than the
rest of the country. The Eastern Cape’s economic growth gap however, has narrowed in recent years, with the
province outperforming the national average in 2011 by 0.4%. Despite this, the Eastern Cape has exhibited the
lowest provincial GDP-R growth rate in both 2014 and 2015.
The provincial economy is forecasted to record a GDP-R growth rate of 0.4% in 2016. Over the duration of the
South African 2016 Medium Term Expenditure Framework, the province’s GDP-R is forecasted to grow by 1.0%,
1.6%, and 1.7% in 2017, 2018 and 2019 in line with national expectations. The Eastern Cape’s GDP-R growth over
the 2017 to 2019 period is expected to be low, closely tracking national performance. Factors that are likely to
positively impact the Eastern Cape’s economy over the short term include: lower inflation, real wage growth
and increased consumer spending due to higher household incomes. An easing of drought conditions and new
electricity generating capacity should also positively contribute to further economic growth.
FIGURE 4.16 EASTERN CAPE GDP-R PERFORMANCE BETWEEN 2009 AND 2019 (CONSTANT 2010 PRICES)
Source: Urban-Econ calculations based on StatsSA, 2016d
The growth rates in Figure 4.16 indicate that the Eastern Cape’s GDP-R rose from R228.9 billion in 2014 to an
estimated R230.3 billion in 2015. Forecasted GDP-R figures for the Eastern Cape suggest that this figure will
increase by R11.0 billion to R241.3 billion in 2019.
FIGURE 4.13 PROVINCIAL AND NATIONAL UNEMPLOYMENT RATES, 2016Q3
FIGURE 4.14 EASTERN CAPE UNEMPLOYMENT RATES
FIGURE 4.15 EASTERN CAPE DISTRICT’S CONTRIBUTION TO GVA-R IN 2015
27.1%21.7%
28.2% 29.6%34.2%
23.5%
30.5% 29.1% 30.4%
21.9%
36.3%
24.8%
41.3% 41.8% 40.4% 40.4%44.6%
32.8%
41.4%36.3%
0%5%
10%15%20%25%30%35%40%45%50%
Sout
h Af
rica
Wes
tern
Cap
e
East
ern
Cape
Nor
ther
n Ca
pe
Free
Sta
te
KwaZ
ulu-
Nat
al
Nor
th W
est
Gaut
eng
Mpu
mal
anga
Lim
popo
Offical Expanded
0%
10%
20%
30%
40%
50%
60%
Ove
rall
Blac
k Af
rican
Colo
ured
Indi
an/ A
sian
Whi
te
Mal
e
Fem
ale
Yout
h 15
-34
15-2
4
25-3
4
35-4
4
45-5
4
55-6
4
No
Educ
ation
Grad
e 0-
7
Seco
ndar
y in
com
plet
e
Seco
ndar
y co
mpl
eted
Terti
ary
2011 2016
Sarah Baartman9.0%
Amathole6.9%
Chris Hani 7.8%
Joe Gqabi 3.4%
O.R.Tambo 10.0%
Alfred Nzo 4.7%
Nelson Mandela Bay
38.7%
Buffalo City19.6%
FIGURE 4.16 EASTERN CAPE GDP-R PERFORMANCE BETWEEN 2009 AND 2019 (CONSTANT 2010 PRICES)
FIGURE 4.17 EASTERN CAPES HISTORICAL TRADE POSITION WITH WORLD MARKETS
FIGURE 4.18 TOTAL EASTERN CAPE MERCHANDISE EXPORTS (RAND, MILLION) BETWEEN 2005 AND 2015
-2%
-1%
0%
1%
2%
3%
4%
180 000
190 000
200 000
210 000
220 000
230 000
240 000
250 000
2009 2010 2011 2012 2013 2014 2015(E) 2016(F) 2017(F) 2018(F) 2019(F)
Perc
enta
ge
Rand
Mill
ions
GDP GDP Growth Rate
-80 000
-60 000
-40 000
-20 000
0
20 000
40 000
60 000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Rand
Mill
ions
Exports Imports Trade Balance
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
0
5 000
10 000
15 000
20 000
25 000
30 000
35 000
40 000
45 000
50 000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Percentage
Rand
s Mill
ions
Value Year-on-year change
57
4.5.2 SECTOR ANALYSIS OF THE EASTERN CAPE ECONOMY
As in the case of 2014, the Eastern Cape economy was largely driven by the finance and trade sectors which
contributed 18.6% and 18.0% respectively to the provinces GDP-R. Another important driver of the provincial
economy was the manufacturing sector contributing 12.5% to the Eastern Cape’s total GDP-R in 2015. The general
government sector made the largest contribution to total GDP-R (20.5%) in 2015. The mining and utilities sectors
were the two smallest contributors to the provincial economy in 2015 at 0.3% and 1.1%, respectively.
The agricultural sector reported a significant contraction of 4.7% in 2015 driven by the adverse impacts of the
drought. This negative growth rate was in contrast to low positive growth in the primary sector between 2010 and
2014, when the sector grew at an average rate of 4.5%. As the drought begins to ease, it is anticipated that the
Eastern Cape agricultural sector will begin to recover in line with the long-term potential of the sector.
Although manufacturing is one of the most critical sectors for sustainable development within the Eastern Cape,
the performance of the manufacturing sector over the last few years has been poor. Growth in the province’s
manufacturing sector during 2015 was negligible, on the back of low domestic and international demand linked
to poor global prospects. Metals, metal products, machinery and equipment; transport equipment; and furniture
and other manufacturing are the major sub-industries contributing to the poor performance of the sector in 2014.
Despite the negligible growth in manufacturing, it represents an increase from the -0.6% growth rate recorded in
2014.
TABLE 4.12 SECTORAL AND SUB-SECTORAL CONTRIBUTION TO PROVINCIAL GVA-R 2015 (CONSTANT 2010 PRICES)
SectorValue (R)
millions
Sectoral
Contribution to
GVA-R
Growth Rate
2014-2015 2010-2015
Primary Sector 4 335 1.9% -4.0% 3.1%
Agriculture, forestry and fishing 3 656 1.6% -4.7% 3.1%
Mining and quarrying 679 0.3% 0.0% 3.0%
Secondary Sector 39 945 17.3% 1.2% 1.3%
Manufacturing 28 771 12.5% 0.1% 1.0%
Utilities 2 536 1.1% -0.2% -0.7%
Construction 8 638 3.7% 5.5% 2.8%
Tertiary Sector 165 725 71.9% 1.5% 1.9%
Wholesale and retail trade, catering and
accommodation 41 547 18.0% 1.6% 2.1%
Transport, storage and communication 18 286 7.9% 1.0% 2.0%
Finance, insurance, real estate and business
services 42 780 18.6% 2.7% 2.6%
Community, social and personal services 47 291 20.5% 0.5% 1.1%
General government 15 822 6.9% 1.7% 1.8%
All industries at basic prices 210 006 91.2% 1.3% 1.8%
Taxes less subsidies on products 20 340 8.8% -9.0% 0.9%
Eastern Cape 230 345 100.0% 0.6% 1.7%
Source: Urban-Econ calculations based on StatsSA, 2016d
Growth in the real gross value added by the finance and business services sector is estimated to have accelerated
to 2.7% in 2015, up from 1.8% in 2014. The strong growth in this sector was mainly evident in the business services
subsector, which accelerated sharply in 2015, increasing by 3.1% during the year. This 2.7% increase was primarily
driven by positive growth in the finance and insurance subsector, which went from registering a negative growth
rate of 2.2% in 2014, to a positive 1.2% in 2015.
Real output growth of the trade sector accelerated from 0.6% in 2014 to an estimated 1.6% in 2015. According
to the SARB (2015), motor vehicles exhibited a downward trend in the latter part of 2015. This was likely to have
adversely affected Eastern Cape vehicle and component manufacturers. While the average consumer confidence
in South Africa has only broken through the FNB/BER Consumer Confidence Index’s 50-point “neutral” mark
three times since 2010, the Eastern Cape was able to perform notably better than the national average in the third
quarter of 2015, registering a positive average in consumer confidence. The highest levels of business confidence
58
in the third quarter of 2015, were exhibited by Eastern Cape wholesalers, who on average, rated their confidence at
93 points, with retail businesses close behind on 84 points (DEDEAT, 2015). The performance of the Eastern Cape
wholesale and retail trade, catering and accommodation sector is anticipated to improve further in 2016 and 2017.
Growth in the sector is forecasted to be between 1.7% and 2.2%, mirroring the national performance (Fin24, 2016).
Real gross value added by the transport, storage and communication sector slowed in 2015, increasing by
0.5% compared to 1.6% in 2014. Moderate output by the sector was to an extent, supported by growth in the
telecommunications subsector, benefiting from an increased number of mobile phone subscribers and a strong
demand for the use of data on mobile phones during 2015 (Bronkhorst, 2015).
4.6 TRADE
The Eastern Cape contributed 4.5% of the total South African exports and 5.2% of the country’s total imported
merchandise in 2015. Between 2014 and 2015, total mechanise imports grew by 18.4% compared to 12.4% for
exported merchandise. These high growth rates resulted in the Eastern Cape’s import and export growth rates
outperforming the national averages of 0.5% and 4.2%, respectively.
Whilst the Northern Cape, Free State and Mpumalanga recorded some of the highest growth rates between 2010
and 2015, they collectively accounted for less than 6% of total South African exports. Growth in merchandise
imports were recorded for the majority of provinces and were in line with the increased imports of petroleum
nationally, which have placed increased pressure on the trade balance.
TABLE 4.13 PROVINCIAL CONTRIBUTION TO NATIONAL IMPORTS AND EXPORTS IN 2015
Exports Imports
Western Cape 11.6% 18.0%
Eastern Cape 4.5% 5.2%
Northern Cape 0.5% 0.1%
Free State 2.6% 0.4%
KwaZulu-Natal 11.6% 10.9%
North West 11.8% 0.5%
Gauteng 51.3% 63.8%
Mpumalanga 2.5% 0.6%
Limpopo 3.6% 0.4%
Source: Urban-Econ calculations based on Quantec, 2017b
The largest provincial contributor to South African merchandise exports was Gauteng, contributing 51.3% of all
exports; followed by North West (11.8%), the Western Cape (11.6%), and KwaZulu-Natal (11.6%). The Eastern Cape
remained the fourth largest contributor to total South African exports in 2015 at 4.5%. The province’s share of total
exports however, rose slightly from 4.2% in 2014. The largest importer by value was the Gauteng province (63.8%),
Western Cape (18.0%) and KwaZulu-Natal (10.9%). The Eastern Cape only accounted for 5.2% of total imports in
2015, up from 4.4% in 2014.
The change in the Eastern Cape’s merchandise imports, exports and trade balance between 2005 and 2015 is
presented in Figure 4.17. Provincial exports in 2015 surpassed their pre-2008 figures on the back of growing
international demand for Eastern Cape exports. Despite increased exports the Eastern Cape remains a net importer
of goods, as evident by the widening trade account deficit since 2011.
59
FIGURE 4.17 EASTERN CAPES HISTORICAL TRADE POSITION WITH WORLD MARKETS
Urban-EcUrban-Econ calculations based on Quantec, 2017b
4.6.1 EXPORTS
The value of the Eastern Cape’s exports increased by R 5.0 billion in 2015, to R45.5 billion, representing a 12.4%
increase from 2014. Similarly to South Africa, this large increase in the value of exports can partially be explained
by the weaker rand, which makes Eastern Cape goods more attractive to international markets.
Eastern Cape merchandise exports have increased notably since 2005, experiencing only two periods of negative
year-on-year growth in 2009 and 2012. The largest of these declines was in 2009 when the value of Eastern Cape’s
exports fell from R44.4 billion to R25.7 billion, equating to a drop of 42.0%. This sharp decline was exclusively
attributable to lower demand for goods on the back of the global recession. The Eastern Cape’s total export
value reached pre-recession levels for the first time in 2015. Despite the province reaching pre-recession export
levels, the average year-on-year growth post-recession has only been 10.5% compared to 33.6% pre-recession.
Eastern Cape exports however, remain in line with the NDP target of 6% year-on-year, averaging 9.1% year-on-year
between 2000 and 2014.
FIGURE 4.18 TOTAL EASTERN CAPE MERCHANDISE EXPORTS (RAND, MILLION) BETWEEN 2005 AND 2015
Source: Urban-Econ calculations based on Quantec, 2017b
The Eastern Cape’s main export regions in 2015 were Europe (45.5%), Asia (24.7%) and the Africa (13.3%). Most
of the Eastern Cape’s major export destinations fall within these regions including Germany (28.6%), China (7.1%),
Hong Kong (4.9%), and Namibia (4.5%). The United States however, which is the Eastern Cape’s second largest
export destination by value (10.7%), does not fall within one of the aforementioned regions. Growth in export trade
has varied considerably across regions, with American and European markets increasing by 26.4% and 23.9%,
respectively, in 2015, whilst export growth to African markets was more moderate at 11.1% over the same period.
FIGURE 4.16 EASTERN CAPE GDP-R PERFORMANCE BETWEEN 2009 AND 2019 (CONSTANT 2010 PRICES)
FIGURE 4.17 EASTERN CAPES HISTORICAL TRADE POSITION WITH WORLD MARKETS
FIGURE 4.18 TOTAL EASTERN CAPE MERCHANDISE EXPORTS (RAND, MILLION) BETWEEN 2005 AND 2015
-2%
-1%
0%
1%
2%
3%
4%
180 000
190 000
200 000
210 000
220 000
230 000
240 000
250 000
2009 2010 2011 2012 2013 2014 2015(E) 2016(F) 2017(F) 2018(F) 2019(F)
Perc
enta
ge
Rand
Mill
ions
GDP GDP Growth Rate
-80 000
-60 000
-40 000
-20 000
0
20 000
40 000
60 000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Rand
Mill
ions
Exports Imports Trade Balance
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
0
5 000
10 000
15 000
20 000
25 000
30 000
35 000
40 000
45 000
50 000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Percentage
Rand
s Mill
ions
Value Year-on-year change
FIGURE 4.16 EASTERN CAPE GDP-R PERFORMANCE BETWEEN 2009 AND 2019 (CONSTANT 2010 PRICES)
FIGURE 4.17 EASTERN CAPES HISTORICAL TRADE POSITION WITH WORLD MARKETS
FIGURE 4.18 TOTAL EASTERN CAPE MERCHANDISE EXPORTS (RAND, MILLION) BETWEEN 2005 AND 2015
-2%
-1%
0%
1%
2%
3%
4%
180 000
190 000
200 000
210 000
220 000
230 000
240 000
250 000
2009 2010 2011 2012 2013 2014 2015(E) 2016(F) 2017(F) 2018(F) 2019(F)
Perc
enta
ge
Rand
Mill
ions
GDP GDP Growth Rate
-80 000
-60 000
-40 000
-20 000
0
20 000
40 000
60 000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Rand
Mill
ions
Exports Imports Trade Balance
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
0
5 000
10 000
15 000
20 000
25 000
30 000
35 000
40 000
45 000
50 000
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Percentage
Rand
s Mill
ions
Value Year-on-year change
60
FIGURE 4.19 EXPORT COMPOSITION BY VALUE BY SOURCE MARKET REGIONS
Urban-Econ calculations based on Quantec, 2017b
The total value of Eastern Cape exports increased by 12.4% year-on-year between 2014 and 2015. Similarly to
the rest of South Africa, the prominence of both African and Asian destinations as export source markets have
increased notably over the last five years. This is evident in the 38.5% and 15.6% average year-on-year growth rate
in the value of exports recorded between 2010 and 2015 for African and Asian source markets, respectively.
In comparison, Eastern Cape’s more established source market (i.e. Europe) has exhibited the lowest growth rate,
with the total export value to source markets in this region, only increasing by an average of 2.2% year-on-year
between 2010 and 2015.
TABLE 4.14 EASTERN CAPE EXPORTS TO REGIONAL SOURCE MARKETS BY VALUE
Value of Exports 2015 (R Millions)10
Contribution to Exports 2015
Change in Exports(R Millions)2014 - 2015
Year on YearGrowth Rate(2014 - 2015)
Five-yearaverage year
on year growth rate 2010 - 2015
Africa 6 035 13.3% 603 11.1% 38.5%
Americas 5 695 12.5% 1 190 26.4% 17.8%
Asia 11 246 24.7% -1 330 -10.6% 15.6%
Europe 20 727 45.5% 3 999 23.9% 2.2%
Oceania 1 808 4.0% 575 46.7% 23.4%
World 45 524 5 033 12.4% 10.0%
Source: Urban-Econ calculations based on Quantec, 2017b
The composition of the top ten commodities exported from the province remained unchanged between 2014 and
2015, baring the inclusion of medicaments at exclusion of combustion engines. Furthermore, the ranking of the
top three exported commodities by value was identical to the previous year. These three commodities included:
passenger motor vehicles, centrifuges and wool. Collectively these three commodities accounted for 53.1% of the
Eastern Cape’s total merchandise exports in 2015 (2014: 51.0%) and generated export revenue of R24.1 billion
(2014: R20.6 billion). If the remaining top seven commodities are included, these ten commodities accounted for
77.1% of the total value of all the Eastern Cape’s exports in 2015, slightly higher than the 76.9% recorded in 2014.
Between 2014 and 2015, the total value of the top ten commodities exported by the Eastern Cape increased by
12.7%, equating to a growth of R3.9 billion. This increase was driven by strong year-on-year growth in the value
of exports of citrus (32.0%), containers (23.5%), and vehicles used to transport passengers (18.6%). Over the 2010
to 2015 period, the average annual growth rate of these commodities was slightly more moderate, with citrus
increasing by 36.4%, containers by 16.4%, and vehicles used to transport passengers by 4.1%.
10 Note export values to not add up to world total due to the exclusion of the ‘not allocated’ category.
FIGURE 4.19 EXPORT COMPOSITION BY VALUE BY SOURCE MARKET REGIONS
FIGURE 4.20 EASTERN CAPE IMPORT COMPOSITION BY VALUE AND SOURCE MARKET REGIONS IN 2015
4.2%
8.9%
19.2%
65.5%
2.2%
13.3%
12.5%
24.7%
45.5%
4.0%
Africa
Americas
Asia
Europe
Oceania
2010 2015
6.1%8.7%
20.6%
63.6%
1.1%
Africa Americas Asia Europe Oceania Not allocated
61
2014 2015
Description Export Value
(R Millions)
Description Export Value
(R Millions)
1. Passenger motor vehicles 10 132 1. Passenger motor vehicles 12 014
2. Centrifuges, including centrifugal dryers 7 708 2. Centrifuges, including centrifugal dryers 8 908
3. Wool, not carded or combed 2 800 3. Wool, not carded or combed 3 231
4. Waste and scrap of precious metal 1 946 4. Waste and scrap of precious metal 2 423
5. Citrus fruit, fresh or dried 1 836 5. Citrus fruit, fresh or dried 2 073
6. Internal combustion piston engines
(diesel or semi-diesel)
1 725 6. Internal combustion piston engines
(diesel or semi-diesel)
1 645
7. Parts and accessories of the motor
vehicles
1 534 7. Parts and accessories of the motor
vehicles
1 483
8. Containers (including containers for the
transport of fluids)
1 333 8. Containers (including containers for the
transport of fluids)
1 173
9. New pneumatic tyres, of rubber 1 084 9. New pneumatic tyres, of rubber 1 117
10. Motor vehicles for the transport of goods
1054 10. Motor vehicles for the transport of goods 1 029
Total 31 151 Total 35 097
TABLE 4.15 EASTERN CAPE’S TOP TEN EXPORT COMMODITIES BY VALUE IN 2014 AND 2015
Source: Urban-Econ calculations based on Quantec, 2017b
4.6.2 IMPORTS
Eastern Cape merchandise imports increased to R56.5 billion in 2015, up by R8.7 billion from the R47.7 billion
recorded in 2014. This represents a year-on-year increase of 18.4% compared to a 9.8% increase recorded between
2013 and 2014. Between 2010 and 2015, Eastern Cape merchandise imports increased by an average annual rate of
14.8%, closely tracking exporting growth (10.0% year-on-year over the period).
In 2015, the main source market regions for Eastern Cape imports were Europe (63.6%), Asian (20.6%) and the
Americas (8.7%). Despite Europe’s importance as a source market for Eastern Cape imports, this region’s total
share of imports has declined somewhat since 2010, when imports from this region accounted for 64.6% of the
province’s imports. In contrast, African source market’s share of total imports has been steadily increasing from
0.8% in 2010 to 6.1% in 2015. This has seen merchandise imports from African source markets rise from R212.6
million in 2010 to R3.2 billion in 2015.
FIGURE 4.20 EASTERN CAPE IMPORT COMPOSITION BY VALUE AND SOURCE MARKET REGIONS IN 2015
Source: Urban-Econ calculations based on Quantec, 2017b
FIGURE 4.19 EXPORT COMPOSITION BY VALUE BY SOURCE MARKET REGIONS
FIGURE 4.20 EASTERN CAPE IMPORT COMPOSITION BY VALUE AND SOURCE MARKET REGIONS IN 2015
4.2%
8.9%
19.2%
65.5%
2.2%
13.3%
12.5%
24.7%
45.5%
4.0%
Africa
Americas
Asia
Europe
Oceania
2010 2015
6.1%8.7%
20.6%
63.6%
1.1%
Africa Americas Asia Europe Oceania Not allocated
62
The Americas accounted for R4.8 billion of the Eastern Cape’s imports in 2015, representing a year-on-year decrease
of 24.2% compared to an average annual import growth rate of 8.4% per year between 2010 and 2015. Imports from
Oceania totalled R447.3 million, the smallest source market for the Eastern Cape.
TABLE 4.16 EASTERN CAPE IMPORTS FROM REGIONAL SOURCE MARKETS BY VALUE
Value of Imports 2015 (R Millions)11
Contribution to Imports 2015
Change in Imports(R Millions)2014 - 2015
Year on YearGrowth Rate(2014 - 2015)
Five-yearaverage year
on year growth rate 2010-2015
Africa 3 437 6.1% 1 151 50.4% 74.5%
Americas 4 898 8.7% 954 24.2% 8.4%
Asia 11 631 20.6% 494 4.4% 13.4%
Europe 36 000 63.6% 6 081 20.3% 14.5%
Oceania 447 0.8% 96 27.3% 5.1%
World 56 568 8 788 18.4% 14.8%
Source: Urban-Econ calculations based on Quantec, 2017b
In terms of value, the top most imported commodities by the Eastern Cape in 2015 were motor vehicles; motor
vehicle components such as original equipment, seats and tires; and compounds used in the manufacturing of
beverages and specialised chemicals. These imports are all associated with major industries in the Eastern Cape
including vehicle manufacturing, beverage production, and chemical production. As the largest manufacturing
industry in the province, components for the automotive industry accounted for an 11.0% of all imports into the
Eastern Cape by value in 2015.
The 21.7% increase in original equipment component imports is likely attributable to the higher number of motor
vehicles manufactured in the Eastern Cape in 2015 that were dependent on imported components as part of their
assembly process. The increase in the value of imported passenger vehicles, which rose by 22.5% between 2014
and 2015 could however, suggest stronger economic growth in the Eastern Cape in the future. This is due to the
fact that motor vehicles purchases are frequently considered leading economic indicators
.
TABLE 4.17 EASTERN CAPE’S TOP TEN IMPORTED COMMODITIES IN 2014 AND 2015
Description
Import Value
(R Millions)Description
Import Value
(R Millions)
1. Motor vehicles principally designed for the
transport of persons
17 933 1. Motor vehicles principally designed for the
transport of persons
21 974
2. Parts and accessories of the motor
vehicles
2 868 2. Parts and accessories of the motor
vehicles
3 516
3. Motor vehicles for the transport of goods 2 826 3. Motor vehicles for the transport of goods 3 370
4. New pneumatic tyres, of rubber 1 176 4. Seats whether or not convertible into beds 1 466
5. Seats whether or not convertible into beds 1 057 5. Insulated wire and cable 1 428
6. Insulated wire and cable 696 6. New pneumatic tyres, of rubber 1 227
7. Mixtures of odoriferous substances and
mixtures (including alcoholic solutions)
652 7. Ceramic wares for laboratory, chemical or
other technical uses
806
8. Parts suitable for use solely or principally
for transmission apparatus
636 8. Mixtures of odoriferous substances and
mixtures (including alcoholic solutions)
684
9. Nucleic acids and their salts 572 9. Heterocyclic compounds 629
10. Prepared binders for foundry moulds or
cores
514 10. Nucleic acids and their salts 572
Total 28 931 Total 35 673
Source: Urban-Econ calculations based on Quantec, 2017b
11 Note import values to not add up to world total due to the exclusion of the not allocated category.
63
The cumulative value of the Eastern Cape’s top ten imported commodities increased by 23.3% in 2015, compared
to only 18.4% for all imported commodities. In 2015 there were only minor changes in the composition of the top
ten products by value imported into the Eastern Cape. New additions included: ceramic wares for laboratory,
chemical or other technical uses (R806.2 million) and heterocyclic compounds (R629.3 million).
4.6.3 TRADE BALANCE
Over the 2010 to 2015 period, the Eastern Cape was unable to register a trade surplus with the last trade surplus
being recorded in 2008 and 2009. The 2009 trade surplus of R789.6 million fell to a trade deficit in 2010, owing
to a 42.0% decline in exports on the back of the global financial crisis. Between 2010 and 2011, the Eastern Cape’s
trade balance, while still in deficit remained comparatively low, with a trade deficit of R22.5 million recorded in 2010
and R1.2 billion in 2011. In 2012, the Eastern Cape’s trade deficit increased significantly, due primarily to a sharp fall
in exports, which over the period decreased by 12.0% coupled with an 11.2% increase in imports.
The Eastern Cape’s trade deficit deteriorated further in 2013, falling to R10.6 billion by the end of the year, before
improving slightly to R7.2 billion by the end of 2014. The trade balance deteriorated further in 2015, increasing by
R3.7 billion to R11.0 billion. Again, this deterioration was driven by higher imports relative to exports.
While not as frequently used as the current account balance to GDP ratio, the ratio between the trade deficit and
GDP serves as a useful tool when analysing what impact a region’s net trade position has on its GDP. The Eastern
Cape’s trade deficit to GDP ratio averaged -2.9% between 2010 and 2015, nearly double the national average for
the same period of -1.5%.
The Eastern Cape’s trade with the rest of the world has grown steadily since 2010, however, as in the case with the
rest of South Africa, this trade has been biased towards the Eastern Cape’s major trading partners. This is evident in
Figure 4.21, which illustrates the Eastern Cape’s trade balance with selected regions in 2015. The Eastern Cape was
able to attain a positive trade balance with Africa (R2.5billion), followed by a R1.3 billion surplus with the Oceania
region. It is however, important to note that the Oceania region only accounts for 4.0% and 0.7% of the Eastern
Cape’s exports and imports, respectively.
The net trading position of those regions that registered a deficit in 2015 (i.e. Asia and Europe) has also deteriorated
notably since 2010, when the Eastern Cape registered a negative trade balance with Asia of R761.3 million and a
surplus of R256.1 million with Europe.
FIGURE 4.21 EASTERN CAPE’S TRADE BALANCE BY REGION IN 2015
Source: Urban-Econ calculations based on Quantec, 2017b
4.6.4 MAJOR TRADING PARTNERS
The top five destinations for Eastern Cape exports, that being Germany, United States, China, Hong Kong and
Namibia, accounted for 55.7% of total provincial exports in 2015 (see Table 4.18). This equated to approximately
R25.3 billion and represented a 21.5% increase from 2014, when these destinations contributed 51.6% to total
provincial exports by value by value (R20.8 billion).
FIGURE 4.21 EASTERN CAPE’S TRADE BALANCE BY REGION IN 2015
FIGURE 4.22 NATIONAL EQUITABLE SHARE ALLOCATION, 2016/17 – 2019/20
-18 000-16 000-14 000-12 000-10 000
-8 000-6 000-4 000-2 000
02 0004 000
Africa Americas Asia Europe Oceania
Rand
, Mill
ions
2010 2015
0
20
40
60
80
100
120
Rand
Bill
ions
2016/17 2017/18 2018/19 2019/20
64
TABLE 4.18 PROFILE OF TOP 5 DESTINATION FOR EASTERN CAPE EXPORTS
Export Destination
Exports
2015
(R Millions)
Export
Growth Rate
(2014 – 2015)
Trade
Balance
2015
(R Millions)
Top Three Exported Commodities
Germany 13 002 34.5% -11 146 Passenger motor vehicles; Centrifuges, including
centrifugal dryer; Parts and accessories of the
motor vehicles
United States 4 850 34.2% 2 920 Centrifuges, including centrifugal dryer; Parts and
accessories of the motor vehicles; Medicaments;
China
3 247 -11.9% -625 Wool; Raw hides, skins and leather
Hong Kong 2 249 6.6% 2 197 Waste and scrap of precious metal; Medicaments;
Citrus fruit, fresh or dried
Namibia12 2 030 12.3% 1 952 Passenger motor vehicles; Motor vehicles for the
transport of goods; Insulated wire and cable
Source: Urban-Econ calculations based on Quantec, 2017b
Similarly to the broader provincial export profile, the main goods exported to these markets are motor vehicles (for
the transport of both goods and passengers), vehicle components (particularly catalytic convertors), citrus, wool
and medicaments.
4.7 FISCAL FRAMEWORK
The modest economic outlook for South Africa, coupled with sluggish international growth, has inhibited the
expansion of the government revenue base, placing considerable pressure on provincial expenditure. In order to
address this, the Eastern Cape government has committed to optimise revenue collection over the 2016 Medium
Term Expenditure Framework (MTEF). This increase in own revenue generation will aid in funding provincial
priorities. The provincial government has also committed to fiscal consolidation through the reduction of
expenditure on compensation to employees – the provincial government’s largest expenditure item.
4.7.1 PROVINCIAL RECEIPTS
Provincial receipts refer to all income received or generated by a provincial government, in this case the Eastern
Cape. Provincial receipts comprise two major line items, namely transfers from national government in the form
of its equitable share and all conditional grants, and all other self-generated revenue (e.g. revenue from fines and
penalties, interest, financial asset transactions, etc.). The Eastern Cape’s annual provincial receipts as well as the
medium-term revenue estimates are presented in Table 4.19.
TABLE 4.19 PROVINCIAL RECEIPTS (RAND, BILLIONS)
Allocations to the provinces constituted 43.0% of the South African national budget in 2016. Over the 2017 MTEF
period, the allocations to the provincial sphere of government is anticipated to increase reflecting the priority
placed on front-line services such as health, education and basic services, as well as the rising cost of these services
due to higher wages, and higher bulk electricity and water costs.
Audited Outcome MainAppro-priation
AdjustedAppro-priation
RevisedEstimate
Medium term estimates
2013/14 2014/15 2015/16 2016/17 2017/18 2018/19 2019/20
Transfers from national 59.2 61.4 64.6 68.3 68.3 68.2 72.9 77.9 83.3
Equitable Share 49.8 51.7 54.8 58.0 58.0 58.0 61.8 66.1 70.9
Conditional grants 9.4 9.7 9.8 10.2 10.2 10.1 11.0 11.7 12.4
Provincial own receipts 1.2 1.5 1.6 1.1 1.6 1.3 1.5 1.5 1.7
Total Provincial Receipts 60.4 63.0 66.3 69.4 69.9 69.6 74.4 79.5 85.0
Source: Eastern Cape Provincial Treasury, 2016
12 Prior to 2014, the Eastern Cape did not register any exports to Namibia.
65
Transfers to the Eastern Cape increased from R68.3 billion in 2016/17 to R72.9 billion. Despite this increase,
provincial allocations have been reduced by R159.4 million over the 2017 MTEF due to the impact of new updates
to the Provincial Equitable Share (PES) formula. In addition, the province is losing R222.6 million due to national
government’s fiscal consolidation in order to fund new priorities in respect of education and health spending.
The province has experienced total reductions of R10.1 billion in the PES from the 2013 MTEF, which was mainly
as a result of the data from the 2011 Census. The Eastern Cape will continue to face funding challenges over the
MTEF period, mainly as a result of higher than budgeted 2015 public-sector wage settlements, which increased
compensation costs above budgeted amounts.
The 2017 MTEF provincial framework provides for a total fiscal envelope of R74.4 billion in the 2017/18 financial
year. This comprises national transfers in the form of PES (R61.8 billion) and conditional grants (R11.0 billion), as well
as the provinces own revenue (R1.5 billion).
4.7.2 EQUITABLE SHARE ALLOCATIONS
The Eastern Cape PES makes up the bulk of the provincial receipts from national government and is intended to
facilitate the province’s ability to deliver on its constitutional obligations. The PES allocation includes the full impact
of data updates which is phased in over the three years of the 2017 MTEF13.
FIGURE 4.22 NATIONAL EQUITABLE SHARE ALLOCATION, 2016/17 – 2019/20
Source: National Treasury, 2016
The Eastern Cape’s share of total PES allocations is anticipated to decrease from 14.1% in 2016/17 to 14.0% in
2019/20. Despite this decrease in the overall share of the national equitable share allocation, the Eastern Cape
remains the province that receives the third highest allocation in absolute terms. The PES of the Eastern Cape is
expected to increase by 6.6% in 2017/18 financial year, the lowest rate of increase across all provinces. The rate
of increase is anticipated to increase slightly in the 2018/19 financial year (7.0%) before reaching 7.3% in 2019/20.
13 The equitable share allocation takes into account the annual revision to the PES formula in accordance with the data from:• Census 2011 school age population• Updated information in respect of 2016 mid-year population estimates• 2016 School Realities Survey (SNAP Survey)• the 2014 GDP by region, • District Health Information Services for patient load data for the periods 2014/15 and 2015/16, • 2012 risk adjusted index for the risk equalisation fund • Insured population from the 2015 General Household Survey • The 2010 Income and Expenditure Survey.
FIGURE 4.21 EASTERN CAPE’S TRADE BALANCE BY REGION IN 2015
FIGURE 4.22 NATIONAL EQUITABLE SHARE ALLOCATION, 2016/17 – 2019/20
-18 000-16 000-14 000-12 000-10 000
-8 000-6 000-4 000-2 000
02 0004 000
Africa Americas Asia Europe Oceania
Rand
, Mill
ions
2010 2015
0
20
40
60
80
100
120
Rand
Bill
ions
2016/17 2017/18 2018/19 2019/20
66
4.7.3 PROVINCIAL PAYMENTS
Provincial expenditure is divided into three clusters (government, social and economic) comprising the various
provincial departments. Table 4.20 illustrates these clusters’ audited expenditure for the periods 2013/14 to
2015/16, their adjusted appropriation for 2016/17, and the indicative 2017 MTEF allocations.
It is evident from Table 4.20, that over the 2017 MTEF, the budget is projected to increase at an average annual
rate of 5.2%, from R69.9 billion in 2016/17 to R81.5 billion in 2019/20. Over the 2017 MTEF, the budget allocation
for the social cluster is projected to have the fastest growth (5.74.4% year-on-year between 2016/17 and 2019/20).
In line with the government priorities, spending over the 2017 MTEF will continue to focus on improving access to
education and health care. In order to achieve this, 79.4%, or R184.4 billion, of the provincial allocation in the 2017
MTEF has been allocated to the social cluster. The cut in the national equitable share allocation however, is likely
to adversely affect this figure.
TABLE 4.20 HISTORIC EXPENDITURE AGAINST 2015 MTEF ALLOCATIONS BY CLUSTER (RAND, BILLIONS)
2013/14 2014/15 2015/16 2016/17 2017/18 2018/19 2019/20 2013/14-
2016/17
2016/17-
2019/20
Audited
Adjusted
appropri-
ation
Medium term estimated Average growth
Governance Cluster 2.2 2.1 2.6 3.0 2.4 2.4 2.6 9.8% 4.9%
Office of the Premier 0.5 0.5 0.6 0.5 0.5 0.5 0.5 4.3% 1.3%
Provincial Legislature 0.4 0.4 0.5 0.5 0.5 0.5 0.6 3.9% 3.8%
Cooperative Govern-
ment and Traditional
Affairs
1.0 1.9 1.0 1.0 1.0 1.0 1.0 0.2% 0.3%
Provincial Treasury 0.3 0.3 0.5 0.9 0.4 0.4 0.4 41.6% -22.7%
Social Cluster 46.6 47.5 50.5 55.0 58.0 61.5 64.9 5.7% 5.7%
Health 17.0 17.5 18.9 20.6 21.5 22.8 24.1 6.6% 5.3%
Social Development 1.9 2.1 2.3 2.4 2.6 2.8 2.9 7.6% 5.9%
Education 26.8 27.0 28.4 31.0 33.0 34.8 36.8 5.0% 5.9%
Sport, Recreation, Arts
and Culture0.7 0.8 0.8 0.8 0.9 1.0 1.0 5.1% 6.0%
Safety and Liaison 0.1 0.1 0.1 0.1 0.1 0.1 0.1 9.5% 4.9%
Economic Cluster 11.3 11.2 12.1 12.1 12.8 13.4 14.2 2.2% 5.5%
Roads and Public
Works3.9 3.8 4.3 4.6 4.7 4.9 5.2 6.1% 4.4%
Rural Development and
Agrarian Reform1.7 1.9 2.0 2.2 2.2 2.3 2.4 8.6% 2.2%
Economic
Development,
Environmental Affairs
and Tourism
1.4 1.1 1.2 1.1 1.3 1.4 1.5 -5.7% 8.8%
Transport 1.5 1.7 1.7 1.7 1.8 1.9 2.1 4.7% 5.9%
Human Settlements 2.8 2.7 2.8 2.4 2.8 2.9 3.0 -5.8% 8.7%
Total 60.1 60.7 65.2 70.0 73.3 77.4 81.6 5.2% 5.2%
Source: Eastern Cape Provincial Treasury, 2016
In 2015/16, all departments except the Department Economic Development, Environmental Affairs and Tourism,
remained within their budget allocations with provincial spending amounting to R65.1 billion of the total adjusted
budget of R66.3 billion. This resulted in an under expenditure of R1.1 billion. The under expenditure was mainly on:
compensation of employees (R705.5 million), goods and services (R496.3 million), and payments of capital assets
by R84.6 million.
The overall growth for the social cluster remains at 5.7% when comparing the two MTEF periods (2013/14 to 2016/17
with 2016/17 to 2019/20). Education and health, which are core service delivery departments, will continue to be
prioritised by the provincial government. Funding allocated to education will focus on improving Early Childhood
67
Development (ECD) and skills training through improving the quality of teaching and learning. The health allocation
will prioritise building capacity to reduce the number of medical legal claims and reducing the communicable and
lifestyle diseases through the Primary Health Care (PHC) approach.
