EC Economic Outlook Book

A DEDEA & Treasury Publication

The Eastern CapeE C O N O M I C P R O F I L E

A N D O U T L O O K

The Eastern Cape Economic Profile and Outlook, 2010, is compiled

using the available information from different sources.

Some of the information is subject to revision.

FOREWORDThe Eastern Cape Economic Profile and Outlook – 2010, is the first of a series of

publications that will be released by the Department of Economic Development &

Environmental Affairs (DEDEA) in partnership with the Provincial Treasury on an annual

basis. This initiative seeks to provide reliable, accurate and up to date information on the

economic sector within the Eastern Cape Province. This document is intended to serve

as a point of reference to the public and the private sector for decision making. It is our

hope that this work will be of great value to those involved in the investment space,

economic planning, research, budgeting processes, to name but a few.

Through this publication, our intention is to provide a single point of reference for the

economic profile of the Eastern Cape, including District Municipalities and the Metro, as

well as key economic sectors. The publication is also forward looking in that it provides a

forecast and an outlook of key variables in the economy over a three- year period. This is

very important to us, as the global economy begins to show signs of recovery from the

economic crisis.

As the Department of Economic Development & Environmental Affairs we have a

mandate to provide leadership and guidance in economic development and planning

discourse. With this in mind I invite all our stakeholders to receive, study, and make use

of this publication. As the first of many to come, our stakeholders can look forward to this

being an annual release and can also look forward to other publications in other areas of

economic thought. I must also commend the team that has worked tirelessly to put this

together, under the leadership of the Head of Department.

Hon Mcebisi Jonas, Mr (MPL)

MEC for Finance, Economic Development & Environmental Affairs

ABBREVIATIONS ........................................................................................................................................ II

TABLES ....................................................................................................................................................... IV

FIGURES ..................................................................................................................................................... V

ACKNOWLEDGEMENTS ............................................................................................................................ VI

EXECUTIVE SUMMARY ............................................................................................................................. VII

FORECASTS ............................................................................................................................................... IX

National ..................................................................................................................................................... IX

Eastern Cape .............................................................................................................................................. X

1 GLOBAL ECONOMIC OUTLOOK AND FORECAST METHODOLOGY ............................................. 1

1.1 Global Economic Outlook ................................................................................................................. 1

1.1.1 USA .................................................................................................................................................... 1

1.1.2 JAPAN ............................................................................................................................................... 2

1.1.3 CHINA ............................................................................................................................................... 3

1.1.4 INDIA ................................................................................................................................................. 3

1.1.5 MAJOR EUROPEAN UNION MEMBER STATES ................................................................................ 4

1.1.6 BRAZIL ............................................................................................................................................... 4

1.2 The Theoretical Framework of the South African Macro-model .............................................. 5

1.3 Eastern Cape Model Conceptualisation ........................................................................................ 5

2 NATIONAL ECONOMIC PERFORMANCE AND OUTLOOK .............................................................. 9

2.1 Overview of current situation .......................................................................................................... 9

2.2 Consumer spending .......................................................................................................................... 10

2.3 Public Fixed Investment ................................................................................................................... 11

2.4 Private Fixed Investment ................................................................................................................. 11

2.5 Labour-Market Condition ................................................................................................................. 12

2.6 Inflation ............................................................................................................................................... 13

2.7 Monetary Policy and Interest Rates .............................................................................................. 13

2.8 Fiscal Policy ....................................................................................................................................... 14

3 THE ECONOMY OF THE EASTERN CAPE ......................................................................................... 17

3.1 Profile of the Eastern Cape ............................................................................................................. 17

3.1.1 Overview ........................................................................................................................................... 17

3.1.2 Population size and age distribution ................................................................................................ 18

3.1.3 HIV and AIDS .................................................................................................................................... 19

3.1.4 Migration .......................................................................................................................................... 20

3.2 Economic Outlook of the Eastern Cape ........................................................................................ 20

3.2.1 Economic performance and projections ........................................................................................... 20

3.2.2 Total Investment in the Eastern Cape .............................................................................................. 21

3.2.2.1 Machinery and other equipment ................................................................................................... 22

3.2.2.2 Building and construction .............................................................................................................. 23

3.2.2.3 Transport equipment ..................................................................................................................... 24

3.2.3 Final Consumption Expenditure in the Eastern Cape ....................................................................... 24

3.2.3.1 Consumption of durable goods in Eastern Cape ........................................................................... 26

3.2.3.2 Consumption of non-durable goods in Eastern Cape .................................................................... 27

3.2.3.3 Consumption of semi-durable goods in Eastern Cape .................................................................. 28

3.2.3.4 Consumption of Services in Eastern Cape .................................................................................... 29

3.2.4 Household Consumption, Saving and Debt ..................................................................................... 29

Tabl

E O

F C

On

TEn

Ts

3.2.5 Trade Position .................................................................................................................................... 30

3.2.5.1 Exports of Goods ............................................................................................................................ 30

3.2.5.2 Imports of Goods ............................................................................................................................ 31

3.2.5.3 Balance of Trade ............................................................................................................................. 32

3.3 Sectoral Analysis of the Eastern Cape .......................................................................................... 33

3.3.1 Sectoral Contribution Analysis in Eastern Cape ............................................................................... 33

3.3.1.1 Primary Sector ................................................................................................................................ 33

3.3.1.2 Secondary Sector ........................................................................................................................... 34

3.3.1.3 Tertiary Sector ................................................................................................................................ 34

3.3.2 Sectoral Growth Analysis in the Eastern Cape ................................................................................. 34

3.3.2.1 Primary Sector ................................................................................................................................ 34