Funding allocated to the economic cluster will focus on transforming the economy so as to create jobs and
sustainable livelihoods; the construction of economic infrastructure; local economic development; supporting
SMMEs; and stimulating rural development, land reform and food security.
4.8 SUMMARY
The Eastern Cape has, in the past years, been unable to outperform the national economy’s GDP performance.
This is despite weak national growth on the back of global economic uncertainty. While this will likely remain true
over the forecasted period, due to the constraints to growth, it is still anticipated that the province will be able to
achieve moderate growth. Economic growth in the Eastern Cape is expected to be between 1.0% and 1.7% between
2017 and 2019, primarily driven by the tertiary sector. Despite weak economic performance, automotive exports
from the Eastern Cape have increased notably on the back of a weaker rand.
Change in the labour market over the past five years has been relatively subdued, both nationally and provincially.
In the Eastern Cape, employment growth was lower than the growth in the provincial labour force. Despite this,
employment growth in the province was slightly higher than the national rate. The provincial labour force expanded
at a more rapid pace than what was observed nationally, partly related to a higher number of job seekers in the
province. These trends combined to put upward pressure on the provincial unemployment rate which rose to 41.3%
in the third quarter of 2016
Working-age individuals within the Eastern Cape are less likely to participate in the labour market than are their
counterparts nationally, leading to high unemployment rates. This pattern is observed across a wide variety of
demographic characteristics, but is particularly strong amongst Black Africans, 15 to 24 year olds, and those with
only some secondary school education.
A more inclusive growth path in the South African context requires a focus on growing employment in a way that
promotes a more labour intensive production structure and makes more intensive use of less skilled workers. This
is particularly true for the Eastern Cape economy. The strengthening of investments in human capital formation (in
education and training, and health) remains a key factor in improving the ability of the labour force to compete in a
global economy. This is particularly important given the Eastern Cape’s poor attainment levels in mathematics. It is
likewise important to ensure that those that have exited the education system early, and who may be marginalised
in the labour market, are able to access further skills development.
68
Photograph: Rob Duker
70
The South African automotive industry is a vital contributor to
manufacturing, employment and foreign exchange earnings.
The industry has developed over the decades to become
a globally competitive, integrated industry which supplies
domestic and international markets with motor vehicles and
automotive components.
Competition in the automotive industry is intensifying globally,
thus South Africa’s sustainability is inextricably linked to
maintaining and improving competitiveness in the local
industry at all tiers of the value chain.
The South African vehicle manufacturing and associated
industries is a major economic contributor and employer in
the country. In 2015 it accounted for a third of South Africa’s
manufacturing output and employed over 110 000 individuals
(ASCCI, 2016). Ranked 21st in the world production rankings,
South Africa has set a target of 1 million cars produced a year
by 2020. Although this target looks set not to be achieved
under the Automotive Production Development Programme
or APDP, the country has seen large scale capital investment
in increasing vehicle assembly.
The component sector with higher employment multipliers, is
a sector that has been identified as a priority for deepening
the automotive value chain and seeing real economic
transformation. The importance of localisation and developing
black industrialists is a key goal of the new Broad Based Black
Economic Empowerment (BBBEE) codes and the Automotive
Supply Chain Competitiveness Initiative (ASCCI) initiative.
This chapter highlights the international trends in the
automotive industry and new disruptive technologies
that could change the automotive landscape. It highlights
national performance on domestic sales, exports, production,
employment and investment. It highlights the performance
of the automotive component sector. The Eastern Cape
automotive industry is discussed in terms of its performance
and in light of the national and international trends. Lastly
new development and investments into the Eastern Cape
are discussed. Eastern Cape support organisations which
are driving competitiveness improvements, productivity and
growth are highlighted. Competitiveness is a theme that is
driving the automotive industry, this chapter analyses how the
Eastern Cape is responding to this calMuch of the focus of this
chapter is on national trends and policy, this is due to the local
automotive industry being a function of national performance
and policy.
4
150 or 36.0% of national component manufacturers located in Eastern Cape
45.9% of South African light vehicle assembly undertaken in Eastern Cape.
48.8% of Light Motor Vehicle Exports produced in the Eastern Cape.
BAIC investment of R11 billion at Coega IDZ. Whilst VWSA invests R4.5 billion on Uitenhage plant. Daimler announces East London as regional base of operations for their new global truck and bus strategy.
Eastern Cape Automotive Industry
71
5.1 International Automotive Industry
5.1.1 International Trends
The international automotive industry is characterised by highly competitive value chains and assembling on high
volume, vehicle platforms which source components from suppliers internationally. Automotive industry investment
decisions are driven by global considerations. There has been a decline in the number of Tier 1 firms internationally,
as larger firms supply an increasing percentage of components on motor vehicles. Cost reductions are an integral
part of the industry and Original Equipment Manufacturers (OEMs) will work with their Tier 1 suppliers on an
open-book costing basis, to decide up-front margins and fair price. OEMs produce only a small percentage of the
vehicle’s value in-house averaging 30%-40%, the remaining value is supplied by a few Tier 1 and a multitude of 2nd,
3rd and lower tier suppliers.
China has cemented its position as the largest automotive market in the world and the largest supplier, bypassing
the US, the traditional leader in production and sales. In 2015 the domestic economy in Brazil and Russia put the
brakes on vehicle sales, this was echoed in general across emerging economies. Whilst over this period the US and
the EU saw a significant improvement in sales.
The international automotive industry was also rocked by a number of scandals in 2016 around emissions
certifications, the delaying of safety recalls and inflated mile-per-gallon statements. This has resulted in feelings
that, internationally, the industry needs to improve its image and that it has lost the confidence of many of its
customers (KPMG, 2016a).
The volatility in international markets is very much a threat going into 2017. In the 2016 KPMG Global Automotive
Executive Survey, CEOs indicated that they were less confident about global economic and business growth
prospects going forward. Global executives however, rated South Africa as the second emerging market to see
growth following Thailand. Global executives see the growth in emerging markets as not being saturated and
that there still exists opportunities for a second round of mass expansion into emerging markets. The favourable
position of South Africa in the minds of global automotive CEOs for future expansion was based on political and
economic stability and the market potential of the region (KPMG, 2016a).
Increasing disruptions to the industry are expected as new technologies take hold. The rising importance of IT
companies and the connected car is expected to shift the customer relationship increasingly away from the OEM
to the tech company (KPMG, 2016a). See Text Box 5.1 for a discussion of disruptive new technology that is on the
horizon for the automotive industry.
The other international trend which will impact on the industry will be the stricter regulations around fuel efficiency.
By 2025 all European and US manufactured models will need to meet the 60 miles per gallon or 4.7 litres per 100
kilometres benchmark. This will be challenging in the face of low oil prices and persistent consumer demand for
SUV and larger models (KPMG, 2016a).
Text Box 5.1: The Countdown to Disruption Starts Now
The automotive industry is developing at a rapid rate. A number of key industry disruptors are on the horizon,
with 80% of global automotive executives predicting that connectivity and digitalisation will disrupt the
industry in the next decade (KPMG, 2016a). “… in design rooms and on factory floors, auto companies were
dabbling with new technologies and vehicle concepts that have the potential to transform the automobile
(and transportation more broadly) in perhaps the most dramatic fashion since Ford rolled out the Model T”
(KPMG, 2016b:1).
The first disruptor is the connected car - a fully digitised vehicle. Sporting Wi-Fi; advanced infotainment
systems and apps; vehicle-to-vehicle communications which will allow vehicles on the road to “talk” to each
other, exchanging basic safety data such as speed and position; real-time location services and routing based
on traffic conditions; and networked Web links that facilitate vehicle diagnostics and repairs (KPMG, 2016b).
The intelligent car - is the forerunner to a fully autonomous car and is in advanced stages of development. An
intelligent car relinquishes some control from the driver to the information systems of the motor vehicle. This
includes self-braking, self-parking, automatic cruise control based on road conditions, automatic accident-
avoidance features, computer-operated power steering, and electric parking brakes, as well as electronic
throttles and engine control.
72
The autonomous car may be the stuff of science fiction but the elements of an autonomous car are now
under development and could be seen in the future on our roads.
The other significant disruptor to the industry is that of electric and hybridised motor vehicles. There take-
up in developed countries looks set to increase with new markets opening up in developing countries such
as South Africa.
These rising technology developments mean that OEMs face a challenge between designing and manufacturing
the traditional powertrain models while trying to stake a claim in the emerging technologies market.
5.1.2 Global Production and Sales Performance
South Africa is ranked 22nd in the world, for motor vehicle production, producing 0.6% of the world’s vehicles in
20161 (OAIC, 2016a). Internationally a production share of 1.0% is considered a significant player in the automotive
industry. The main producer of automobiles in the world is China with 12.8 million units produced or 27.7% of
world’s production. The second largest producer is the US which in 2016 produced 13.4% of the world’s production
or 6.2 million vehicles. Other major producers are Japan, Germany, South Korea and India.
Table 5.1 Ranking of Global Motor Vehicle Producers, 2016
Ranking Country Production Units Proportion
1 China 12 892 154 27.7%
2 US 6 255 476 13.4%
3 Japan 4 494 583 9.7%
4 Germany 3 193 975 6.9%
5 South Korea 2 195 843 4.7%
6 India 2 186 655 4.7%
7 Mexico 1 737 313 3.7%
8 Spain 1 621 017 3.5%
9 Canada 1 244 808 2.7%
10 France 1 142 000 2.5%
11 Brazil 1 016 680 2.2%
12 United Kingdom 944 778 2.0%
13 Thailand 903 380 1.9%
14 Turkey 725 477 1.6%
15 Czech Republic 722 236 1.6%
16 Russia 612 130 1.3%
17 Indonesia 601 461 1.3%
18 Italy 572 740 1.2%
19 Slovakia 570 000 1.2%
20 Iran 562 374 1.2%
21 Poland 378 365 0.8%
22 South Africa 288 372 0.6%
Source: OICA, 2016a
1 The year ending 2nd quarter 2016.
73
China surpassed the US as the world’s largest producer in the 2000s. The rise of China as the dominating global
automotive producer is highlighted in Figure 5.1, indicating market share of production over time. In 2006 the US
was the world’s largest producer with 16.3% of global production. This dropped dramatically to 10.8% by 2011, with
China increasing its production share from 10.3% to 23.0%. In addition, countries such as Japan and Germany have
seen a decline in their market share of production, Japan from 16.6% in 2006 to 9.7% in 2016 and Germany from
8.4% in 2006 to 6.9% in 2006. South Africa’s percentage of global production is small by global standards and
has declined over the period from 0.8% in 2006 to 0.6% in 2016. Developing economies such as Mexico and India
have seen an increase in their share of global production over the period. The remainder of motor manufacturing
countries making up ‘other’ have seen their proportion of manufacturing decline over the period.
Figure 5.1 Composition of Global Production over TimeFigure 5.1 Composition of Global Production Over Time
Figure 5.2 Composition of International Motor Vehicle Sales, 2016
10.4%23.1% 26.5% 27.7%
16.3%
10.8%13.4% 13.4%16.6%
10.5%10.2% 9.7%8.4% 7.7%6.8% 6.9%
0.8% 0.7%0.7% 0.6%
41.7% 39.0% 33.9% 33.2%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2006 2011 2015 2016
China USA Japan Germany India Mexico South Africa Other
China33%
USA11%
Japan 6%Germany 5%
UK 4%India 4%
France 3%Italy 3%
South Korea 3%Brazil 2%
Other26%
Passenger Vehicle Sales
USA44%
China15%
Canada 6%
Japan 4%India 3%
Mexico 2%
France 2%
Other24%
Commerical Vehicle Sales
Source: OICA, 2016a
The motorisation rate is the number of passenger cars per 1 000 inhabitants. It is used as a measure of the prevalence
of motor vehicles in a locality and offers comparisons of economic development and environmental issues. A high
motorisation rate corresponds with a high level of economic development and asset ownership. On the other hand,
having a high motorisation rate means a burden on the environment associated with extensive use of fossil fuels,
high contribution to air pollution and more extensive road networks which encroach on public space and fragment
natural habitats. It can also imply a lack of alternatives such as safe and efficient public transport. South Africa in
2014 had a motorisation rate of 180, only slightly above the world average rate of 179. The country in the world with
the highest motorisation rate is Puerto Rico with 892 vehicles per 1 000 inhabitants. The average motorisation rate
in Africa is 44 compared to Europe where it is 480. South Africa has the third highest motorisation rate in Africa,
with the highest in Libya (415) followed by the Congo (405). The low motorisation rate offers opportunities for
increasing market share but also implies that South Africa has a relatively small domestic market (OICA, 2016c).
Figure 5.2 indicates the composition of international vehicle sales, with China the largest market for passenger cars
(32.6%) followed by the US with 10.4%. The largest market for commercial vehicles is still the United States with
44.2% of the market, followed by China with 15.0% (OICA, 2016b).
74
Figure 5.2 Composition of International Motor Vehicle Sales, 2016
Source: OICA, 2016b
Globally there was a 2.0% growth in passenger car sales and a 5.1% increase in commercial vehicles sales year on
year between the 2nd quarter 2015 and 2nd quarter 2016 (OICA, 2016b). This points to a positive trend going for-
ward as many economies, especially developed economies bounce back. However economic volatility in developed
and emerging markets is still a major concern going into 2017, and could see mixed sales results across regions.
5.2 South African Automotive Industry
The automotive industry in South Africa accounts for
7.5% of GDP of which 4.8% is based on manufacturing
and 2.7% on retail. Total industry contribution has
increased from 7.2% of GDP in 2014. The automotive
industry accounts for 33.5% of manufacturing output
up from 30.2% in 2014. Automotive exports accounted
for 14.6% of all South African exports in 2015.
Automotive assemblers employed 31 260 people, and
component manufacturers employed 82 100 in 2016
(AIEC, 2016). The APDP has had a significant impact
on attracting and retaining vehicle assemblers as seen
in the R7.6 billion capital investment by assemblers in
2016. The most recent performance data is provided
below and a more detailed analysis of national sales,
production, export and employment trends is provided
in this section.
‘The MIDP, implemented in 1995, and its
successor, the APDP, implemented in 2013,
represent some of the most innovative and successful programmes to retain a domestic vehicle and component manufacturing
industry, which has continued to contribute
positively to the South African economy and
society. In South Africa, the automotive sector
is the mainstay of the national industrial base.
Accounting for 7.5% of GDP (breakdown –
4.8% manufacturing and 2.7% retail), 33.5%
of manufacturing output and 14.6% of all
South African exports in 2015, the industry
demonstrates what can be accomplished
when constructive collaboration between
stakeholders takes place.’ (AIEC, 2016:18)
5.2.1 National Performance in 2016
In 2016 up to the 3rd quarter, domestic sales were slow and it has proved a difficult year for the domestic market,
however export sales did somewhat offset the drop. By the 3rd quarter 2016 employment levels improved by an
additional 205 jobs or 0.8% increase in employment on the 2nd quarter 2016. This brought employment in vehicle
manufacturing to 31 389, an increase on the average number of persons employed in 2015 at 31 260 and a further
increase on the 29 715 persons employed in 2014 (NAAMSA, 2016c).
The rand’s depreciation in 2016 offered some benefits for importers of original equipment components. The supply
of locally produced components in 2016 was satisfactory but volume reductions have placed cost pressures on
component manufacturers. Component prices were affected by production price movements which were higher
than consumer price inflation. With vehicle production inflation standing at 14.0% for the first three quarters of
2016 (NAAMSA, 2016a).
At the beginning of 2016 industry capital expenditure was projected at R7.6 billion. This figure does not include the
BAIC investments which will be accounted for in the 2017 figures (NAAMSA, 2016a).
Figure 5.1 Composition of Global Production Over Time
Figure 5.2 Composition of International Motor Vehicle Sales, 2016
10.4%23.1% 26.5% 27.7%
16.3%
10.8%13.4% 13.4%16.6%
10.5%10.2% 9.7%8.4% 7.7%6.8% 6.9%
0.8% 0.7%0.7% 0.6%
41.7% 39.0% 33.9% 33.2%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2006 2011 2015 2016
China USA Japan Germany India Mexico South Africa Other
China33%
USA11%
Japan 6%Germany 5%
UK 4%India 4%
France 3%Italy 3%
South Korea 3%Brazil 2%
Other26%
Passenger Vehicle Sales
USA44%
China15%
Canada 6%
Japan 4%India 3%
Mexico 2%
France 2%
Other24%
Commerical Vehicle Sales
75
Figure 5.3 South African Total Production, Local Sales and Export Volumes
0
100 000
200 000
300 000
400 000
500 000
600 000
700 000
800 000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Uni
ts
Local sales Exports Domestic Production
Figure 5.4 Growth in South African Total Production, Local Sales and Export Volumes
-60%
-40%
-20%
0%
20%
40%
60%
80%
Local sales Exports Domestic Production
Projected
The Utilisation of Production Capacity which is a measure of how busy production lines have been was variable
with heavy and medium commercial having high utilisation rates whilst passenger car and light commercial having
lower than average utilisation rates (NAAMSA, 2016a).
By the 3rd quarter 2016 there was a sharp decline in exports to African countries (-53.6%) between 3rd quarter
2016 and 3rd quarter 2015. This was due to changes in Nigeria and Zimbabwe’s ad-hoc duties, new restrictions into
Algeria and general weaker economic conditions in Africa due to low commodity prices. Exports to Asia increased
by 47.7% to 36 319 units by 3rd quarter 2016 and there was also an improvement in growth of exports to Europe
by 8.7% (NAAMSA, 2016a).
Figure 5.3 provides a historic overview of South African vehicle production and sales trends over a 16-year period
and projections for 2016 and 2017. Production levels increased from 2010 onwards as the APDP incentives came
into effect. Growth in 2010 production increased by 26.2% after a sizeable decline after the economic crisis in 2009
(NAAMSA, 2016c).
The prospects are not rosy for the domestic car market and especially new vehicle sales. With a weaker economic
outlook for 2017 and increasing pressure on the consumer’s disposable income. Double digit price increases in
car prices due to earlier rand weakness have also increased the cost of buying. Local sales which includes both
domestically produced and imported vehicles declined in 2015 after four years of positive growth. Expectations for
local sales are down in 2016 by -11.5% at 547 000 units. NAAMSA projected that with a national economic growth
rate of 1.5% in 2017, new vehicle sales could improve somewhat to 565 000 units, a year-on-year growth of 3.3%
(NAAMSA, 2016c).
Exports have been steadily increasing since 2010, with expectations in 2016 of an increase to 346 000 units and by
2017 to increase by a further 8.7% to 376 000 (NAAMSA, 2016c).
Figure 5.3 South African Total Production, Local Sales and Export Volumes
Source: NAAMSA, 2016c
76
Figure 5.5 Composition of Domestic Vehicle Sales by Vehicle Type, 2015
Figure 5.7 Composition of Domestic Market – Locally Produced vs Imported Passenger Motor Vehicles
Passenger Car67%
Light Commercial 28%
Medium and Heavy Commercial
5%
27.3%
73.0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Local Sales Domestically Produced Passenger Car Imports
Figure 5.6 Growth in Domestic sales by Vehicle Type
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
Passenger Car Light Commercial Medium and Heavy Commercial Total
Projected
Figure 5.4 Growth in South African Total Production, Local Sales and Export Volumes
Source: NAAMSA, 2016c
5.2.2 Domestic Motor Vehicle Sales
Domestic motor vehicle sales in South Africa totalled 617 749 in 2015, the majority of these sales were in the
passenger car market with 412 670 units sold or 66.8% of the market. Light commercial vehicles make up 28.3% of
the market with 174 544 units. Other commercial vehicles which includes medium, heavy commercial vehicles make
up the remainder of the sales with 4.9% or 30 535 units (NAAMSA, 2016c).
Figure 5.5 Composition of Domestic Vehicle Sales by Vehicle Type, 2015
Source: NAAMSA, 2016c
Figure 5.6 indicates that all most all growth in domestic vehicle sales of all vehicle types was negative in 2015. There
was a sizeable correction in sales post the financial crisis in 2010 with sales of all vehicle types rising. Since then
growth rates have been falling. In 2015 growth in passenger vehicle sales was down -6.0%, light commercial was
up 0.5%, other commercial was down -3.2%. The decline in passenger car sales is attributed to the weak economic
climate domestically, increases in interest rates, consumer spending pressures and the inflationary pressures on the
cost of vehicles due to the depreciating rand, increasing retail prices (NAAMSA, 2016c).
Growth in domestic sales for 2016 is expected to decline by 11.5% with the passenger cars and other commercials
leading the decline, with a drop of -12.8% and -11.6% respectively. In 2017 growth is expected to rebound to 3.3%;
led by passenger cars (3.9%) and other commercial vehicles (3.7%).
Figure 5.3 South African Total Production, Local Sales and Export Volumes
0
100 000
200 000
300 000
400 000
500 000
600 000
700 000
800 000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Uni
ts
Local sales Exports Domestic Production
Figure 5.4 Growth in South African Total Production, Local Sales and Export Volumes
-60%
-40%
-20%
0%
20%
40%
60%
80%
Local sales Exports Domestic Production
Projected
77
2 2016 data is projected data from NAAMSA as at the time of writing as 4th quarter data was unavailable.
The South African automotive market is one of the most competitive trading markets in the world, with 55 brands
and 2 872 passenger car model derivatives to choose from (AIEC, 2016). Thus South Africa has the largest choice
to market–size ratio in the world (AIEC, 2016). Figure 5.7 indicates how the composition of domestic sales of
passenger cars between locally produced and imported models, has changed over time. Figure 5.8 meanwhile
indicates the number of imported cars into South Africa. In the early 2000s, the majority of passenger cars were
local produced compared to today where 73% of sales in 2015 are imported models. This is due to manufacturers
producing a greater variety of models for the consumer, as well as OEMs moving to single platform models at
their plants globally. Passenger vehicles which are produced in South Africa include the BMW 3 Series, the General
Motors Chevrolet Spark, Ford Ranger and Everest, Mercedes-Benz C Class, Toyota Corolla and Fortuner and
Volkswagen Polo new model and Vivo (AIEC, 2016).
Figure 5.7 Composition of Domestic Market – Locally Produced vs Imported Passenger Motor Vehicles
Source: NAAMSA, 2016c
Figure 5.5 Composition of Domestic Vehicle Sales by Vehicle Type, 2015
Figure 5.7 Composition of Domestic Market – Locally Produced vs Imported Passenger Motor Vehicles
Passenger Car67%
Light Commercial 28%
Medium and Heavy Commercial
5%
27.3%
73.0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Local Sales Domestically Produced Passenger Car Imports
Figure 5.6 Growth in Domestic sales by Vehicle Type
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
Passenger Car Light Commercial Medium and Heavy Commercial Total
Projected
Source: NAAMSA, 2016c
Figure 5.5 Composition of Domestic Vehicle Sales by Vehicle Type, 2015
Figure 5.7 Composition of Domestic Market – Locally Produced vs Imported Passenger Motor Vehicles
Passenger Car67%
Light Commercial 28%
Medium and Heavy Commercial
5%
27.3%
73.0%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Local Sales Domestically Produced Passenger Car Imports
Figure 5.6 Growth in Domestic sales by Vehicle Type
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
Passenger Car Light Commercial Medium and Heavy Commercial Total
Projected
Figure 5.6 Growth in Domestic sales by Vehicle Type2
78
Figure 5.8 Total Number of Motor Vehicles and LCVs Imported into South Africa
0
50000
100000
150000
200000
250000
300000
350000
400000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Uni
ts
Motor Vehicles Light Commercial Vehicles (LCV)
13%10%
Gauteng44%
Free State4%
Eastern Cape5%
Western Cape15%
North West15%
Northern Cape1%
Mpumalanga6%
Limpopo4%
KZN6%
Figure 5.9 Overall New Vehicle Market Share by Manufacturer, 2015
Toyota 20%
VW/ Audi16%
Ford13%AMH
10%
GM9%
Nissan8%
Mercedes Benz5%
BMW Group4%
Renault3%
Honda2% Other
10%
3 Values exclude those imported vehicles that are re-exported.
Figure 5.8 Total Number of Motor Vehicles and LCVs Imported into South Africa3
Of the top 10 most popular car models for buyers in SA in 2015, five are locally produced commercial vehicles: the
Toyota Hilux, Ford Ranger, Nissan NP 200, Chevrolet Utility and Isuzu KB. Four are locally produced passenger cars
that being the VW Polo Vivo, VW Polo, Toyota Corolla and Corolla Quest and Mercedes C-Class. The only import
to make the top 10 is the Toyota Etios imported from India (AIEC, 2016).
Figure 5.9 indicates the top selling brands in South Africa. Toyota South Africa continues to be the market leader in
domestic new vehicle sales with 19.9% of the market in 2015. A lead it has maintained for 36 years. The other brands
leading in the South African market are Volkswagen Audi (15.9%), Ford Motor Company (12.7%) and AMH Holdings
(9.6%), which offers the Kia and Hyundai brands (AIEC, 2016).
Figure 5.9 Overall New Vehicle Market Share by Manufacturer, 2015
Source: Quantec, 2016
Diesel vehicle sales are increasing in popularity year-on-year, diesel vehicles now account for 32.1% of new
passenger and light commercial vehicles sold. Whilst hybrid car sales have also seen an increase in South Africa for
2015 but off a small base. In 2015, 502 hybrids were sold nationally whilst all-out electric vehicles increased to 79
units sold. The main electric vehicle models available on the South African market are the Nissan Leaf, BMW i3 and
BMW i8 (AIEC, 2016). See Text Box 5.2 for a discussion of the national research platform based in the Eastern Cape
which is pioneering the adoption of e-mobility in South Africa.
Figure 5.8 Total Number of Motor Vehicles and LCVs Imported into South Africa
0
50000
100000
150000
200000
250000
300000
350000
400000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Uni
ts
Motor Vehicles Light Commercial Vehicles (LCV)
13%10%
Gauteng44%
Free State4%
Eastern Cape5%
Western Cape15%
North West15%
Northern Cape1%
Mpumalanga6%
Limpopo4%
KZN6%
Figure 5.9 Overall New Vehicle Market Share by Manufacturer, 2015
Toyota 20%
VW/ Audi16%
Ford13%AMH
10%
GM9%
Nissan8%
Mercedes Benz5%
BMW Group4%
Renault3%
Honda2% Other
10%
79
The majority of new vehicle sales were through dealer networks and these accounted for 79.7% of sales whilst the
car rental industry is a significant buyer and this accounted for 12.5% of new sales in 2015 (AIEC, 2016).
Total domestic car sales – thus including passenger and commercial vehicle sales – were predominantly in the three
largest provincial economies of the country, that being Gauteng with the single largest share at 35.9% of national
car sales in 2015. Followed by KwaZulu-Natal with 13.5% and the Western Cape with 12.2%. The Eastern Cape sales
made up 4.3% or 25 112 vehicles in 2015 (Quantec, 2016).
The Eastern Cape has seen three consecutive years of negative growth in car sales 2013-2015, this is on the back
of three years of positive car sales growth from 2010 to 2012. This mirrors the national domestic car sale market
which saw negative year-on-year growth in 2014 (-1.9%) and 2015 (-4.0%) after a period between 2010 and 2013 of
positive growth (Quantec, 2016).
Figure 5.10 Composition of National Total Domestic Vehicles Sales by Province, 2015
Source: Quantec, 2016
Figure 5.11 Eastern Cape Total Domestic New Vehicle Sales Year-on-Year Growth
Source: Quantec, 2016
Figure 5.8 Total Number of Motor Vehicles and LCVs Imported into South Africa
0
50000
100000
150000
200000
250000
300000
350000
400000
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Uni
ts
Motor Vehicles Light Commercial Vehicles (LCV)
13%10%
Gauteng44%
Free State4%
Eastern Cape5%
Western Cape15%
North West15%
Northern Cape1%
Mpumalanga6%
Limpopo4%
KZN6%
Figure 5.9 Overall New Vehicle Market Share by Manufacturer, 2015
Toyota 20%
VW/ Audi16%
Ford13%AMH
10%
GM9%
Nissan8%
Mercedes Benz5%
BMW Group4%
Renault3%
Honda2% Other
10%
Figure 5.11 Eastern Cape Total Domestic New Vehicle Sales Year on Year Growth
Figure 5.12 Automotive Industry Capital Investment
Figure 5.13 Employment in Vehicle Manufacturers
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As regards, heavy and medium motor vehicle sales in the domestic market, truck sales were down in 2015 in part
due to weaker economic conditions and business confidence. The bus market saw the largest percentage decline
(10.7% with only 1 119 units sold). The medium commercial segment saw 10 458 units sold in 2015 down from 11 017
in 2014 (NAAMSA, 2016c).
5.2.3 Motor Industry Exports
South African automotive exports have shown resilience in the face of adverse economic conditions globally. Exporting
is seen as a necessary step to improve the competitiveness of the local automotive industry. South Africa’s total export
earnings for 2015 totalled R151.5 billion up from R115.7 billion in 2014 a 30.9% increase (AIEC, 2016). Whilst component
exports increased in 2015 to R49.6 billion up from R45.7 billion in 2014. Automotive exports make up 14.6% of total
South African exports by value. The South African auto industry exports to 140 countries around the world and has seen
30 countries in 2015 double their export value. South Africa’s main export partner in terms of value is Germany, but in
terms of volume it is the United Kingdom with the main export region being the EU (AIEC, 2016).
5.2.3.1 Motor Vehicle Exports
Vehicle exports totalled R101.9 billion in 2015 up from R70.1 billion in 2014 (AIEC, 2016). The performance of exports
has been favourable but will remain a function of the performance of global markets and the policies of the OEMs
whether to continue manufacturing lines in a particular country or another. The outlook for exports in 2016 and
2017 is also favourable as there has been economic improvements experienced in the United States and the EU.
Whilst Asian export markets continue to grow.
Table 5.2 Exports as Percentage of Production – Light vehicles
In 2015 passenger car and light commercial vehicle exports made up 67.0% and 42.3% of all local production. With
228 459 passenger cars and 102 664 light commercial vehicles exported. Growth in exports has been positive for
passenger cars rising 47.5% compared to a drop of 13.4% for light commercials (AIEC, 2016).
Table 5.3 Exports as Percentage of Production – Medium and Heavy Commercial Vehicles
Medium and Heavy Commercial Vehicles
Domestic Volume Export Volume ExportsPercentage
ofProduction
Growthyear-on-year
2011 26 656 1 076 2.9 -6.7%
2012 27 841 1 076 3.7 34.0%
2013 30 924 1 206 3.8 12.1%
2014 31 558 1 414 4.3 17.2%
2015 30 535 1 124 3.6 -20.5%
Source: NAAMSA, 2016c and AIEC, 2016
Table 5.3 provides export breakdown for medium and heavy commercial vehicles. The percentage of local
production exported is much lowered for other commercial vehicles at only 1 124 units or 3.6% in 2015. This was
also a decline on the previous year when 1 414 vehicles were exported (AIEC, 2016).
The main export destinations for the national automotive industry has traditionally been Europe, Japan and North
Passenger Cars Light Commercial Vehicles
ExportVolume
ExportsPercentage
ofProduction
Growthyear-on-year
Export Volume
ExportsPercentage
ofProduction
Growthyear-on-year
2011 187 529 60.1 3.2% 84 125 43.6 47.7%
2012 151 659 55.7 -18.3% 123 443 50.4 46.7%
2013 151 893 57.3 -0.9% 121 345 48.9 -1.7%
2014 154 920 55.8 2.0% 118 585 46.4 -2.3%
2015 228 459 67.0 47.5% 102 664 42.3 -13.4%
Source: NAAMSA, 2016c and AIEC, 2016
81
America. These long-standing trading partners continue to be of great importance to the country, however newer
trading partners have shown significant growth such as China, other Asian countries and Africa. The wider exposure
of South African exports also mitigates the impact of a country or region specific economic slumps. Table 5.4
indicates the main export destinations by value for South Africa.
Table 5.4 South Africa’s Top 5 Export Countries by Value, 2014 and 2015
Country 2014 Value R Millions
2014 Ranking
2015 Value R Millions
2015Ranking
Germany 21 651.5 1 34 992.1 1
USA 17 145.0 2 20 946.9 2
Belgium 8 157.9 3 13 162.2 3
Namibia 8 322.1 4 9 440.0 4
Japan 6 616.8 5 7 809.5 5
Source: AIEC, 2016
5.2.3.2 Component Exports
The South African components industry is made up of a wide range of manufacturers producing parts from
catalytic converters and exhaust systems, trim, harnesses, electronics, just-in-time assemblies, bearings, shocks,
filters, plugs, machined and plastic components, tyres and toughened glass (ASCCI, 2016). These component
manufacturers are clustered near OEMs, around the country. In the Eastern Cape, Nelson Mandela Bay has 30% and
Buffalo City 6% of all national component manufactures (ASCCI, 2016).
The automotive component industry is highly competitive and consists of a number of sub-sectors, different cost
drivers, supply chain challenges and requirements. The first-tier component manufacturers are 75% foreign owned
multi-national corporations whilst domestic firms are represented within the 2nd and 3rd tier manufacturers. The
lower tiers are where the most improvements around competitiveness are required.
Exports of components were aided by weakening rand, the growth in motor vehicle exports and the flexibility of
domestic component manufacturers’ production as well as trade agreements with SADC, EU and the US. Main
export destinations are traditional markets but the increase in exports to Thailand, India and Taiwan has shown a
growth in demand from developing countries. India grew from South Africa’s 18th largest export market to its 8th
largest market in a year with exports totalling R1.4 billion and Thailand from 11th largest to 7th largest with exports
of R1.6 billion. The growth of India and Thailand as export destinations is highlighted in Table 5.6.
Table 5.5 South Africa’s Top 5 Export Automotive Component Countries by Value, 2014 and 2015
Country R Millions Ranking R Millions Ranking
Germany 12 486.9 1 13 681.9 1
USA 4 721.0 2 6 518.9 2
Namibia 2 660.9 3 2 790.7 3
UK 2 862.5 4 2 624.1 4
Spain 1 979.6 5 2 226.7 5
Source: NAAMSA, 2016c and AIEC, 2016
Table 5.6 Growth of India and Thailand as Export Destinations for Automotive Component Countries by Value, 2014 and 2015
Country 2014 Value R Millions
2014 Ranking
2015 Value R Millions
2015Ranking
Thailand 1 020.5 1 13 681.9 7
India 560.3 2 6 518.9 8
Source: AIEC, 2016
82
Figure 5.11 Eastern Cape Total Domestic New Vehicle Sales Year on Year Growth
Figure 5.12 Automotive Industry Capital Investment
Figure 5.13 Employment in Vehicle Manufacturers
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The main component export in value terms from the country are catalytic converters totalling R20.3 billion (AIEC,
2016). A breakdown of the main components sold to the German and United States markets are provided in Table
5.7.
Table 5.7 Main Component Exports to Germany and US
Country Total Component Exports
Top 10 Exports
Germany R13.6 billion
1. Catalytic Converters = R8.9billion2. Engine Parts = R974.4 million3. Stitched Leather seats/ parts = R581.2 million4. Radiators = R430.4 million5. Shock Absorbers = R322.0 million6. Tyres = R270.5 million7. Clutches / Shaft Couplings = R202.0 million8. Axles = R190.8 million9. Filters = R131.0 million10.Transmission shafts / cranks =R120.1 million
US R6.5 billion
1. Catalytic Converters = R4.0 billion2. Engine Parts = R922.5 million3. Radiators = R227.9 million4. Silencers/ exhausts = R177.5 million5. Tyres = R133.8 million6. Automotive tooling = R116.7 million7. Axles = R80.8 million8. Gear Boxes = R59.4 million9. Shock Absorbers / Suspension Parts = R57.6 million10.Gauges / Instruments = R52.2 million
Source: AIEC, 2016
5.2.4 Investment and Employment
The strategic location of South Africa to enter African markets and the security and support offered by APDP
makes for a compelling investment destination for OEMs. Thus the South African vehicle manufacturing base has
continued to increase with sizeable capital investments. In 2015 capital investment of R6.6 billion was recorded
with investment rising in 2016 to an estimated R7.6 billion.
Figure 5.12 Automotive Industry Capital Investment
Source: NAAMSA, 2016
Capital investment in the industry is reported annually by NAAMSA. The 2016 figure is a projection and is calculated
at the start of the calendar year. It thus does not include the BAIC investment into the country which will be
accounted for in the 2017 data. The high levels of investment in recent years and new investment coming on board
is attributed to the APDP support programme and the higher levels of production of export assemblers.
The main investment item within this capital investment is on production facilities, local content and export
investment which in 2016 accounted for R5.9 billion or 77.6% of the R7.6 billion. The next largest category of
investment was R1.1 billion or 15.4% on land and buildings and R464 million or 7.3% on support infrastructure
(NAAMSA, 2016).
83
Figure 5.13 Employment in Vehicle Manufacturers
Source: NAAMSA, 2016c
Employment in the automotive manufacturing sector has remained relatively stable over the last 4 years. In the
third quarter of 2016, NAAMSA estimated employment in vehicle manufacturing to be 31 389 persons. As can be
seen from Figure 5.13 employment in vehicle assembly underwent a major structural adjustment after 2008, with
employment levels not coming back to these levels 8 years on. This correlates to the increasingly competitive,
internationalised environment in which local assemblers operate in.
Employment in the industry as a whole includes retail and component manufacturers totalling 313 360 jobs in 2015.
A 0.3% increase in industry employment on the previous year of 312 505. Employment in component manufacturing
was 82 100 in 2015, down 0.8% from 82 790 in 2014 (AIEC, 2016).
5.3 Eastern Cape Automotive Industry
The Eastern Cape automotive industry is centred on Port Elizabeth and Uitenhage in the Nelson Mandela Bay
Metro and East London in the Buffalo City Metro. The key features of the Eastern Cape automotive industry are
summarised in Table 5.8 and Figure 5.14.
Figure 5.14 Eastern Cape and its Automotive Clusters
EASTERN CAPE
30% OF NATIONAL COMPONENT
MANUFACTURERS
6% OF NATIONAL COMPONENT
MANUFACTURERS Nelson Mandela Bay
East london
Figure 5.11 Eastern Cape Total Domestic New Vehicle Sales Year on Year Growth
Figure 5.12 Automotive Industry Capital Investment
Figure 5.13 Employment in Vehicle Manufacturers
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Source: Based on AIEC, 2016
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Table 5.8 Eastern Cape Automotive Industry
Mercedes-Benz South Africa is based in East London and its factory has the manufacturing capacity of 420 C-Class
Mercedes per day and an annual output of up to 100 000 units. In addition, the factory produces Freightliner and
Fuso trucks, and the Mercedes-Benz bus chassis. These operations are likely to expand as Daimler has designated
MBSA’s East London factory as the regional base for its Southern African truck and bus strategy. Based within the
East London IDZ are twelve Tier 1 suppliers to MBSA including Johnson Controls, Feltex, TI Automotive, Foxtex-
Ikwhezi and Molan Pino. They provide a range of components including tyres and rims, moulded carpets, seat pads
and head rests, seats, brake and fuel pipes, fuel tanks, exhaust manifolds, catalytic converter silencers and exhaust
systems.
NMBM has three automotive assemblers namely FAW for trucks, General Motors South Africa and Volkswagen SA.
The largest of these assemblers is Volkswagen which has the capacity to produce approximately 150 000 units
per annum expanding from 120 000 units in 2015. Ford produces engines in Port Elizabeth for the domestic and
international markets at its Struandale plant. The most recent entrant is FAW, a Chinese owned heavy commercial
vehicle manufacturer anticipated to produce 5 000 trucks per annum for both the domestic and international
market.
The production of light motor vehicles in the Eastern Cape as a percentage of the total light vehicle production
in South Africa is approximately half at 45.9%. Compared to Gauteng which has 30.9% of this production and
KwaZulu-Natal with 22.9%. Whilst Eastern Cape light motor vehicles exports as a percentage of total SA automobile
exports are 48.8%, compared to 33.3% in Gauteng and 17.1% in KwaZulu-Natal.
Competitiveness levels in the province are not expected to differ significantly from the national average. A
benchmarking study undertaken in 2015 on South African component manufacturers found that the sector is under
pressure but that there were positives emerging (SAABC, 2016). The benchmarking study looked at three areas,
growth, competitiveness and productivity. The study found that the satisfaction of OEMs with local suppliers
declined since 2012 and 2015 by 11%. This said OEMs were reported in the study to be interested in increasing
their local buying allocation. The largest opportunity was identified in increasing the range of products supplied to
OEMs, developing new products and then increasing the volume of components sold. In terms of competitiveness
– cost competitiveness was compared between South African firms and Thailand, India and US proxies. The analysis
found that South African manufacturers had a cost disadvantage to Thailand and Indian counterparts but were
more competitive than US firms. However, year-on-year South African cost competitiveness has improved with
inventory costs being the biggest cost challenge for local firms. Thai and Indian suppliers were also found to benefit
more from their countries trade protection whilst South Africa and the US were not as well protected (SAABC,
2016).
Productivity in South African component manufacturing has improved and there have been real advances. This is
despite SA being at a disadvantage compared to other country competitors. Productivity can be measured by the
value-added levels per unit of total employee cost, which have improved from 2.7 in 2012 to 2.9 in 2015. Nominal
employee value added has improved by R347 000 in 2012 to R446 000 in 2015. Absenteeism has continued to
improve and spend on training as a percentage of the remuneration bill (SAABC, 2016).
Key Indicators Eastern Cape
Names of OEM manufacturing Plants Volkswagen Group SA (VWSA) Mercedes-Benz SA (MBSA)
General Motors Southern Africa Ford Motor Company of Southern Africa - engine plant
Number of component manufacturers 150
Percentage of Component Manufacturers in Eastern Cape 30.0% in NMBM 6.0% in BCM
Motor Vehicle Parc4 as a percentage of SA’s Motor Vehicle Parc
6.6%
Production of Light Motor Vehicles (MV) as a percentage of the total light vehicle production in SA
45.9%
Light MV exports as a percentage of total SA automobile exports
48.8%
Source: AIEC, 2016 and ASCCI, 2016
4 Motor Vehicle parc is the number of motor vehicles in a country or region.
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5.3.1 Eastern Cape Support Institutions
The key support organisation within the Eastern Cape for the automotive industry are AIDC-EC and ECAIF.
The Eastern Cape Automotive Industry Forum (ECAIF) is a platform for automotive component manufacturers in
the Eastern Cape to grow their businesses through coordinated development and unlocking synergies. ECAIF was
established in late 2015 and is driven by the industry itself; the provincial Department of Economic Development,
Environmental Affairs and Tourism (DEDEAT) funds a full time facilitator to administer the forum. The forum is
based on cluster development principles and seeks economies of scale to ensure the local cluster is competitive.
ECAIF’s objective is to facilitate competitiveness of the Eastern Cape automotive component manufacturers by
driving initiatives to enable this competitiveness. Membership and participation in ECAIF is open to all manufacturers,
in the automotive component sector in the province and in the supply-chain of the automotive industry (ECAIF,
2016). Participation in ECAIF has assisted manufacturers to improve productivity and competitiveness. Members
have benefited through development of the supply chain, accessing specialised training, undertaking efficiency
programs, and opening up channels of communication to government. ECAIF is organised under three working
groups focused on logistics, skills and energy.
In 2016 ECAIF approached the Competition Commission to secure approval for combined tendering of manufacturers
for logistic and purchasing services. The outcome of the competition commission is expected in early 2017 (Coffey,
2016).
Key activities by ECAIF have included; undertaking opportunity identification for black owned suppliers in the
value chain; partnerships have been created around skills training, with VWSA offering training workshops on
Problem Solving and Lean Manufacturing free of charge to members. A skills portal has also been created to
assist manufacturers to fill workshops by pooling the potential audience across the cluster as well as to notify of
skilled personnel available for recruitment. Energy efficiency presentations have been convened to inform other
manufacturers about a new initiative or lessons learnt.
ECAIF has embraced the need for transformation in the industry and is looking to innovative best practise examples
to assist manufacturers to be BBBEE compliant. This includes ECAIF work through Automotive Supply Chain
Competitiveness Initiative or ASCCI to bring the ASCCI Black Supplier (manufacturers) development programme
to the Eastern Cape. The programme includes benchmarking black owned suppliers and the provision of an industry
mentor whilst developing the suppliers in areas highlighted by the benchmark. ASCCI will pilot this new initiative in
early 2017 that offers a thorough methodology to provide concentrated support to black suppliers to grow.
The Automotive Industrial Development Corporation - Eastern Cape (AIDC-EC) was established in 2003 and is a
wholly owned enterprise of the Eastern Cape Development Corporation. The AIDC-EC is the implementing agency
of national and provincial government in providing subsidised technical and manufacturing related services and
programmes to South African companies. The organisation has refocused itself to be more service and demand
driven in order to be self-sufficient. It works with ECAIF to implement projects and initiatives identified by the
forum. It aligns itself with ASCCI and provides support to reach the ASCCI objectives in the Eastern Cape. The
organisation went through an organisational change in 2015 and is looking to 2017 to cement its new strategy with
positive growth in its programmes.
The organisation’s strategic goals are to:
• Enhance and develop skills, create jobs, contribute to continuous growth and sustainability.
• Create wellness awareness for a healthy workforce and increase productivity.
• Increase global competitiveness and become a viable investment.
• Develop supply chain and logistic solutions to promote business development (AIDC-EC, 2015b).
The AIDC-EC has a strong focus on pursuing technical engineering excellence by providing world-class services.
Its four main focus areas are:
• Supplier development
• Supply chain development
• Skills development and training including wellness.
The AIDC-EC main activities in these areas includes:
• Working with ASCCI to develop a simple guide to Total Quality Management Systems.
• Administer Jobs Fund and offer matriculants on-the-job experience before entering university.
86
• With the support of the GIZ the AIDC-EC offers wellness programmes, this has assisted to alleviate the
financial burden of preventable diseases on employers and employees. The programme saw 422 persons
trained in the automotive industry.
• The AIDC-EC has also developed an employee engagement model which they are rolling out and which
they partnered with NMMU to develop.
• The organisation provides Six Sigma Training. This was offered to 25 participants. Whilst Six Sigma
projects implemented across industry secured a saving of R12 million and a 200% savings on investment.
• The AIDC-EC Total Productive Maintenance (TPM) programme trained 656 people and achieved results
within industry.
• The Cleaner Production Programme assisted firms to lower their energy usage on average 10%for new
entrants to the programme and by a further 5.7% for firms with existing energy management programmes.
• The AIDC-EC’s Automotive Experiential Career Development Programme for grade 12 learners which
assisted 30 learners with bursaries to study engineering.
• Implemented customised shop floor training programmes.
• Implemented phase 2 of the Job Fund to train and recruit 45 unemployed engineering graduates.
• Launched the Buffalo City Automotive Aftermarket Incubator in Mdantsane which has created 4 new
businesses (AIDC-EC, 2016b).
Another interesting automotive support organisation in the field of e-mobility which is located in the Eastern Cape is profiled in Text Box 5.2.
The uYilo e-Mobility Technology Innovation Programme (EMTIP), a programme funded by the Technology Innovation Agency (TIA) and hosted by NMMU in Port Elizabeth, in the Eastern Cape. The programme was launched in 2013 with the aim of fast tracking the development and commercialisation of key technologies that will primarily support the electric vehicle (EV) industry, with supplementary support towards electric mobility (e-mobility) as a whole. R&D on most electric vehicles such as the Nissan Leaf and BMWi3 is undertaken by international partner OEMs at their overseas headquarters. Thus there is limited scope for South Africans to enter this arena. The opportunities for South Africa lies in niche e-mobility applications, public transport take-up and creating a landscape for EV adoption. There is a great need to
prevent the country being left behind. Areas to focus on include developing local testing capacity, developing a new generation of engineers and mechanics with skills appropriate to e-mobility and developing the public and private infrastructure needed to support the growth of e-mobility. There are opportunities to localise manufacturing of niche EVs and charging stations.
uYilo is based on the NMMU North campus within eNtsa and is the national innovation programme for e-mobility in South Africa. In order to allow the South African consumer and municipalities to take up the adoption of electric vehicles, a number of interventions are needed in the landscape which includes standardisation of charging points and plugs for the South African markets, development of light lithium ion battery technology and identification of unique applications for the market. uYilo’s role in South Africa’s adoption of e-mobility is over and above specialised facilities and engineering services it offers, the programme facilitates and co-ordinates projects and initiatives across the country between industry, government and academia to support the local industry.
uYilo has a number of unique offerings including an EV systems laboratory. This offers a platform to facilitate EV compatibility with products from a variety of global suppliers to accelerate the development and deployment of electric vehicle technologies into South Africa. The live testing environment (LTE) serves as a simulator for the EV ecosystem to facilitate universal connectivity between EVs and the electric charging infrastructure. The facility supports analysis, development, and testing of EVs and smart grid technologies to aid in the development and optimisation of advanced technologies to expand commercial applications. The uYilo battery testing laboratory supports local battery manufacturing companies by providing accurate and reproducible testing services during the evaluation of new storage solutions whilst providing validation of existing battery technologies. The importance of battery technology and testing in e-mobility is critical, as the battery in an EV is the single most expensive component on the vehicle and thus lighter, longer lasting and more affordable batteries are key in consumer take up of eMobility. Materials characterisation laboratory: was established at the NMMU in 2000 and was formerly known as Port Elizabeth Technikon Materials Resource Centre (PETMRC). The centre performs an invaluable service to the local lead-acid battery industry based in Port Elizabeth and East London by performing routine analysis on raw materials used in the production of lead-acid batteries.
Text Box 5.2: Eastern Cape leading South Africa’s Development of E-Mobility
87
Key programme successes have included:
• A founding member of the Electric Vehicle Infrastructure Alliance (EVIA) a public and private sectorcollaboration within the e-mobility industry. uYilo has played a significant role in the establishmentof this organisation.
• SANAS accreditation on lead acid battery testing.• Global ISO 17025 implementation within battery testing laboratory operations.• Offer advanced equipment for lithium ion testing• Piloted projects that included renewable energy integration with energy management and load
levelling for charging vehicles. The programme has undertaken a number of demonstrations to testvarious applications of e-mobility in conjunction with their partners. These include the developmentof a fleet of shared eBikes at NMMU campuses. The sharing scheme offers a powered bicycle transportfor students and staff to travel around campus. Another example of this was a demonstration of theuse of EV in a safari tourism operation. uYilo partnered with Shamwari Game Reserve to test a gameviewer in the rough terrain of the Eastern Cape. The game viewer was charged through solar panelsmounted on a dedicated car port. The experiment produced significant data on how to refine thisapplication.
• Integration of an electric vehicle battery pack repurposed for stationery storage charging of electricvehicles.
• uYilo partnered with Nissan South Africa to field test the Nissan Leaf. The Leaf is utilised to determineuser patterns, usage modes and energy cycles, the intent being to facilitate the development of other technologies.
In order to help kick start the localisation of manufacturing in the industry, a Kick-Start Fund was launched. The fund aims to fast-track the development processes for those projects that currently cannot attract funding from other funding mechanisms but are of strategic importance to furthering SA’s e-mobility. Successful projects will have to demonstrate the intent to further develop and commercialise the technology.
The Eastern Cape offered the opportunity to establish this technology platform as it could link up with the key national technology contributions of eNtsa which was established over 13 years ago and the support base of NMMU. Additionally, the Eastern Cape is host to multiple automotive, battery and general manufacturing companies. uYilo’s relevance to the Eastern Cape is summed up by Hiten Parmar, Deputy Director; ‘uYilo serves to provide specialised facilities and services, seeded funding and skill development and training towards an emerging industry within the automotive industry in South Africa. These key activities serve towards bridging the skills gap across the automotive industry contributing towards improving the income distribution across citizens of the Eastern Cape. Seed funding initiatives contribute towards SMME and entrepreneurship development that further improve the factors of production within regional GDP contributions. Support facilities and services provide opportunity for further advancements of the industry’s goods and services thereby increasing competition with a net effect of increasing profit margins of manufacturing firms.” (Parmar, 2016).
The Eastern Cape is also well placed to develop e-mobility culture as we have shorter driving distances in our cities than other South African cities. There is an existing base of automotive knowledge that could be re-skilled in EV systems. As more consumers take up e-mobility the spinoffs are great including making a significant contribution to lower carbon emissions and reduced air pollution. The development of a new industry could also offer new business opportunities for manufacturing and localisation in South Africa and specifically in the Eastern Cape.
Future plans for uYilo includes extending the battery testing equipment to include a 60V testing of Lithium ion battery modules and to offer accredited Lithium Ion battery testing. This would make the centre the only one in South Africa offering accredited testing of Lithium ion batteries. The live testing environment is also to be expanded to include global technologies of vehicle-to-grid, allowing energy to be transferred from the electric vehicle to the grid network (Parmar, 2016).
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5.3.2 Notable Developments for the Eastern Cape
There have been several major developments in
the Eastern Cape automotive industry in the last
few years. The most significant however, has
been the investment by BAIC (see Box 5.3 for a
discussion on this investment). Other significant
developments have also taken place at firms
such as Volkswagen, Goodyear, and Daimler as
disused below.
Volkswagen South Africa celebrated 65 years in
South Africa in 2016. The firm was established in
1946 in South Africa. The vehicle manufacturer
has come a long way from a 12 Studebakers per
day production line to its current production
capacity at its Uitenhage plant of 600 units a
day, manufacturing Polos and Polo Vivos for the
domestic and international markets. Volkswagen
operations in South Africa have produced over
3.4 million vehicles over the company’s lifespan
(Volkswagen, 2016).
In late 2015 Volkswagen announced its
commitment to invest R4.5 billion into
it Uitenhage operations. This includes
an estimated R3 billion in production facilities
and quality systems‚ R1.5 billion in local
supplier capacity and a further estimated R22
million for the development and training of
employees (Timeslive, 2015). It will be the first
time that a version of the Modular Transverse
Matrix platform will be utilised in South Africa
and will offer the latest technologies and driver
assistance systems.
Text Box 5.3 R11 billion Investment by BAIC in the Eastern Cape Automotive Industry
BAIC International is the fourth largest passenger and
light commercial vehicle manufacturer in China, with a
global footprint in 20 countries around the world. The
company produces 2.5 million vehicles annually, 3.6
times greater than South Africa, and is targeting the pro-
duction 5.0 million vehicles by 2020. In 2015, BAIC had a
turnover in excess of R0.5 trillion (CDC, 2016).
BAIC has selected the Coega IDZ as the site for its 85 000
m2, R11 billion new automotive factory in South Africa.
This investment, which is considered the “Biggest auto-
motive investment in African in the last 40 years”, will
create 2 500 direct employment opportunities during
construction as well as a further 7 500 indirect jobs (dti,
2016; RNews, 2016).
Construction has begun on a completely knocked down
(CDK) vehicle manufacturing plant in the Coega IDZ.
Once operational, it is anticipated that the BAIC fac-
tory will have an initial production capacity of 30 000
units per annum. This will increase to a full capacity of
100 000 units per annum over a five-year period. It is an-
ticipated that 50% of all vehicles produced by the facto-
ry will be for export, with BAIC specifically targeting the
African market. Initial sod turning occurred in November
2016, with initial production anticipated for January 2018.
Once fully operational, the factory will create between
800 and 1 500 permanent jobs. In addition to the facto-
ry, BAIC plans to assist in the creation of a new supplier
base and construct employee accommodation near St
George’s Strand (CDC, 2016).
The export market is the key market for the vehicles developed as part of this investment. VWSA Managing Director
indicated that South Africa was not a logical production location given its small share of global production but
a key decision-making factor for the investment was the favourable terms of the APDP for investment by auto
assemblers (Timeslive, 2015).
Goodyear South Africa also looks set to reinvest into its Uitenhage plant with an investment of R670 million. The
investment is set to increase production of high value added (HVA) consumer tyres in hope of driving profitable
growth and meeting market demand. New state of the art technology is to be introduced at the plant and this will
enable Goodyear to meet growing demand for HVA consumer tyres in sub-Saharan Africa, including South Africa.
These improvements in technology are expected to induce double digit growth through to 2020. Furthermore, the
investment will improve the plant’s capability to produce Low Rolling Resistance Tyres, which are increasingly in
demand due to their fuel efficiency. Investment plans are expected to be completed by the end of 2016 or early
2017, and no interruptions to existing tyre lines are expected (Fin24, 2015; dti, 2016).
Other notable milestone, is the 10th anniversary of start of operations of Foxtec-Ikwhezi based in the East
London IDZ. The past 10 years has seen it grow to supply the majority of Mercedes-Benz plants in the world. The
plant employs 100 people and manufactures assembly ready forged aluminium suspension link arms. The firm, a
partnership between Otto Fuchs KG and a local shareholder Ikwhezi Investment Holdings, was praised as a model
of bringing the best of German and South African expertise together (Daily Dispatch, 2016).
In February 2016 the Daimler group announced that South Africa would be their regional base of operations for
their new global truck and bus strategy. This is expected to bring significant new investment to the East London
Mercedes-Benz plant. The creation of a new regional centre to look at markets in Southern Africa, will mean the
East London plant has the opportunity to supply into SA, Namibia, Botswana, Swaziland, Lesotho, Mozambique,
Zimbabwe, Zambia and Malawi (dti, 2016).
89
apdp
impo
rt d
uty
vehi
cle a
ssem
bly
allo
wen
ce
prod
ucti
on in
cent
ive
auto
mot
ive i
nves
tmen
t sc
heme
duty cashrebates
Source: adapted from NAACAM, 2016
5.4 National Automotive Policy and Programmes
Support for the South African automotive industry exists at two levels from the national government in the form
of APDP and ASCCI. As well as trade assistance in the form of import duties, tax incentives, competitiveness
programmes and national engagements. Secondly support is offered at a regional level in specific geographic
areas this includes industry fora and clusters and the opportunities from geographical clusters (AIEC, 2016).
The leading national policies guiding the automotive industry in South Africa are the Automotive Production and
Development Programme (APDP), which replaced the Motor Industry Development programme (MIDP) in 2013.
Other key industrial policies which impact on the industry include the Industrial Policy Action Plan (IPAP), Black
Business Empowerment Supplier Development Programme (BBESDP) and Manufacturing Competitiveness
Enhancement Programme (MCEP). This section looks at the implications of the APDP on the outlook for the
automotive industry, the impetus around transformation that is being undertaken through the BBBEE codes and
the ASCCI.
5.4.1 Automotive Production and Development Programme (APDP)
The APDP replaced the MIDP in January 2013 and is based on four strategic pillars which include:
• Import duty
• Vehicle Assembly Allowance (VAA)
• Production Incentive (PI)
• Automotive Investment Scheme (AIS)
Figure 5.16 APDP Strategic Pillars
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Text Box 5.4: The Specifics of the APDP
The key allowances of the APDP are as follows:
Tariffs: Import duties on vehicles and components have been frozen at 2012 levels (25% on light vehicles and
20% on components) through to 2020. A preferential agreement with the European Union results in imported
vehicles from the EU paying only 18% on the duty.
Vehicle Assembly Allowance (VAA): This support includes a duty-free import credit issued to assemblers
based on 20% of the ex-factory vehicle price in 2013, reducing by 1% in 2014 and from 2015 to 18% of the
value of light motor vehicles produced domestically. The equivalent value of this to the vehicle assemblers is
the allowance multiplied by the duty rate, so 4% in 2013 reducing to 3.6% in 2015. The allowance is based on
local production so exported vehicles, which don’t pay duty on imported parts, can receive the full allowance.
Production Incentive: From 2013 this support starts at 55% reducing annually by 1% to 50% of value added
(52% by 2016) and additionally is in the form of duty-free import credits. ‘Vulnerable products’ earn a PI
of 80% in 2013 and 2014, reducing to 50% by 2020. The incentive is earned by the end producer or the
vehicle assembler or the component manufacturer in the case of component exports. Materials are usually
excluded from value added however materials that have been locally beneficiated for the automotive industry
specifications have an incentive applied at 25% of their value addition.
Incentives to medium and heavy commercial vehicles are still being considered by the national department of
Trade and Industry. Component manufacturers for medium and heavy commercial vehicles can earn a PI but
this incentive cannot be passed on to the assembler as in the case of light vehicles (NAACAM, 2016).
The successes of APDP can be seen in annual growth in export production despite the domestic market declining.
The programme as well as work that has commenced in 2016 draw up the APDP extension post 2020 has seen
consistent growth in investment as seen in Figure 5.12. In 2016 capital investment into the country totalled an
estimated R7.6 billion.
In 2015 the dti published the findings of its review of the APDP and found that the target of producing 1 million
vehicles per year by 2020 would not be achieved. This was due to a number of reasons including that the global
economy was still recovering from the 2008/09 economic crisis and the current domestic economic conditions
would also prevent the local manufacturing base from expanding and deepening to meet this target. The original
APDP framework was developed in 2008 but the domestic economy had changed significantly since then. In order
to keep steering the industry towards increased vehicle production and local content a number of proposals were
implemented, these were:
1. A post APDP support framework will be developed during the course of 2016 to provide policy certainty
for investments post 2020.
2. The volume threshold for vehicle production will be reduced from 50 000 units to 10 000 units per annum
in order to allow new entrants into the local industry.
3. The Volume Assembly Allowance (VAA) will be offered on a sliding scale based on volume commencing
at 10% for 10 000 units to 18% for 50 000 units from January 2016.
4. A suitable capital incentive (AIS) level will be provided for new entrants at the less than 50 000 per annum
threshold (details will be captured in guidelines that should be finalised by April 2016).
5. The production incentive for catalytic converters will be frozen at 2017 levels of 65% rather than continue
the phase down.
6. The qualification for component suppliers to earn APDP benefits will be tightened in order to avoid these
benefits being earned on non-core automotive products and therefore preference will be afforded to
those products that add value in the value chain.
7. The dti will engage the National treasury in an effort to secure improved investment support for tooling as
a means of encouraging further component localisation (dti, 2015).
NAACAM responded to the APDP review to indicate that they wished the review had yielded more specific
requirements for local manufacturing of components citing local content percentages which have declined over
the APDP period. The lowering of entry requirements for VAA would encourage new market entrants but this
was not supported with local content provisions. The APDP is seeing positive impacts in the area of new OEM
investments see the concern is that this would create more competition with components suppliers with imports
and not assist in deepening the sector (NAACAM, 2016).
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The main critiques of the APDP have come from the component manufacturers who indicated that the support
within APDP for this sector fell short of their expectations. Some critiques of the programme include:
• That there is a continual increase in imported vehicle share of the market, and these tariffs are lower than
in other developing countries. These duties can also be rebated against thus in some cases the effective
protection is zero.
• Component exporters earn significantly lower production incentive than under MIDP, this was expected to
affect exports. This would particularly affect exporters with a high raw material content, such as catalytic
converters and aluminium based products. Thus special consideration was given by authorities to products
with a high material content through vulnerable products exception. These products include alloy wheels,
aluminium products, cast iron products, catalytic converters, flexible couplings, leather interiors, brass
components and steel jacks.
• The critique was on the overall programme that when the allowances and incentives are accumulated the
vehicle assemblers received, on average, a higher benefit in 2013 than in 2012 under MIDP. The extent of
the increase is based on the ratio of exports to local market. Whilst component manufacturers in total
received less overall support as a result of the removal of the export incentive. NAACAM argues that the
vehicle assemblers have been generously incentivised but that has not incentivised OEMs to increase local
content. Also that component manufacturers should receive a more direct benefit from the programme,
as unless the localisation of manufacturing of components is supported it may become increasingly
unsustainable to assembly vehicles in South Africa (NAACAM, 2016).
• Also the threshold of a production of 50 000 units per annum means that smaller production facilities
would not benefit from the incentive programme (Innovation Group, 2016).
The dti announced in April of 2016 that it would start work on developing the successor programme to the APDP.
New investments that have been announced recently from BMW South Africa, VWSA and Ford have model
production cycles that extend beyond 2020, thus making it necessary for work to commence on the post APDP
support. Although too early to speculate the extended programme should include support features similar to the
current APDP’s Automotive Investment Scheme (AIS) and the productive asset allowance. The focus will continue
to be on job creation and localisation support (Venter, 2016). In announcing the review Minister Davies indicated
that it would be necessary to “strike a balance” between component manufacturers and assemblers (Engineering
News, 2016: 3). It was however noted that the component manufacturers were dependent on the assemblers for
their existence.
5.4.2 Transformation
The revised Broad Based Black Economic Empowerment (BBBEE) came into effect in May 2015 and have placed
transformation and compliance in the spotlight for the automotive industry. Industry has come out with concern
over the implementation of the revised BBBEE codes, with locally based multinational manufacturers such as
Volkswagen indicating they will find it impossible to achieve a Level 4 rating, as they cannot score on the ownership
criteria. This would entail some form of ownership transaction and for multinationals this is a tough ask as they
are unwilling to dilute their ownership. NAAMSA has indicated that they are considering an Automotive Industry
Sector Charter due to the difficulty compliance is creating. OEMs have indicated that the thresholds and targets for
automotive industry are unachievable and all OEMS but General Motors could possibly reach Level 8 with General
Motors achieving a Level 7 (Cokayne, 2016).
VWSA undertook a Black Supplier Day in Uitenhage to find nationally black owned manufacturers in any sector who
could move into automotive manufacturing. The results of the nationally advertised event were the identification
of 41 companies. This fell way short of the VWSA’s needs as it requires 500 suppliers to comply. Transformation
and meeting the revised codes has been identified as a key challenge for the industry going forward. VWSA has
implemented actions around the creation of a trust with an incubator to help develop future suppliers. There is
however expected to be more pressure on existing suppliers to change their ownership structures so as to continue
supplying (IOL, 2016).
There is a lack of black owned manufacturing businesses in the country. Past efforts at BBBEE in automotive have
been successful in quick wins around procurement from BBBEE owned support services however this has not
gone further into the value chain. The new codes offer an opportunity to support and develop black industrialists
and create greater economic empowerment in the industry. A number of supplier development models are being
piloted. AIDC Gauteng has undertaken an Automotive Incubation Centre approach of which its first incubate
graduated in 2016. The centre was established to develop and support Black-owned businesses during the first
critical phase of their development and to move on to perform value-added sub-assembly work for Tier 1 suppliers
on the Ford Ranger assembly (AIDC, 2016). Other initiatives that have been undertaken include education and
awareness building on BBBEE codes with component manufactures as undertaken by ECAIF.
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5.4.3 Competitiveness and Supplier Development
ASCCI was established with the purpose of “coordinating supply chain developing activities within the South
African automotive industry.” (ASCCI, 2016:4). In global terms SA is a small player in the automotive industry, and
this means that component manufacturers are at a disadvantage to suppliers based in the main markets. ASCCI
was established to respond to key challenges in the SA component industry that being
1. Uncompetitive operating efficiencies
2. Uncompetitive input costs and in particular material costs
3. Limited investment in new processes and product technology
4. Inadequate economies of scale
5. Insufficient economic transformation
6. Maintaining an enabling policy and regulatory environment
7. Availability and quality of skills
8. Globalisation and global sourcing policy
ASCCI is a stakeholder driven initiative and draws on the expertise of government, OEMS and suppliers to drive
the organisation. ASCCI intends to increase “supplier Manufacturing Value Add (MVA) in support of growing
local vehicle production output, increasing employment, enabling local supply chain capabilities, increasing local
content, and advancing transformation.” (ASCCI, 2016:5). ASCCI is the primary point of coordination between
international and national funders wishing to finance South African automotive development activities. ASCCI’s
focus lies in pursuing localisation opportunities, supplier capability development and providing strategic insights.
Thus activities focused on supporting supplier production capabilities, that increase local content, spanning
competitive local material inputs through to investment in new supplier process technologies and that develop
insight into critical policy, regulatory and related issues.
Its strategic priorities in supplier capability include:
a) Base operating standards
b) World class manufacturing
c) Shop floor skills
d) Scare skills programme
ASCCI focuses on localising MVA, through developing an understanding of localisation blockages, enablers and
opportunities and facilitating investments to Tier 1 and 2 suppliers. ASCCI aims for 41.0% local content through the
implementation of four targeted programmes. ASCCI is supporting the development of a number of business cases
that intend to establish the motivation for localising a range of materials and components.
Key activities include:
a) Raw material pricing and beneficiation
b) Tier 1 localisation
c) Tier 2 localisation
d) Technology Investment
ASCCI stakeholders have committed to supporting the initiative into its 2nd phase with a new business plan
developed to guide operations for 2017-2020.
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5.5 Conclusion
The SA automotive industry is influenced by macro-economic trends with growth faltering to the rest of Africa, but
sales picking up in Asia, the US and the EU. The industry outlook in 2017 sees good export growth but domestic
sales recovering somewhat from 2016, but still low. Thus the industry has a mixed outlook for 2017.
Local competitiveness remains below optimal levels, but the positives are that there is greater policy certainty
in the industry than before, seeing large scale capital investment from new and existing assemblers. At a firm
level, some South African firms are thriving whilst others are battling, with limited export exposure and subdued
domestic sales. Domestic production is expected to rise in the future on the basis of the APDP incentives and an
increase in exports is expected (NAAMSA, 2016).
Internationally trends that are considered disruptive are on the horizon this includes the rise of e-mobility and the
autonomous car. Cost competitiveness between and within OEMs will continue to be a major driver for the industry.
In the Eastern Cape two local OEMs are expecting expansion in the future with investments at Volkswagen and
MBSA plants. A new entrant into the market was FAW Trucks which is based at the Coega IDZ. A sizeable capital
investment for the province and the country is BAIC’s investment in a R11 billion manufacturing facility at the Coega
IDZ.
The Eastern Cape component manufacturers has established an automotive cluster which is pursuing cluster
activities and has seen great buy-in from the industry. The Forum is hoping to hear the outcome of it submissions
to the Competition Commission in the 2017 as regards joint logistics and purchasing collaboration efforts.
On the policy side a review of the APDP in 2015 has seen shifts in the focus of the programme, with the 1 million
production target now phased out. The dti has commenced in 2016 with work on the extension to the APDP which
will take the programme past 2020. The impact of APDP can be seen in the growing export units and value and
the capital investment into the country. Yet it faces criticism from the component manufacturers that it does
not do enough to support the industry and deepen localisation. With component manufacturing having higher
employment multipliers than assembly it is in the interest of policy makers to promote incentives that sustain local
industry.
In 2017 the key trends that will be driving developments in the industry include the new BBBEE codes and compliance
thereof, as well as pursuing transformation in the supply chain. As regards sales the low national growth rate and
limited consumer confidence will impact on local domestic sales. Whilst exports although favourable need to be
cognisant of the high levels of volatility in international markets. As work on the APDP extension gains momentum
within the dti, more analysis and debate will emerge on the how the APDP will be extended to meet the needs of
the South African Automotive industry.
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4
In volatile economic times, the importance of collaborations
cannot be underestimated. Collaboration was key to South
Africa warding off a ratings downgrade. When institutions stop
working in silos and start working together, the development
possibilities are endless.
This chapter highlights a number of ongoing collaborations
in the province: from the Maritime Cluster forming in Buffalo
City to take forward maritime interests; to organised business
lobbing and taking action on issues of water, electricity tariffs,
city cleanliness and investment promotion; to partnerships
between municipalities, municipal entities and government
departments to reinvigorate urban areas and tourism
attractions. This chapter celebrates partnerships driving the
Eastern Cape.
The business and investment environment was subdued
in 2016, with the notable exception of the R11 billion Beijing
Automotive Group (BAIC) investment into the Coega IDZ.
Whilst other long standing projects saw renewed urgency and
impetus, such as the Nooitgedacht Water Scheme and the N2
Wild Coast Road.
This chapter looks at a few selected developments which
are pushing the development agenda forward. These include
industrial and urban development, tourism, agriculture, private
sector investment, the waste economy, ocean economy and
infrastructure. It also looks at pressing issues that will influence
the province in 2017, such as the continued drought conditions.
6.1 Coega Industrial Development Zone
The Coega IDZ estimated that it contributes 0.16% to the
annual provincial gross geographic product (CDC, 2017a).
In 2016, the Coega Development Corporation (CDC) signed
17 new investors with a cumulative investment value of R27
billion. The IDZ now has a total of 36 operational investors
(CDC, 2017a).
R11 billion investment by the Beijing Automotive Group (BAIC) in the Coega IDZ automotive assembly plant.
Buffalo City Maritime Cluster established.
Coega positions itself as energy hub of South Africa. Gas to power investment sought.
Nelson Mandela Bay Business Chamber wins court challenge with NERSA. Thus, reducing the effective electricity tariffs applied.
Drought conditions in the Eastern Cape continue into 2017 with some areas under water restrictions. Farmers still counting the cost of drought.
STRATEGICINITIATIVES
97
The organisation achieved international accreditation of its quality management systems through the International
Organisation for Standardisation (ISO) and achieved ISO 9001:2015 quality assurance. It also achieved OHSAS
occupational health and safety accreditation, and ISO 14001:2015 environmental management accreditation. The
CDC also saw success through its skills programme, with 50 learners trained in boiler making, welding, fitting and
turning and joinery. The CDC pursued its broader economic development objectives by facilitating the accreditation
of 218 SMMEs throughout the Eastern Cape (CDC, 2017b).
The CDC has sought investments into the Coega IDZ, which ensure maximum benefit for local SMMEs. In the
2015/16 financial year it achieved a 37.2% procurement of its goods and services through SMMEs (CDC, 2016c). A
project that saw the use and development of SMME’s was the construction of 777 houses in Motherwell on behalf
of the Department of Human Settlements. The CDC project managed the R100 million project, which made use of
31 emerging contractors (CDC, 2017c).
The CDC is positive about the coming year, with six projects already under construction as of the beginning of the
year. Five of these projects are expected to be operational by the close of the 2016/17 financial year. Key activities
in 2017 include:
1. Commencing construction of the BAIC plant.
2. The implementation of energy-related projects such as the gas-to-power programme with the 1,000
MW of the power facility allocated to the IDZ (CDC, 2017a).
Progress on the BAIC site has been steady with phase 1 of the preparations already completed that being
geotechnical investigations; surveying and clearance of the site (CDC, 2017a). The R11 billion BAIC development
has forged closer ties between the province and the Republic of China. In November 2016, the CDC welcomed the
Vice President of the People’s Republic of China, Dr LI Yuanchao and this was followed by a visit by the Consul
General, Kang Yong in January 2017.
The Coega IDZ is positioning itself as the energy hub of South Africa with its Liquefied Natural Gas (LNG) gas-to-
power project. The CDC in partnership with various state owned companies, has, over the past decade, undertaken
extensive preliminary work into the location of a Gas-to-Power Project. The CDC has identified opportunities in the
LNG value chain, including onshore and offshore activities. The project would entail the generation of approximately
2500 MW of electricity, ‘which would satisfy the bulk of the Eastern Cape’s electricity requirements together with
opportunities in the broader level, such as localisation and conversion to gas’ (CDC, 2016a). The development of a
LNG gas-to-power industry in the Coega IDZ could see stable supply of electricity, increased industrialisation, and
job creation (CDC, 2016a).
The CDC has identified three sites within the IDZ for the potential development of the LNG project. These sites are
in the process of having Environmental Impact Assessments (EIA) undertaken. Apart from its strategic location
near the largest city in the Eastern Cape, the sites are also in close proximity to Eskom’s power station and service
corridor (CDC, 2016a).
Apart from gas-to-power energy options, the Coega IDZ is positioning itself to be an energy hub, with space
and infrastructure to support conventional and renewable energy projects as well as nuclear. The IDZ has also
positioned itself to offer manufacturing and localisation opportunities for the energy sector (CDC, 2016b).
6.2 East London IDZ (ELIDZ)
As the first Industrial Development Zone in South Africa, the East London IDZ (ELIDZ) has shown its effectiveness
within the IDZ programme and is re-establishing itself within the recently adopted Special Economic Zone (SEZ)
programme. In the September 2016 Government Gazette, the ELIDZ was announced as having been granted SEZ
status under the Act. Thus being the first SEZ to be proclaimed in the country (RSA, 2016). Future investors and
existing tenants of the ELIDZ could qualify for a range of incentives under the SEZ Act, which includes:
• Zero VAT and import duties on materials
• A 14 percentage point cut in company tax from 29% to 15%
• Reduced employment taxes
• Training incentives (The Daily Dispatch, 2017a).
To comply with the SEZ Act has meant that the ELIDZ has had to implement new policies whilst at the same
time pursuing its investor programme. The greatest challenge facing the ELIDZ was on how to grow its revenue
base without compromising its mandate to the province and its investors (ELIDZ, 2016). The ELIDZ was happy to
announce at its annual general meeting in January 2017 that the institution had succeeded in growing its revenue
base by 27% year-on-year. This was through growth in income from rentals, sales and provision of services and was
98
attributed to growth in the zone’s investors and expansion of existing tenants. Growing own-generated income
was identified as a key indicator of the positive shareholder value created by the organisation (The Daily Dispatch,
2017b).