3.3.2.2 Secondary Sector ........................................................................................................................... 35

3.3.2.3 Tertiary Sector ................................................................................................................................ 35

3.4 Labour-Market Analysis in Eastern Cape ..................................................................................... 36

3.4.1 Employment ....................................................................................................................................... 38

3.4.1.1 Employment by province ................................................................................................................ 38

3.4.1.2 Employment Sectoral Analysis ....................................................................................................... 39

3.4.1.2.1 Employment Industry’s Contribution to Total Employment ......................................................... 39

3.4.1.2.2 Formal Employment by Industry .................................................................................................. 39

3.4.1.2.3 Informal Employment by Industry ............................................................................................... 41

3.4.1.3 Employment by Occupation and by Skills ...................................................................................... 42

3.4.1.3.1 Employment by Occupation ......................................................................................................... 42

3.4.1.3.2 Employment by Skills .................................................................................................................. 43

3.4.2 Unemployment Rate in Eastern Cape ............................................................................................... 43

3.4.3 Labour Remuneration ........................................................................................................................ 44

3.4.3.1 Remuneration by Gender ............................................................................................................... 45

3.4.3.2 Remuneration by Age Group .......................................................................................................... 46

3.4.3.3 Remuneration by sector ................................................................................................................. 46

4 ECONOMIC PERFORMANCE OF DISTRICT MUNICIPALITIES ...................................................... 49

4.1 Amatole District Municipality ......................................................................................................... 49

4.1.1 Total Population by Age Group ......................................................................................................... 49

4.1.2 Total Population Affected by HIV ...................................................................................................... 50

4.1.3 Household Income and Expenditure ................................................................................................. 50

4.1.4 Sectoral Contribution Analysis ......................................................................................................... 51

4.1.5 Sectoral Growth Analysis ................................................................................................................. 52

4.1.6 Access to Services ............................................................................................................................ 53

4.1.6.1 Access to Water ............................................................................................................................. 53

4.1.6.2 Access to Energy ............................................................................................................................ 54

4.1.6.3 Access to Sanitation ...................................................................................................................... 54

4.1.6.4 Access to Telephones .................................................................................................................... 55

4.1.6.5 Access to Refuse ........................................................................................................................... 55

4.1.7 Types of Dwelling ............................................................................................................................. 56

4.1.8 Formal Employment by Sector .......................................................................................................... 56

4.1.9 Informal and Formal Employment by Skill ........................................................................................ 57

4.1.10 Dependency Ratio ........................................................................................................................... 57

4.1.11 Level of Education ........................................................................................................................... 57

Tabl

E O

F C

On

TEn

Ts

4.1.12 Number of People in Poverty .......................................................................................................... 58

4.1.13 Distribution of Households by Income ........................................................................................... 58

4.1.14 Human Development Indicator (HDI) .............................................................................................. 59

4.1.15 Urbanisation ................................................................................................................................... 59

4.2 Alfred Nzo District Municipality ..................................................................................................... 60


4.2.2 Total Population Affected by HIV ..................................................................................................... 60




4.2.6 Access to Services ........................................................................................................................... 63

4.2.6.1 Access to Water ............................................................................................................................ 63

4.2.6.2 Access to Energy ........................................................................................................................... 63

4.2.6.3 Access to Sanitation ..................................................................................................................... 64


4.2.6.5 Access to Refuse ........................................................................................................................... 65




4.2.10 Dependency Ratio .......................................................................................................................... 67

4.2.11 Level of Education .......................................................................................................................... 67

4.2.12 Number of people in poverty .......................................................................................................... 68



4.2.15 Urbanisation ................................................................................................................................... 69

4.3 Cacadu District Municipality .......................................................................................................... 70


4.3.2 Total Population by Affected HIV ..................................................................................................... 70

4.3.3 Household income and expenditure ................................................................................................ 71



4.3.6 Access to Services ........................................................................................................................... 73

4.3.6.1 Access to Water ............................................................................................................................ 73

4.3.6.2 Access to Energy ........................................................................................................................... 74



4.3.6.5 Access to Refuse ........................................................................................................................... 75

4.3.7 Type of Dwelling ............................................................................................................................... 75





4.3.12 Number of People in Poverty ......................................................................................................... 78



4.3.15 Urbanisation ................................................................................................................................... 79

4.4 Chris Hani District Municipality ..................................................................................................... 80





4.4.5 Sectoral Growth Analysis ................................................................................................................ 82


4.4.6.1 Access to Water ............................................................................................................................ 83

4.4.6.2 Access to Energy ........................................................................................................................... 84



4.4.6.5 Access to Refuse ........................................................................................................................... 85







4.4.13 Income Distribution ........................................................................................................................ 88


4.4.15 Urbanisation ................................................................................................................................... 89

4.5 Nelson Mandela Bay Metropolitan ............................................................................................... 90






4.5.6 Access to services ............................................................................................................................ 94

4.5.6.1 Access to water ............................................................................................................................. 94

4.5.6.2 Access to Energy ........................................................................................................................... 94

4.5.6.3 Access to Sanitation...................................................................................................................... 94

4.5.6.4 Access to telephones .................................................................................................................... 95

4.5.6.5 Access to Refuse ........................................................................................................................... 95

4.5.7 Type of dwelling ............................................................................................................................... 96

4.5.8 Formal Employment by sector .......................................................................................................... 96

4.5.9 Informal and Formal Employment by skill ........................................................................................ 97





4.5.14 Human Development Indicator (HDI) ............................................................................................. 99

4.5.15 Urbanisation ................................................................................................................................... 99

4.6 O.R. Tambo District Municipality ................................................................................................... 100

4.6.1 Total Population by Age Group ........................................................................................................ 100


4.6.3 Household income and expenditure ................................................................................................ 101

4.6.4 Sectoral Contribution Analysis ........................................................................................................ 102


Tabl

E O

F C

On

TEn

Ts


4.6.6.1 Access to Water ............................................................................................................................. 103

4.6.6.2 Access to Energy ............................................................................................................................ 104



4.6.6.5 Access to Refuse ........................................................................................................................... 105

4.6.7 Types of dwelling .............................................................................................................................. 105

4.6.8 Formal Employment by sector .......................................................................................................... 106

4.6.9 Informal and Formal employment by skill ........................................................................................ 107



4.6.12 Number of people in poverty .......................................................................................................... 109

4.6.13 Distribution of households by income ............................................................................................ 109


4.6.15 Urbanisation .................................................................................................................................... 110

4.7 UKhahlamba District ........................................................................................................................ 111


4.7.2 Total Population Affected by HIV ...................................................................................................... 111





4.7.6.1 Access to Water ............................................................................................................................ 114

4.7.6.2 Access to Energy ............................................................................................................................ 115



4.7.6.5 Access to Refuse ............................................................................................................................ 116





4.7.11 Level of Education ........................................................................................................................... 118


4.7.13 Distribution of Household by Income ............................................................................................. 119

4.7.14 Human Development Indicator ....................................................................................................... 119

4.7.15 Urbanisation .................................................................................................................................... 120

5 STRATEGIC IMPLICATIONS ................................................................................................................ 123

APPENDIX .................................................................................................................................................. 126

II

AFF Agriculture, Forestry and Fishing

AIDS Acquired Immune Deficiency Syndrome

COICOP Classification of Individual Consumption by Purpose

CON Construction

Cons. Consumption

CPI Consumer Price Index

CPIX Consumer Price Index Excluding Mortgage

CSPS Community, Social and Other Personal Services

DM District Municipality

Dur. Goods Durable Goods

EAP Economically Active Population

EC Eastern Cape

EGW Electricity, Gas and Water

EMEs Emerging Market Economies

Equip. Equipment

FCEH Final Consumption Expenditure by Household

FET Further Education and Training

FIBS Finance, Insurance and Business Services

GDP Gross Domestic Product

GDP_R Gross Domestic Product by Region

GGS General Government Services

GVA Gross Value Added

HDI Human Development Indicator

HIV Human Immune Virus

ILO International Labour Office

IMF International Monetary Fund

Inv. Investment

ITC International Trade Classification

MAN Manufacturing

MDG Millennium Development Goal

MPC Monetary Policy Committee

MQ Mining and Quarrying

NCA National Credit Act

Non-Dur. Goods Non-Durable Goods

OGSS Other General Government Services

PPI Producer Price Index

ABB

REVI

ATIO

NS

III

PPP Public-Private Partnerships

Priv. Private

Prop. Cons. Propensity to Consume

Prop. Sav. Propensity to Save

PSBR Public Sector Borrowing (Budget) Requirement

Pub. Public

Q-Q/Q-on-Q Quarter to Quarter / Quarter on Quarter

SA South Africa

SARB South African Reserve Bank

SEC Sector

Semi- Dur. Goods Semi-Durable Goods

Serv. Services

STATSSA Statistics South Africa

TSC Transport Storage and Communication

US United States

VCI Visual Condition Index

WRTCA Wholesale & Retail Trade, Catering and Accommodation

IV

TABl

ES

SEcTION 2Table 2.1: South African Gross Domestic Product, 2007-2013

Table 2.2: Final Consumption Expenditure by Household, 2008-2013

Table 2.3: Public Fixed Investment, 2008-2013

Table 2.4: Private Fixed Investment, 2008-2013

Table 2.5: Labour Market, 2007-2013

Table 2.6: Price Inflation, 2007-2013

Table 2.7: Monetary Sector and Exchange Rate, 2007-2013

Table 2.8: Public Finance, 2006/07-2011/12

SEcTION 3Table 3.1: Gross Domestic Product of the Eastern Cape, 2007-2013

Table 3.2: Total Gross Domestic Fixed Investment of the Eastern Cape, 2007-2013

Table 3.3: Machinery and Other Equipment Investment in the Eastern Cape, 2007-2013

Table 3.4: Building and Construction Investment in the Eastern Cape, 2007-2013

Table 3.5: Transport Equipment Investment in the Eastern Cape, 2007-2013

Table 3.6: Final Consumption Expenditure by Households in the Eastern Cape, 2007-2013

Table 3.7: Consumption of Durable Goods in the Eastern Cape, 2007-2013

Table 3.8: Consumption of Non-durable Goods in the Eastern Cape, 2007-2013

Table 3.9: Consumption of Semi-durable Goods in the Eastern Cape, 2007-2013

Table 3.10: Consumption of Services in the Eastern Cape, 2007-2013

Table 3.11: Consistent Major Export Contributors in the Eastern Cape, 1995-2008

Table 3.12: Consistent Major Import Contributors in the Eastern Cape, 1995-2009

Table 3.13: Eastern Cape Primary Sector Average Growth, 1995-2008

Table 3.14: Eastern Cape Secondary Sector Average Growth, 1995-2008

Table 3.15: Eastern Cape Tertiary Sector Average Growth, 1995-2008

Table 3.16: Key Labour Market Indicators in Eastern Cape, 3Q2008 – 3Q2009

Table 3.17: Eastern Cape and South Africa Labour Market, 1Q2000 – 2Q2009

Table 3.18: Employment by Province, 3Q2008 – 3Q2009

Table 3.19: Formal Employment by Industry in Eastern Cape, 1Q2000 – 2Q2009

Table 3.20: Informal Employment by Industry in Eastern Cape, 1Q2000 – 2Q2009

Table 3.21: Employment by Occupation in the Eastern Cape, 2004-2008

Table 3.22: Unemployment Rate and Number by province, 3Q2008 – 3Q2009

Table 3.23: Labour Remuneration by Gender in Eastern Cape, 2008

Table 3.24: Labour Remuneration by Age Group in Eastern Cape, 2008

Table 3.25: Labour Remuneration by Industry in Eastern Cape, 2008

SEcTION 4NB. The following tables are applicable to all district municipalities and the metropolitan.