Some key investments which are close to implementation for the ELIDZ include:
• A 12 million petroleum storage and distribution depot
• A R240 million waste plant which recycles oil to diesel fuel
• A R54 million aquaculture plant to farm cob
• In addition another aquaculture investment, valued at R68 million (The Daily Dispatch, 2017a).
Whilst new investments on the horizon are estimated at over R800 million include:
• A R1 billion rand aquaculture investment
• A R500 million ICT electronics plant
• A R200 million metal extraction recycling plant
• A R100 million pharmaceutical and medical products plant
• An expansion of Yanfeng Interiors an automotive supplier to Mercedes Benz (The Daily Dispatch, 2017a).
Apart from meeting its own revenue generation targets, the ELIDZ also has a mandate towards creating new
investment, increasing industrialisation, employment creation and technology advancement (The Daily Dispatch,
2017b). The institution has also shown excellence in its financial and management controls and these had resulted
in a clean financial audit for the 2015/16 financial year (The Daily Dispatch, 2017b).
6.3 Industrial Parks
The Department of Trade and Industry (dti) is spearheading a programme to upgrade and revitalise industrial parks
around South Africa. Policy interventions have been identified to revive industrial parks throughout the Eastern
Cape, some of these industrial areas were established during the homeland era and once had thriving industry. As
the industrial incentives fell away, businesses relocated, leaving deserted industrial areas on the outskirts of towns.
Dimbaza outside King Williams Town has been allocated R344 million towards its revitalisation, through a three-
year project funded by the dti and managed by the Eastern Cape Development Corporation (ECDC) (Daily Dispatch,
2016a). Whilst Queendustria in Queenstown and Vulindlela Heights in Mthatha will see additional investment valued
at R44 million from the dti. Fort Jackson outside East London, has had a R6 million upgrade to its street lighting,
fencing and security infrastructure, funded by the ECDC (DEDEAT, 2016).
6.4 Urban Development
A number of urban redesign and planning projects have been embarked on which have seen increased investments
into Eastern Cape cities and the revival of neighbourhoods.
The Mandela Bay Development Agency (MBDA) progressed with the implementation of the R60 million Safety and
Peace project, in the Northern Areas of Port Elizabeth, funded by the German Development Bank/kfW. The project
based in Helenvale is in full swing with projects that look at the physical space as well as community development
projects to reduce crime, violence and gangsterism (MBDA, 2016a).
Another urban redevelopment project in the metro is the Singaphi Street upgrade, with phase 2 of the project
nearing completion. The project is seen as a connector between the city and the Red Location Museum. It is hoped
that the project will assist local residents in beautifying the area and easing congestion. The area has important
heritage assets in terms of its people, arts and culture, and the upgrade is hoped to increase the profile of the area
with locals and tourists alike.
The Tramways Building in the Baakens Valley has been beautifully restored and revived into a vibrant office and
exhibition space. The building now plays host to a number of social, cultural and business events. The Baakens Valley
has welcomed a number of family-orientated events that are firmly establishing it as an integrated, cosmopolitan,
recreational area in the city of Port Elizabeth. An inner city project to create linkages between the Baakens Valley,
the beachfront and the CBD will kick-off in 2017. The project will start work on a redesign of the Vuyisile Mini
Square, the public space in front of the city hall and municipal offices in the heart of Govan Mbeki Avenue. The
redesign will focus on converting the square into the ‘people’s meeting place’ and would also allow for the hosting
of cultural events (The Herald, 2016a). Other construction elements include glow-in-the-dark walkways between
the CBD and the Baakens Valley, parking upgrades in Flemming Street and a pedestrian bridge over the river. The
project will also focus on bringing in local SMMEs, with unskilled labour being sourced directly from the suburb of
Central (The Herald, 2016a).
99
Restoration work on the Campanile comes to a close in 2017. The Nelson Mandela Bay Municipality (NMBM) tasked
the MBDA to assist in restoring the 93-year-old Campanile. The restoration work will include necessary structural
restoration as well as routine maintenance. The 23 cast bronze bells were removed for restoration work. The MBDA
has also introduced elements to make the Campanile representative as a monument belonging to all citizens of the
Metro (MBDA, 2016b).
6.5 Infrastructure Development
Infrastructure is continually identified as a challenge to economic development in the province. A major
infrastructure project valued at R3.5 billion, that will soon come to fruition, is that of the N2 toll road along the
Wild Coast. Authorisation of the brownfields components of the route are already underway. This includes the
upgrade of the existing road and bypasses for the highly congested regional towns of Butterworth and Dutywa.
The controversial component of the project is the greenfields construction between Port Edward and Port St
Johns. An environmental monitoring committee has been established by SANRAL to monitor activities in the area
and their environmental impacts. The committee will ensure that all environmental authorisations and requirements
are met. SANRAL has assured the public that the route was designed to minimise environmental impacts and that
some of the environmental measures that will be undertaken include a search and rescue for endangered plants
and animals (Engineering News, 2016). The greenfields component will include the development of 560 km of new
road infrastructure and nine bridges, of which the largest will span the Mtentu and Msikaba rivers (SANRAL, 2016).
A number of key regional tourism routes will be taken over by SANRAL from the province. These include the R335,
R342 and R336 routes into the major citrus export and international tourism destination of Addo (SANRAL, 2016).
A major infrastructure project is the next phase of the Nooitgedacht Low Level Scheme. The Nooitgedacht pipeline
connects the Gariep Dam with the Nelson Mandela Bay Metro, and was originally developed as a water security
back-up plan. Its upgrade now forms a crucial part of the future water supply plans for the region, securing supply
for the future. The delay in the completion of the scheme has meant that there has been an over-utilisation of the
Kromme and Gamtoos River Systems. The full completion was planned for 2014, but funding for the project dried
up, leading to delays. The project was restarted with phase 2, valued at R1.2 billion, scheduled to be completed in
July 2018. Phase 2 will increase the city’s water supply capacity from 90 to 210 megalitres a day (The Herald, 2017),
whilst Phase 3 is set for completion by end of 2018 (NMBBC, 2016a).
The greatly anticipated upgrade to the Celia Makiwane Hospital in Mdantsane, East London saw its first phase
being handed over in February 2016. The upgrade has taken nine years to complete. The project is estimated to
cost R1.3 billion with the first phase comprising of maternity, neo-natal, paediatric, and general wards, as well as
theatres and a neo-natal ICU. The facility, when completed, will also boast a renal dialysis unit, scope room and
psychiatric facilities; as well as medical equipment valued at R300 million. The hospital will slowly move from its
original buildings to the new site. The intention is for the new building to begin admitting its first patients by mid-
2017 (Daily Dispatch, 2016b and 2016c).
East London may have one of the smallest airports in the country, but due to its excellence in passenger service,
has been ranked 2nd in a poll of regional airports in Africa. The East London and Port Elizabeth International
Airports run by Airports Company South Africa (ACSA), were voted in 2nd and 3rd place, in the Skytrax World
Airport Awards in the category: Best African Regional Airport. In first place was Durban’s King Shaka International
Airport. The awards are based on 13.25 million international customer surveys across the globe during the survey
period. The surveys look at passengers’ ratings of check-in, arrivals, transfers, shopping, security, immigration and
departures. The awards are often referred to as the ‘passenger choice awards’ as they are based on passengers’
reviews of the airports (Skytrax World Airport Awards, 2016; Invest Buffalo City, 2016a).
6.6 Agriculture
Despite some rains in January 2017, the province is still in the grips of the worst drought in 30 years. The majority
of the Eastern Cape has been affected by continued drought conditions, especially the western half and coastal
areas of the province. The Algoa water system is under strain; in January 2017, the combined capacity of the five
dams suppling the NMBM was at 56%, compared with 94.4% at the same time last year (DWA, 2017). With limited
summer rains and only winter rains to look forward to, water restrictions have been put in place in the metro. The
Gamtoos Irrigation Board has also asked consumers to reduce usage as both agriculture and consumers draw from
the water in the Gamtoos Irrigation Scheme. In January 2017, the Amathole water system dam levels were also
lower than normal. The six dams serving Buffalo City were at 73.7% in January, and despite having no restrictions
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in place, this was lower compared to last year when dams were at 94.5% (Amathole Water, 2017; DWA, 2017).
Throughout 2016, communities affected by the drought in the Eastern Cape needed water tankers to supply the
towns.
Agriculture in the province faces a precarious year ahead; “Eastern Cape farmers are buckling under the pressure
of the drought – we are on tenterhooks,” reported Agri-Eastern Cape President, Doug Stern (RNews, 2016a:1). The
lack of rain in the farming areas between East London and Tsitsikamma, has become critical. Farmers drawing
from the Fish River Irrigation Scheme, are experiencing water restrictions due to poor inflows into the Gariep Dam.
The province is predominantly a livestock farming area and thus, livestock farmers have been the most affected
by the drought. Livestock production is down, as farmers are keeping smaller herds and feed is being bought in to
supplement diminished grazing. Agri-SA warned that farmers were coming under increasing cash flow pressures
and small and emerging farmers were some of the worst affected (RNews, 2016a). There has also been slow
expenditure of the R95 million set aside for drought relief in the province. By November 2016, only 26% of drought
relief had been spent (Daily Dispatch, 2016d).
Dairy farming outside Port St Johns was given a boost when a state-of-the-art dairy parlour was handed over to
the Mantusini community. This project forms part of the Department of Rural Development and Land Reform’s
(DRDLR) Strategy on Rural Agrarian Economic Transformation aimed at poverty alleviation; job creation and
the creation of sustainable community-owned enterprises (DRDLR, 2016). The Mantusini Community Trust was
established in 2005 to develop dairy farming in the area. It has however been held back by a lack of funding,
until a R18 million funding request to the department was successful. The construction of the dairy facility and
the acquisition of additional dairy cattle was undertaken in partnership between the DRDLR and the provincial
department of agriculture (DRDAR) (Daily Dispatch, 2016e).
6.7 Ocean Economy
In October 2016, Transnet unveiled its ‘People’s Port’ Plan for the Port Elizabeth harbour. The port will still be
a service-driven harbour but with added recreational facilities. The plan includes restaurants, retail, a maritime
museum, passenger terminal, statute of Nelson Mandela, canal walkway, and bunkering for small vessels. Transnet
is on track and committed to move the oil tanks by 2019 from Port Elizabeth harbour, but has had to revise the
move of the manganese ore dumps backwards to 2020. The waterfront development project is expected to be
initiated in January 2019 and would not be dependent on when the manganese terminal was relocated, according
to Transnet (The Herald, 2016b).
The 2nd Eastern Cape Maritime Summit, organised by the Border-Kei Chamber of Business, was held in East
London from the 27-28th of October 2016. The summit received 220 delegates from around the country. One of the
resolutions taken during the Maritime Summit was to establish a Buffalo City Maritime Cluster. The establishment of
the Buffalo City cluster follows the successes experienced by the eThekwini cluster and more recently, the NMBM
maritime clusters. The purpose of the Buffalo City Municipality (BCM) Maritime Cluster is to be a unified voice for
stakeholders so as to lobby for investments and ensure the region’s maritime interests are profiled at a national and
international level. The cluster is relevant to all businesses in the Buffalo City region whose revenues are linked to
the sea whether in import/export, transport, logistics, tourism, ship repairs and services, water safety, aquaculture,
or fishing. The cluster will be lobbing for investments and improvements to the policy and processes that impact
the maritime economy (BKCOC, 2016a; Daily Dispatch, 2016f).
The ELIDZ is moving ahead with 30 hectares for the development of an Aquaculture Development Zone (ADZ).
The ELIDZ will finalise the construction of aquaculture facilities and has already secured two investors in the form
of MT2 Fish Cultures and Brightwater Aquaculture. It is anticipated that the ADZ could develop 2 000 permanent
jobs (DEDEAT, 2016).
6.8 Waste Economy
There is a growing awareness and recognition of the need to introduce and inculcate circular economy principles
and practices into all industries in South Africa. The need to do so has moved from solely an environmentally-friendly
driven motivation, to the understanding that the creation of a new industry will generate increased new economic
activity. “A circular economy seeks to restore, renew and regenerate and aims to keep products, components, and
materials at their highest value at all times. By looking at resources differently through a circular economy lens,
there can be sustainable businesses and economic growth” (McCallum, 2017:1).
NMMU has entered into an agreement with the Recycling and Economic Development Initiative of South Africa
(REDISA) in respect of the establishment of the Centre for Rubber Science and Technology. REDISA, through this
agreement, approached NMMU to jointly host a national circular economy conference. Thus, NMMU, in partnership
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with REDISA, and the Regional Innovation Forum will host the first South African Circular Economy Conference
from the 2nd to the 3rd of March 2017 in Port Elizabeth.
South Africa has been slow to investigate and implement circular economy principles and practices and as such, it
is of critical importance to create platforms for industry representatives and academic researchers to engage and
network with international and national experts and share innovative ideas and best practice. It is intended that
the South African Circular Economy Conference will be an annual or bi-annual event (CEC, 2016; McCallum, 2017).
A company that is making ‘cents’ of recycling is the ECDC funded PolyFibre Pty Ltd. The project received a R22
million cash injection from the Department of Trade and Industry’s Employment Creation Fund, to establish a
factory in Port Alfred as well as three recycling collection centres, in Stutterheim, Dutywa, and Somerset East. The
project was initialled funded by ECDC who assisted the project in obtaining South African Bureau of Standards
(SABS) approval for its product and establish recycling centres in Port Alfred and Grahamstown. The company
uses plastic waste and pineapple fibre to create moulded window and door frames, outdoor furniture, and utility
bags (The Herald, 2016c).
6.9 Tourism
Roads, especially in the Wild Coast, have been a major impediment to safe access to the beautiful destinations
along the coastline. The province’s R456 million rural access roads intervention, has prioritised roads that lead to
tourism resorts and facilities. It is hoped this will assist in increasing tourism to rural tourism nodes (DEDEAT, 2016).
The NMBM and Buffalo City have positioned themselves as destinations for sport tourism. NMBM is gaining a
reputation as the premier open water swimming destination in South Africa and even internationally. Whilst Buffalo
City hosted a number of well supported sporting events in 2016 (BKCOC, 2016b). In 2017 the IRONMAN 70.3 will
be hosted by Buffalo City on the 29th of January 2017. The IRONMAN African Championship South Africa will be
hosted by NMBM on the 2nd of April 2017 (WTC, 2017).
The 2016 NMBM Championship IRONMAN event saw 1 859 entrants of which 91% were from outside the metro
and 616 were international athletes. The event brought in an estimated R68 million in direct spend to the city,
with 45 000 bednights sold. The IRONMAN 70.3 event in Buffalo City saw 2 852 participants of which 176 were
international athletes. BCM saw an estimated R56 million in direct spend from the event. These ultra-endurance
events are set to expand in the Eastern Cape, with NMBM being awarded the rights to host the 2018 70.3 IRONMAN
World Championship. This event alone, is expected to bring in an estimated R300 million into the local economy.
The event will be televised worldwide and will offer the NMBM media coverage and exposure on the scale last seen
at the 2010 FIFA World Cup (World Endurance Africa Holdings, 2016a, 2016b and 2016c).
The Eastern Cape tourism and hospitality industry excelled at the national Lilizela Tourism Awards. These awards
recognise and reward tourism players and businesses across South Africa. Winners in the accommodation -
backpacking and hostelling sub-category included Amapondo Backpackers in Port St Johns and Tube ‘n Axe
Backpackers Lodge in Storms River. Lemon Tree Lane Bed and Breakfast in Port Elizabeth won in the Bed and
Breakfast category. Town Lodge Port Elizabeth was a winner in the hotel category. Thunzi Bush Lodge and
Beach Break won in the self-catering accommodation category. In the BBBEE category, Stormsriver Adventures
(Tsitsikamma Canopy Tour) won. In the visitor experience categories the Eastern Cape nabbed a number of awards,
for the Bloukrans Bungee, the Chokka Trail, and Raggy Charters (Lilizela Awards, 2016).
Another tourism award is gaining popularity in the market due to it being based purely on the consumer reviews,
that being the TripAdvisor Travellers’ Choice Awards. These awards are based on the millions of reviews undertaken
worldwide of hotels, restaurants and attractions. In 2016, two Eastern Cape hotels made the Top 25 Hotels in South
Africa; these were the Southern Sun Hemmingways Hotel in East London, and Umngazi River Bungalows and Spa
on the Wild Coast. In the Bargain Hotel category, Kob Inn on the Wild Coast was voted 13th and Tsitsikamma
Village Inn in Storms River was 15th. The Eastern Cape’s top destinations on the Trip Advisor 2016 Travellers Choice
Awards for South Africa was Graaff-Reinet in 7th position followed by Storms River in 9th position (Trip Advisor,
2016).
6.10 Sports, Heritage, Arts and Culture
Bayworld Museum was once the main tourist attraction on the Port Elizabeth beachfront, attracting visitors to its
dolphin shows, marine animals and museum exhibits. The museum complex includes the Port Elizabeth Museum,
Oceanarium, the Snake Park, and No. 7 Castle Hill Museum. The museum played an important role in Port Elizabeth
as an educational institution, as it offered a venue for school fieldtrips and weekend entertainment for families.
The museum complex however, fell on hard times; becoming rundown and thus necessitating the relocation of
the dolphin exhibit. The redevelopment of Bayworld was identified in 2004 by the MBDA as a key project for the
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city under its Vision 2020 projects. In the initial feasibility study, it was found that Bayworld could fulfil a gap in
the edutainment/edu-tourism segment, and that this upgrade would have a significant impact on the economy of
NMBM and the province as a whole. In 2004, the cost of renovations was estimated at R148 million. Obstacles have
been encountered as to who is responsible for upgrading the facility as the land is owned by the NMBM, but the
museum buildings and contents are the property of the provincial government. In 2016, eleven years of negotiation
culminated in the signing of a co-operative governance agreement between the Eastern Cape Department of
Sports, Recreation, Arts and Culture and the Nelson Mandela Bay Municipality. Through a council resolution, the
NMBM mandated the MBDA to manage the redevelopment. The goal for the MBDA will be to revive the Bayworld
complex into a ‘world-class tourist attraction and flagship heritage institution for both the Nelson Mandela Bay and
the Eastern Cape Province’ (NMBBC, 2016b). A partnership has also been entered into with NMMU to develop a
range of maritime and marine education programmes. The MBDA is looking at 2018 to have a funding agreement
in place and 2019 to start construction (NMBBC, 2016b).
On the 18th of July 2016, the Mandela Museum in Mthatha was officially reopened by National Minister of Arts and
Culture, Mr Nathi Mthethwa. The event formed part of International Mandela Day celebrations to commemorate
Nelson Mandela’s birthday. The facility has been undergoing a major facelift over the last five years, at a cost of
R63 million (Daily Dispatch, 2016g).
Border Cricket has been appointed as an Implementing Agent by the National Lotteries Board of South Africa to
build the Tshabo Sports Complex, near Berlin. The community of Tshabo, consisting of 26 villages, is the beneficiary
of the sports complex, under the auspices of the Tshabo Trust. A R30 million grant from the National Lottery
Distribution Fund has made the project possible. The sports centre will include multi-disciplinary sports fields, a
clubhouse, supporter stands, and floodlights etc. Activities to be catered for at the complex will include volleyball,
tennis, swimming, basketball, an outdoor gym, skateboard and a play park (BKCOC, 2016b).
6.11 Energy Sector
DEDEAT continues to pursue renewable and sustainable energy investment and localisation within the province
through a number of interventions including, its support programme to SMMEs in the energy sector. The project
has moved into its fourth phase and has engaged 330 prospective SMMEs, and provided follow-up support to
16 selected SMMEs who have secured over R17 million in contracts, both within and outside the energy sector
(DEDEAT, 2016). This has advanced the participation of SMMEs in the renewable energy value chain.
Apart from the investment previously mentioned in energy projects within the ELIDZ, the institution is also rolling
out its implementation of Grid Simulation Labs. The project is funded by the Development Bank of South Africa
and will allow for artisans to be upskilled especially in the construction and the maintenance of renewable projects.
The University of Fort Hare, which celebrated its centenary in 2016, is also making strides in the field of biogas.
The university has constructed a 180 kilowatt biogas digester that will convert animal waste to gas and electricity
(DEDEAT, 2016).
The lack of scientific research into the field of hydraulic fracturing in South Africa necessitated DEDEAT to partner
with NMMU to undertake scientific research on shale gas resources in the province. The research programme is
providing crucial data to policy makers. There are currently 30 Masters and Doctoral research studies under way,
that have been sponsored by this programme (DEDEAT, 2016).
6.12 Private Sector Initiatives
The past year has been one of low business confidence and volatile markets, yet a number of Eastern Cape
businesses are investing back into the province. A selection of some of these investments is provided in this section.
The BBF Safety Group’s Port Elizabeth plant invested R16 million in production equipment which should increase
capacity by 30%. The DESMA Double Density Polyurethane moulding machine, procured by the firm will allow
it to compete with imports into the country. Production capacity will increase from 2 000 pairs of shoes a day
to over 5 000 a day. Thus improving localisation of safety clothing for the Eastern Cape and the South African
manufacturing industry (NMBBC, 2016c).
Billson Trucks PE has committed to invest R100 million to expand their Deal Party facility. This state of the
art upgrade would allow the facility to supply and service Volvo and UD trucks in the Port Elizabeth area. The
investment is also hoped to revive the Deal Party industrial area and encourage other businesses to reinvest into
their properties (MyPE, 2016).
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The Nelson Mandela Bay Business Chamber lobbied extensively throughout 2015 and 2016 to reduce electricity
tariffs for businesses in the metro. The proposed increase in electricity tariffs by the National Energy Regulator of
South Africa (NERSA) was opposed by the local business chamber due to the additional cost it would impose on
doing business. The NERSA tariff increase was also expected to have a knock-on effect, by increasing municipal
electricity tariffs when they were reviewed in July 2017. The Nelson Mandela Bay Chamber of Business along with
four local companies, opposed the electricity regulators price increases, in court. In August 2016 the North Gauteng
High Court found in favour of the application brought against NERSA and Eskom. The judgement had national
implications, with the business chamber estimating that it would save consumers between R40 billion and R60
billion over the period. Thus the electricity tariff for the year starting in April 2017 will potentially be restricted to
estimated 3.5%, as opposed to the approved 8% (NMBBC, 2016d and 2016e).
The Border Kei Chamber of Business (BKCOB) won the SACCI-affiliated Chamber of the Year award, and two awards
from the Institute of Waste Management (IWMSA) (BKCOB, 2016a). The BKCOB launched a number of initiatives
in 2016, including Invest Buffalo City. Invest Buffalo City will work on investment attraction and promotion, and
collaborate closely with the Buffalo City Municipality on key initiatives to assist the Metro. Planned projects include:
an environmental awareness and clean-up campaign, the development of an investment framework, as well as
working towards a positive self-image of local businesses (RNews, 2016b). The website offers a one-stop-shop for
investors wanting to invest in the metro, including local contact information, cost of doing business estimates and
step-by-step guides for business registration processes. Invest Buffalo City builds upon the concept of “Invest,
Work, Live and Play – One Click away” (RNews, 2016b; Invest Buffalo City, 2016b). The initiative has established
partnerships with the Buffalo City Municipality, ELIDZ, the Eastern Cape Development Corporation and German
government development agency- the GIZ. Invest Buffalo City is also engaging with Mercedes-Benz South Africa,
the Buffalo City Metropolitan Development Agency, the Airports Company South Africa and the Eastern Cape
Provincial Government (RNews, 2016b).
The BKCOB initiated the Call-2-Action Campaign, which is aligned to the three main pillars of the Metro Growth
and Development Strategy (MGDS). That being a ‘green and clean city’, a ‘productive and innovative city’ and a
‘well-governed city’. A memorandum of understanding has been signed between the BKCOB and the Buffalo City
Municipality, and numerous consultation meetings held to establish the programme. The programme has started
with clean ups in the CBD, but plans to expand to other pilot areas in the city. Seven workers have been sourced for
the project from the communities in the CBD with the assistance of their Ward Councillors. The project is expected
to employ at least 28 individuals in total. The clean-up initiative includes the collection of litter, placement of litter
bins and recycling containers, the cleaning of roads and roadside gutters and clearing of vegetation and grass
cutting (BKCOB, 2016a).
6.13 Summary
The year ahead is expected to bring greater volatility, with uncertain international events. Although higher growth
is expected domestically, it will be a year of a moderate economic recovery domestically. In a world of greater
uncertainty – partnerships and collaboration have become more important than ever before, to grow and attract
business and to pursue inclusive growth.
The largest investment for the province in 2017 will come from the BAIC investment into a new automotive plant
at the Coega IDZ. The Coega IZ is also looking at developing itself as an energy hub for the country and has
completed planning for a Liquefied Natural Gas (LNG) gas-to-power project.
The ELIDZ has made great strides in own revenue generation and has over R800 million in its investment pipeline.
Whilst the dti is funding the revitalisation of previously thriving industrial parks that are scattered across the
Eastern Cape.
Infrastructure is a major impediment to provincial growth, but 2017 sees some important but long delayed
investments taking place. These include the hand-over of the Celia Makiwane Hospital, construction of the next
phase of the Nooitgedacht low level scheme and the R3.5 billion N2 Wild Coast toll road and prioritisation of
upgrades to key rural tourism access roads. The Nooitgedacht low level scheme will bring the NMBM closer to
securing water resources for the future.
Tourism is seeing an uptick, there are also a number of investments and events planned for the Eastern Cape.
The Buffalo City and NMBM continue to draw large scale sporting events and have positioned themselves as
sports tourism destinations. In 2018 Nelson Mandela Bay will play host to the 70.3 IRONMAN World Championship,
bringing unprecedented media coverage to the Eastern Cape.
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appendices
107
sarah baartman districtMunicipality
sarah baartman household density
sarah baartman population density
Sarah Baartman462 937
7.9People per
km2
Dr Beyers Naudépop = 79 059
Kou Kammapop = 40 929
Kouga pop = 100 240
Blue Crane Routepop = 37 245
Sundays River Valleypop = 57 304
Ndlambe pop = 64 616
pop = 83 544Makana
Total Population
Dr Beyers NaudéAvg HH Size = 4.0
Kou KammaAvg HH Size = 3.7
Kouga Avg HH Size = 3.3
Blue Crane RouteAvg HH Size = 3.7
Sundays River ValleyAvg HH Size = 3.7
Ndlambe Avg HH Size = 3.2
Sarah Baartman129 403
Total Households
3.6Average Household
Size
Avg HH Size = 3.7Makana
1 Unless otherwise stated, all district data is 2015 based on Quantec Standardised Regional Database.
Population and Households1
The district recorded a population growth rate of 1.2% in
2015 (2014: 1.2%), and accounted for only 6.7% of the total
provincial population. The low growth rate meant that the
population only increased by just over 5 000 people from 457
348 in 2014, to 462 937 in 2015. Between 2010 and 2015, the
SBDM population grew by 1.3% (Eastern Cape: 1.4%; South
Africa: 1.6%).
Sarah Baartman had the second smallest youth population
in the province after Joe Gqabi in 2015, with only 143 295
individuals being classified as between the ages of 15 and 35
years old. This constituted 31.0% of the total population, lower
than the provincial value of 33.6%. Sarah Baartman had the
lowest number of minors (younger than 15 years old) in the
province with only 125 282 or 27.1% of total population. This
low proportion of children relative to the total working age
population meant that the district has a more positive child
dependency ratio of 41.4; thus being the third lowest in the
Eastern Cape.
The low proportion of children in Sarah Baartman compared
to the rest of the Eastern Cape had a positive impact on the
overall Dependency Ratio, or the ratio of persons not in the labour force (under 15 and over 64 years) and those within the labour force or the working age population. The 2015
Dependency Ratio for Sarah Baartman was 53.0, compared
to the provincial figure of 66.6. The old age dependency
ratio (i.e. individuals older than 65 years relative to the labour
force) in Sarah Baartman was 11.2. These were some of the
lowest dependency ratios in the province and suggests a well
distributed population age structure.
The number of households in Sarah Baartman increased from
128 146 in 2014 to 129 403 in 2015; an increase of only 1 257
households. This was equivalent to a year-on-year increase of
1.0%, lower than both the national (1.6%) and provincial (1.4%)
growth rates over the same period. Unlike other parts of the
Eastern Cape, the overwhelming (86.3%) majority of these
households lived in formal dwellings. This was the second
highest proportion in the province; only surpassed by the
NMBM where 87.4% of households resided in formal dwellings.
This high level of access to formal housing means that there
has been little growth over the last five years as evident by
the fact that 86.1% of households lived in a formal structure
in 2010.
The Sarah Baartman District Municipality (SBDM), which is located in the Eastern Cape, is bordered by the Chris Hani and Amathole Districts. It surrounds the NMBM. Sarah Baartman covers an area
of roughly 58 245 km2 and comprises seven local municipalities, namely: Dr Beyers Naudé, Blue Crane Route, Makana, Ndlambe, Sundays River Valley, Kouga and Kou-Kamma. The Dr
Beyers Naudé Local Municipality was established after the August 2016 local government elections by the merging of Camdeboo, Ikwezi and Baviaans local municipalities.
108
4
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
8%4%
19%
7%
35%
19%
8%
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher EducationOther
Education Levels of education attainment in Sarah Baartman indicate
that 8.1% of the population over the age of 20 years old had
no formal schooling. A further 27.0% of the population over
20 years old had attained either their matric (19.0%) or had
some form of higher education (8.0%). This makes Sarah
Baartman, the district with the third highest educational
attainment levels in the province after the two metros. The
high level of educational attainment can partially be attributed
to the presence of Rhodes University in Makana and a satellite
campus of the East Cape Midlands TVET in Dr Beyers Naudé.
At 147 266, Sarah Baartman had the fourth lowest number of
learners in the Eastern Cape after Joe Gqabi, BCM and Alfred
Nzo in 2014. This represented a decline of 1 450 learners from
2013. These learners were accommodated in approximately
333 public and private schools across the district. The sharp
reduction in educators between 2013 and 2014, resulted in a
deterioration of the learner to educator ratio, which rose from
28.9 in 2013 to 45.6 in 2014 (DBE, 2016).
The 2016 Community Survey indicated how respondents in
Sarah Baartman rated their overall satisfaction with public
schools in the district. The majority of respondents (59.1%)
in Sarah Baartman indicated that they felt that the quality of
public schools in the district were good. This was slightly above
the provincial average of 56.4%. There was however, notable
variation across the district. Local municipalities such as
Ndlambe (75.5%) and Dr Beyers Naudé (71.3%) had high levels
of satisfaction with public schools, while municipalities such as
Sundays River Valley (38.1%) and Kou-Kamma (38.8%) had low
levels of satisfaction. Across the district, 1.6% of respondents
indicated that they had no access to public schools, while 7.7%
indicated that they did not make use of public schools (the
second highest in the province).
Highest Educational Level
in 2015:
HIGHER EDUCATION
8%MATRIC
19%
86% live informal dwellings
2% live in traditional dwellings
11% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
sarah baartman dwelling type
POPULATIONTotal Population 462 937
0 - 14 years 125 282
15 - 35 years 143 295
15 - 64 years 302 657
Average Household Size 3.6
Population Density 7.9 persons/km2
Provincial Population Percentage 6.7%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 147 266 4
Education Attainment Levels Percentage of district population (aged 20 years +)
Proportion Change
Provincial Rank
No Education 8.1% 3
Matric 19.0% 3
Higher Education 8.0% 3
109
Health2,3
The ‘maternal mortality in facility rate’ has been on the decline
over the past 3 years. The indicator declined by 49.30% in 2014
to 61.7 deaths per 100 000 live births, which is significantly
lower than the provincial average of 148.3 deaths per 100 000
live births. As a result, the district has the 8th lowest maternal
mortality rate in South Africa. Infant mortality represented
by the still birth in facility ratio for Sarah Baartman was 19.4
deaths per 1 000 births (Eastern Cape: 19.6 per 1 000 births)
and is showing a downward trend, declining by 3.48% in 2014.
Child mortality can be referred to by indicators measuring
case fatality rates in diarrhoea, pneumonia and malnutrition
in children under the age of 5 years. ‘Child under 5 diarrhoea
case fatality rates’ were 1.5% in 2014 compared to 5.2% the
provincial average. This represented an 11.76% decline in 2014
and was the second lowest fatality rate in the province. ‘Child
under 5 pneumonia fatality rates’ were 2.5% in 2014 for Sarah
Baartman compared to 4.2% for the province. This indicator
has been historically very low and has ranged between 2.6%
and 0.8%; in 2014 the indicator increased by 4.17% from 2.4% to
2.5%. ‘Child under 5 malnutrition fatalities’ have been in steady
decline over the period 2009-2013. The indicator recorded
for 2014 was 3.9%, which is significantly below the provincial
average of 11.9%. Sarah Baartman has the lowest rate of ‘child
under 5 malnutrition’ case fatalities in the province and is
ranked 6th nationally.
‘Immunisation coverage of children under the age of 1 year’
in the district is 80.1%, which is comparable to the provincial
average of 80.9% but below the national average of 89.8%. ‘TB
incidence rates’ are particularly high in the district, but have
been declining over the last three years.
The TB incidence rate in 2014 was 1 127.1 per 100 000, the
highest incidence in the province.
‘HIV testing coverage’ Sarah Baartman has the second lowest coverage for HIV testing in the province. This is also below the national average of 31.2%.
30%
The ‘maternal mortality in facility rate’ dropped 49.3% in 2014 to 61.7 deaths per 100 000 live births. As a result the district had the 8th lowest maternal mortality rate in South Africa.
8TH LOWEST MATERNAL MORTALITY RATE IN SA
2 Based on Massyn et al. 2015.
3 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwisestated.
Health3 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
19.4 19.6
Maternal Mortality - Per 100 000 live births (2014)
61.7 148.3
Immunisation Rate under 1 years (2014)
80.1% 80.9%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 1.5% 5.2%
Pneumonia case fatality rate (2014) 2.5% 4.2%
Malnutrition case fatality (2014) 3.9% 11.8%
TB Incidence per 100 000 population (2014)
1 127.1 792.3
TB Cure rate (percentage) (2013) 81.7% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
30.4% 36.2%
socio-economic and poverty indicators Sarah Baartman
Provincial
Poverty Headcount (2016) 4.5% 12.7%
Poverty Intensity (2016) 42.2% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 8 354 R 7 085
POVERTY HEADCOUNT (2016)
Sarah Baartman
4%Provincial
13%
CHILDREN UNDER 1 YEARS (2014)
Provincial81%
poverty indicators
IMMUNISATION RATE
Sarah Baartman
80%
Sarah Baartman has the highest TB incident rates in the country at 1 127.1 per 100 000. Despite this, the district has the highest TB treatment success rate in the province.
110
sarah baartman EMPLOYMENT By skill level
1O%
27%
3O%
32%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
13%4%
6%21%
23%15%
8%6%
3%1%
0%0%
No incomeR1 - R4 800
R4 801 - R 9 600R9 601 - R 19 200
R19 201 - R 38 400R38 401 - R 76 800
R76 801 - R153 600R153 601 - R307 200R307 201 - R614 400
R614 401 - R1 228 800R1 228 801 - R2 457 600
R2 457 601 and more
10% 10%
26%
15%
7%9% 10%
25%15%
41%
25%
6%
16% 16%
DrBe
yers
Nau
dé
Blue
Cra
neRo
ute
Mak
ana
Ndla
mbe
Sund
aysR
iver
Val
ley
Koug
a
Kou-
Kam
ma
2005 2015
64%60%
66% 69%76%
66%72%
36%40%
34% 31%24%
34%28%
DrBe
yers
Nau
dé
Blue
Cra
neRo
ute
Mak
ana
Ndla
mbe
Sund
ays R
iver
Valle
y
Koug
a
Kou-
Kam
ma
Formal Employment Informal Employment
UNemployment rates
1O%
27%
3O%
32%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
13%4%
6%21%
23%15%
8%6%
3%1%
0%0%
No incomeR1 - R4 800
R4 801 - R 9 600R9 601 - R 19 200
R19 201 - R 38 400R38 401 - R 76 800
R76 801 - R153 600R153 601 - R307 200R307 201 - R614 400
R614 401 - R1 228 800R1 228 801 - R2 457 600
R2 457 601 and more
10% 10%
26%
15%
7%9% 10%
25%15%
41%
25%
6%
16% 16%
Dr B
eyer
s Nau
dé
Blue
Cra
ne R
oute
Mak
ana
Ndl
ambe
Sund
ays R
iver
Val
ley
Koug
a
Kou-
Kam
ma
2005 2015
64%60%
66% 69%76%
66%72%
36%40%
34% 31%24%
34%28%
DrBe
yers
Nau
dé
Blue
Cra
neRo
ute
Mak
ana
Ndl
ambe
Sund
ays R
iver
Valle
y
Koug
a
Kou-
Kam
ma
Formal Employment Informal Employment
sarah baartman household income distribution
1O%
27%
3O%
32%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
13%4%
6%21%
23%15%
8%6%
3%1%
0%0%
No incomeR1 - R4 800
R4 801 - R 9 600R9 601 - R 19 200
R19 201 - R 38 400R38 401 - R 76 800
R76 801 - R153 600R153 601 - R307 200R307 201 - R614 400
R614 401 - R1 228 800R1 228 801 - R2 457 600
R2 457 601 and more
10% 10%
26%
15%
7%9% 10%
25%15%
41%
25%
6%
16% 16%
DrBe
yers
Nau
dé
Blue
Cra
neRo
ute
Mak
ana
Ndl
ambe
Sund
aysR
iver
Val
ley
Koug
a
Kou-
Kam
ma
2005 2015
64%60%
66% 69%76%
66%72%
36%40%
34% 31%24%
34%28%
DrBe
yers
Nau
dé
Blue
Cra
neRo
ute
Mak
ana
Ndl
ambe
Sund
ays R
iver
Valle
y
Koug
a
Kou-
Kam
ma
Formal Employment Informal Employment
The ‘TB Cure rate all types’ however, is higher than the
provincial average at 81.7% and increased by 4.61% in
2013. Sarah Baartman also has the highest TB treatment
success rate in the province; and has over the last three
years of measurement, seen an improvement in this rate.
The district is ranked 10th nationally in terms of its TB
success rate. The ‘HIV testing coverage of the population
aged 15-49 years’ was 30.4%, which is below the provincial
average of 36%, but comparable to the national average
of 32.1%.