Table: Population by Age Group, 1995-2009

Table: People affected by HIV, 2000-2007

Table: Household Income and Expenditure, 1995-2009

Table: Sectoral Growth, 1995-2008

Table: Access to Water, 1995-2009

Table: Access to Energy, 1995-2009

Table: Access to Sanitation, 1995-2009

Table: Access to Telephones, 1995-2008

V

Table: Access to Refuse, 1995-2009

Table: Access to Housing, 1995-2009

Table: Formal employment by sector, 1995-2008

Table: Informal and Formal Employment by Skill, 1995-2008

Table: Dependency Ratio, 1995-2009

Table: Level of Education, 1995-2009

Table: Literacy level, 1995-2009

Table: People in Poverty, 1995-2009

Table: Distribution of Households by Income, 1995-2009

Table: Human Development Indicator, 1995-2009

Table: Urbanisation, 1995-2009

SEcTION 1Figure 1: Conceptual Model of the Eastern Cape Economy

SEcTION 3Figure 3.1: Distribution of the Eastern Cape’s Population by Age and Gender, 2009

Figure 3.2: Gross Domestic Product of Eastern Cape

Figure 3.3: Growth Rate of Investment in the Eastern, 1996-2013

Figure 3.4: Household Consumption in the Eastern Cape, 1996-2013

Figure 3.5: Growth Rate of Consumption of Durable Goods -

Eastern Cape vs. S.A., 1996-2008

Figure 3.6: Growth Rate of Consumption of Non-durable Goods -


Figure 3.7: Growth Rate of Consumption of Semi-durable Goods -


Figure 3.8: Growth Rate of Consumption of Services - Eastern Cape vs. S.A.,

1996-2008

Figure 3.9: Propensity to Consume, Eastern Cape vs. S.A., 1995-2009

Figure 3.10: Propensity to Save Eastern Cape vs. S.A., 1995-2009

Figure 3.11: Balance of Trade of the Eastern Cape, 1995-2008

Figure 3.12: Industry Share of the Eastern Cape Output, 1995-2008

Figure 3.13: Industry Employment share of the total in Eastern Cape, 1Q2000 – 2Q2009

Figure 3.14: Composition of Employment by Skills

Figure 3.15: Labour Remuneration per Income Category in the Eastern Cape

1995 – 2009

SEcTION 4Figure 4.1: GVA Contribution for Amatole, 1995-2008

Figure 4.2: GVA Contribution for Alfred Nzo, 1995-2008

Figure 4.3: GVA Contribution for Cacadu, 1995-2008

Figure 4.4: GVA Contribution for Chris Hani, 1995-2008

Figure 4.5: GVA Contribution for Nelson Mandela, 1995-2008

Figure 4.6: GVA Contribution for O.R. Tambo, 1995-2008

Figure 4.7: GVA Contribution for Ukhahlamba, 1995-2008

FIG

URE

S

VI

Ac

KNO

WlE

DG

EMEN

TS

This publication was commissioned by the Honorable MEC Mcebisi Jonas, and

prepared by the CDC Research Unit under the overall guidance of Njabulo Sithebe.

Invaluable comments were received from Sybert Liebenberg, Ronney Ncwadi,

Phumla Ndaba, Pepi Silinga, Khwezi Tiya and Idriss Mouchili.

TEAMNjabulo Sithebe (Head: CDC Research Unit)

Senzeni Mtetwa (Senior Economist / Study Leader)

Semiyou Rafiou (Senior Economist / Study Leader)

Nonesi Golotile (Research Assistant)

Mawande Jiyana (Research Assistant)

Nomzamo Kolo (Research Assistant)

Unathi Lutshaba (Research Assistant)

Vukani Nkasa (Research Assistant)

Vuyokazi Tyida (Research Assistant)

VII

THE GlOBAl EcONOMYIn 2008, the growth of the world economy fell from 3.8 percent in 2007 to 2.2 percent.

At the beginning of the year 2009, the common expectation was that global growth

would slow further to reach the lowest rate since World War II. Global output and trade

plummeted in the last months of 2008 as the contagion set off by the United States (US)

financial crisis spread from the developed world to emerging economies.

The world economy has started to enter recovery due to high-frequency indicators

pointing to stronger growth in the second half of the year and global activity rising by

3 percent during the second quarter of 2009, from a 6.5 percent contraction in the first

quarter. However, employment continues to drop, household wealth continues to decline

and consumer demand continues to go down owing to the high rate of bankruptcies

in companies. Worldwide, disruption in the provision of credit has curtailed household

spending and business investment.

THE SOUTH AFRIcAN EcONOMYIn the mid 2000s, South Africa experienced unprecedented levels of growth which were

underpinned by favorable external conditions coupled with strong domestic demand and

rapid credit extension. These conducive conditions raised growth to 5 percent on average

in 2004 to 2007 and lowered the rate of unemployment by approximately 5 percentage

points, while strengthening public finances and maintaining single-digit inflation, and

improving external reserves.