Socio-EconomicThe poverty headcount in Sarah Baartman was 4.2% in
2016; the second lowest in the province after BCM. This
measure is based on the South African Multidimensional
Poverty Index (SAMPI). The SAMPI is an index that
is constructed using eleven indicators across four
dimensions; namely health, education, living standards
and economic activity. The poverty headcount shows
the proportion of households that are considered to
be “multidimensional poor” in the district. The poverty
intensity, which refers to the average proportion of
indicators in which multidimensional poor households
are deprived, in the district was 42.2% in 2016, marginally
below the provincial average of 43.3%. The average
weighted household income for Sarah Baartman in 2011
was R8 354 (2015 prices). This was the third highest in the
province after only the BCM and the NMBM. The provincial
average weighted household income was R7 085 (2015
prices).
labour marketSarah Baartman had the second lowest unemployment
rate in the province, with only 23.1%4 of the labour force
classified as unemployed, compared to the Eastern
Cape’s 29.5%. This rate equated to 45 379 unemployed
in the district. Of those employed, 32.5% were in the
informal sector, 57.6% were in low skilled or semi-skilled
occupations, and only 10.0% were in skilled jobs. Makana
had the highest unemployment rate at 41.1%, followed by
Dr Beyers Naudé at 24.9% and Ndlambe at 24.7%. The
lowest unemployment rates were exhibited in Sundays
River Valley at 6.3%. Sundays River Valley also had the
highest proportion of formal employment at 75.6%,
followed by Kou-Kamma with 72.4%.
Economic output5 Total GVA-R output for Sarah Baartman in 2015 was R18.8
billion, making it the fourth largest economy in the Eastern
Cape and accounting for 8.9% of total provincial GVA-R.
Sarah Baartman’s GVA-R increased by 1.6% year-on-
year between 2014 and 2015. In 2015, the tertiary sector
was the largest economic contributor followed by the
secondary and primary sectors. These sectors contributed
R13.7 billion, R3.7 billion and R1.3 billion to total district
GVA-R, respectively, in 2015. GVA-R growth rates for the
primary, secondary and tertiary sectors between 2014 and
2015 were: -5.1%, 3.1% and 1.8%.
4 The official unemployment rate does not consider discouraged job-seekers (i.e. individuals who were not employed, wanted to work, were available to work/start a business but did not take active steps to find work during the last four weeks). 5 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.
Formal vs informal employment
1O%
27%
3O%
32%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
13%4%
6%21%
23%15%
8%6%
3%1%
0%0%
No incomeR1 - R4 800
R4 801 - R 9 600R9 601 - R 19 200
R19 201 - R 38 400R38 401 - R 76 800
R76 801 - R153 600R153 601 - R307 200R307 201 - R614 400
R614 401 - R1 228 800R1 228 801 - R2 457 600
R2 457 601 and more
10% 10%
26%
15%
7%9% 10%
25%15%
41%
25%
6%
16% 16%
DrBe
yers
Nau
dé
Blue
Cra
neRo
ute
Mak
ana
Ndla
mbe
Sund
aysR
iver
Val
ley
Koug
a
Kou-
Kam
ma
2005 2015
64%60%
66% 69%76%
66%72%
36%40%
34% 31%24%
34%28%
Dr B
eyer
s Nau
dé
Blue
Cra
ne R
oute
Mak
ana
Ndla
mbe
Sund
ays R
iver
Valle
y
Koug
a
Kou-
Kam
ma
Formal Employment Informal Employment
111
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 18 817 1.6% 3.1% 4
GVA-R/Capita R 40 674 0.3% 1.2% 3
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 1 308 -5.1% 4.8%
Agriculture, forestry and fishing
R 1 292 -5.2% 4.9% 7
Mining and quarrying R 16 0.0% -0.3% 10
Secondary Sector R 3 718 3.1% 4.4%
Manufacturing R 2 440 2.4% 4.6% 4
Electricity, gas and water R 290 0.3% 0.9% 9
Construction R 987 6.0% 5.2% 8
Tertiary Sector R 13 792 1.8% 2.6%
Trade, catering and accommodation
R 4 061 1.7% 2.6% 1
Transport R 1 401 2.6% 4.3% 5
Finance and business services
R 3 704 3.4% 3.1% 2
Community services R 3 284 0.1% 1.6% 3
General government R 1 342 1.0% 2.2% 6
Total R 18 817 1.6% 3.1%
GROSS VALUE ADDED-REGIONAL for Sarah
Baartman grew by 1.6% to R18.8 billion in 2015 from R18.5
billion in 2014.
1.6%INCREASEIN GVA-R
Local Municipal Economic ContributionKouga Local Municipality was the largest contributor to the
Sarah Baartman economy in 2015, accounting for R5.0 billion in
total output and 26.7% of district GVA-R. The second and third
largest contributors to economic output were Makana (R3.6
billion) and Dr Beyers Naudé (R2.9 billion). Collectively, these
three economies account for 61.6% of all district GVA-R in 2015.
GVA-R PER CAPITA in Sarah Baartman increased by 1.2%
between 2010 and 2015. 1.2%
INCREASEIN GVA-R PER
CAPITA
or as percentage 3% (2012)Contribution: R18.8 Billion
1.6% Growth
4rd highest in the EC
sarah baartman GVa-r contribution
Sectoral Contribution to Economic Activity The total GVA-R of the primary sector declined by 5.1% between
2014 and 2015 to R1.3 billion. This was primarily attributable
to the R71 million decrease in the GVA-R of the agriculture,
forestry and fisheries sub-sector. Despite this contraction, Sarah
Baartman had the largest primary sector in the province in 2015.
The 3.1% growth in the secondary sector’s GVA-R between 2014
and 2015 was impacted by the 6.0% and 2.4% growth in the
GVA-R of the construction and manufacturing sub-sectors,
respectively. The tertiary sector, which accounted for 73.3%
of the total GVA-R of Sarah Baartman and employed 93 560
people, grew by 1.8% between 2014 and 2015. This growth
was led by a R123 million, or 3.4%, increase in the GVA-R of
the finance and business services sub-sector. The community
and social services, as well as the general government services
sub-sectors’ GVA-R increased in absolute terms by R14 million
and R4 million, respectively, between 2014 and 2015. These
sub-sectors remained the largest employers in 2015, accounting
for 26.8% of total district employment and employing 40 100
people.
112
6 Stats SA, 2016
-1%
0%
1%
1%
2%
2%
3%
3%
4%
4%
5%
-5% 0% 5% 10% 15% 20% 25% 30%
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
provision of services6
Sarah Baartman had the second highest proportion (94.6%) of
households with access to piped water in the province in 2015.
Only the NMBM had a higher level of piped water provision at
96.9%. The high level of provision meant that there has been
little growth in piped water access since 2010, when 94.2%
of households had access to these services. The high level of
access resulted in a correspondingly high level of satisfaction,
with 48.5% of households indicated that the quality of water
provision in 2016 was good.
Similarly to water access, Sarah Baartman had the second
highest proportion of households that use electricity as
their main source of lighting. The district also had success in
expanded electricity access between 2010 and 2015. This was
evident by the fact that the proportion of households that use
electricity as their main source of lighting increased from 86.5%
in 2010 to 87.1% in 2015. Satisfaction levels with electricity
provision likewise remained high, with 57.4% of households
indicating good quality provision of electricity in 2016.
ACCESS TO SERVICES:
Households that have access to piped water
201595%
2010 - 2015+ 1150
households
sarah baartman SECTOR CONTRIBUTION
GVA-R Growth Rate
Local Municipality GVA-R (Millions)
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
Contribution to Joe Gqabi GVA-R
GVA-R Rank in District
Dr Beyers Naudé R2 951 1.6% 3.2% 15.7% 3
Blue Crane Route R 1 292 1.9% 3.7% 6.9% 7
Makana R 3 623 1.4% 2.6% 19.3% 2
Ndlambe R 2 533 1.1% 2.1% 13.5% 4
Sundays River Valley R 1 653 2.1% 4.9% 8.8% 6
Kouga R 5 017 2.0% 3.2% 26.7% 1
Kou-Kamma R 1 749 0.5% 2.9% 9.3% 5
Economic performanceGVA-R per capita allows for the comparison of different
economies relative to their populations. A rise in GVA-R per
capita can indicate an improvement in productivity. Between
2014 and 2015, Sarah Baartman’s GVA-R per capita increased by
0.3% to R40 647, making it the third highest in the province. This
was above the provincial GVA-R per capita figure of R30 392.
Horizontal: Sector contribution to SBDMVertical: Sector Growth in 2014/2015Size: People Employed
ACCESS TO SERVICES:
Households that have access to electricity
for lighting
201587%
2010 - 2015+ 1020
households
113
7 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling in line with the RDP standard. 8 Access to electricity is measured by the number of households that use electricity as their main source of lighting. 9 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet. 10 Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 7 Electricity 8 Sanitation 9 Refuse Removal 10
Dr Beyers Naudé 19 977 98.4% 92.0% 87.2% 82.1%
Blue Crane Route 10 112 95.2% 87.1% 84.7% 80.8%
Makana 22 371 95.0% 89.4% 74.0% 89.5%
Ndlambe 20 260 93.9% 86.0% 59.6% 80.5%
Sundays River Valley 15 480 86.6% 79.7% 58.4% 64.0%
Kouga 30 011 96.1% 86.5% 75.3% 84.7%
Kou-Kamma 11 192 94.8% 87.4% 75.5% 70.3%
Sarah Baartman 129 403 94.6% 87.1% 73.2% 80.4%
Almost three quarters (73.2%) of households in Sarah
Baartman had access to sanitation services in 2015;
representing a marginal increase from the 72.8%
households that had access in 2010. This meant that 94
713 households made used of either a flush or chemical
toilet; the minimum RDP standard in 2015. Only 11 632
households or 9.0% had no access to sanitation services
in Sarah Baartman. Satisfaction levels with sanitation
services were also high as evidenced by the 51.0% of
households that indicated the quality of provision in
2016 was good.
Sarah Baartman had the second highest (80.4%)
proportion of households that have their refuse removed
by a local authority either weekly or less frequently. Only
13.6% of households in the district made use of their own
refuse dump. This high level of provision was reflected
in the equally positive satisfaction levels in the district –
58.2% of households indicated that the quality of their
refuse collection was good in 2016.
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
201573%
2010 - 2015+ 740
households
114
Amathole DistrictMunicipality
The Amathole District Municipality (ADM) is bordered by the Sarah Baartman, Chris Hani, O.R. Tambo Districts and the Buffalo City Metro (BCM). The district covers an area of roughly 23 577
km2 and is comprised of six local municipalities: Amahlathi, Great Kei, Mbhashe, Mnquma, Ngqushwa, Raymond Mhlaba. The Raymond Mhlaba Local Municipality was established
after the August 2016 local elections by the merging of Nkonkobe and Nxuba local municipalities.
1 Unless otherwise stated all district data is 2013 based on Quantec Standardised Regional Database
Raymond MhlabaAvg HH Size = 3.6
AmahlathiAvg HH Size = 3.6
MnqumaAvg HH Size = 3.6
MbhasheAvg HH
Size = 4.2
Great KeiAvg HH Size = 3.8
NgqushwaAvg HH Size = 3.3 Amathole
254 222Total Households
3.7Average Household
Size
Raymond Mhlabapop = 159 845
Amahlathipop = 130 107
Mnqumapop = 266 534
Mbhashepop = 268 178
Great Keipop = 41 396
Ngqushwapop = 76 552 Amathole
942 612
43.6People per
km2
Total Population
amathole population density
Amathole household density
Raymond MhlabaAvg HH Size = 3.6
AmahlathiAvg HH Size = 3.6
MnqumaAvg HH Size = 3.6
MbhasheAvg HH
Size = 4.2
Great KeiAvg HH Size = 3.8
NgqushwaAvg HH Size = 3.3 Amathole
254 222Total Households
3.7Average Household
Size
Raymond Mhlabapop = 159 845
Amahlathipop = 130 107
Mnqumapop = 266 534
Mbhashepop = 268 178
Great Keipop = 41 396
Ngqushwapop = 76 552 Amathole
942 612
43.6People per
km2
Total Population
Population and households1
The district’s population rose steadily in the last year reaching
942 612 in 2015. This equated to a population growth rate of
1.6% between 2014 and 2015, which was higher than the Eastern
Cape’s population growth rate over the same period of 1.5%.
Over the 2010 to 2015 period however, the district’s population
grew at a slower rate (1.2%) that the province (1.4%).
There are approximately 229 940 youth, classified as those
between the ages of 15 and 34 years old in Amathole. This
accounts for 31.8% of the district’s total population, similar to
the provincial average of 33.6%. Amathole has a moderately
high proportion of minor children, with 33.9% of the district’s
population classified as 14 years and younger. This low
proportion of children means that the district has a child
dependency ration (i.e. children below the age of 14 years old
to the total working age population) of 58.9, the fourth lowest
in the Eastern Cape.
The overall Amathole Dependency Ratio, or the ratio of
persons not in the labour force (under 15 and over 64 years) to
the working age population is 74.0. This is above the provincial
figure of 66.6. Whilst the old age dependency ratio (i.e.
individuals older than 65 years relative to the labour force) in
Amathole at 15.1 is also well above the provincial average (11.2).
The district further has the highest old age dependency ratio
in the province.
The high overall and old age dependency ratios are worth
noting for Amathole as they imply increased pressure on the
productive population to support dependents. It indicates a
smaller base to draw taxes on to support state interventions
for the youth and aged. This measure however is premised on
the assumption that those over the age of 65 years’ lack other
sources of income.
In line with population growth the number of households in
Amathole increased from 249 878 in 2014 to 254 222 in 2015,
equivalent to a 1.7% year-on-year increase. This is greater than
the provincial figure (1.4%) but slightly less than the national
figure (1.6%). Of these 254 222 households, approximately
52.8% live in formal dwellings, while a further 41.7% are live in
traditional dwellings.
116
Education
Educational attainment levels in Amathole were low, with
13.4% of the population over 20 years old having not attained
any schooling in 2015. This was above the provincial average
of 10.7%. Only 13.5% of the population over 20 years had
attained Matric, and a further 6.1% had attained some form
of higher education versus 19.4% and 8.6% respectively in
the province. Despite the low level of tertiary educational
attainment Amathole has one university (University of Fort
Hare), one public TVET in Mnquma, a campus of the King
Sabata Dalindyebo TVET College in Mbhashe and a campus of
the Lovedale TVET College in Raymond Mhlaba.
Amathole had approximately 315 629 learners enrolled across
1 614 public and private schools in 2014. This represented a
decline of 52 856 learners from 2010. This reduction in learners
resulted in a corresponding decline in the number of educators
which fell from 13 340 in 2010 to 11 827 in 2015. Despite these
declines, the learner-to-educator-ratio improved from 27.6 in
2010 to 26.7 in 2015 (DBE, 2016)2.
As part of the 2016 Community Survey respondents in
Amathole were asked to rate their overall satisfaction with
public schools within the district. The overwhelming majority
(52.3%) of respondents indicated that they felt that the schools
in the district were good. However, this varied notably across
the district with only 47.0% respondents in Mbhashe indicating
that the quality of public schools was good compared to 69.9%
in Amahlathi. Across the district 1.8% of respondents indicated
that they had no access to public schools or did not make use
of public schools (1.6%).
2 The Department of Education learner statistics by district municipality does not separate the Buffalo City Metro from the Amathole District. To obtain statistics for Amathole, the figures for the East London school district (which largely corresponds to the borders of the Buffalo City Metro) have been removed from the totals to obtain a figure for Amathole.
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
13%
21%
7%
35%
6%
13%
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher Education
3%
POPULATIONTotal Population 942 612
0 - 14 years 319 087
15 - 35 years 299 440
15 - 64 years 541 594
Average Household Size 3.7
Population Density 43.6 persons/km2
Provincial Population Percentage 13.6%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 315 629 2
Education Attainment Levels Percentage of district population (aged 20 years +)
Proportion Change
Provincial Rank
No Education 13.4% 4
Matric 13.5% 6
Higher Education 6.1% 5
Highest Educational Level
in 2015:
HIGHER EDUCATION
6%MATRIC
13%
53% live informal dwellings
42% live in traditional dwellings
50% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
amathole dwelling typeAmathole has the third highest number of traditional
dwellings in the province after Alfred Nzo and O.R. Tambo.
This represents a slight improvement from 2010 when 94.5%
households were classified as living in either a formal (52.3%)
or traditional (42.2%) dwelling.
117
‘HIV testing coverage’ Amathole has the highest coverage for HIV testing in the province and third highest nationally.
Amathole saw an increase of 62% in malnutrition case fatalities in under fives, up from 8.7% to 14.1%.
51.9%
INCREASEIN UNDER 5
MALNUTRITIONFATALITIES
3 Based on Massyn et al. 2015.4 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwise stated.
Health3 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
13.4 19.6
Maternal Mortality - Per 100 000 live births (2014)
58.8 148.3
Immunisation Rate under 1 years (2014)
86.6% 80.9%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 3.0% 5.2%
Pneumonia case fatality rate (2014) 2.6% 4.2%
Malnutrition case fatality (2014) 14.1% 11.8%
TB Incidence per 100 000 population (2014)
651.1 792.3
TB Cure rate (percentage) (2013) 78.6% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
51.9% 36.2%
socio-economic and poverty indicators Amathole Provincial
Poverty Headcount (2016) 18.7% 12.7%
Poverty Intensity (2016) 42.5% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 4 346 R 7 085
CHILDREN UNDER 1 YEARS (2014)
Provincial81%
poverty indicators
IMMUNISATION RATE
POVERTY HEADCOUNT (2016)
Amathole19% Provincial
13%
Amathole70%
Health3,4
In 2014, ‘maternal mortality in-facility rate’ stood at 58.8 per
100 000 live births, which is below the provincial average of
148.3 per 100 000 and national average of 132.5 deaths per
100 000. Amathole has the lowest ‘maternal mortality rate’ of
a district in the Eastern Cape. However, trends in this indicator
have been difficult to discern over time due to a small data
sample within the district. The ‘stillbirth rate’ has decreased
from 17.1 per 1000 births to 13.4 per 1000 births, a 21.64% drop
from 2013 to 2014. The district has the second lowest stillbirth
rate nationally. Child mortality can be referred to by indicators
analysing case fatality rates in diarrhoea, pneumonia and
malnutrition in children under the age of 5 years. ‘Child under
5 diarrhoea case fatality rates’ have fluctuated for Amathole
DM over the 2009-2014 period, but exhibited a general
improvement from 5.3% in 2013 to 3% in 2014. ‘Child under 5
pneumonia fatality rates’ have also fluctuated over this period,
with the most recent at 2.6% in 2014, below the provincial
average. ‘Child under 5 malnutrition fatalities’ have increased
from 8.7% to 14.1% between 2013 and 2014, which is above
provincial average. ‘Immunisation coverage of children under
the age of 1 year’ in the district is 86.6%, which is above the
provincial figures of 80.9% and below the national figures
of 84.4%. ‘TB incidence rates (all types)’ have fluctuated
between 537.1 and 647.2 per 100 000 over the 2009-2013
period, with 651.5 suspected cases per 100 000 in 2014. ‘TB
treatment success rates for all cases’ have increased from
71.9% in 2012, to 78.6% in 2013, above the national average of
77.9% but below the national target of 82%. The ‘HIV testing
coverage of the population aged 15-49 years was 51.9%; this
was higher than the national average of 32.1% and the highest
coverage in the province. The district was ranked 3rd nationally
for this indicator.
118
Formal vs informal employment
AMATHOLE EMPLOYMENT By skill level
15%7%
11%27%
24%8%
5%3%
1%0%0%0%
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
48%
35%
18%
31%
53%
38%
37%
27%
20%
32%
39%
50%
Mbh
ashe
Mnq
uma
Grea
t Kei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
61%64%
60%
68%72%
39%36%
64%
36%40%
32%28%
Mbh
ashe
Mnq
uma
Grea
t Kei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
2005 2015
18%
24%
23%
36%
Skilled Semi - SkilledLow Skilled Informal Employment
Formal Employment Rate Informal Employment Rate
UNemployment rates15%
7%
11%27%
24%8%
5%3%
1%0%0%0%
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
48%
35%
18%
31%
53%
38%
37%
27%
20%
32%
39%
50%
Mbh
ashe
Mnq
uma
Gre
at K
ei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
61%64%
60%
68%72%
39%36%
64%
36%40%
32%28%
Mbh
ashe
Mnq
uma
Gre
at K
ei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
2005 2015
18%
24%
23%
36%
Skilled Semi - SkilledLow Skilled Informal Employment
Formal Employment Rate Informal Employment Rate
AMATHOLE household income distribution
15%7%
11%27%
24%8%
5%3%
1%0%0%0%
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
48%
35%
18%
31%
53%
38%
37%
27%
20%
32%
39%
50%
Mbh
ashe
Mnq
uma
Gre
at K
ei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
61%64%
60%
68%72%
39%36%
64%
36%40%
32%28%
Mbh
ashe
Mnq
uma
Gre
at K
ei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
2005 2015
18%
24%
23%
36%
Skilled Semi - SkilledLow Skilled Informal Employment
Formal Employment Rate Informal Employment Rate
5 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.6 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling.
15%7%
11%27%
24%8%
5%3%
1%0%0%0%
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
48%
35%
18%
31%
53%
38%
37%
27%
20%
32%
39%
50%
Mbh
ashe
Mnq
uma
Grea
t Kei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
61%64%
60%
68%72%
39%36%
64%
36%40%
32%28%
Mbh
ashe
Mnq
uma
Grea
t Kei
Amah
lath
i
Ngq
ushw
a
Raym
ond
Mla
ba
2005 2015
18%
24%
23%
36%
Skilled Semi - SkilledLow Skilled Informal Employment
Formal Employment Rate Informal Employment Rate
Socio-Economic
The poverty headcount in Amathole was 18.7% in 2016; the
third highest in the province after Alfred Nzo and O.R. Tambo.
This measure is based on the South African Multidimensional
Poverty Index (SAMPI). The SAMPI is an index that is
constructed using eleven indicators across four dimensions;
namely health, education, living standards and economic
activity. The poverty headcount shows the proportion of
households that are considered to be “multidimensional
poor” in the district. The poverty intensity, which refers to the
average proportion of indicators in which multidimensional
poor households are deprived, in the district was 42.5% in
2016, marginally below the provincial average of 43.3%. The
average monthly weighted household income for Amathole
in 2011 was R4 346 (2015 prices), and is the second lowest in
the province, ahead of only Alfred Nzo. The provincial average
weighted household income is R7 085 (2015 prices).
Labour Market
With respect to employment, Amathole had an official
unemployment rate of 34.8%5 (5.3% higher than the provincial
average of 29.5%). This equated to 91 216 unemployed people
within the district. Of those employed in Amathole, 103 681
were formally employed which was approximately 64.4%
of the labour force. This was 5.5% lower than the provincial
average of 69.9%. There was also a notable informal sector,
which employed 67 376 people, or 35.6% of the employed
population. This was higher than the province, where 30.3% of
employment was informal.
Economic Output6
Amathole had a comparatively small economic output in 2015,
having the 3rd lowest GVA-R in the Eastern Cape. Amathole
produced R14.5 billion in GVA-R output in 2015; this was up
1.6% on the 2014 figure. The largest sectoral contribution to
GVA-R came from the tertiary sector at R12.4 billion. The
primary sector contributed R493.0 million and the secondary
sector contributed R1.6 billion. GVA-R growth was experienced
by all but the primary sector between 2014 and 2015, with this
sector contracting by 4.1%. In comparison, the secondary and
tertiary sectors grew by 4.3% and 1.5%, respectively. Amathole
contributed only 6.9% of the total Eastern Cape GVA-R, and
was the 6th largest contributor.
Sectoral Contribution to Economic ActivityThe primary sector comprises of predominantly the agricultural,
forestry and fisheries sub-sector, which contributed R394.4
million in 2015 but contracted by 5.0% year-on-year. The
manufacturing sub-sector made up 49.1% of the secondary
sector’s total output. Manufacturing contributed R805.1 million
towards GVA-R and exhibited a strong positive growth of 1.1%
for the period. All sub-sectors of the tertiary sector displayed
positive growth in 2015, with finance and business services
exhibiting the highest growth rate at 4.7% and community
services growing by 2.0%.
119
AMATHOLE SECTOR CONTRIBUTION
-1%
0%
1%
2%
3%
4%
5%
6%
-10% 0% 10% 20% 30% 40% 50%
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
Horizontal: Sector contribution to ADMVertical: Sector Growth in 2014/2015Size: People Employed
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 14 534 1.4% 2.6% 6
GVA-R/Capita R 15 419 0.0% 2.3% 6
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 493 -4.1% 3.4%
Agriculture, forestry and fishing
R 394 -5.0% 4.6% 8
Mining and quarrying R 99 -0.6% -0.1% 10
Secondary Sector R 1 641 4.3% 3.0%
Manufacturing R 805 1.1% 1.6% 5
Electricity, gas and water
R 235 2.0% 1.5% 9
Construction R 600 9.9% 6.1% 7
Tertiary Sector R 12 400 1.5% 2.5%
Trade, catering and accommodation
R 2 765 1.3% 2.1% 2
Transport R 836 0.3% 1.6% 5
Finance and business services
R 2 237 4.7% 5.3% 3
Community services R 5 109 0.3% 2.0% 1
General government R 1 454 2.0% 2.0% 4
Total R 14 534 1.6% 2.6%
GROSS VALUE ADDED-REGIONAL for Amathole grew by 1.4% to R14.5 billion in 2015
from R14.3 billion in 2014.
GVA-R PER CAPITA in Amathole increased by 2.3% between 2010
and 2015
1.4%INCREASEIN GVA-R
2.3%INCREASE
IN GVA-R PER CAPITA
Local Municipal Economic ContributionGeographically the majority of Amathole’s economic activity is
ascribed to the Mnquma Local Municipality which contributes 30.5% or
R4.4 billion to total output. The second and third largest contributors
to economic output are Raymond Mhlaba with R3.0 billion (20.8%) and
Amahlathi with R2.6 billion (18.2%).
or as percentage 3% (2012)Contribution: R14.5 Billion
1.4% Growth
6th highest in the EC
AMATHOLE GVa-r contribution
The trade sub-sector contributed R2.7 billion in 2015 and was
the district’s largest private sector employer, employing 28 621
persons in 2015. General government was the largest employer
in the district, employing 23 656 persons as well as the largest
contributor to GVA-R with R5.1 billion in 2015.
120
Local Municipality GVA-R (Millions)
GVA-R Growth Rate Contribution to District GVA-R
GVA-R Rank in District
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
Amahlathi R2 644 1.8% 2.8% 18.2% 3
Great Kei R 904 1.0% 2.5% 6.2% 5
Mbhashe R 2 629 2.8% 3.6% 18.1% 4
Mnquma R 4 439 0.9% 1,9% 30.5% 1
Ngqushwa R 898 2.4% 3.0% 6.2% 6
Raymond Mhlaba R 3 020 1.4% 2.6% 20.8% 2
Economic performance
GVA-R per capita offers a measure of the performance of an
economy relative to another economy. A rise in GVA-R per
capita can indicate an improvement in productivity. Amathole’s
2015 GVA-R per capita was R15 419, almost unchanged from
the R15 420 recorded in 2014. GVA-R per capita is low in
comparison to the other districts as its 6th lowest in the
province, and below both the national (R50 511) and provincial
(R30 392) GVA-R per capita figures.
provision of services7
Just over half (54.9%) of households in Amathole have access
to piped water in 2015, representing a marginal increase from
the 53.9% households that had access in 2010. This low level of
access means that 50 145 households in the district are reliant
on dams, rivers and springs for their water supply. Accordingly,
20.7% of households in 2016 indicated that the quality of water
provision in the district was poor.
Almost 70% of households in Amathole use electricity as their
main source of lighting, 1.1% higher than in 2010. Approximately
29.2% of households, however are dependent on either candles
or paraffin for lighting. Satisfaction levels with electricity
remain high with 49.0% of households indicating good quality
provision of electricity in 2016.
7 Access to water is measured using the RDP standard which requires that water be accessible to a household within 200 metres of a home.8 Access to electricity is measured by the number of households that use electricity as their main source of lighting.9 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet.10 Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 7 Electricity 8 Sanitation 9 Refuse Removal 10
Mbhashe 62 666 29.6% 49.9% 4.5% 3.6%
Mnquma 72 686 43.9% 61.5% 14.6% 16.1%
Great Kei 10 640 72.2% 80.1% 34.1% 34.2%
Amahlathi 35 673 71.6% 82.6% 21.7% 20.6%
Ngqushwa 22 331 71.2% 91.4% 5.9% 7.2%
Raymond Mhlaba 43 453 83.6% 89.0% 37.6% 32.6%
Amathole 247 450 54.9% 69.9% 17.2% 16.5%
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
201517%
2010 - 2015+ 2070
households
ACCESS TO SERVICES:Households that have access to piped water
201555%
2010 - 2015+ 2310
households
ACCESS TO SERVICES:Households that have
access to electricity for lighting
201570%
2010 - 2015+ 3000
households
Amathole has one of the lowest levels of sanitation provision in the Eastern Cape with only 17.2% of households
having access to a flush or chemical toilet, the minimum RDP standard. Over a third (35.2%) of households however
have no access to sanitation services. This equates to 89 459 households. Satisfaction levels with sanitation services
are also low as evident by the 18.3% of households that indicated the quality of provision in 2016 was poor.
Amathole has the third fewest number of households that have their refuse removed by a local authority either
weekly or less frequently. Almost two thirds (63.9%) of households in the district make use of their own refuse
dump. This low provision is reflected in the equally low satisfaction levels in the district – 26.7% of households
indicated that the quality of their refuse collection was poor in 2016.
121
CHRIS HANI districtMunicipality
1 Unless otherwise stated, all district data is 2015 based on Quantec Standardised Regional Database.
Population and households1
The population of Chris Hani grew by 1.6% between 2014 and
2015 (2013 to 2014: 1.5%), increasing to 837 404 persons in
2015. This district accounted for 12.1% of the total Eastern Cape
population in 2015. Between 2010 and 2015, the population
growth of Chris Hani was lower than both the Eastern Cape
(1.4%) and South African (1.6%) population growth rates over
the same period.
There were approximately 262 517 youth, classified as those
between the ages of 15 and 35 years old, in Chris Hani in 2015.
This age segment accounted for 31.3% of the total district
population, and was lower than the provincial figure of 33.6%.
Chris Hani has a minor child population (i.e. individuals younger
than 14 years old) equal to 34.7% of the total population,
resulting in a child dependency ratio of 60.6. The child
dependency ratio, which measures the ratio of children below
the age of 15 years old to the total working age population, in
Chris Hani was higher than provincial figure of 55.4.
The Dependency Ratio, or the ratio of persons not in the labour force (under 15 and over 64 years) to those within the labour force (or the working age population) for Chris Hani
was 74.3, which was above the provincial figure of 66.6. The
old age dependency ratio, which measures the proportion of
individuals older than 65 years relative to the labour force in
Chris Hani was 13.8 (Eastern Cape: 11.2).
The high dependency ratios are likely to place greater strain
on the working age population who will have to allocate
resources to support those younger or older than themselves.
The number of households in Chris Hani increased from 220
779 in 2014 to 224 497 in 2015. This represented a year-on-year
increase in household numbers in the district of 1.7%, slightly
lower than the household growth rate between 2010 and 2015
(1.5%). The number of households in the district also grew at
a faster rate than both the Eastern Cape (1.4%) and South
Africa (1.6%) between 2014 and 2015. Similarly to the rest of
the Eastern Cape, the majority (62.0%) of these households
resided in formal dwellings, while only 38.0% resided in either
an informal (2.1%) or traditional (35.9%) dwellings.
The Chris Hani District Municipality (CHDM) is bordered by the Joe Gqabi, O.R. Tambo,and Sarah Baartman Districts in the Eastern Cape. The district comprises six local municipalities namely:
Inxuba Yethemba, Enoch Mgijima, Intsika Yethu, Emalahleni, Engcobo, and Sakhisizwe; and covers an area of roughly 36 114 km2. The Enoch Mgijima Local Municipality was established
after the August 2016 local government elections by the merging of the Lukhanji, Tsolwana and Inkwanca local municipalities.
chris hani household density
Inxuba YethembaAvg HH Size = 3.5
Enoch MgijimaAvg HH Size = 3.7
EmalahleniAvg HH Size = 3.7
Intsika Yethu Avg HH Size = 3.5
Avg HH Size = 4.1Chris Hani224 497
Total Households
3.7Average Household
Size
Engcobo
MbhasheAvg HH Size = 3.9
chris hani population density
Inxuba Yethembapop = 66 129 Enoch Mgijima
pop = 260 439
Emalahlenipop = 125 567 Sakhisizwe
pop = 67 179
Intsika Yethu pop = 152 987
Engcobo pop = 164 320
Chris Hani837 404
23.2People per
km2
Total Population
122
4
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
14%
22%
7%33%
15%
7%
2%
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher EducationOther
Highest Educational Level
in 2015:
HIGHER EDUCATION
7%MATRIC
15%
62% live informal dwellings
2% live in traditional dwellings
35% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
chris hani dwelling typeeducation
Chris Hani had the third highest percentage (14.3%) of
individuals over the age of 20 years old that had no formal
schooling in the Eastern Cape during 2015. Only the O.R.
Tambo (17.0%) and Joe Gqabi (14.7%) Districts had a higher
proportion. Approximately 14.9% and 7.0% had achieved either
Matric or some form of higher education, respectively. This was
below the provincial averages of 19.4% and 8.6%. The district
has one TVET college and a satellite campus of the Walter
Sisulu University both located in Enoch Mgijima. This TVET
however, has two satellite campuses located in Joe Gqabi.
Chris Hani had 249 386 learners in 2014, 1 745 or 0.7% higher
than in 2013. These learners were enrolled in 744 public and
private schools across the district. Despite the increase in
learners, the number of educators declined by 6.6% between
2013 and 2014. This resulted in the learner to educator ratio
deteriorating from 27.3 in 2013 to 29.5 in 2015 (DBE, 2016).
The 2016 Community Survey provides detail on how
respondents in Chris Hani rated their overall satisfaction with
public schools within the district. The district had the second
highest proportion of respondents that indicated that the
quality of public schools in the district was good at 63.6%.
This figure however, varied across the district with Inxuba
Yethemba registering the highest number of respondents that
indicated that the quality of public schools was good (71.1%)
and Engcobo the lowest (51.3%). Outside of the metros and
Sarah Baartman, Chris Hani had the highest proportion of
respondents that indicated that they did not make use of
public schools (1.7%).
POPULATIONTotal Population 837 404
0 - 14 years 290 940
15 - 35 years 262 517
15 - 64 years 480 399
Average Household Size 6.2
Population Density 23.2 persons/km2
Provincial Population Percentage 12.1%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 249 386 3
Education Attainment Levels Percentage of district
population (aged 20 years +)
Pro-portion Change
Provincial Rank
No Education 14.3% 7
Matric 14.9% 4
Higher Education 7.0% 4
123
2 Based on Massyn et al. 2015. 3 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwise stated.
Health2,3
The ‘maternal mortality in facility rate’ has fluctuated between
2009 and 2013, remaining relatively stable over the last two
years at 199.8 deaths per 100 000 live births. This however,
is an 18.58% increase in this indicator between 2013 and 2014,
making it the second highest maternal mortality rate in the
province. The ‘maternal mortality in facility rate’ for Chris Hani
District is above the provincial average of 148.3 per 100 000
and national average of 132.5 per 100 000. The ‘stillbirth rate’
has increased from 17.8 to 18.5 per 1000 births, between 2013
and 2014; an increase of 3.93%. Child mortality can measured
by analysing case fatality rates in diarrhoea, pneumonia and
malnutrition in children under the age of 5 years. ‘Child under
5 diarrhoea case fatality rates’ have decreased over the last
two years for Chris Hani, from 5.8% in 2013 to 4.4% in 2014.
‘Child under 5 pneumonia fatality rates’ have fluctuated
over the period 2009-2014, with the most recent indicator
measuring 3.5% in 2014, below the provincial average. ‘Child
under 5 malnutrition fatalities’ have risen, from 9.7% to 10%
between 2013 and 2014, below the provincial average of 11.8%.
‘Immunisation coverage of children under the age of 1 year’
in the district is 83.6%, which is above the provincial figure of
80.9% and below the national average of 84.4%. ‘TB incidence
rates’ have fluctuated between 910.7 and 707.9 per 100 000
over the 2009-2014 period. In 2014, the indicator measured
707.9 suspected cases per 100 000 of TB (all cases). ‘TB
treatment success rates for all cases’ have increased from
75.7% in 2009 to 76.6% in 2013, currently below the national
average of 77% and the national target of 82%. The ‘HIV testing
coverage of the population aged 15-49 years’ was 42.5%; this
was higher than the national and provincial averages of 32.1%
and 36.0%. It is the second highest testing coverage within the
province.
CHILDREN UNDER 1 YEARS (2014)
Provincial81%
IMMUNISATION RATE
POVERTY HEADCOUNT (2016)
Chris Hani16% Provincial
13%
poverty indicators
Chris Hani84%
Health2 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
18.5 19.6
Maternal Mortality - Per 100 000 live births (2014)
199.8 148.3
Immunisation Rate under 1 years (2014)
83.6% 80.9%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 4.4% 5.2%
Pneumonia case fatality rate (2014) 3.5% 4.2%
Malnutrition case fatality (2014) 10.0% 11.8%
TB Incidence per 100 000 population (2014)
707.9 792.3
TB Cure rate (percentage) (2013) 76.6% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
42.5% 36.2%
socio-economic and poverty indicators Chris Hani
Provincial
Poverty Headcount (2016) 16.4% 12.7%
Poverty Intensity (2016) 43.1% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 5 415 R 7 085
Infant mortality increased from 17.8 still births per 1 000 in 2013 to 18.5 still births per 1 000 in 2014.
3.9%
‘HIV testing coverage’ Chris Hani has the second highest coverage for HIV testing in the province after Amathole.
43%
124
CHRIS HANI EMPLOYMENT By skill level
14%6%
11%26%
23%9%
5%4%
2%
0%0%0%
19%
26%
24%
31%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
78%69%
64%67% 68%
62%
22%31%
36% 33%32%
38%
Inxu
baYe
them
ba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isizw
e
Formal Employment Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
15%
30%
51%55%
48%
37%
29%
40%
51%
66%
43%
56%
Inxu
ba Y
ethe
mba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isizw
e
2005 2015
UNemployment rates
14%6%
11%26%
23%9%
5%4%
2%
0%0%0%
19%
26%
24%
31%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
78%69%
64%67% 68%
62%
22%31%
36% 33%32%
38%
Inxu
baYe
them
ba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isizw
e
Formal Employment Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
15%
30%
51%55%
48%
37%
29%
40%
51%
66%
43%
56%
Inxu
ba Y
ethe
mba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isiz
we
2005 2015
SOCIO-ECONOMIC
Chris Hani had a poverty headcount of 16.4% in 2016;
the fourth highest in the province after Alfred Nzo, O.R.
Tambo and Chris Hani. This measure is based on the
South African Multidimensional Poverty Index (SAMPI).
The SAMPI is an index that is constructed using eleven
indicators across four dimensions; namely health,
education, living standards and economic activity. The
poverty headcount shows the proportion of households
that are considered to be “multidimensional poor” in the
district. The poverty intensity, which refers to the average
proportion of indicators in which multidimensional poor
households are deprived, in the district was 43.1% in 2016,
only slightly below the provincial average of 43.3%. The
average monthly weighted household income for Chris
Hani in 2011 was R5 415 (2015 prices), and was the fourth
highest in the province, after the two metros and the
Sarah Baartman District. The provincial average weighted
household income is R7 085 (2015 prices).
labour market
Chris Hani had the highest unemployment rate in the
province, with approximately 45.2%4 of the labour force
classified as unemployed in 2015 compared to the Eastern
Cape unemployment rate of 29.7%. There were 126 642
employed persons in Chris Hani, of which 62.8% were
formally employed; equating to 87 754 individuals. The
informal sector in Chris Hani, employed 47 056 persons in
2015, or 37.2% of the total number of employed individuals
in the labour force. The highest unemployment rate was
exhibited in Emalahleni at 66.1%, followed by Sakhisizwe
at 56.3%. The lowest unemployment rate was exhibited
in Inxuba Yethemba at 28.5%. The highest proportion of
formal employment to informal employment was in Inxuba
Yethemba at 77.5%. The local municipality with the largest
proportion of informal employment was Sakhisizwe,
where 37.8% of the labour force was classified as working
in the informal sector. Skilled occupations make up 19.3%
of total employment compared to 50.0% for semi-skilled
and low-skilled employment.
Economic output5
Chris Hani had one of the smallest economies in the Eastern
Cape, accounting for only 7.8% of the total provincial GVA
in 2015. This was equivalent to a total GVA-R in 2015 for
the district of R16.3 billion. Between 2014 and 2015, Chris
Hani’s GVA-R output increased by 1.9%, making it the
fourth fastest growing district in the Eastern Cape over the
period. This was above both the national and provincial
GVA-R growth rates over the same period, which were
1.2% and 1.3% respectively. The tertiary sector was the
largest contributor to total GVA-R in 2015, followed by the
secondary and primary sectors. These sectors contributed
R13.9 billion, R1.8 billion and R502 million respectively, to
total Chris Hani’s GVA-R in 2015. GVA-R growth rates for
the primary, secondary and tertiary sectors between 2014
and 2015 were: -5.2%, 5.7% and 1.7%, respectively.
Formal vs informal employment
14%6%
11%26%
23%9%
5%4%
2%
0%0%0%
19%
26%
24%
31%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
78%69%
64%67% 68%
62%
22%31%
36% 33%32%
38%
Inxu
baYe
them
ba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isizw
e
Formal Employment Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
15%
30%
51%55%
48%
37%
29%
40%
51%
66%
43%
56%
Inxu
ba Y
ethe
mba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isizw
e
2005 2015
4 The official unemployment rate does not consider discouraged job-seekers (i.e. individuals who were not employed, wanted to work, were available to work/start a business but did not take active steps to find work during the last four weeks).
5 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.
CHRIS HANI household income distribution (2011)
14%6%
11%26%
23%9%
5%4%
2%
0%0%0%
19%
26%
24%
31%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
78%69%
64%67% 68%
62%
22%31%
36% 33%32%
38%
Inxu
baYe
them
ba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isizw
e
Formal Employment Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
15%
30%
51%55%
48%
37%
29%
40%
51%
66%
43%
56%
Inxu
ba Y
ethe
mba
Enoc
h M
gijim
a
Ints
ika
Yeth
u
Emal
ahle
ni
Engc
obo
Sakh
isiz
we
2005 2015
125
Sectoral Contribution to Economic Activity
The primary sector’s total GVA-R decreased by R27 million
in 2015, equating to a 5.2% decrease between 2014 and 2015.
This was primarily attributable to a 5.8% decrease in the
GVA-R of the agriculture, forestry and fisheries sub-sector.
The secondary sector’s GVA-R grew by 5.7% between 2014
and 2015, driven by strong positive growth of 11.7% and 1.8%
for the construction and manufacturing sub-sectors. These
increases were offset by the low, 0.6% growth in the GVA-R
of the utilities sub-sector. The tertiary sector, which accounted
for 85.3% of the total GVA-R of Chris Hani and which employed
87 306 people, grew by 1.7% year-on-year in 2015. This growth
was led by a 4.5%, or R111 million, increase in the GVA-R of
the finance and business Services sub-sector. The community
and social services and general government services sub-
sectors grew over the period by R41 million (2.5%) and R44
million (0.8%), respectively. These two sub-sectors remained
the largest employers in 2015, accounting for 44.6% of total
district employment and employing 49 874 people.
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 16 303 1.9% 3.0% 5
GVA-R/Capita R 19 468 0.3% 2.2% 4
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 502 -5.2% 4.0%
Agriculture, forestry and fishing
R 438 -5.8% 4.9% 8
Mining and quarrying R 64 -0.8% -0.4% 10
Secondary Sector R 1 889 5.7% 4.4%
Manufacturing R 857 1.8% 3.0% 6
Electricity, gas and water
R 208 0.6% 1.1% 9
Construction R 823 11.7% 7.3% 7
Tertiary Sector R 13 912 1.7% 2.8%
Trade, catering and accommodation
R 2 928 1.1% 1.9% 2
Transport R 1 084 0.6% 1.9% 5
Finance and business services
R 2 603 4.5% 5.0% 3
Community services R 5 641 0.8% 2.6% 1
General government R 1 656 2.5% 2.8% 4
Total R 16 303 1.9% 3.0%
GROSS VALUE ADDED-REGIONAL for Chris Hani
grew by 1.9% to R16.3 billion in 2015 from R15.9 billion in 2014.
1.9%INCREASEIN GVA-R
GVA-R PER CAPITA in Chris Hani increased by 2.2% between 2010
and 2015. 2.2%
INCREASEIN GVA-R PER
CAPITA
Contribution: R16.3 Billion5th highest in the EC
1.9% Growth
CHRIS HANI GVa-r contribution
126
CHRIS HANI SECTOR CONTRIBUTIONAgriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
-1%
0%
1%
2%
3%
4%
5%
6%
7%
-10% 0% 10% 20% 30% 40% 50%
Horizontal: Sector contribution to CHDMVertical: Sector Growth in 2014/2015Size: People Employed
Local Municipal Economic Contribution
The largest GVA-R contributor to the Chris Hani economy
in 2015 was the Enoch Mgijima Local Municipality, which
accounted for R7.8 billion in total GVA-R output and 48.1%
of district GVA-R. The next largest economies were Inxuba
Yethemba and Engcobo, respectively. These two economies
jointly accounted for a further R4.5 billion in district GVA-R,
accounting for approximately 27.6% of all district GVA-R in 2015.
This highlighted the marginal contribution of the remaining three
municipalities to the district economy.
Economic performance
GVA-R per capita allows comparison of different economies
relative to their populations. A rise in GVA-R per capita can
indicate an improvement in productivity. Between 2014 and
2015, Chris Hani’s GVA-R per capita increased by 0.3% to R19
468. This was below the provincial GVA-R per capita figure of
R30 457.
provision of services6
Access to pipped water varies considerably across the district,
with 98.8% of households in Inxuba Yethemba having access
to piped water, compared to only 43.9% of households in
Engcobo. Across the district, on average, 72.7% of households
had access to piped water – the fourth highest in the Eastern
Cape. Accordingly, only a small number (10.4%) of households
were dependent on dams, rivers and streams for their water. The
high level of access to piped water correlates with the quality
of the service. In 2016, 41.4% of Community Survey respondents
indicated that the quality of provision was good.
Local Municipality GVA-R (Millions)
GVA-R Growth Rate Contribution to District GVA-R
GVA-R Rank in District
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
Inxuba Yethemba R2 842 2.0% 3.4% 17.4% 2
Enoch Mgijima R 7 848 1.6% 2.8% 48.1% 1
Intsika Yethu R 1 558 2.3% 3.1% 9.6% 4
Emalahleni R 1 197 2.1% 2.8% 7.3% 5
Engcobo R 1 665 3.0% 3.3% 10.2% 3
Sakhisizwe R 1 911 2.4% 3.3% 7.3% 6
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
201533%
2010 - 2015+ 1050
households
6 StatsSA, 2016.
127
7 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling in line with the RDP standard. 8 Access to electricity is measured by the number of households that use electricity as their main source of lighting. 9 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet. 10 Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 7 Electricity 8 Sanitation 9 Refuse Removal 10
Inxuba Yethemba 18 799 98.8% 95.5% 90.1% 84.9%
Enoch Mgijima 70 051 91.8% 90.5% 62.9% 56.3%
Intsika Yethu 42 512 51.5% 64.6% 3.1% 3.1%
Emalahleni 33 268 75.7% 78.5% 13.1% 8.7%
Engcobo 39 189 43.9% 50.7% 4.6% 3.2%
Sakhisizwe 16 980 77.9% 79.2% 23.3% 15.2%
Chris Hani 220 799 72.7% 76.2% 32.8% 28.7%
In Chris Hani, 76.2% of households used electricity as the primary
means of lighting. This represented an increase of 71.5% from
the 1.2% households that used electricity as their main means
of lighting in 2010. Chris Hani had one of the lowest proportion
of household’s dependent on candles for lighting at 10.3%.
Satisfaction levels were also high, with 50.1% of respondents in
the 2016 Community Survey indicating that they were satisfied
with the quality of electricity provision in the district.
Chris Hani has the fifth lowest provision of sanitation services in
the Eastern Cape. In 2015, only 32.8% of households in the district
had access to such services, compared to a provincial average of
42.3%. The result of this low level of provision, was that over two
thirds of households in Chris Hani were dependent on either a pit
latrine (35.8%) or had no access to any form of sanitation services
(30.5%). Satisfaction levels were likewise low, with 41.7% of
households in the district rating the quality of sanitation provision
either average (23.0%) or poor (18.7%) in 2016.
Although below the provincial average, Chris Hani had the
fourth highest proportion (28.7%) of households in the province
with access to periodic refuse removal by a municipal authority.
There is notable variation across the district, as less than 10%
of households in Emalahleni, Engcobo and Intsika Yethu have
access to regular refuse removal services. More than a quarter of
households (27.2%) in 2016 however, rated this service as good.
ACCESS TO SERVICES:
Households that have access to electricity
for lighting
201576%
2010 - 2015+ 2760
households
ACCESS TO SERVICES:Households that have access to piped water
201573%
2010 - 2015+ 2580
households
128
joe gqabi districtMunicipality
1 Unless otherwise stated, all district data is 2015 based on Quantec Standardised Regional Database.
Population and households1
The district had a population of 370 329 people in 2015,
exhibiting a population growth of 1.7% between 2014 and
2015 (Eastern Cape: 1.5%). This was slightly higher than 2014,
when the population increased by 1.5%. Between 2010 and
2015, the district’s population grew by 1.4% compared to a
provincial and national population growth rate of 1.4% and
1.6%, respectively. The district remains relatively isolated and
rural and has a small population, which made up only 5.4% of
the provincial population.
The total youth population in Joe Gqabi (i.e. those between the
ages of 15 and 35 years old) was 124 759 in 2015, or 33.7%, of
the population. This was marginally higher than the provincial
figure of 33.6%. The child dependency ratio, which measurers
the ratio of children below the age of 15 years old to the total
working age population, in Joe Gqabi in 2015 was 58.8. This
was higher than the provincial figure of 55.4.
The Joe Gqabi Dependency Ratio, or the ratio of persons not in the labour force (under 15 and over 64 years) and those within the labour force or the working age population was 71.5,
compared to the provincial figure of 66.6. This made it the
fourth lowest in the province. The old age dependency ratio
in comparison, which measurers the proportion of individuals
older than 65 years relative to the labour force in Joe Gqabi
was 12.9, the third highest in the province.
The high overall and old age dependency ratios are worth
noting for Joe Gqabi as they imply increased pressure on the
productive population to support dependents. It indicates a
smaller base to draw taxes on to support state interventions
for the youth and aged. This measure however, is premised on
the assumption that those over the age of 65 years’ lack other
sources of income.
The number of households in Joe Gqabi increased by 1.7%
between 2014 and 2015 from 102 265 in 2014 to 103 969 in
2015. This year-on-year increase in household numbers in
the district was above both the provincial (1.5%) as well as
the national (1.7%) household growth rate over the same
period. Similar to the Eastern Cape, the majority (60.4%) of
households in Joe Gqabi resided in formal dwellings, with a
further 35.3% residing in traditional dwellings.
The Joe Gqabi District Municipality (JGDM) is situated in the northern most part of the Eastern Cape and borders the Free State Province. The district is bordered by the O.R. Tambo, Chris
Hani, Joe Gqabi and Alfred Nzo Districts. The district is comprised of three local municipalities: Elundini, Senqu and Walter Sisulu. Joe Gqabi covers an area of roughly 25 663 km2. The
Walter Sisulu Local Municipality was established after the August 2016 local government elections by the merging of the Gariep and Maletswai local municipalities.
Joe Gqabi population density
Joe Gqabi
370 329
14.4People per
km2
Walter Sisulupop = 81 843
Senqupop = 142 623
Elundinipop = 145 863
Total Households
Joe Gqabi household density
Joe Gqabi103 969
Total Households
3.6Average Household
Size
Avg HH Size = 3.6Walter Sisulu
Avg HH Size = 3.5Senqu
Avg HH Size = 3.6Elundini
130
4
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
15%
23%
7%33%
14%
6%
2%
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher EducationOther
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher EducationOther
Highest Educational Level
in 2015:
HIGHER EDUCATION
6%MATRIC
14.7%
60% live informal dwellings
4% live in traditional dwellings
35% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
Joe Gqabi dwelling typeThe number of households residing in formal dwellings has
declined from 2010, when 60.6% of households stayed in
formal structures.
EDUCATION
Joe Gqabi’s levels of educational attainment were low, with
14.7% of the population aged 20 years having had no formal
schooling in 2015. This made the Joe Gqabi the district with
the second highest proportion of individuals with no schooling
in the province. A further 14.3% of the population had attained
their Matric, while 6.2% of the population had achieved some
form of higher education. There are no universities or TVET
colleges that are exclusively based in the district. The district
does however, have two TVET satellite campus for the Ingwe
and Ikhala TVET Colleges.
Joe Gqabi has the fewest number of learners in the province
with only 104 818. These learners were spread across 354
public and private schools across the district. According to
the Department of Basic Education the number of learners
has decreased from 2010, when the district was home to 113
607 learners (DBE, 2016). The reduction in learners led to an
improvement in the learner to educator ratio which decreased
from 29.8 in 2010 to 27.6 in 2014.
In the 2016 Community Survey, respondents in Joe Gqabi were
asked to rate their overall satisfaction with public schools in
the district. The overwhelming majority (61.0%) of respondents
indicated that they felt that the schools in the district were
good – the fourth highest in the province. This however, varied
somewhat across the district, with two thirds of respondents
in Elundini and Walter Sisulu indicating that the quality of
public schools in the district was good compared to only 51.1%
in Senqu. Across the district, 1.3% of respondents indicated
that they had no access to public schools. A further 1.2% of
respondents did not make use of public schools.
POPULATIONTotal Population 370 329
0 - 14 years 127 055
15 - 35 years 124 759
15 - 64 years 215 983
Average Household Size 3.6
Population Density 14.4 persons/km2
Provincial Population Percentage 5.4%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 104 818 8
Education Attainment Levels Percentage of district
population (aged 20 years +)
Proportion Change
Provincial Rank
No Education 14.7% 6
Matric 14,3% 5
Higher Education 6.2% 5
131
2 Based on Massyn et al. 2015. 3 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwise stated.
Health2,3
The ‘maternal mortality in facility rate’ for Joe Gqabi was
104.6 deaths 100 000 live births in 2014. This is below the
provincial average of 148.3 deaths per 100 000 live births. The
infant mortality rate provided by the ‘stillbirth in facility rate’
indicates a declining trend with 16.5 deaths per 1 000 births, a
decrease of 1.2% between 2013 and 2014. Child mortality can
be referred to by indicators measuring case fatality rates in
diarrhoea, pneumonia and malnutrition in children under the
age of 5 years. ‘Child under 5 diarrhoea case fatality rates’ were
lower than the provincial average of 5.2% and were measured
at 3.0% in 2014. This indicator declined by 50% over the two
periods, indicating a positive improvement. ‘Child under 5
pneumonia fatality rates’ fluctuated between 2009-2014, with
the highest rate recorded in 2009 at 11.7% and the lowest in
2014 at 0.5%. The 2014 rate of 0.5% showed a 95.23% decrease
in the rate between 2013 and 2014. The district has the lowest
rate of ‘child under 5 pneumonia fatalities’ in the province. It
is also significantly lower than the provincial average of 4.2%
and is the lowest rate within the province. The 95% decrease
resulted in the district being ranked 7th nationally. ‘Child under
5 malnutrition fatalities’ have significantly improved since
2009 and 2010, when the rate was 27% but the rate declined
significantly to 14.3% in 2012, only to increase in 2013 to
16%. Since 2013, the rate has significantly decreased to 11.3%
in 2014 indicating a 29.34% decrease in the rate between
2013 and 2014. This is above the provincial average of 11.8%.
‘Immunisation coverage of children under the age of 1 year’
in the district is 73.9%, which is below the provincial average
of 80.9%. Positively, the immunisation coverage increased by
15.65% between 2013 and 2014. ‘TB incidence rates’ have been
in steady decline over the last five years, with 2011 marking the
lowest rate is 5 years, with 894.4 cases per 100 000.
POVERTY HEADCOUNT (2016)
Joe Gqabi13% Provincial
13%
poverty indicators
CHILDREN UNDER 1 YEARS (2014)
Provincial81%
IMMUNISATION RATE
Joe Gqabi74%
Health2 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
16.5 19.6
Maternal Mortality - Per 100 000 live births (2014)
104.6 148.3
Immunisation Rate under 1 years (2014)
73.9% 80.9%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 3.0% 5.2%
Pneumonia case fatality rate (2014) 0.5% 4.2%
Malnutrition case fatality (2014) 11.3% 11.8%
TB Incidence per 100 000 population (2014)
674.7 792.3
TB Cure rate (percentage) (2013) 73.1% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
32.1% 36.2%
socio-economic and poverty indicators JoeGqabi
Provincial
Poverty Headcount (2016) 13.4% 12.7%
Poverty Intensity (2016) 43.7% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 4 885 R 7 085
The drop of 95% resulted in the district being ranked 7th nationally
95% DROP IN CHILD IN
FACILITY MORTALITY
RATE DUE TO PNEUMONIA
‘HIV testing coverage’ Joe Gqabi had the fourth lowest coverage for HIV testing in the Eastern Cape32%
Joe Gqabi has the third lowest infant mortality rate represented by the still birth rate in the Eastern Cape after Alfred Nzo and Amathole.
16.5 deaths per 1000
births
132
Joe Gqabi household income distribution (2011)
13%4%
6%
21%23%
15%8%
6%3%
1%0%0%
12% 11% 10%12%
26%
14%
7%5%
8%6%
22%20%
15%
40% 39%
24%
7%
18%15% 15%
2004 2014
10.3%
22.6%
31.8%
35.3%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
59%68% 64% 68%
58%65%
72%63%
73%
41%32% 36% 32%
42%35%
28%37%
27%
Formal Employment Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
Joe Gqabi EMPLOYMENT By skill level
Highly Skilled SkilledSemi- and Unskilled Informal Employment
14%
7%
11%
27%
22%
8%
5%
3%
1%
0%
0%
0%
No income
R1 - R4 800
R4 801 - R 9 600
R9 601 - R 19 200
R19 201 - R 38 400
R38 401 - R 76 800
R76 801 - R153 600
R153 601 - R307 200
R307 201 - R614 400
R614 401 - R1 228 800
R1 228 801 - R2 457 600
R2 457 601 and more
28% 30%
13%
20%
36%
30%
Elundini Senqu Walter Sisulu
2005 2015
63%67%
72%
37%33%
28%
Elundini Senqu Walter Sisulu
Formal EmploymentInformal Employment
14%
26%
28%
32%
UNemployment rates
Highly Skilled SkilledSemi- and Unskilled Informal Employment
14%
7%
11%
27%
22%
8%
5%
3%
1%
0%
0%
0%
No income
R1 - R4 800
R4 801 - R 9 600
R9 601 - R 19 200
R19 201 - R 38 400
R38 401 - R 76 800
R76 801 - R153 600
R153 601 - R307 200
R307 201 - R614 400
R614 401 - R1 228 800
R1 228 801 - R2 457 600
R2 457 601 and more
28% 30%
13%
20%
36%
30%
Elundini Senqu Walter Sisulu
2005 2015
63%67%
72%
37%33%
28%
Elundini Senqu Walter Sisulu
Formal EmploymentInformal Employment
14%
26%
28%
32%
In 2014, the rate was significantly lower at 674.7 cases per
deaths. This is significantly below the provincial average
of 792.3 cases per 100 000. Joe Gqabi has the third lowest
incidence rate in the province. ‘TB treatment success rates
for all cases’ dropped by 6.8% to 73.1% in 2013; below both
the national average of 77% and the national target of
83%. The ‘HIV testing coverage of the population aged 15-
49 years’ was 32.1%, below the provincial average of 36%
but the same as the national average (32.1%).
SOCIO-ECONOMIC
Joe Gqabi had a poverty headcount of 13.4% in 2016.
This made it the district with the fourth lowest poverty
headcount after the NMBM, Sarah Baartman and the
BCM. This measure is based on the South African
Multidimensional Poverty Index (SAMPI). The SAMPI
is an index that is constructed using eleven indicators
across four dimensions; namely health, education, living
standards and economic activity. The poverty headcount
shows the proportion of households that are considered
to be “multidimensional poor” in the district. The poverty
intensity, which refers to the average proportion of
indicators in which multidimensional poor households are
deprived, in the district was 43.7% in 2016; only slightly
above the provincial average of 43.3%. The average
monthly weighted household income for Joe Gqabi in 2011
was R4 885 (2015 prices), and was the fourth highest in the
province, after the two metros and the Sarah Baartman
District. The provincial average weighted household
income is R7 085 (2015 prices).
labour market
Joe Gqabi had an unemployment rate of 28.0%; lower
than the provincial average of 29.5%4. There were 26 639
persons who were unemployed in the district compared to
68 704 persons who were employed. Of those employed,
67.8% were employed in the formal sector; equating to
46 581 individuals. There was a notable informal sector,
employing 22 123 persons across the district. Senqu had
the highest unemployment rate 36.3%, while Elundini with
its unemployment rate of 20.3% had the lowest. Walter
Sisulu had the highest proportion of formal employment
to total employment, with 71.6% of the labour force
working in the formal sector. Skilled employment made
up only 14.3% of total employment compared to 53.6% for
semi-skilled (25.9%) and low skilled (27.7%) employment.
Economic output5
Joe Gqabi had the smallest economy in the Eastern Cape
in terms of total GVA-R, contributing only 3.4% of the total
provincial output in 2015. Joe Gqabi’s total GVA-R in 2015
was R7.1 billion, which was 2.3% higher than in 2014. The
tertiary sector was the largest contributor, accounting for
R5.8 billion or 81.2% of the district’s total GVA-R output
in 2015. In comparison, the secondary sector contributed
R974 million, and the primary sector only R379 million.
Highly Skilled SkilledSemi- and Unskilled Informal Employment
14%
7%
11%
27%
22%
8%
5%
3%
1%
0%
0%
0%
No income
R1 - R4 800
R4 801 - R 9 600
R9 601 - R 19 200
R19 201 - R 38 400
R38 401 - R 76 800
R76 801 - R153 600
R153 601 - R307 200
R307 201 - R614 400
R614 401 - R1 228 800
R1 228 801 - R2 457 600
R2 457 601 and more
28% 30%
13%
20%
36%
30%
Elundini Senqu Walter Sisulu
2005 2015
63%67%
72%
37%33%
28%
Elundini Senqu Walter Sisulu
Formal EmploymentInformal Employment
14%
26%
28%
32%
Formal vs informal employment
4 The official unemployment rate does not consider discouraged job-seekers (i.e. individuals who were not employed, wanted to work, were available to work/start a business but did not take active steps to find work during the last four weeks).5 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.
133
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
-5% 0% 5% 10% 15% 20% 25% 30% 35% 40%
Most sub-sectors exhibited positive growth rates between
2014 and 2015, with the secondary and tertiary sectors growing
by 6.4% and 2.2%, respectively. The primary sector however,
contracted by 5.7% between 2014 and 2015.
Sectoral Contribution to Economic Activity
The largest GVA-R contributor to the primary sector in 2015
was the agriculture, forestry and fisheries sub-sector, which
contributed R336 million but contracted by 6.3% between
2014 and 2015. The manufacturing sub-sector was the largest
GVA-R contributor to the secondary sector in 2015, accounting
for 48.6% of this sector’s output. Manufacturing contributed
R473 million towards GVA-R and displayed a growth rate
between 2014 and 2015 of 1.2%. This growth rate was the third
lowest GVA-R sub-sector growth rate after the agriculture,
forestry and fisheries, and mining sub-sectors. The general
government services sub-sector accounted for 40.9% of the
tertiary sectors GVA-R in 2015. The sub-sector also exhibited
a GVA-R growth rate of 1.3% between 2014 and 2015. The
trade sub-sector was Joe Gqabi’s largest employer, employing
12 942 people and contributing R1.2 billion in GVA-R in 2015.
The community and social services sub-sector, although the
second largest employer in Joe Gqabi and employing 12 324
people, only contributed R691 million in GVA-R in 2015.
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 7 187 2.3% 3.7% 8
GVA-R/Capita R 19 407 0.7% 4.6% 4
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 379 -5.7% 3.8%
Agriculture, forestry and fishing
R 336 -6.3% 4.4% 8
Mining and quarrying R 43 -0.7% -0.1% 10
Secondary Sector R 974 6.4% 5.4%
Manufacturing R 473 1.2% 3.7% 5
Electricity, gas and water
R 103 2.4% 2.8% 9
Construction R 397 14.6% 8.8% 7
Tertiary Sector R 5 834 2.2% 3.4%
Trade, catering and accommodation
R 1 288 1.6% 2.4% 2
Transport R 442 1.4% 3.0% 6
Finance and business services
R 1 027 5.2% 6.5% 3
Community services R 2 386 1.3% 3.0% 1
General government R 691 3.0% 3.2% 4
Total R 7 187 2.3% 3.7%
GROSS VALUE ADDED-REGIONAL for Joe Gqabi
grew by 2.3% to R7.1 billion in 2015 from R7.0 billion in 2014.
2.3%INCREASEIN GVA-R
GVA-R PER CAPITA in Joe Gqabi increased by 4.6% between 2010
and 2015. 4.6%
INCREASEIN GVA-R PER
CAPITA
2.3% Growth
Contribution: R7.1 billion Lowest in EC
Joe Gqabi GVa-r contribution
134
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
-5% 0% 5% 10% 15% 20% 25% 30% 35% 40%
Joe Gqabi SECTOR CONTRIBUTION
Horizontal: Sector contribution to JGDMVertical: Sector Growth in 2014/2015Size: People Employed
Local Municipal Economic Contribution Walter Sisulu was the largest contributor to the Joe Gqabi
economy in 2015, generating R2.9 billion in total GVA-R and
accounting for 40.7% of districts total GVA-R. The second and
third largest contributors to economic output were Senqu with
R2.3 billion (32.5%) and Elundini with R1.9 billion (26.8%).
Local Municipality GVA-R (Millions)
GVA-R Growth Rate Contribution to District GVA-R
GVA-R Rank in District
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
Elundini R 1 926 2.5% 3.4% 26.8% 3
Senqu R 2 334 2.9% 3.9% 32.5% 2
Walter Sisulu R 2 927 1.8% 3.7% 40.7% 1
Economic performanceGVA-R per capita allows comparison of different economies
relative to their populations. A rise in GVA-R per capita can
indicate an improvement in productivity. Joe Gqabi’s 2015
GVA-R per capita was R19 407; 0.7% higher than the R19 281
recorded in 2014. Joe Gqabi had the fourth highest GVA-R per
capita figure in the Eastern Cape, yet is still below the provincial
figure of R30 457.
provision of services6
Just over three quarters (62.9%) of households in Joe Gqabi
had access to piped water in 2015, representing a marginal
increase from the 62.1% households that had access in 2010.
This comparably high level of access meant that only 16.3%
were dependant on dams, rivers and springs for their water
supply. Despite this high level, over 50.0% of respondents in
the 2016 Community Survey indicated that the quality of water
provision in the district was either average (27.4%) or poor
(25.8%).
Almost 70.0% of households in Joe Gqabi used electricity
as their main source of lighting; 1.2% higher than in 2010.
Approximately 27.3% of households however, were dependent
on either candles (18.4%) or paraffin for lighting (8.9%).
Satisfaction levels with electricity remained moderately high,
with 47.3% of households indicating good quality provision
of electricity in 2016. This however, was below the provincial
average of 50.1%.
ACCESS TO SERVICES:Households that have
access to piped water
201563%
2010 - 2015+ 1000
households
6 StatsSA, 2016.
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
201526%
2010 - 2015+ 360
households
135
7 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling in line with the RDP standard. 8 Access to electricity is measured by the number of households that use electricity as their main source of lighting. 9 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet. 10 Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 7 Electricity 8 Sanitation 9 Refuse Removal 10
Elundini 40 425 39.0% 46.3% 10.8% 13.3%
Senqu 40 581 68.7% 81.8% 13.5% 12.8%
Walter Sisulu 22 963 94.6% 87.0% 76.4% 82.8%
Joe Gqabi 103 969 62.9% 69.1% 26.3% 28.5%
Joe Gqabi had the fourth lowest levels of sanitation provision in
the Eastern Cape, with only 26.3% of households having access
to a flush or chemical toilet, the minimum RDP standard. Just
under a quarter (24.2%) of households however, had no access
to sanitation services. This equated to 25 160 households. Despite
the comparably low level of provision, satisfaction levels with
sanitation services were relatively high. According to the 2016
Community Survey, 46.0% of respondents indicated that the
quality of provision in 2016 was good – the fourth highest in the
province.
Only 28.5% of the households in Joe Gqabi had access to refuse
removal services provided by a local authority either weekly or
less frequently. Over half (55.1%) of households in the district
therefore, had to make use of their own refuse dump. This low
provision was reflected in the equally low satisfaction levels
– 39.8% of households indicated that the quality of their refuse
collection was either average or poor in 2016.
ACCESS TO SERVICES:
Households that have access to electricity
for lighting
201569%
2010 - 2015+ 1140
households
136
O.R. Tambo districtMunicipality
1 Unless otherwise stated, all district data is 2015 based on Quantec Standardised Regional Database.
Population AND HOUSEHOLDS1
The population of O.R. Tambo was 1 447 364 in 2015, making
it the most populous district in the province. As the most
populous district, O.R. Tambo accounted for 20.9% of the
total population of the province in 2015. The district exhibited
a population growth of 1.6% between 2014 and 2015 (Eastern
Cape: 1.5%), compared to a growth rate of 1.5% between 2013
and 2014 (Eastern Cape: 1.5%; South Africa: 1.6%).
The youth population, those between the ages of 15 and 34,
totals 596 442 persons in 2015, or 39.3% of the population;
the highest in province and well above the provincial figure
of 33.3%. This high proportion of minor children resulted in a
high child dependency ratio of 71.2, which measures the ratio
of children below the age of 15 years old to the total working
age population. This was the second highest child dependency
ratio in the province after Alfred Nzo.
Given the high number of children and the elderly in O.R.
Tambo, the overall Dependency Ratio, or the ratio of persons
not in the labour force (under 15 and over 64 years) to those within the labour force or the working age population, was
81.0; which was well above the provincial figure of 66.6. The
old age dependency ratio, which measurers the proportion of
individuals older than 65 years relative to the labour force in
O.R. Tambo was 9.8; the third lowest in the province.
The high overall and youth dependency ratios are worth
noting for O.R. Tambo as they imply increased pressure on
the productive population to support dependants. It also
indicates a smaller base from which taxes can be drawn to be
used to support state interventions for the youth and aged.
This measure however, is premised on the assumption that
those over the age of 65 years lack other sources of income.
There were approximately 319 344 households in O.R. Tambo
in 2015, compared to 313 744 households in the district in 2014.
This represents a year-on-year increase of 1.8%. This growth in
the number of households in the district was higher than both
the provincial and national household growth rates of 1.5% and
1.7%, respectively, over the same period. Unlike other parts of
the Eastern Cape, the majority (54.8%) of these households
resided in traditional dwellings. This was notably higher than
the provincial average, where only 28.3% of households lived
in traditional dwellings.
The O.R. Tambo District Municipality (ORTDM) is situated within the Eastern Cape Province. The district is bordered by the Amathole, Chris Hani, Joe Gqabi and Alfred Nzo Districts. The district
comprises five local municipalities, namely: King Sabata Dalindyebo, Nyandeni, Ingquza Hill, Mhlontlo and Port St. Johns. O.R. Tambo covers an area of roughly 12 095 km2.
O.R. Tambo population density
Mhlontlopop = 198 724
Nyandeni pop = 307 808
Ngquza Hill pop = 295 624
Port St. Johns pop = 164 827
King Sabata Dalindyebo
pop = 480 381O.R.Thambo
1 447 364
119.7People per
km2
Total Population
O.R. Tambo household density
O.R.Thambo319 344
Total Households
4.5Average Household
Size
MhlontloAvg HH Size = 4.3
Nyandeni Avg HH Size = 4.7 Port St. Johns
Avg HH Size = 4.9King Sabata Dalindyebo
Avg HH Size = 4.3
Ngquza HillAvg HH Size = 4.3
138
4
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
17%
19%
6%
34%
15%
7%
2%
O.R Tambo
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher EducationOther
Highest Educational Level
in 2015:
HIGHER EDUCATION
7%MATRIC
15%
54% live in traditional dwellings
1% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
There has however, been a slight improvement in the number
of households that live in formal dwellings, with this figure
increasing from 43.1% in 2010, to 43.8% in 2015.
EducationO.R. Tambo had the highest proportion of individuals over
the age of 20 years old that had no formal schooling in the
Eastern Cape. In 2015, 17.0% of the districts over 20 population
had no formal schooling compared to a provincial average of
10.7%. The poor level of educational attainment was further
highlighted by the fact that only 22.3% of the districts’
population had attained either their matric (15.4%) or some
form of higher qualification (6.9%). These attainment levels
were lower than all but Alfred Nzo. Despite the low level of
tertiary attainment in the district, it benefits from having one
university – Walter Sisulu (King Sabata Dalindyebo), a TVET
college (King Sabata Dalindyebo) and a satellite campus of the
Ingwe TVET College in Ingquza Hill.
In line with the age profile of the district, O.R. Tambo had the
highest number of learners in the province at 671 562 in 2014
(DBE, 2016). These learners were spread across 1 625 public
and private schools in the district. Despite accounting for 34.5%
of the total number of learners in the province during 2014,
the O.R. Tambo has seen a substantial decrease in the number
of learners since 2010 when the district had 711 511 learners.
The reduction in learners saw a corresponding decline in the
number of educators, resulting in a worsening of the educator
to learner ratio from 33.6 in 2010 to 43.8 in 2014.
The 2016 Community Survey asked respondents in O.R. Tambo
to rate their overall satisfaction with public schools in the
district. The overwhelming majority (53.0%) of respondents
indicated that they felt that the schools in the district were
good – the third lowest in the province. These satisfaction
levels however, varied somewhat across the district with less
than 50.0% respondents in King Sabata Dalindyebo (46.2%)
and Ingquza Hill (49.6%) indicating that the quality of public
schools in the district were good compared to 67.3% in
Nyandeni. Across the district, 1.5% of respondents indicated
that they had no access to public schools or did not make use
of public schools (1.5%).
POPULATIONTotal Population 1 447 364
0 - 14 years 569 442
15 - 35 years 513 092
15 - 64 years 799 726
Average Household Size 4.5
Population Density 119.7 persons/km2
Provincial Population Percentage 20.9%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 671 562 1
Education Attainment Levels Percentage of district
population (aged 20 years +)
Proportion Change
Provincial Rank
No Education 17.0% 8
Matric 15.4% 8
Higher Education 6.9% 7
43% live informal dwellings
O.R. TAMB0 dwelling type
139
2 Based on Massyn et al. 2015. 3 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwise stated.
Health2,3
The ‘maternal mortality in facility rate’ in the OR District
Municipality in 2014 was 198.5 deaths per 100 000 live births.
The infant mortality rate as measured by the ‘stillbirth in
facility rate’, has remained relatively steady around 23.9 per
1 000 births (2014). This is above the provincial average of
19.6 per 1 000 births and the highest in the province. Child
mortality can be referred to by indicators measuring case
fatality rates in diarrhoea, pneumonia and malnutrition in
children under the age of 5 years. ‘Child under 5 diarrhoea case
fatality rates’ have on the decline over the last 5 years, with
the rate declining by 34.69% in 2014 to 9.6%. This is almost
double the provincial average of 5.2%, but the highest value
for a district in the Eastern Cape. ‘Child under 5 pneumonia
fatality rates’ have been in decline over the past few years,
from a high in 2009 at 20.7 to the low of 5.3% in 2014. This is
above the provincial average of 4.2%, and the second highest
rate recorded in the province. ‘Child under 5 malnutrition
fatalities’ have been declining over the last five years; and in
2014 this indicator decreased by 47.03% to 11.6%, lower than
the provincial average of 11.8%. ‘Immunisation coverage of
children under the age of 1 year’ in the district is 74.9%, an
increase of 29.14% from 2013, which is below the provincial
and national averages of 80.9% and 89.9%. O.R Tambo has the
third lowest immunisation coverage of a district in the Eastern
Cape. ‘TB incidence rates (all cases)’ have declined by 12.73%
in 2014 to 764.1 cases per 100 000. This is above the provincial
average of 792.3 cases per 100 000. It is significant to note
that TB incidence rates have been on a 5-year steady decline
and have declined from a high of 998.3 cases per 100 000 in
2010, to the current 764.1 cases per 100 000.