Despite these positive results yielded by prudent economic policies, South Africa still

faces a number of medium-term challenges. The country’s output growth has remained

lower than in many Emerging Market Economies (EMEs), even during the economic

expansion. Strong employment growth over the same period failed to bring down a very

high unemployment rate of over 23 percent despite the lower labor-force participation

compared to other emerging economies. Another challenge that plagued South Africa

was increasing income inequality, which is still among the highest in the world.

Output and demand remain weak in spite of the slow recovery in the economy. In the

fourth quarter of 2008, output contracted by 1.8 percent quarter-on-quarter (Q-on-Q)

and by more than 6.7 percent in the first quarter of 2009. The annual output for 2009 is

expected to contract further by about 2 percent, with a slow recovery towards the end of

the year. It would not be until 2011 before output starts to show strong signs of recovery,

when it is expected to tread at about 3.8 percent before climbing to about 4.2 percent in

2012 and 4.5 percent in 2013. While the projected growth may be disappointing in light of

the experience of the mid-2000s, it is still encouraging given the severity of the external

shocks.

THE EASTERN cAPE EcONOMYThe Eastern Cape output is expected to remain fragile in spite of the slow recovery in the

economy. The outlook shows that by 2010, the Eastern Cape Province would again start

seeing positive growth as global recovery and consumption levels improve to those last

seen before the financial crisis. The province’s output would start showing relative strong

signs of recovery by 2011, when it is expected to tread at about 3.5 percent before rising

ExEc

UTI

VE S

UM

MA

RY

VIII

ExEc

UTI

VE S

UM

MA

RY

to about 3.9 percent in 2012 and 4.2 percent in 2013.

Growth in fixed investment spending contracted in 2009, registering an annual growth

rate of 2.04 percent from 12.44 percent in 2007. Nevertheless, while the growth rates

in investment in assets remain relatively strong, the trend is clearly declining. While

total fixed investment spending for 2009 is likely to disappoint, forecasts pronounce a

rebound in 2011, 2012 and 2013 due to government’s investment plan. In line with the

total investment in the Eastern Cape, machinery and other equipment would contract in

2009 and bounce back from 2010 onwards due to the global economic recovery and rising

investment confidence.

Building and construction was the second-largest contributor to the total gross domestic

fixed investment in the Eastern Cape for the past 14 years. In 2009/10, a decline is

expected as most construction projects would be completed for the 2010 World Cup.

Given the rural development priorities of national government, investment in building and

construction is expected to start showing signs of improvement from 2011 and pick up

further in 2012 before declining slightly in 2013.

The average annual contribution of transport equipment to the total gross domestic fixed

investment was about 14 percent between 1995 and 2008. In 1996, this sector of fixed

investment observed negative growth of about minus 2 percent associated with the first

South African currency crisis, before an all-time low of approximately minus 27 percent

in 1999 from 11 percent growth the previous year. This sector proves susceptible to both

domestic and international macroeconomic shocks. Projections show that investment in

transport equipment would improve from 2009 onwards, with robust growth rates from

2010 to 2013.

The Eastern Cape is ranked fourth in terms of consumption expenditure after KwaZulu-

Natal. The total spending growth in the province declined from 8.9 percent in 2006 to 8.6

percent in 2007, before plummeting to 0.9 percent in 2008 and is expected to contract by

0.7 percent in 2009. Final consumption is expected to start improving from 2010, given

the easing in monetary policy and the improving economic climate.

STRATEGIc INITIATIVESThe Eastern Cape Province faces a number of challenges such as underdevelopment,

high unemployment, high dependency ratios, high poverty levels, and a skewed economic

structure. This is a province which is more than 60 percent rural and yet is driven by

the tertiary sector, with a total contribution of more than 70 percent to the economy

– a serious anomaly. There is a dire need to reconfigure the structure of the provincial

economy. The starting point in this undertaking is to consider the natural comparative

advantage of the province such as land endowment and favourable climatic conditions.

The province needs to revive its primary sector and invest in market-oriented agricultural

infrastructure. This includes infrastructure that supports on-farm production (irrigation,

energy, transportation, pre- and post-harvest storage), ensures efficient trading and

exchange (telecommunications, covered markets), adds value to the domestic economy

(agro-processing and packaging facilities), and enables produce to move rapidly and

efficiently from farmgate to processing facilities, and on to wholesalers (transportation

and bulk storage).

IX

NB. All figures are at constant 2000 prices

NATIONAl

SOUTH AFRIcAN GROSS DOMESTIc PRODUcT FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 5.1 3.1 -2.0 2.0 3.8 4.2 4.5

GDP* (Rm) 1,233,930 1,271,717 1,246,111 1,270,787 1,319,577 1,375,619 1,437,222

FINAl cONSUMPTION ExPENDITURE BY HOUSEHOlD FOREcASTS

2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) -2.2 2.3 3.8 4.2 4.5

FCEH* (Rm) 864,346 845,330 864,773 897,633 935,335 977,426

PUBlIc FIxED INVESTMENT FOREcASTS

2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 7.1 5.7 7.5 7.8 -0.1

Gross Fixed Pub. Inv.* (Rm) 95,379 102,182 108,017 116,123 125,181 125,038

PRIVATE FIxED INVESTMENT FOREcASTS

2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 1.3 1.3 3.8 5.6 5.2

Gross Fixed Priv. Inv.* (Rm) 189,416 191,902 194,430 201,896 213,221 224,206

PRIcE (ANNUAl PERcENTAGE cHANGE)

FOREcASTS

2007 2008 2009 2010 2011 2012 2013

CPI inflation (annual average) 7.1 11.5 7.1 5.9 5.1 4.4 4.5

GDP deflator 9 10.8 7.5 8.6 7.1 6 5.9

PPI (annual average) 10.9 14.3 1.4 5.3 5.8 5.6 5.5

MONETARY SEcTOR AND ExcHANGE RATE FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Repo Rate (year-end %) 11 11.5 7 8.5 7 7 7