‘HIV testing coverage’ O.R. Tambo has the third highest coverage for HIV testing in the province after Amathole and Chris Hani
Infant mortality decreased from 25.6 still births per 1 000 in 2013 to 23.9 still births per 1 000 in 2014
CHILDREN UNDER 1 YEARS (2014)
O.R.Tambo81%
IMMUNISATION RATE
POVERTY HEADCOUNT (2016)
O.R.Tambo19% Provincial
13%
poverty indicators
O.R.Tambo75%
Health2 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
23.9 19.6
Maternal Mortality - Per 100 000 live births (2014)
198.5 148.3
Immunisation Rate under 1 years (2014)
74.9% 72.3%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 9.6% 5.2%
Pneumonia case fatality rate (2014) 5.3% 4.2%
Malnutrition case fatality (2014) 11.6% 11.8%
TB Incidence per 100 000 population (2014)
764.1 792.3
TB Cure rate (percentage) (2013) 76.2% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
40.8% 36.2%
socio-economic and poverty indicators O.R. Tambo
Provincial
Poverty Headcount (2016) 19.2% 12.7%
Poverty Intensity (2016) 43.5% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 4 770 R 7 085
6.6%
41%
O.R. Tambo has the third highest maternal mortality in facility ratio in the province at 198.5 per 100 000 births.
140
24%
16%7%
14%
21%8%
5%
3%1%
0%
0%0%
O.R. Tambo Household Income Distribution
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
33%
44%
49%
33%28%
23%26%
44%
23% 22%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dalin
dyeb
o
2005 2015
20%
27%
20%
33%
Skilled
Low skilled
Semi-skilled
Informal Employment
66%70% 69% 69%
66%
34%30% 31% 31%
34%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dal
indy
ebo
Formal Employment Informal Employment
O.R. Tambo EMPLOYMENT By skill level
UNemployment rates
24%
16%7%
14%
21%8%
5%
3%1%
0%
0%0%
O.R. Tambo Household Income Distribution
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
33%
44%
49%
33%28%
23%26%
44%
23% 22%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dalin
dyeb
o
2005 2015
20%
27%
20%
33%
Skilled
Low skilled
Semi-skilled
Informal Employment
66%70% 69% 69%
66%
34%30% 31% 31%
34%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dal
indy
ebo
Formal Employment Informal Employment
‘TB treatment success rates for all cases’ increased by
5.83% in 2013 to 76.2%. This is aligned to the provincial
average of 77%, but below both the national average of
77.9% and the national target of 82%. The ‘HIV testing
coverage of the population aged 15-49 years’ was 40.8%,
which is above the provincial average of 36% and which is
the third highest in the province.
SOCIO-ECONOMIC
The poverty headcount in O.R. Tambo in 2016 was 19.2%;
the second highest in the province after Alfred Nzo. This
measure is based on the South African Multidimensional
Poverty Index (SAMPI). The SAMPI is an index that
is constructed using eleven indicators across four
dimensions; namely health, education, living standards
and economic activity. The poverty headcount shows
the proportion of households that are considered to
be “multidimensional poor” in the district. The poverty
intensity, which refers to the average proportion of
indicators in which multidimensional poor households are
deprived, in the district was 43.5% in 2016, only slightly
above the provincial average of 43.3%. The average
monthly weighted household income for O.R. Tambo was
R4 770 (2015 prices) in 2011, and was the third lowest in
the province, ahead of only Alfred Nzo and Amathole.
The district’s household income is below the provincial
average of R7 085 (2015 prices).
labour market
O.R. Tambo had an unemployment rate of 26.5%4, which
was lower than the provincial average of 29.5%. In 2015,
there were 84 276 persons who were unemployed in O.R.
Tambo. Of those employed, there were 148 110 persons
employed in the formal sector, compared to 85 049
persons in the informal sector. This equated to 36.5% of
total employed. Nyandeni had the highest unemployment
rate in the district at 43.8%; followed by Port St Johns
at 26.1%. The local municipality with the largest formal
sector was Port St Johns where 69.6% of the labour force
was formally employed. This was followed by Mhlontlo
at 69.0%. The proportion of skilled employment in the
district was 20.0% compared to 46.7% for semi- and low-
skilled employment.
Economic output5
O.R. Tambo has the 3rd largest economy in the Eastern
Cape in terms of total GVA-R, contributing 10.0% of the
total provincial output in 2015. O.R. Tambo generated
R20.9 billion in GVA-R output in 2015; 2.0% higher than
the 2014 figure. The largest sectoral contribution to
GVA-R came from the tertiary sector at R18.5 billion.
The primary sector contributed R450 million, while the
secondary sector contributed R1.9 billion. All but the
agriculture, forestry and fisheries sub-sector exhibited a
positive economic growth rate between 2014 and 2015,
with the primary, secondary and tertiary sectors growing
by -1.7%, 5.0% and 1.8%, respectively.
Formal vs informal employment
24%
16%7%
14%
21%8%
5%
3%1%
0%
0%0%
O.R. Tambo Household Income Distribution
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
33%
44%
49%
33%28%
23%26%
44%
23% 22%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dalin
dyeb
o
2005 2015
20%
27%
20%
33%
Skilled
Low skilled
Semi-skilled
Informal Employment
66%70% 69% 69%
66%
34%30% 31% 31%
34%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dal
indy
ebo
Formal Employment Informal Employment
4 The official unemployment rate does not consider discouraged job-seekers (i.e. individuals who were not employed, wanted to work, were available to work/start a business but did not take active steps to find work during the last four weeks).
5 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.
O.R. Tambo household income distribution (2011)
24%
16%7%
14%
21%8%
5%
3%1%
0%
0%0%
O.R. Tambo Household Income Distribution
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
33%
44%
49%
33%28%
23%26%
44%
23% 22%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dalin
dyeb
o
2005 2015
20%
27%
20%
33%
Skilled
Low skilled
Semi-skilled
Informal Employment
66%70% 69% 69%
66%
34%30% 31% 31%
34%
Ingq
uza
Hill
Port
St J
ohns
Nya
nden
i
Mhl
ontlo
King
Sab
ata
Dal
indy
ebo
Formal Employment Informal Employment
141
0%
1%
2%
3%
4%
5%
6%
-10% 0% 10% 20% 30% 40% 50%
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
This was followed by Mhlontlo at 69.0%. The proportion of
skilled employment in the district was 20.0% compared to
46.7% for semi- and low-skilled employment.
Sectoral Contribution to Economic Activity
The agricultural, forestry and fisheries sub-sector was the
largest component of the primary sector, contributing R245
million in GVA-R in 2015. Despite the size of the sector, it
exhibited negative growth between 2014 and 2015, contracting
by 3.2% over the period. The construction sub-sector
accounted for 44.0% of the total output of the secondary
sector. construction contributed R854 million towards GVA-R,
but displayed a growth rate between 2014 and 2015 of 9.6%.
The general government sub-sector was the largest tertiary
sector contributor, accounting for 39.7% of the sectors
GVA-R but only grew at a rate of 0.8%. The trade sub-sector
contributed R3.9 billion in 2015 and was the district’s largest
employer, employing 36 171 people in 2015. The community
and social services sub-sector, was the third largest employer
employing 31 036 people, and the 4th largest contributor to
GVA-R with R2.0 billion in 2015.
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 20 904 2.0% 3.1% 3
GVA-R/Capita R 14 443 0.4% 1.8% 7
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 450 -1.7% 1.9%
Agriculture, forestry and fishing
R 245 -3.3% 3.8% 9
Mining and quarrying R 205 0.1% 0.1% 10
Secondary Sector R 1 939 5.0% 3.9%
Manufacturing R 755 2.0% 2.3% 7
Electricity, gas and water R 329 1.0% 2.0% 8
Construction R 854 9.6% 6.5% 6
Tertiary Sector R 18 515 1.8% 3.0%
Trade, catering and accommodation
R 3 971 2.1% 3.0% 2
Transport R 1 474 0.4% 2.1% 5
Finance and business services
R 3 655 3.8% 4.7% 3
Community services R 7 342 0.8% 2.6% 1
General government R2 073 2.5% 2,6% 4
Total R 20 904 2.0% 3.1%
GROSS VALUE ADDED-REGIONAL for O.R. Tambo
grew by 2.0% to R20.9 billion in 2015 from R20.4 billion in
2014.
2.0%INCREASEIN GVA-R
GVA-R PER CAPITA in O.R. Tambo increased by 1.8% between
2010 and 2015. 1.8%
INCREASEIN GVA-R PER
CAPITA
2.O% Growth
Contribution: R20.9 billion3rd highest in EC
O.R. TAMBO GVa-r contribution
142
0%
1%
2%
3%
4%
5%
6%
-10% 0% 10% 20% 30% 40% 50%
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
O.R. TAMBO SECTOR CONTRIBUTION
Horizontal: Sector contribution to ORTDMVertical: Sector Growth in 2014/2015Size: People Employed
Local Municipal Economic Contribution
Geographically, the majority of O.R. Tambo’s economic activity
was ascribed to King Sabata Dalindyebo, which contributes
55.5% or R11.5 billion to the district’s total GVA-R in 2015.
The second and third largest contributors to district GVA-R
were Nyandeni with R2.9 billion (14.3%) and Ingquza Hill with
R2.9 billion (14.1%). These high contributions are attributable
to these municipalities large populations and their associated
buying power, accounting for 41.7% of the districts population.
Economic performance
GVA-R per capita allows comparison of different economies
relative to their populations. A rise in GVA-R per capita can
indicate an improvement in productivity. O.R. Tambo’s 2015
GVA-R per capita was R14 443, increasing by 0.4% from
R14 388 in 2014. The GVA-R per capita of O.R. Tambo was
particularly low, with the district having the 7th lowest value in
the province. The district’s GVA-R per capita is also below the
provincial figure of R30 392.
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
201512%
2010 - 2015+ 630
households
ACCESS TO SERVICES:Households that have access to piped water
201538%
2010 - 2015+ 2060
households
Local Municipality GVA-R (Millions)
GVA-R Growth Rate Contribution to District GVA-R
GVA-R Rank in District
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
Ingquza Hill R2 938 1.8% 2.9% 14.1% 3
Port St. Johns R 1 134 1.8% 3.0% 5.4% 4
Nyandeni R 2 985 2.5% 3.8% 14.3% 2
Mhlontlo R 2 250 2.0% 3.0% 10.8% 5
King Sabata Dalindyebo R 11 597 2.0% 3.0% 55.5% 1
6 StatsSA, 2016.
provision of services6
Just over three quarters (62.9%) of households in Joe Gqabi
had access to piped water in 2015, representing a marginal
increase from the 62.1% households that had access in 2010.
This comparably high level of access meant that only 16.3%
were dependant on dams, rivers and springs for their water
supply.
143
7 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling in line with the RDP standard. 8 Access to electricity is measured by the number of households that use electricity as their main source of lighting. 9 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet. 10 Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 7 Electricity 8 Sanitation 9 Refuse Removal 10
Ngquza Hill 60 204 20.6% 62.9% 3.2% 3.8%
Port St. Johns 33 978 24.5% 68.3% 3.1% 3.5%
Nyandeni 66 019 28.9% 71.3% 2.1% 2.1%
Mhlontlo 46 437 45.9% 72.7% 4.2% 5.7%
King Sabata Dalindyebo 112 706 52.7% 73.3% 27.2% 26.1%
O.R. Tambo 319 344 37.7% 70.3% 11.6% 11.5%
O.R. Tambo had the second lowest levels of sanitation provision in
the Eastern Cape, with only 11.6% of households having access to
a flush or chemical toilet; the minimum RDP standard. Of greater
concern was that 30.3% of households had no access to sanitation
services in 2015. This equates to 96 761 households. Satisfaction
levels with sanitation services were accordingly very poor.
According to the 2016 Community Survey, 24.6% of respondents
indicated that the quality of provision in 2016 was poor – the
highest percentage in the province.
Only 11.5% of the households in O.R. Tambo had access to refuse
removal services provided by a local authority either weekly, or
less frequently. Just under three quarters (65.4%) of households in
the district were therefore, forced to make use of their own refuse
dump to dispose of their waste. This low provision was further
reflected in the equally low satisfaction levels with refuse removal
services in the district. This was evident by the 51.1% of households
that indicated that the quality of their refuse collection was either
average or poor in 2016.
Despite this high level, over 50.0% of respondents in the 2016
Community Survey indicated that the quality of water provision
in the district was either average (27.4%) or poor (25.8%). O.R.
Tambo had the lowest proportion of households (37.7%) in the
province with access to piped water in 2015. Although the levels
remain low, they represented a marginal increase from 2010 when
only 36.7% of households in the district had access to piped water.
The low proportion of households with access to piped water in
the district meant that a significant portion of households (42.9%)
were dependent on dams, rivers and springs for their water supply.
The low level of provision led to 63.5% of respondents of the 2016
Community Survey indicating that the quality of provision was
either poor (39.9%) or average (23.6%). This was the highest in
the province.
Just over 70.0% of households in O.R. Tambo used electricity
as their main source of lighting in 2015. This represented a 2.0%
improvement from 2010 when only 68.3% of households used
electricity as their main source of lighting. A significant proportion
of the population (23.6%) however, was still dependent on candles
for lighting. Satisfaction levels with electricity provision remained
comparably high with 74.9% of households indicating either a
good (42.5%) or average (32.4%) quality of electricity provision
in 2016. This however, was below the provincial average of 78.4%.
ACCESS TO SERVICES:Households that have access to electricity
for lighting
201570%
2010 - 2015+ 3930
households
144
alfred nzo districtMunicipality
1 Unless otherwise stated, all district data is 2015 based on Quantec Standardised Regional Database.
Population and households1
The district had a population of 849 217 people in 2015 and
exhibited a population growth of 1.6% between 2014 and 2015
(2013 – 2014: 1.5%), and a growth of 1.4% since 2010. Over
the 2014 to 2015 period Alfred Nzo’s population grew at a
faster rate than the Eastern Cape (1.5%) but lower than South
Africa (1.7%). Between 2010 and 2015, the district’s population
increased at the same rate as the provincial average (1.4%) but
at a slower rate than the national average (1.6%).
Alfred Nzo had approximately 281 761 individuals classified as
youth, namely those between the ages of 15 and 35 years old.
This equated to approximately 33.2% of the total population,
slightly lower than the provincial figure of 33.6%. Alfred Nzo
had the second highest number of minor children in the Eastern
Cape, with approximately 350 527 in 2015. The population
aged 14 years and younger, accounted for 41.3% of the total
district population, the highest proportion in the province. The
high number of minors in Alfred Nzo had a negative impact on
the Child Dependency Ratio, or the ratio of children (under 15
years old) to those within the labour force or the total working
age population. The Alfred Nzo Child Dependency Ratio in
2015 was 78.3, the highest in the province and well above the
provincial figure of 55.4.
In 2015, the Alfred Nzo overall Dependency Ratio was 89.7
compared to the provincial figure of 66.6. This was the highest
Dependency Ratio in the province, and is likely to adversely
impact the productivity of the labour force. This is due to
the additional state resources needed to be allocated from
productive activities to support the economically dependent.
The Old Age Dependency Ratio (i.e. individuals older than 65
years relative to the labour force) in the district, in contrast,
was relatively low at 11.4, only slightly above the provincial
value of 11.2.
There were approximately 181 174 households in Alfred Nzo
in 2015, 3 171 (1.5%) higher than in 2014. This was slightly less
than the national figure (1.6%) but greater than the Eastern
Cape value of 1.4%. Over 50% of these 181 174 dwellings were
classified as traditional (56.8%), making Alfred Nzo the district
with the highest proportion of such dwellings in the Eastern
Cape.
The Alfred Nzo District Municipality (ANDM) is bordered by the O.R. Tambo and Joe Gqabi Districts, and covers an area of approximately 10 731 km2. The district is comprised of four local
municipalities, namely: Umzimvubu, Matatiele, Mbizana and Ntabankulu.
Alfred Nzo population density
Alfred Nzo849 217
79.1People per
km2
Matatielepop = 215 923
Ntabankulupop = 131 313
Mbizana pop = 298 535
Umzimvubupop = 203 446
Total Population
Alfred Nzo household density
Alfred Nzo181 174
Total Households
4.7Average Household
Size
MatatieleAvg HH Size = 4.1
NtabankuluAvg HH Size = 5.0
Mbizana Avg HH Size = 5.8
UmzimvubuAvg HH Size = 4.1
146
4
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
14%
24%
7.1%36%
13%
6%
0.8%
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher EducationOther
Highest Educational Level
in 2015:
HIGHER EDUCATION
6%MATRIC
14%
42% live informal dwellings
1% live in traditional dwellings
56% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
alfred nzo dwelling typeA further 42.0% of these dwellings were classified as formal,
with the remaining dwellings being informal in nature. This
represents a slight improvement from 2010, when 98.8% of
dwellings in Alfred Nzo were classified as either traditional
(57.4%) or formal (41.3%).
Education
Education levels among the population of 20 years and older
in Alfred Nzo were low. The district was ranked lowest in
terms of both the percentage of the provincial population
with a matric and with higher education. Only 13.0% of the
population over 20 years had attained Matric, and only 5.6%
had attained some form of higher education versus 19.4% and
8.6%, respectively, in the Eastern Cape. The low level of higher
educational attainment can be attributed to the district only
having a single institute of higher learning, namely the Ingwe
TVET College in Umzimvubu.
There were an estimated 142 205 learners in Alfred Nzo in
2014, spread across 480 of public and private schools. This
represented a 1.0% increase between 2013 and 2014 equivalent
to 1 363 additional learners. Despite this increase in learners,
the number of educators in the district increased to 5 501. This
resulted in the learner to educator ratio improving from 29.0 in
2013, to 25.9 in 2014.
Respondents in Alfred Nzo have one of the highest levels
of satisfaction regarding public schools in the province.
Approximately 63.9% of the 2016 Community Survey
respondents in Alfred Nzo indicated that the quality of public
schools was good. This satisfaction was highest in Umzimvubu
and Mbizana, where 66.5% respondents indicated the quality of
public schools is good compared to only 58.6% in Ntabankulu.
Only 0.8% of those surveyed across the district indicated that
they did not make use of public schools, the lowest level in the
Eastern Cape.
POPULATIONTotal Population 849 217
0 - 14 years 350 527
15 - 35 years 281 761
15 - 64 years 447 546
Average Household Size 4.7
Population Density 79.1 persons/km2
Provincial Population Percentage 12.3%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 142 205 6
Education Attainment Levels Percentage of district
population (aged 20 years +)
Proportion Change
Provincial Rank
No Education 1 3.9% 4
Matric 13.0% 8
Higher Education 5.6% 8
147
2 Based on Massyn et al. 2015. 3 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwise stated.
Health2,3
The ‘maternal mortality in facility rate’ has fluctuated over the
last four years between 155.6 and 55.5 deaths per 100 000 live
births and was 67.9 per 100 000 in 2014, a 44.9% decrease.
The mortality rates have tended to vary widely in the Alfred
Nzo District and this is attributed to small sample, making
trends difficult to discern. The ‘maternal mortality rate’ is
below the provincial average of 148.3 deaths per 100 000 live
births. The ‘stillbirth rate’ has decreased from 16.9 per 1 000
births, to 14.9 per 1 000 births, a 12% decrease. Child mortality
can be referred to by indicators measuring case fatality rates
in diarrhoea, pneumonia and malnutrition in children under
the age of 5 years. ‘Child under 5 diarrhoea case fatality
rates’ in Alfred Nzo account for 7.7% of cases admitted and
diagnosed. This indicator has dropped by 23% over the period
2013 to 2014, but is still above the provincial average of 5.2%
and significantly is the second highest provincially. The ‘child
under 5 pneumonia case fatality rate’ dropped 17.28% from
8.1% in 2013, to 6.7% in 2014. The ‘child under 5 severe acute
malnutrition case fatality rate’ remained steady at 18.1%. Levels
of child under 5 fatalities from three preventable illnesses
are among the highest in the country within the Alfred Nzo
District. The particularly high fatality rates may also be in part
due to poor data quality. Immunisation coverage of children
under the age of 1 year in the district has decreased from 73.2%
to 72.3% in 2014. The percentage coverage indicator is in line
with the provincial average, but some way from the national
target of 95% immunisation coverage. ‘TB Incidence rates (all
cases)’ have declined annually in the past 5 years from 918.1 per
100 000, to 599.2 per 100 000 in 2014. This is the lowest ‘TB
incidence rate’ in the province and is also below the provincial
average of 792.3 cases per 100 000. The district is ranked 20th
nationally for this indicator.
POVERTY HEADCOUNT (2016)
Alfred Nzo22% Provincial
13%
poverty indicators
CHILDREN UNDER 1 YEARS (2014)
Provincial81%
IMMUNISATION RATE
Alfred Nzo72%
Ranked amongst the worst performing districts in South Africa for immunisation coverage, child fatalities from diarrhea and pneumonia; and delivery to mothers under the age of 18 years
‘HIV testing coverage’ Alfred Nzo has the fifth lowest coverage for HIV testing in the province only slightly below the provincial average of 36.2%
32%
Health2 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
14.9 19.6
Maternal Mortality - Per 100 000 live births (2014)
67.9 148.3
Immunisation Rate under 1 years (2014)
72.3% 80.9%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 7.7% 5.2%
Pneumonia case fatality rate (2014) 6.7% 4.2%
Malnutrition case fatality (2014) 18.1% 11.8%
TB Incidence per 100 000 population (2014)
599.2 792.3
TB Cure rate (percentage) (2013) 61.7% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
32.4% 36.2%
socio-economic and poverty indicators Alfred Nzo
Provincial
Poverty Headcount (2016) 22.0% 12.7%
Poverty Intensity (2016) 44.3% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 4 010 R 7 085
Child under 5 diarrhoea case fatality rates’ in Alfred Nzo accounts for 7.7% This is still above the provincial average (5.2%) and significantly, the third highest nationally.
148
Alfred Nzo EMPLOYMENT By skill level
15%7%
14%26%
21%7%
4%3%
1%0%0%0%
19%
22%
22%
36%
Highly skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
36%
28%
46%
60%
29%
20%
37%
54%
2005 2015
68%
60%63%
71%
32%
4.0%37%
2.9%
Formal Employment Informal Employment
Umzimvubu Matatiele Mbizana Ntabankulu
Umzimvubu Matatiele Mbizana Ntabankulu
UNemployment rates
15%7%
14%26%
21%7%
4%3%
1%0%0%0%
19%
22%
22%
36%
Highly skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
36%
28%
46%
60%
29%
20%
37%
54%
2005 2015
68%
60%63%
71%
32%
4.0%37%
2.9%
Formal Employment Informal Employment
Umzimvubu Matatiele Mbizana Ntabankulu
Umzimvubu Matatiele Mbizana Ntabankulu
The ‘TB treatment success rate (all cases)’ however, has
dropped from 73.5% in 2012 to 72.1% in 2013, and is below
the national target of 82%. HIV testing coverage of the
population aged 15-49 years, stands at 32.4%, which is
lower than the national average of 36%.
SOCIO-ECONOMIC
Alfred Nzo had the highest poverty headcount in the
Eastern Cape at 25.6%, well above the provincial average
of 14.4%. This measure is based on the South African
Multidimensional Poverty Index (SAMPI). The SAMPI
is an index that is constructed using eleven indicators
across four dimensions, namely health, education, living
standards and economic activity. The poverty headcount
shows the proportion of households that are considered
to be “multidimensional poor” in the district. The poverty
intensity, which refers to the average proportion of
indicators in which multidimensional poor households are
deprived, in the district was 41.9%, in line with the Eastern
Cape average of 41.9%. The average monthly weighted
household income for Alfred Nzo was R4 010 (2015
prices); the lowest in the province, and below the Eastern
Cape average figure of R7 085 (2015 prices).
labour market4
Alfred Nzo had an unemployment rate of 31.0%4; 1.5%
higher than the provincial average of 29.5%. This is
equivalent to 65 427 unemployed persons using the
official definition of unemployment. Approximately 145
702 persons in Alfred Nzo were employed; of this figure,
63.7% or 86 813 persons were employed in the formal
sector. The highest unemployment rates were experienced
in Ntabankulu (54.4%), followed by Mbizana at 36.8%. The
lowest unemployment rate in the district was in Matatiele,
where 20.3% of the labour force was unemployed.
Matatiele had the largest proportion of employment
in the informal sector with 40.4%. The lowest informal
sector employment as a proportion of total employment
was in Ntabankulu (29.5%). Low skilled or semi-skilled
employment made up 44.4% of all employment in the
district.
Economic output5
Alfred Nzo had the second smallest economy in the Eastern
Cape in terms of total GVA-R, contributing only 4.7% of
the province’s output. In 2015, Alfred Nzo generated R9.8
billion in GVA-R, which was 2.9% higher than the GVA-R
recorded in 2014. The Alfred Nzo tertiary sector was the
largest economic contributor, accounting for R8.6 billion
or 87.8% of total GVA-R output in 2015. In comparison,
the secondary sector contributed R946.8 million and the
primary sector only R247.6 million. All but the primary
sector exhibited positive growth rates between 2014 and
2015. While the primary sector contracted by 2.9%, the
secondary and tertiary sectors grew by 9.6%, and 2.4%,
respectively, over the 2014 to 2015 period.
Formal vs informal employment
15%7%
14%26%
21%
7%4%
3%1%
0%0%0%
19%
22%
22%
36%
Highly skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
36%
28%
46%
60%
29%
20%
37%
54%
2005 2015
68%
60%63%
71%
32%
4.0%37%
2.9%
Formal Employment Informal Employment
Umzimvubu Matatiele Mbizana Ntabankulu
Umzimvubu Matatiele Mbizana Ntabankulu
4 The official unemployment rate does not consider discouraged job-seekers (i.e. individuals who were not employed, wanted to work, were available to work/start a business but did not take active steps to find work during the last four weeks).
5 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.
Alfred Nzo household income distribution (2011)
15%7%
14%26%
21%7%
4%3%
1%0%0%0%
19%
22%
22%
36%
Highly skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
36%
28%
46%
60%
29%
20%
37%
54%
2005 2015
68%
60%63%
71%
32%
4.0%37%
2.9%
Formal Employment Informal Employment
Umzimvubu Matatiele Mbizana Ntabankulu
Umzimvubu Matatiele Mbizana Ntabankulu
149
Sectoral Contribution to Economic Activity
The largest contributor to the primary sector in 2015 was
the agriculture, forestry and fisheries sub-sector, which
contributed R245.2 million to GVA-R but contracted by 2.9%
between 2014 and 2015. The construction sub-sector was the
largest GVA-R contributor to the secondary sector in 2015,
accounting for 57.4% of the sector’s GVA-R (R543.4 million).
The manufacturing sub-sector contributed a further R311.7
million towards GVA-R and exhibited a GVA-R growth rate of
2.2% between 2014 and 2015. The finance and business services
sub-sector accounts for 17.0% of the tertiary sector’s GVA-R in
2015. This sub-sector also exhibited the second highest GVA-R
growth rate between 2014 and 2015 at 5.0%. The trade sub-
sector, in comparison, only grew at 1.9% between 2014 and
2015 but contributed R1.9 billion to the districts GVA-R (22.9%)
and was the largest employer in Alfred Nzo, employing 20
268 people. The general government and community services
sub-sectors contributed 40.2% and 12.2%, respectively, to the
tertiary sector’s GVA-R in 2015. These two sectors also employ
over 45.4% of the total formally employed labour force in
Alfred Nzo. The transport and communication sub-sector is
the smallest component of the tertiary sector, employing only
2 509 people and contributing R659.4 million (6.7%) to the
district’s total GVA-R in 2015.
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 9 812 2.9% 3.8% 7
GVA-R/Capita R 11 555 1.2% 2.7% 8
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 248 -2.9% 1.8%
Agriculture, forestry and fishing
R 134 -4.3% 4.5% 8
Mining and quarrying R 114 -1.2% -0.6% 9
Secondary Sector R 947 9.6% 6.7%
Manufacturing R 312 2.2% 3.5% 7
Electricity, gas and water
R 92 2.5% 3.6% 10
Construction R 543 15.8% 9.9% 6
Tertiary Sector R 8 618 2.4% 3.6%
Trade, catering and accommodation
R 1 974 1.9% 2.7% 2
Transport R 659 1.4% 2.8% 5
Finance and business services
R 1 468 5.0% 6.4% 3
Community services R 3 467 1.4% 3.4% 1
General government R 1 049 3.3% 3.4% 4
Total R 9 812 2.9% 3.8%
GROSS VALUE ADDED-REGIONAL for Alfred Nzo
grew by 2.9% to R9.8 billion in 2015 from R9.5 billion in 2014.
2.9%INCREASEIN GVA-R
GVA-R PER CAPITA in Alfred Nzo increased by 2.7% between 2010
and 2015. 2.7%
INCREASEIN GVA-R PER
CAPITA
2.9% Growth
Contribution: R9.8 billion2nd highest in EC
ALFRED NZO GVa-r contribution
150
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
-10% 0% 10% 20% 30% 40% 50%
Alfred Nzo SECTOR CONTRIBUTION
Horizontal: Sector contribution to ANDMVertical: Sector Growth in 2014/2015Size: People Employed
Local Municipal Economic Contribution
Matatiele was the largest contributor to the Alfred Nzo
economy, accounting for R3.5 billion in total output and 35.9%
of the district’s total GVA-R. The second and third largest
contributors to economic output were Umzimvubu with R2.7
billion (27.8%) and Mbizana with R2.6 billion (27.1%).
Local Municipality GVA-R (Millions)
GVA-R Growth Rate Contribution to District GVA-R
GVA-R Rank in District
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
Mbizana R2 728 2.5% 3.4% 27.8% 2
Ntabankulu R 3 524 3.3% 4.1% 35.9% 1
Umzimvubu R 2 663 2.8% 4.0% 27.1% 3
Matatiele R 897 2.9% 3.7% 9.1% 4
Economic performance
GVA-R per capita allows comparison of different economies
relative to their populations. A rise in GVA-R per capita can
indicate an improvement in productivity. Alfred Nzo’s 2015
GVA-R per capita was R11 555; 1.2% higher than the R11 417
recorded in 2014. Despite this improvement, Alfred Nzo has
the lowest GVA-R per capita figure in the Eastern Cape, 62.0%
below the provincial figure of R30 457. The GVA-R per capita
in Alfred Nzo is also well below the national figure of R50 511.
provision of services
Alfred Nzo had the second lowest number of households with
access to piped water in the Eastern Cape after O.R. Tambo. In
2015, only 38.0% of households, equivalent to 66 371 dwellings
had access to piped water. This, however, represented an
increase from 2010, when only 37.1% of households had access
to piped water. This low level of service provision was also
highlighted in the 2016 Community Survey, which indicated
that the quality of water provision was either poor (33.8%) or
that respondents did not have any access (22.9%).
ACCESS TO SERVICES:Households that have access to piped water
201538%
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
20157%
2010 - 2015+ 190
households
2010 - 2015+ 1170
households
151
7 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling in line with the RDP standard. 8 Access to electricity is measured by the number of households that use electricity as their main source of lighting. 9 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet. 10 Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 7 Electricity 8 Sanitation 9 Refuse Removal 10
Mbizana 50 217 47.7% 45.7% 6.8% 7.7%
Ntabankulu 52 958 58.2% 44.8% 11.8% 12.5%
Umzimvubu 51 867 9.9% 60.0% 2.2% 2.6%
Matatiele 26 132 34.8% 23.2% 4.5% 4.4%
Alfred Nzo 181 174 38.0% 46.3% 6.6% 7.2%
Alfred Nzo had the lowest provision of sanitation services in the
Eastern Cape, with only 6.6% of households having access to the
minimum RDP standard. This was well below provincial average of
42.6% of households. Approximately 122 915 households (67.9%)
in Alfred Nzo relied on pit latrines for sanitation. Satisfaction
levels however, remained high with 68.3% of respondents in the
2016 Community Survey indicating that the quality of sanitation
services was either good (43.7%) or average (24.6%).
Alfred Nzo had the fewest number of households in the province
that have their refuse removed by a local authority either
weekly or less frequently. The overwhelming majority (72.5%) of
households made use of their own refuse dumps. This equates
to 131 341 households. This satisfaction level amongst Alfred Nzo
households in terms of the quality of refuse removal services
was equally low, with only 13.9% households rating the quality of
this provision as good in 2016.
The provision of electricity used by households in Alfred Nzo for
lighting was also very low, with only 46.3% of households having
such access in 2015. This was marginally higher than in 2010 when
45.0% of households had access to electricity. The predominant
source of lighting amongst households after electricity in Alfred
Nzo, was candles (45.4%). Despite the poor level of provision,
satisfaction levels were high, with 45.9% of households indicating
the quality of the provision of electricity was good in 2016.
ACCESS TO SERVICES:Households that have
access to electricity for lighting
201546%
2010 - 2015+ 1450
households
152
nelson mandela bayMetro
Population and households1
Between 2014 and 2015, the population of the Nelson Mandela
Bay Metro increased by 1.3% (2013 to 2014: 1.3%) to 1 194
106. The NMBM had the second highest population in the
Eastern Cape after the O.R. Tambo District, and accounted for
approximately 17.3% of the provincial population in 2015. Over
the 2010 to 2015 period, the NMBM registered a population
growth rate of 1.3%, below both the Eastern Cape (1.4%) and
South African (1.6%) population growth rates.
The youth population, or those people classified as being
between the ages of 15 and 35 years old, accounted for 33.4%
of the total NMBM population, or 398 482 individuals. This
was aligned to the provincial average proportional figure of
33.3%. The NMBM has the lowest number of minor children
as a percentage of the total population in the Eastern Cape,
with only 25.5% of the population below the age 15 years old.
The low proportion of minor children in the NMBM resulted
in the metro having the lowest child dependency ratio in the
province at 37.5. The child dependency ratio, which measures
the ratio of children below the age of 15 years old to the
total working age population, in the NMBM was lower than
provincial figure of 55.4.
The Dependency Ratio, being a ratio that measures the
number of persons not in the labour force (under 15 and over
64 years) to those within the labour force (or the working
age population), for the NMBM was 46.8. Positively this was
well below the provincial ratio of 66.6, and the lowest in the
province. The old age dependency ratio, which measurers the
proportion of individuals older than 65 years relative to the
labour force in the NMBM, was 9.3 (Eastern Cape: 11.2).
The low dependency ratios are particularly positive as they
are likely to place considerably less strain on the working age
population to allocate resources to support those younger or
older than themselves.
Between 2014 and 2015 the number of households in NMBM
increased by 1.0% from 333 104 in 2014 to 336 328 in 2015.
Despite having a household growth rate lower than both the
Eastern Cape (1.5%) and South African (1.7%) averages over
the period, the metro accounted for 18.8% of all households
in the province.
The Nelson Mandela Bay Metro (NMBM) is the commercial engine of the Eastern Cape Province and is surrounded by the Sarah Baartman District. The metro covers an area of approximately
1 959 km2.
1 Unless otherwise stated all district data is 2013 based on Quantec Standardised Regional Database
NMBM336 328
Total Households
3.6Average Household
Size
Nelson Mandela Bay Metro population density
Nelson Mandela Bay Metro household density
NMBM1 194 106
609.6People per
km2
154
4
Household growth rates in the Eastern Cape (1.4%) and
South Africa (1.6%) also outperformed the NMBM average
(1.0%) between 2010 and 2015. The NMBM had the greatest
proportion of households living in formal dwellings in the
province at 87.4%. A further 42 127 of households in the NMBM
lived in either informal (12.2%) or traditional (0.3%) dwellings.
Education
Just over a quarter (29.5%) of the NMBM’s adult population
had attained their Matric and a further 11.7% had some form
of higher education qualification. This places the NMBM as
the Eastern Cape district with the highest proportion of its
population with Matric and the second highest district in terms
of higher education attainment. Levels of no schooling were
accordingly very low, with only 3.4% of the population over
the age of 20 years old having no formal education. The high
proportion of individuals with tertiary education in the metro
is partly attributable to the presence of a university (NMMU) as
well as two TVET colleges, one in Port Elizabeth and the other
in Uitenhage.
The metro had 181 352 learners across all school types in 2014,
an increase of 4.3% from the previous year. These learners
were accommodated in 263 schools in the metro, of which
9.9% were classified as independent. Between 2013 and 2014,
the NMBM added 1 871 educators, which resulted in the learner
to educator ratio improving from 29.8 in 2013 to 23.5 in 2015
(DBE, 2016).
In the 2016 Community Survey respondents were asked to rate
their overall satisfaction with public schools in the area in which
they normally reside. In the NMBM, only 47.5% of respondents
indicated that the quality of public schools in the metro was
good. This was the lowest level in the Eastern Cape as well
as being below the provincial average of 56.4%. The NMBM
however, had a significant percentage of respondents (30.8%)
that indicated that the quality of public schools was average.
The metro also had the highest proportion of respondents who
made use of private schools (9.8%).
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
3%
9% 5%
40%
30%
12%
2%
No Schooling Some Primary Primary SchoolSome Secondary Matric Higher EducationOther
POPULATIONTotal Population 1 194 106
0 - 14 years 304 691
15 - 35 years 398 482
15 - 64 years 813 430
Average Household Size 3.6%
Population Density 609.6 persons/km2
Provincial Population Percentage 17.3%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 181 352 4
Education Attainment Levels Percentage of district
population (aged 20 years +)
Proportion Change
Provincial Rank
No Education 3.4% 1
Matric 26.6% 1
Higher Education 11.7% 2
Highest Educational Level
in 2015:
HIGHER EDUCATION
12%MATRIC
27%
87% live informal dwellings
0% live in traditional dwellings
12% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
Nelson Mandela Bay metro dwelling type
155
Health2,3
The ‘maternal mortality in facility rate’ was 125.3 deaths per
100 000 live births, which is lower than the provincial average
of 148.3 deaths per 100 000 live births. The ratio however, has
been decreasing, with a decrease of 5.72% between 2013 and
2014. Infant mortality as measured by the ‘stillbirth in facility
rate’ was 19 per 1 000 births in 2014. This is on par with the
provincial average of 19.6, but it is on the increase; the rate
increased by 7.34% in 2014. Child mortality can be referred
to by indicators measuring case fatality rates in diarrhoea,
pneumonia and malnutrition in children under the age of 5
years. ‘Child under 5 diarrhoea case fatality rates’ have been
in decline over the period 2010-2014, with 1.4% recorded in
2014 a decline of 44% from 2013. As a result of this decrease,
the district is ranked 10th nationally. ‘Child under 5 pneumonia
fatality rates’ was 4.7% in 2014, above the provincial average
of 4.2%, and increasing by 9.3% between 2013 and 2014. The
indicator increased year-on-year in the most recent year of
assessment; the overall trend over the last 5 years has been
positive with fatality rates on the decline, excluding 2013 and
2014. ‘Child under 5 malnutrition fatalities’ have been on a long-
term decline within the metro; however, there was an increase
in 2014, recording a 12.6% fatality rate. This is higher than
the provincial average of 11.8%. The ‘immunisation coverage
of children under the age of 1 year’ in the metro is 87.6% for
2014 (Eastern Cape: 80.9%). ‘TB incidence rates’ are higher in
the Nelson Mandela Bay Metro (1 009.1 cases per 100 000)
than the provincial average (792.3 cases per 100 000). This
represents the second highest TB incidence by district in the
Eastern Cape. The indicator has been in decline in the metro
over the four years of assessment, with the indicator declining
by 2.59% in 2014.