USD / ZAR (annual average) 7.06 8.25 8.62 8.26 8.58 9 9.44

EUR / ZAR (annual average) 9.66 12.13 11.7 11.5 12.15 13.12 13.5

lABOUR MARKET (ANNUAl PERcENTAGE cHANGE)

FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Population Growth Rate 1.1 1.1 1.07 1.04 1.04 1.05 1.05

Unemployment Growth Rate 22.7 21.9 24.9 24.3 23.4 22.1 20.7

Employment Growth Rate 0 10.2 1.1 0.4 1.4 1.8 1.8

Nominal Unit Labour Cost 4.2 11.5 9.2 8.6 5.3 5.2 5.1(excluding agriculture sectors)

Labour Productivity 2.4 1.1 0.6 0.9 2.3 2.3 2.5 (excluding agriculture sectors)

FORE

cA

STS

X

PUBlIc FINANcE DATA FOREcASTS

2006/7 2007/8 2008/9 2009/10 2010/11 2011/12

Feb 2008 Revised Actual Actual Budget Estimates Medium Term Estimates

National Government (Rb)

Deficit ( - ) / Surplus ( + ) 11 18.3 14.3 -22.8 -95.6 -83.3 -67.7

Revenue 481.2 559.8 625.4 611.1 643 7.9.1 781.2

Expenditure 470.2 541.5 611.1 633.9 738.6 792.4 849

As a percentage of GDP

Deficit ( - ) / Surplus ( + ) 0.6 0.9 0.6 -1 -3.9 -3.1 -2.3

State Debt Cost 2.9 2.6 2.2 2.4 2.2 2.2 2.3

Total Net Loan Debt 26.4 23.4 19.7 22.6 25.6 27.1 27.4

PSBR* -0.3 -0.6 1.2 3.9 7.5 6.5 5.3

FORE

cA

STS

EASTERN cAPE

GDP_R FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 2.7 -2.4 1.6 3.5 3.9 4.2

GDP_R* (Rm) 96,704 99,269 96,922 98,499 101,954 105,973 110,424

GROSS DOMESTIc FIxED INVESTMENT (GDFI) FOREcASTS

2007 2008 2009 2010 2011 2012 2013

GDFI Growth Rate (%) 7.3 2.7 2 8.1 9.4 7.3

GDFI share of GDP (%) 19.2 20 21.1 21.1 22.1 23.3 24

GDFI (Rm) 18,521 19,879 20,408 20,825 22,521 24,646 26,453

GROSS DOMESTIc FIxED INVESTMENT: MAcHINERY AND OTHER EqUIPMENT FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 10 -0.2 7.6 10.8 9.7 10

Machinery and Other Equipment (Rm) 7,418 8,157 8,137 8,852 9,696 10,637 11,696

GROSS DOMESTIc FIxED INVESTMENT: BUIlDING AND cONSTRUcTION FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 11.4 3 -0.9 0.6 8.3 7.6

Buildings and Construction Works (Rm) 7,670 8,545 8,797 8,721 8,774 9,505 10,231

GROSS DOMESTIc FIxED INVESTMENT: TRANSPORT EqUIPMENT FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) -3.9 1.8 3.8 10.3 11.3 9.2

Transport Equipment (Rm) 2,910 2,796 2,847 2,955 3,260 3,629 3,962

XI

FINAl cONSUMPTION ExPENDITURE BY HOUSEHOlDS FOREcASTS

2007 2008 2009 2010 2011 2012 2013


FCEH share of GDP (%) 76.4 75.1 76.4 78 79 80.1 81.4

FCEH (Rm) 73,887 74,544 74,058 76,797 80,576 84,931 89,845

DURABlE GOODS: TOTAl FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 3.7 1.1 7.2 5.4 4.5 3.1

Durable Goods (Rm) 8,584 8,903 8,999 9,645 10,168 10,621 10,949

NON-DURABlE GOODS: TOTAl FOREcASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 1 -3.2 2.9 4.2 5.2 6.2

Non-durable Goods (Rm) 26,211 26,477 25,631 26,376 27,486 28,917 30,700

SEMI-DURABlE GOODS: TOTAl FOREcASTS

2007 2008 2009 2010 2011 2012 2013


Semi-durable Goods (Rm) 13,270 13,162 14,002 14,925 15,977 17,510 19,214

SERVIcES: TOTAl FOREcASTS

2007 2008 2009 2010 2011 2012 2013


Services (Rm) 25,822 26,001 25,425 25,852 26,944 27,883 28,982

1

1 GLOBAL ECONOMIC OUTLOOK AND FORECAST METHODOLOGY

1.1 GLOBAL ECONOMIC OUTLOOKThe economic data showed that in 2008, the growth of the world economy fell from 3.8

percent in 2007 to 2.2 percent. At the beginning of the year 2009, the common expectation

was that global growth would slow further this year to reach the lowest rate since World

War II. Global output and trade plummeted in the last months of 2008 as the contagion set

off by the United States (US) financial crisis spread from the developed world to emerging

economies.

According to the October 2009 World Economic Outlook, the world economy has started

to enter recovery due to high-frequency indicators pointing to stronger growth in the

second half of the year and global activity rising by 3 percent during the second quarter of

2009 from a 6.5 percent contraction in the first quarter. However, employment continues

to drop, household wealth continues to decline and consumer demand continues to go

down owing to the high rate of bankruptcy in companies. Worldwide, disruption in the

provision of credit has curtailed household spending and business investment.

1.1.1 USA

The US economy is forecast to contract by 2.75 percent in 2009 due to the sharp

contraction during the first half of the year. As 2009 progressed, output data confirms that

the U.S. economy is stabilising owing to the result of the monetary, financial and fiscal

policy interventions which helped stabilise consumer spending, the housing default rate

and financial markets. Despite these positive upshots, business investment continues to

sink, the savings rate is still climbing and the markets remain stressed.

These events will collectively depress investment and consumption. Intertwining these

occurrences with the impact of rising unemployment, it is apparent that growth will remain

lethargic, reaching 1.5 percent for the entire year of 2010. The short-lived nature of the

fiscal stimulus and subdued growth in tradingpartnereconomies also play a considerable

role in depressing growth further. A number of developments, including the rise of more

than 10 percent in the level of unemployment in the second half of 2010 and a core

inflation of below 1 percent through the greater part of the year, are expected.

2

There is great uncertainty around the near-term outlook for the economy, which

is underpinned by the scale of shocks and the hazy outlook for the rest of the world.

However, the strong policy response and a rapid recovery in emerging markets could

reverse the uncertainty that has plagued the U.S. economy, leading to a healthy rise

in confidence, improving financial conditions and promoting strong aggregate demand

growth. Although the economic turmoil is easing, downside risks still remain a concern.

In particular, continued cutting down on household debt, also known as deleveraging,

and rising unemployment, may have serious ramifications on consumption than initially

forecast. Also, accelerating corporate and commercial property defaults could delay the

improvement in financial conditions.

The medium-term outlook sees potential growth likely to fall below 2 percent for a

considerable time. History tells that previous financial crises are followed by large

permanent output losses relative to precrisis trends due to the nature of impaired financial

systems, which generally take time to heal. In return, this slows down investment and

innovation. High cyclical unemployment could also raise structural unemployment. On the

demand side, although the personal saving rate has already climbed to about 5 percent, it

may have to rise further given the need to rebuild the balance sheets of households.

The fiscal legacy of the crisis is a high and rising debt trajectory that could become

unsustainable without significant medium-term measures. Such financial crises give birth

to high and rising debt trails that also pose the risk of being unsustainable if no adequate

medium-term measures are put in place.

Forecasts show that deficits will be around 10 percent of gross domestic product

(GDP) for the period 2009/10 and 2010/11. However, it’s not all gloom and doom; the

economic climate shows that these deficits will gradually decline to levels below 10

percent afterwards, while the level of gross government debt will continue rising swiftly,

reaching 108.2 percent of GDP by 2014 from 84.8 percent of GDP in 20091. In light of

the impending healthcare and pension pressures brought about by aging population,

this is bad news for the US economy. These pressures are made more transparent by

the current budget proposal, which augments medium-term forecasts by using growth

assumptions that tend to be optimistic. Adjustments may become a necessity to ensure

long-term fiscal sustainability, particularly on the revenue side, considering that non-

defence optional spending is approaching historical lows. The critical state of the U.S.

healthcare system cannot be ignored. It is plagued by inefficiencies, as proven by the fact

that similar healthcare results are achieved at different costs across the U.S. states. This

necessitates that coverage be expanded in a budget-neutral manner, and that measures

to curb the rate of cost growth be put in place to help preserve debt sustainability.

1.1.2 JAPAN

Japan’s ongoing dependence on exports is the primary cause of the abrupt impact of the

global downturn on its economy. As the worldwide credit crunch pinched off external

demand, export of goods and services plunged by 35 percent in December 2008 from

the previous year. Industrial output fell by 9.4 percent in December following a decrease

of 8.5 percent in November. Industries which were particularly hit are transport (including

automotive), machinery and electrical-equipment manufacturing.

1 IMF World Economic Outlook, October 2009

3

Firmly entrenched in recession territory last year, Japan’s economy returned to growth

(2.3 percent) in the second quarter of 2009 after a range of fiscal stimulus measures by

the government. On average, Japan’s economy is expected to shrink by 5.4 percent in real

terms in 2009, with sluggish growth of 1.7 percent in 2010. Inflation will remain negative

until 2012, given the significant slack in the economy.

1.1.3 CHINA

China’s economic growth rate would slow in 2009. GDP growth fell to 6.8 percent in the

fourth quarter of 2008, down from 9 percent in the third quarter. Growth is forecast to

chug along at 8.5 percent in 2009 and 9.0 percent in 2010. While this appears positively

robust compared to the rest of the world, it is less than the strong 13-percent rate the

country achieved in 2007. Exports to the West have slowed, but a number of other factors

have contributed to China’s economic deceleration. The housing construction industry has

come to a virtual standstill as a result of the government’s efforts to deflate a potential

real-estate bubble. The government has introduced a number of fiscal stimulus measures,

such as transportation investment (which is expected to be expanded by 70 percent in

2009), public housing spending, rebates on purchases of consumer goods and additional

healthcare. The inflation rate is expected to be around an annual average of minus 0.1 in

2009 and 0.6 percent in 2010. The current-account surplus would remain substantial, but

is likely to fall to 7.8 percent of GDP in 2009 and rise to 8.6 percent in 2010.

1.1.4 INDIA

India’s economy is expected to slow in 2009. After dropping from a growth rate of 9.4

percent in 2007 to 7.3 percent in 2008, 2009 is expected to post gains of only 5.4 percent,

while the growth rate for the coming year is expected to be about 6.4 percent. India

continues to face inflationary pressures from high resource utilisation and strong credit

growth. The inflation rate jumped from 6.4 percent in 2007 to 8.3 percent in 2008, and is

expected to reach 8.7 percent in 2009 before decreasing slightly to around 8.4 percent in

2010. The country’s current-account balance stood at minus 2.2 percent of GDP in 2008. It

is expected to be flat in 2009 and drop to minus 2.5 percent of GDP in 2010. In an attempt

to boost liquidity, the government has eased restrictions on lending in the property sector

and increased the availability of export credit finance.