‘TB treatment success rates for all cases’ have increased by
an impressive 8% in 2013 from 71.8% to 77.7%. The ‘HIV testing
‘HIV testing coverage’ NMBM has the lowest coverage for HIV testing in the province and the fifth lowest in the country.
23%
The NMBM has one of the highest ‘TB incidence rates for all cases’ in the country and is ranked 49th in the country out of 52.
49TH
POSITION NATIONALLY
Health3 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
19 19.6
Maternal Mortality - Per 100 000 live births (2014)
125.3 148.3
Immunisation Rate under 1 years (2014)
87.6% 80.9%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 1.4% 5.2%
Pneumonia case fatality rate (2014) 4.7% 4.2%
Malnutrition case fatality (2014) 12.6% 11.8%
TB Incidence per 100 000 population (2014)
1009.1 792.3
TB Cure rate (percentage) (2013) 77.7% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
23.2% 36.2%
socio-economic and poverty indicators Amathole Provincial
Poverty Headcount (2016) 3.0% 12.7%
Poverty Intensity (2016) 42.3% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 11 663 R 7 085
POVERTY HEADCOUNT (2016)
Provincial13%
NMBM3%
CHILDREN UNDER 1 YEARS (2014)
Provincial81%
poverty indicators
IMMUNISATION RATE
NMBM88%
2 Based on Massyn et al. 2015.3 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwise stated.
156
nelson mandela bay metro EMPLOYMENT By skill level
16%4%
6%16%
17%13%
11%9%
6%2%
0%0%
26%
19%
24%
22%
25%
26%
23%
20%
29%
26%
22%
27%
17%
25%23%
19%
NM
BM
City
ofCa
peTo
wn
Buffa
lo C
ity
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
2005 2015
17%
25%
20%
37%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
NM
BM
City
ofCa
peTo
wn
Buffa
lo C
ity
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
Formal Employment Informal Employment
75%75%
70%66%
76% 74% 73% 72%
25% 25% 30%34%
24% 26% 27% 28%
UNemployment rates across metros16%
4%6%
16%17%
13%11%
9%
6%2%
0%0%
26%
19%
24%
22%
25%
26%
23%
20%
29%
26%
22%
27%
17%
25%23%
19%N
MBM
City
of C
ape
Tow
n
Buffa
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ity
Man
gaun
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eThe
kwin
i
Ekur
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ni
City
of
Joha
nnes
burg
City
of
Tshw
ane
2005 2015
17%
25%
20%
37%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
NM
BM
City
ofCa
peTo
wn
Buffa
lo C
ity
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
Formal Employment Informal Employment
75%75%
70%66%
76% 74% 73% 72%
25% 25% 30%34%
24% 26% 27% 28%
Nelson Mandela Bay metro household income distribution
16%4%
6%16%
17%13%
11%9%
6%2%
0%0%
26%
19%
24%
22%
25%
26%
23%
20%
29%
26%
22%
27%
17%
25%23%
19%
NM
BM
City
ofCa
peTo
wn
Buffa
lo C
ity
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
2005 2015
17%
25%
20%
37%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
NM
BM
City
ofCa
peTo
wn
Buffa
lo C
ity
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
Formal Employment Informal Employment
75%75%
70%66%
76% 74% 73% 72%
25% 25% 30%34%
24% 26% 27% 28%
coverage of the population aged 15-49 years’ was 23.2%,
which was below the provincial and national averages of
36.0% and 32.1%.
Socio-Economic
In 2016, the NMBM had a poverty headcount of 3.0%;
the lowest in the Eastern Cape. This measure is based
on the South African Multidimensional Poverty Index
(SAMPI). The SAMPI is an index that is constructed using
eleven indicators across four dimensions; namely health,
education, living standards and economic activity. The
poverty headcount shows the proportion of households
that are considered to be “multidimensional poor” in the
district. The poverty intensity, which refers to the average
proportion of indicators in which multidimensional poor
households are deprived, in the metro was 42.3% in 2016;
the second lowest in the province and also below the
Eastern Cape average of 43.3%. The monthly average
weighted household income for NMBM in 2011 was
R11 663 (2015 prices); the highest in the province. The
provincial monthly average weighted household income
in comparison was R7 085 (2015 prices).
labour market
The Nelson Mandela Bay Metro’s unemployment rate
of 28.9%4 was below the provincial figure of 29.5%, and
the 4th highest in the Eastern Cape. There were 159 877
persons who were classified unemployed under the
official definition of unemployment in 2015. In comparison
to the other metros in South Africa, the NMBM had the
highest unemployment rate. The metro with the second
highest unemployment rate was Mangaung at 26.8%. Out
of total employment in the NMBM, 20.4% was in skilled
occupations compared to 54.3% in semi- and low-skilled
jobs. A further 25.3% of the NMBM labour force was
employed in the informal sector.
Economic output5
The Nelson Mandela Bay Metro had the largest economy
in the Eastern Cape, accounting for 38.7% of the total
provincial GVA-R in 2015. Total GVA-R grew by 0.9%
between 2014 and 2015, reaching R81.2 billion in 2015.
The metro’s low GVA-R growth rate was well below the
provincial growth rate figure, which over the same period
increased by 1.3%. The NMBM also had the lowest GVA-R
growth rate amongst all the districts in the Eastern Cape.
The negative growth rates in the primary and secondary
sectors were responsible for the metro’s poor GVA-R
growth rate over the period.
4 The official unemployment rate does not consider discouraged job-seekers (i.e. individuals who were not employed, wanted to work, were available to work/start a business but did not take active steps to find work during the last four weeks).5 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.
157
The tertiary sector contributed R59.9 billion to the metro’s
total GVA-R in 2015, followed by the secondary (R20.7 billion)
and primary (R483 million) sectors. Between 2014 and 2015,
the tertiary sector exhibited the strongest GVA-R growth rate
(1.3%), whilst the primary and secondary sectors shrunk by
-2.1% and -0.2%, respectively.
Sectoral Contribution to Economic Activity
The primary sector experienced the sharpest decrease between
2014 and 2015, with GVA-R decreasing by R11 million. This was
attributable to the R12 million decrease in the GVA-R of the
agriculture, forestry and fisheries sub-sector over the period.
In contrast, the mining and quarrying sub-sector increased
by R1 million in absolute terms over the same period. The
secondary sector’s -0.2% decrease in GVA-R between 2014
and 2015 was driven primarily by a -0.5% and -1.7% reduction
in the GVA-R of the electricity and water and construction
sub-sectors. The manufacturing sub-sector’s GVA-R, in
comparison, remained flat with no growth. The tertiary sector
remained the main driver of the Nelson Mandela Bay Metro’s
economy, contributing R59.9 billion in GVA-R and accounting
for 76.1% of the metro’s employment. The strong growth in
the tertiary sector over the 2014 to 2015 period was driven
by growth in all sub-sectors but particularly the finance and
business services as well as the trade sub-sectors. These sub-
sectors GVA-R increased by 2.0% and 1.3% respectively, jointly
adding an additional R574 million to the metro’s total GVA-R
in 2015. The community and social services sub-sector, which
employed the second highest number of people in the metro
after the trade sub-sector, saw its GVA-R increase in absolute
terms by R23 million (0.5%) between 2014 and 2015.
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 81 218 0.9% 2.1% 1
GVA-R/Capita R 68 016 -0.4% 0.3% 1
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 483 -2.3% 4.4%
Agriculture, forestry and fishing
R 405 -2.9% 5.4% 9
Mining and quarrying R 77 1.1% 0.6% 10
Secondary Sector R 20 736 -0.2% 1.7%
Manufacturing R 17 463 0.0% 1.4% 2
Electricity, gas and water R 667 -0.5% 0.3% 8
Construction R 2 606 -1.7% 3.8% 7
Tertiary Sector R 59 999 1.3% 2.2%
Trade, catering and accommodation
R 15 554 1.3% 2.1% 3
Transport R 9 037 1.1% 2.7% 5
Finance and business services
R 18 929 2.0% 2.2% 1
Community services R 12 157 0.6% 2.2% 4
General government R 4 323 0.5% 1.6% 6
Total R 81 218 0.9% 2.1%
GROSS VALUE ADDED-REGIONAL for NMBM grew by
0.9% to R81.2 billion in 2015 from R80.5 billion in 2014.
GVA-R PER CAPITA in NMBM increased by 0.3% between 2010
and 2015.
0.9%INCREASEIN GVA-R
0.3%INCREASE
IN GVA-R PER CAPITA
Contribution: R81.2 Billion
0.9% Growth
Lowest in the EC
nelson mandela bay metro GVa-r contribution
nelson mandela bay metro Formal vs informal
16%4%
6%16%
17%13%
11%9%
6%2%
0%0%
26%
19%
24%
22%
25%
26%
23%
20%
29%
26%
22%
27%
17%
25%23%
19%
NM
BM
City
ofCa
peTo
wn
Buffa
lo C
ity
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
2005 2015
17%
25%
20%
37%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
NM
BM
City
of C
ape
Tow
n
Buffa
lo C
ity
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
of
Joha
nnes
burg
City
of
Tshw
ane
Formal Employment Informal Employment
75%75%
70%66%
76% 74% 73% 72%
25% 25% 30%34%
24% 26% 27% 28%
158
nelson mandela bay metro SECTOR CONTRIBUTION
-2%
-1%
0%
1%
2%
3%
4%
5%
-5% 0% 5% 10% 15% 20% 25% 30%
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
Economic performance
GVA-R per capita allows comparison of different economies
relative to their populations. A rise in GVA-R per capita can
indicate an improvement in productivity. Between 2014 and 2015,
Nelson Mandela Bay Metro’s GVA-R per capita decreased by
0.4% to R68 016. This was the second highest in the Eastern Cape
and above the provincial GVA-R per capita figure of R30 392.
ACCESS TO SERVICES:Households that have access to piped water
201597%
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
201589%
2010 - 2015+ 2670
households
2010 - 2015+ 3040
households
provision of services6
The NMBM’s household access to piped water was 96.9% in
2015, the highest in the Eastern Cape, but only 0.3% higher
than the 96.6% of households that had access to piped
water in 2010. This low increase in the level of access is not
uncommon given the high level of provision already evident in
the metro. Accordingly, less than 1.0% of the households in the
metro were dependent on dams, rivers and streams for their
water supply. The high level of provision is also reflected in the
satisfaction level of households, with 59.3% indicating that the
quality of water provision was good in 2016. This represents
the second highest level in the province after the BCM.
The NMBM had the highest proportion (90.3%) of households
in the Eastern Cape that made use of electricity as their
primary source of lighting in 2015. This figure represented a
0.6% improvement from 2010 when 89.7% used electricity as
their primary means of lighting. Satisfaction levels were also
high with 57.5% of respondents in the 2016 Community Survey,
indicating that they were satisfied with the quality of electricity
provision in the NMBM.
Local Municipality GVA-R Growth Rate Contribution to District GVA-R
GVA-R Rank in District
GVA-R (Millions)
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
NMBM R 81 218 0.9% 2.1% 38.7% 1
6 StatsSA, 2016.
nelson mandela bay metro Formal vs informal
Horizontal: Sector contribution to NMBM Vertical: Sector Growth in 2014/2015Size: People Employed
159
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 7 Electricity 8 Sanitation 9 Refuse Removal 10
NMBM 336 328 96.9% 90.3% 89.2% 91.4%
As in the case with water and electricity provision, just under
90.0% of households in the NMBM have access to sanitation
services. Only 9 486 households had no access to any form
of sanitation services (2.8%) in 2015. Despite the high level of
sanitation provision, the NMBM has the highest proportion of
households that were dependent on bucket toilets (6.5%) in
the Eastern Cape. This was equivalent to 21 696 households.
According to the 2016 Community Survey, 58.6% of households
in the metro consider the quality of the provision of sanitation
services as good – the highest in the province.
As was the case with other basic services, the NMBM had
the highest proportion of households that had their refuse
removed by a local authority either weekly or less frequently.
Just over 90.0% of households in the metro made use of
refuse removal services provided by a local authority. The
high level of provision was also reflected in the quality of the
service, with 62.0% of households indicating that the quality of
provision was good in 2016.
7 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling.8 Access to electricity is measured by the number of households that use electricity as their main source of lighting.9 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet.10Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
ACCESS TO SERVICES:Households that have
access to electricity for lighting
201590%
2010 - 2015+ 2730
households
160
Buffalo cityMetro
1 Unless otherwise stated, all district data is 2015 based on Quantec Standardised Regional Database.
Population and households1
The population of the Buffalo City Metro grew by 1.7% in 2015
year-on-year (2014: 1.7%), increasing to 805 885. This was
higher than the provincial population growth rate (1.5%) and
in line with the national figure (1.7%). The metro had the third
smallest population and only accounted for 11.7% of the total
provincial population. The BCM however, had the second
highest population density in the Eastern Cape after NMBM,
at 317.8 per km2.
The total youth population (individuals between the ages of
15 and 35 years old) in the BCM in 2015 was 296 086. This
represented the highest proportion of youth relative to the
total population (36.7%) in the province. The corresponding
provincial average was 33.6%. Buffalo City had the second
lowest number of minors as a percentage of the total
population in the Eastern Cape, with only 26.2%, of the
population or 211 461 individuals under the age of 15 years old.
The low proportion of children below the age of 15 years old
relative to the total working age population, resulted in a child
dependency ratio of 38.7 for BCM. This was the second lowest
in the province.
The ratio of persons not in the labour force (under 15 and over
64 years) to those within the labour force (or the working age
population), i.e. the overall Dependency Ratio, for Buffalo City
was 47.3 in 2015. This was well below the provincial figure
of 66.6, and the second lowest in the Eastern Cape after the
NMBM. The BCM had the lowest old age dependency ratio (i.e.
individuals older than 65 years relative to the labour force) in
the Eastern Cape at 8.7. This value was below the provincial
average (11.2) and in line with the national figure of 8.7.
A low overall and old age dependency ratio is likely to place
less strain on the state to allocate resources to support
children and the elderly. It further indicates a larger productive
base from which to draw taxes to support state interventions
for the youth and aged. This measure is dependent on the
assumption that those over the age of 65 years lack other
sources of income.
The Buffalo City Metro (BCM) covers approximately 2 536 km2 and is bounded to the south-east by the Indian Ocean and is completely surrounded by the Amathole District. In May 2011,
the metro was separated from the Amathole District and converted into a metropolitan municipality.
BCM population density
Buffalo City805 885
317.8People per
km2
Total Population
BCM household density
Buffalo City236 991
Total Households
3.4Average Household
Size
162
4
EDUCATION ATTAINMENT LEVELS(POPULATION OVER 20 YEARS)
5%10% 5%
37%
27%
14%
4%
No Schooling Some Primary Primary School Some Secondary
Matric Higher Education Other
Highest Educational Level
in 2015:
HIGHER EDUCATION
14%MATRIC
27%
4% live in traditional dwellings
22% live ininformal dwellings
EDUCATION ATTAINMENT LEVELS
The metro had the third highest number of households living
in formal dwellings after the NMBM and the Sarah Baartman
District. The remaining 12 610 of households lived in either
informal (22.7%) or traditional (4.6%) dwellings.
Education
Education levels in the BCM were higher than in other parts
of the province, with 13.5% of the population over the age of
20 years old having achieved some form of higher education
qualifications compared to a provincial average of 5.65.0%.
A further 26.6% of the metro’s population had attained their
Matric, and only 4.9% had no formal schooling. The BCM had
the second highest proportion of individuals with post Matric
qualifications in the province, but the highest proportion
of those with tertiary qualifications. Despite not having a
university, both Fort Hare and Walter Sisulu have satellite
campuses in the metro. In addition to this, the metro has two
TVET colleges namely Buffalo City (East London) and King
Hintsa (King Williams Town).
There were approximately 134 667 learners in the BCM in 2015,
equating to an increase on 7 902 learners from the 126 765
learners in 2010. These leaners were spread across 319 public
and private schools in the metro. This gave the metro a learner
to educator ratio of 28.1 in 2015, slightly higher than the 26.1
recorded in 2010 (DBE, 2016)2.
The 2016 Community Survey asked respondents in BCM how
they would rate their overall satisfaction with public schools
in the metro. Approximately 40.5% of respondents rated the
quality of public schools between poor (9.7%) and average
(30.8%), with a further 11.9% indicating that they either did
make use of public schools (9.8%) or had no access to public
schools (2.2%). Only 47.5% of respondents indicated that they
felt that the quality of public schools in the BCM was good.
This was the lowest proportion in the province and well below
the provincial figure of 56.4%.
POPULATIONTotal Population 805 885
0 - 14 years 211 461
15 - 35 years 296 086
15 - 64 years 547 025
Average Household Size 3.4
Population Density 317.8 persons/km2
Provincial Population Percentage 11.7%
EDUCATION Value Growth Provincial Rank
Number of Learners (2014) 134 667 7
Education Attainment Levels Percentage of district
population (aged 20 years +)
Proportion Change
Provincial Rank
No Education 4.9% 2
Matric 26.6% 2
Higher Education 13.5% 1
72% live informal dwellings
Buffalo City Metro dwelling type
2 The Department of Education learner statistics by district municipality does not separate the Buffalo City Metro from the Amathole District. To obtain statistics for BCM, the figures for the East London school district (which largely corresponds to the borders of the Buffalo City Metro) have been removed from the Amathole total to obtain a figure for the metro.
163
3 Based on Massyn et al. 2015. 4 At the time of publishing this report the 2015/16 District Health Review had not been released and thus the discussion on health is for the 2014 year unless otherwise stated.
Health3,4
The ‘maternal mortality in facility rate’ has fluctuated between
2010 and 2014, from 204.5 deaths per 100 000 live births in
2010, to 133.3 per 100 000 in 2013. It has decreased substantially
from 2010 to 2014; however, in 2014, the trend increased once
again to 206.7, the highest in this period. The metro had the
highest maternal mortality rate in the province. The ‘stillbirth
death rate in facility’ stands at 21.3 deaths per 1 000 live births
and is the second highest in the province and above the
national target of 20.7 per 1 000 births. Child mortality can be
referred to by indicators measuring by analysing case fatality
rates in diarrhoea, pneumonia and malnutrition in children
under the age of 5 years within facilities of patients diagnosed
with the illness. ‘Child under 5 diarrhoea case fatality rates’
have decreased by 15.38% from 2.6% in 2013, to 2.2% in 2014;
this being well below the provincial average. The ‘child under
five pneumonia case fatality rate’ has fluctuated over the last
five years from 4.7% in 2010 to a low of 2.1% in 2013, rising
to 2.4% by 2014. The ‘Child under five acute malnutrition
case fatality rate’ also fluctuated over the same period,
but decreased between 2013 and 2014 by 26.36% to 9.6%.
Although the metro’s child mortality rates have fluctuated
over the last five years, it should be noted that its performance
for these indicators is near or on target with national targets.
‘Immunisation coverage of children under the age of 1 year’ in
the metro is significantly positive at 96.4%. Buffalo City has
the highest immunisation coverage provincially and is ranked
among the top ten (10th) districts nationally. ‘TB incidence
rates (all types)’ increased by 3.95% from 791.4 per 100 000 in
2013 to 822.7 per 100 000 in 2014.
CHILDREN UNDER 1 YEARS (2014)
Provincial81%
IMMUNISATION RATE
POVERTY HEADCOUNT (2016)
Provincial13%
Buffalo City7%
poverty indicators
Buffalo City96%
96%
32%
Health3 Indicator Growth Provincial Average
Infant Mortality - Still birth rate in facility per 1 000 births (2014)
21.3 19.6
Maternal Mortality - Per 100 000 live births (2014)
206.7 148.3
Immunisation Rate under 1 years (2014)
96.4% 80.9%
Child-under-5-years Mortality
Diarrhoea case fatality rate (2014) 2.2% 5.2%
Pneumonia case fatality rate (2014) 2.4% 4.2%
Malnutrition case fatality (2014) 9.5% 11.8%
TB Incidence per 100 000 population (2014)
822.7 792.3
TB Cure rate (percentage) (2013) 77.9% 77.3%
HIV Testing Coverage (Ages 15 - 39) (2014/2015)
31.6% 36.2%
socio-economic and poverty indicators Buffalo City
Provincial
Poverty Headcount (2016) 7.3% 12.7%
Poverty Intensity (2016) 42.8% 43.3%
Average Weighted Monthly Household Income(2011, 2015 prices)
R 10 639 R 7 085
Immunisation coverage of children under the age of 1 year in the metro is high at 96.4% of children under one year. Buffalo City has the highest immunisation coverage provincially and is ranked among the top 10 districts nationally.
HIV testing coverage ‘BCM has the third lowest coverage for HIV testing in the province ahead of only NMBM and Sarah Baartman
164
Buffalo City Metro EMPLOYMENT By skill level
17%5%
7%19%
17%11%
9%8%
5%2%
0%
0%
24%
19%
26%
22%
25%26%
23%
20%22%
26%
29%27%
17%
25%23%
19%
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
2005 2015
76%
70%
75% 75%
66%74%
73% 72%
30% 25% 25%34%
24% 26%27% 28%
Formal Employment Informal Employment
19%
29%
23%
30%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
UNemployment rates across metro17%
5%7%
19%17%
11%9%
8%5%
2%0%
0%
24%
19%
26%
22%
25%26%
23%
20%22%
26%
29%27%
17%
25%23%
19%
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
of
Joha
nnes
burg
City
of
Tshw
ane
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
2005 2015
76%
70%
75% 75%
66%74%
73% 72%
30% 25% 25%34%
24% 26%27% 28%
Formal Employment Informal Employment
19%
29%
23%
30%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
This is higher than the provincial average of 792.3 per
100 000. ‘TB treatment success rates for all cases’ was
77.7% on a par with the provincial average but below the
national target of 77.9%. The ‘HIV testing coverage of the
population aged 15-49 years’ was 31.6%, which is aligned
to the national average of 32.1%.
SOCIO-ECONOMIC
The Buffalo City Metro had the third lowest poverty
headcount in the province during 2016 at 7.3%. This was
however, still notably higher than the poverty headcount
recoded in the NMBM (3.0%) and Sarah Baartman
(4.5%). This measure is based on the South African
Multidimensional Poverty Index (SAMPI). The SAMPI
is an index that is constructed using eleven indicators
across four dimensions; namely health, education, living
standards and economic activity. The poverty headcount
shows the proportion of households that are considered
to be “multidimensional poor” in the district. The poverty
intensity, which refers to the average proportion of
indicators in which multidimensional poor households are
deprived, however, was the third lowest in the province
in 2016 at 42.8%. This was marginally below the Eastern
Cape average of 43.3%. The average monthly weighted
household income for the metro was R10 639 (2015
prices), the second highest in the Eastern Cape after the
NMBM (R11 663; 2015 prices).
labour market
The unemployment rate in the Buffalo City Metro was
22.4%5, the lowest in the Eastern Cape and 7.1% below
the provincial average. This was equivalent to 82 838
unemployed persons using the official definition of
unemployment. The BCM also had the third lowest
unemployment rate when compared to other South
African metros, with only City of Tshwane and eThekwini
having lower unemployment rates. The majority (70.3%)
of the 286 674 employed in the BCM are employed in the
formal sector, with only 29.7% of total employment being
in the informal sector.
Economic output6
The Buffalo City Metro had the second largest economy
in the Eastern Cape after the Nelson Mandela Bay Metro,
accounting for 19.6% of the total provincial GVA-R in 2015.
The total GVA-R for the BCM in 2015 was R41.2 billion,
equating to a year-on-year increase of 0.9%. This was well
below the provincial GVA-R growth rate, which over the
same period, increased by 1.3% year-on-year. The largest
contributor to the total GVA-R of the metro in 2015 was the
tertiary sector (R32.6 billion), followed by the secondary
(R8.1 billion) and primary (R473 million) sectors. Between
2014 and 2015, the tertiary sector exhibited the strongest
GVA-R growth rate, increasing by 1.2% year-on-year. In
comparison, over the same period, the primary sector
contracted by 2.1%, while the secondary sector exhibited
almost no growth.
Formal vs informal employment
17%5%
7%19%
17%11%
9%8%
5%2%
0%
0%
24%
19%
26%
22%
25%26%
23%
20%22%
26%
29%27%
17%
25%23%
19%
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
of
Joha
nnes
burg
City
of
Tshw
ane
2005 2015
76%
70%
75% 75%
66%74%
73% 72%
30% 25% 25%34%
24% 26%27% 28%
Formal Employment Informal Employment
19%
29%
23%
30%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
2
5 The official unemployment rate does not consider discouraged job-seekers (i.e. individuals who were not employed, wanted to work, wereavailable to work/start a business but did not take active steps to find work during the last four weeks).6 Economic output, sectoral contribution to economic activity, local municipal economic contribution, and economic performance are indicated at basic prices in constant 2010 prices.
Buffalo City Metro household income distribution (2011)
17%5%
7%19%
17%11%
9%8%
5%2%
0%
0%
24%
19%
26%
22%
25%26%
23%
20%22%
26%
29%27%
17%
25%23%
19%
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
Buffa
lo C
ity
City
of C
ape
Tow
n
NM
BM
Man
gaun
g
eThe
kwin
i
Ekur
hule
ni
City
ofJo
hann
esbu
rg
City
ofTs
hwan
e
2005 2015
76%
70%
75% 75%
66%74%
73% 72%
30% 25% 25%34%
24% 26%27% 28%
Formal Employment Informal Employment
19%
29%
23%
30%
Highly Skilled SkilledSemi- and Unskilled Informal Employment
No incomeR 1 - R 4 800
R4 801 - R 9 600R 9 601 - R 19 200
R 19 201 - R 38 400R 38 401 - R 76 800
R 76 801 - R 153 600R 153 601 - R 307 200R 307 201 - R 614 400
R 614 401 - R 1 228 800R 1 228 801 - R 2 457 600
R 2 457 601 and more
165
Sectoral Contribution to Economic Activity
The principle contributors to the primary sectors contraction
in GVA-R was the agricultural, forestry and fisheries sub-
sector, which declined by 2.9% from R424 million in 2014 to
R412 million in 2015. The construction sub-sector added R98
million in additional GVA-R between 2014 and 2015; however,
1.5% and 2.5% contractions by the manufacturing and utilities
sub-sectors respectively, resulted in almost no growth in the
secondary sector over the period.
The tertiary sector remained the main driver of the Buffalo
City Metro’s economy, contributing R32.6 billion in GVA-R
and accounting for 79.2% of the metro’s total GVA-R. The
R379 million increase in the total GVA-R of the tertiary
sector between 2014 and 2015 was driven by positive GVA-R
growth rates in all but the general government sub-sector.
General government GVA-R declined 0.2% year-on-year but
remained the third largest employer in 2015, employing 35
602 and accounting for 17.0% of the Buffalo City Metro’s total
employment. The finance and business services sub-sector
followed by the trade and community and social services sub-
sectors, exhibited the strongest GVA-R growth rates in the
tertiary sector, growing by 1.7%, 1.5% and 0.9%, respectively.
GVA-R Value(2015)
Growth 2014-2015
CGARGrowth2005-2015
EC Rank
GVA-R (Millions) R 41 229 0.9% 1.9% 2
GVA-R/Capita R 51 161 -0.8% 0.4% 2
SEctor GVA-R (Millions,
2015)
Growth2014-2015
CGARGrowth2005-2015
Rank District
Contribution
Primary Sector R 473 -2.1% 6.8%
Agriculture, forestry and fishing
R 413 -2.9% 7.6% 9
Mining and quarrying R 60 3.2% 2.6% 10
Secondary Sector R 8 103 0.0% 1.0%
Manufacturing R 5 665 -1.5% 0.3% 4
Electricity, gas and water R 612 -2.5% -2.3% 8
Construction R 1 826 5.7% 5.6% 7
Tertiary Sector R 32 654 1.2% 2.0%
Trade, catering and accommodation
R 9 005 1.8% 2.4% 2
Transport R 3 353 0.4% 1.8% 5
Finance and business services
R 9 157 1.8% 2.2% 1
Community services R 7 905 -0.2% 1.4% 3
General government R 3 234 1.7% 2.3% 6
Total R 41 230 0.9% 1.9%
GROSS VALUE ADDED-REGIONAL for BCM grew by 0.9% to R41.2 billion in 2015 from R40.8 billion in 2014.
0.9%INCREASEIN GVA-R
GVA-R PER CAPITA in BCM increased by 0.4% between 2010
and 2015. 0.4%
INCREASEIN GVA-R PER
CAPITA
Contribution: R41.2 Billion
0.9% Growth
2rd highest in the EC
Buffalo City Metro GVa-r contribution
166
-2%
-1%
0%
1%
2%
3%
4%
5%
-5% 0% 5% 10% 15% 20% 25% 30%
Agriculture, forestry and fishing
Mining and quarrying
Manufacturing
Electricity, gas and water
Construction
Wholesale and retail trade, catering and accommodation
Transport, storage and communication
Finance, insurance, real estate and business services
Community, social and personal services
General government
Buffalo City mETRO SECTOR CONTRIBUTION
Horizontal: Sector contribution to BCMVertical: Sector Growth in 2014/2015Size: People Employed
Economic performance
GVA-R per capita allows comparison of different economies
relative to their populations. A rise in GVA-R per capita can
indicate an improvement in productivity. Between 2014 and
2015, Buffalo City Metro’s GVA-R per capita decreased by 0.8%
to R51 161 from R51 580. This was the second highest in the
Eastern Cape after the NMBM, and above both the national and
provincial GVA-R per capita figure of R50 511 and R30 392,
respectively.
provision of services7
Buffalo City Metro household access to piped water stood at
90.7% in 2015; only 0.5% higher than the 90.2% of households
that had access to piped water in 2010. This low increase in
the level of access is not uncommon given the high level of
provision already evident in the metro. The high level of access
to piped water in the metro means that only 434 households
relied on dams, rivers and springs for their water supply. The
high level of provision was further reflected in the satisfaction
level of households, with 61.2% indicating that the quality of
water provision was good in 2016. This represented the highest
level of satisfaction for this service in the province.
ACCESS TO SERVICES:Households that have access
to sanitation at the RDP standard
201571%
2010 - 2015+ 2200
households
ACCESS TO SERVICES:Households that have access to piped water
201591%
2010 - 2015+ 2980
households
Local Municipality GVA-R (Millions)
GVA-R Growth Rate Contribution to District GVA-R
GVA-R Rank in District
Year-on-year (2014 - 2015)
CAGR between (2005 - 2015)
Buffalo City Metro R 41 229 0.9% 1.9% 19.6% 2
7 StatsSA, 2016
167
8 This includes all households that have piped water inside their dwelling, within their yard, or less than 200 metres from their dwelling in line with the RDP standard. 9 Access to electricity is measured by the number of households that use electricity as their main source of lighting. 10 Access to sanitation is measured using the RDP standard which requires that households have access to a waterborne flush toilet, conservancy tank or non-waterborne VIP toilet. 11 Access to refuse removal is measured by household’s ability to access refuse collection services from a local authority in line with the National Waste Management Strategy.
municipality Total Number of Households
Percentage of Households in 2015 with access to:
Water 8 Electricity 9 Sanitation 10 Refuse Removal 11
Buffalo City Metro 236 991 90.7% 80.7% 70.9% 70.9%
The Buffalo City Metro had the third highest number of households
that have their refuse removed by a local authority either weekly
or less frequently. Over two thirds (70.9%) of households in the
metro made use of refuse removal services provided by a local
authority. The high level of provision was however, not reflected in
the satisfaction with service, with 50.0% of households indicating
that that the quality of provision was either average (25.8%) or
poor (24.2%).
ACCESS TO SERVICES:Households that have
access to electricity for lighting
201581%
The Buffalo City Metro had the third highest proportion (80.7%) of
households that used electricity as their primary means of lighting
in the Eastern Cape. This figure was largely unchanged from the
2010 figure of 177 285 households (80.1%). Despite this high level
of provision, 39.7% of households in 2016 indicated that the quality
of provision was either average (26.4%) or poor (13.3%).
As in the case with water and electricity provision, over 70.0% of
households in the BCM had access to sanitation services, with only
68 880 households having no access to sanitation services (11.4%)
or being dependent on a bucket toilet (1.2%) or pit latrine (16.5%).
The high level of provision does however, not address the quality
of provision. According to the 2016 Community Survey, 53.6% of
households in the metro consider the quality of the provision of
sanitation services as good – the second highest in the province.
2010 - 2015+ 2600
households
168
Consumer Price Index (CPI): The Consumer Price Index is a specific bundle of goods that is measured period by
period and compared; to identify what the price changes have been over the goods and the whole bundle. These
price changes are the rate at which inflation occurs. CPIX is the same bundle of goods, without interest rates on
mortgage bonds, and is the inflation measure targeted by the Reserve Bank.
Deflation: a situation in which there is a reduction in the general price level – thus the rate of inflation is negative.
Discouraged work seekers (Non-searching unemployed): is a person who was not employed during the reference
period, wanted to work, was available to work/start a business but did not take active steps to find work during
the last four weeks, provided that the main reason given for not seeking work was any of the following: no jobs
available in the area; unable to find work requiring his/her skills; lost hope of finding any kind of work.
Disinflation: is a decrease in the rate of inflation – a slowdown in the rate of increase of the general price level of
goods and services.
Employed: Comprises all working-age individuals who, during the reference week, did any work for at least one
hour or had a job or business.
Expanded unemployment: Comprises all working-age individuals who were not employed seven days prior to
the interview, but were available to work. The expanded unemployed definition therefore includes all individuals
unemployed according to the narrow definition of unemployment and all discouraged work seekers.
Gross Domestic Product (GDP): GDP is the market value of all officially recognised final goods and services
produced within a country in a year, or other given period of time.
Gross Fixed Capital Formation: This is a component of the expenditure on GDP, and shows how much of the
new value added in the economy is invested rather than consumed. It can also serve as a predictor for future
economic growth.
Gross Value Added (GVA): GVA is a measure in economics of the value of goods and services produced in an area,
industry or sector of an economy. GVA + taxes on products - subsidies on products = GDP.
Inflation Rate: Inflation is a persistent increase in the general price level of goods and services in an economy over
a period of time. A chief measure of price inflation is the inflation rate, the annualised percentage change in a
general price index (normally the consumer price index) over time.
Informal Employment: Identifies persons who are in precarious employment situations irrespective of whether or
not the entity for which they work is in the formal or informal sector. Persons in informal employment therefore
comprise all persons in the informal sector, employees in the formal sector, and persons working in private
households who are not entitled to basic benefits such as pension or medical aid contributions from their employer,
and who do not have a written contract of employment.
Informal sector: The informal sector is comprised of (1) employees working in establishments that employ fewer
than five employees and who do not deduct income tax from their wages; and (2) employers, own account workers
and individuals helping unpaid in household businesses that are not registered for either income tax or value-
added tax.
4
DEFINITIONS
171
Labour absorption rate or employment-to-population ratio: Is the proportion of the working-age population that
is employed.
Labour force: Comprises all individuals within the working-age population who are willing and capable of working,
and therefore includes the employed and the unemployed.
Labour force participation rate: Represents the proportion of the working-age population who are members of the
labour force (i.e. who are either employed or unemployed).
Motor Vehicle parc: the number of motor vehicles in a country or region.
Nominal vs. Real Values/Rates: A nominal value is an economic value expressed in monetary terms (that is, in units
of a currency). By contrast, a real value is a value that has been adjusted from a nominal value to remove the effects
of general price level changes over time.
Not Economically Active: Persons aged 15–64 years who are neither employed nor unemployed in the reference
week.
Percentage points: A unit of one percent.
Poverty Headcount: The poverty headcount shows the proportion of households that are considered to be
“multidimensional poor” in a defined area. Multidimensional poor is determined based on the South African
Multidimensional Poverty Index (SAMPI). The SAMPI is an index that is constructed using eleven indicators across
four dimensions, namely health, education, living standards and economic activity.
Poverty Intensity: The intensity of poverty is the average proportion of indicators in which multidimensional poor
households are deprived.
Producer Price Index (PPI): The Producer Price Index measures the changes in prices over a bundle of goods from
producers, as opposed to point of sale. The index is a more volatile index on average, with larger changes.
Real Effective Exchange Rate (REER): seeks to measure the value of a country’s goods against those of another
country, a group of countries, or the rest of the world, at the prevailing nominal exchange rate.
Real Interest Rate: The real interest rate is the money rate of interest corrected for the change in purchasing power
of money by subtracting the inflation rate.
Regional Gross Domestic Product (GDP-R): This is the gross domestic product, or gross geographic product, of
a region within a country.
Repo rate: The repo rate is the discount rate at which the South African Reserve Bank repurchases government
securities from commercial banks, depending on the level of money supply it decides to maintain in
the country’s monetary system.
Unemployed: Comprises all working-age individuals who were not employed during the reference week, but were
available to work and actively sought employment or had taken steps to start their own business during the four
weeks prior to the interview, or had not actively sought work but had a job or business to start at a definite date in
the future and were available. This is known as the narrow or official definition of unemployment.
Unemployment rate: Represents the proportion of the labour force that is unemployed. Both narrow/official and
broad/expanded unemployment rates can be calculated.
Working-age population: Comprises all individuals aged between 15 and 65 years, whether or not they are
economically active.
172
Executive Summary
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STATSSA, 2016a. Quarterly Labour Force Survey 3rd quarter 2016. Statistics South Africa: Pretoria.
STATSSA, 2016b. Gross Domestic Product, Quarter 3: 2016. Pretoria: Stats SA.
CHAPTER 1: INTRODUCTION
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NATIONAL TREASURY, 2016e. Media Statement: Fitch affirms South Africa’s ratings at BBB- and revises the outlook. [Online]. Available: http://www.treasury.gov.za. [Accessed December 2016].
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CHAPTER 4: EASTERN CAPE ECONOMIC PROFILE
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CHAPTER 5: EASTERN CAPE AUTOMOTIVE INDUSTRY
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CHAPTER 6: STRATEGIC INITIATIVES
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