In summary, the risks associated with growth are steadily becoming domesticated. Most

of the improvement in activity is still owed to measures that could turn out to be short-

lived, such as the rebounding capital markets, adjustment in inventory, and expansionary

fiscal and monetary policy. The risk that these factors bring is a recovery that will not be

self-sustaining unless activity spreads to other regions. On the positive side, the policy

stimulus in China could shore up recoveries in other parts of Asia.

The greatest challenge that still lies ahead is the timing of the withdrawal of policy support.

Two questions that would need careful consideration are when and how to withdraw

policy support while ensuring a successful transition to more balanced, medium-term

growth. The dependence of Asia on export demand has resulted in the global imbalances

that have made the region susceptible to developments in demand. This in turn would

force the growth composition to be driven by internal demand as global demand proves

unlikely to pick up in the short-term.

4

1.1.5 MAJOR EUROPEAN UNION MEMBER STATES

The economies of the European Union (EU) were in deep recession at the beginning of the

year, and are currently showing signs of slow recovery. In 2007, when the financial crisis

was surging through the United States and Britain, the EU was enjoying comparatively

faster growth and lower unemployment. All of this changed in late 2008, when the credit

crunch stormed Europe’s shores just when businesses and consumers needed access

to credit to tide them over the tough times. Also, the economy was hit by high energy

prices, an overvalued currency and a sharp fall in domestic demand from the EU’s trading

partners. The EU GDP contracted less than expected in the second quarter of 2009.

Countries like France and Germany showed positive growth, while GDP in the United

Kingdom contracted less than expected.

After expanding by an estimated 1.2 percent in 2008, the German economy is forecast to

decline by 5.3 percent in 2009 before rebounding to 0.3 percent in 2010. During the crisis,

the German government introduced a series of fiscal stimulus packages to boost growth

and stimulate consumption. Consumer price inflation is expected to fall from its 2008 level

of 2.8 percent to 0.1 percent in 2009 and 0.2 percent in 2010. Also, the unemployment

rate is expected to rise from 8 percent in 2009 to 10.7 percent in 2010.

The economy of the United Kingdom, after stalling in 2008 with a growth rate of 0.7

percent, is expected to contract by 4.4 percent in 2009 before rebounding to 1.7 percent in

2010. Squeezed by the credit crunch and falling consumer demand, businesses have shed

inventories, halted investment and shed staff. Consumer spending has been bolstered

by falling energy prices and declining mortgage rates. However, rising unemployment

and an increase in precautionary saving would exert downward pressure on household

expenditure. Inflation stood at 3.6 percent in 2008 but would fall to an average of 1.9

percent in 2009 and 1.5 percent in 2010. The unemployment rate is estimated to rise from

7.6 percent in 2009 to 9.3 percent in 2010.

The economy of France is expected to decline to minus 2.4 percent in 2009, from a

growth rate of 0.3 percent in 2008. A gradual recovery is expected to take root in 2010

with 0.9 percent positive growth, but it would depend on the stabilisation of financial

markets and a resurgence of global activity. The recent growth observed is the fruit of

the French government’s plans to alleviate the impact of the slowdown through stimulus

measures in the form of higher public investment, the shoring-up of corporate liquidity and

support for unemployed and low-paid workers. France’s unemployment rate is expected

to rise from 9.5 percent in 2009 to 10.3 percent in 2010. Consumer-price inflation is also

expected to increase from 0.3 percent in 2009 to 1.1 percent in 2010.

1.1.6 BRAZIL

Brazil’s economy is forecast to decline significantly in 2009, compared with the 5.1 percent

growth pace of 2008. It is expected to be around minus 0.7 percent before entering positive

territory once again, with an expected growth rate of around 3.5 percent in 2010. The

Brazilian economy is expected to pick up faster than the rest of South America because

of its large domestic market, high degree of integration into the world and diversification

of its export market. Also, inflation is expected to slow down from 4.8 percent in 2009 to

4.1 on average in 2010. However, the current-account deficit is expected to increase from

minus 1.3 in 2009 to minus 1.9 in 2010.

5

1.2 THE THEORETICAL FRAMEWORK OF THE SOUTH AFRICAN MACRO-MODELThe model used is a Keynesian demand-side model, and was built to provide a theoretical

structure for understanding the linkages between key macro-economic variables. The

following four sectors of the South African economy are modelled:

The real sector, including the external sector, estimates private consumption and •

investment, total government expenditure, and exports and imports to determine an

aggregate demand function for the economy;

The monetary sector deals with the estimation of the main monetary variables, •

namely the broad monetary aggregate;

The price-sector estimates equations, which try to capture the influencing factors on •

the domestic price level; and

The labour sector could not be estimated because of the poor quality of available •

data.

The starting point for specifying the real sector of a comprehensive macro-econometric

model for any economy is the national identity from the national accounts. The national

identity, or aggregate demand for domestic consumption, is the sum of consumption,

investment, government expenditure and the trade balance:

Y = C + I + G + (X - M)

where,

Y is the real GDP; C is the final consumption expenditure by household; I is the real

private investment expenditure and G is the real government expenditure. X denotes real

exports and M real imports. Additional equations provide links between these demand-

side equations. These linking equations would include the consumer-price index and the

producer-price index; the GDP deflator (measuring the general price level in the economy);

various Rand exchange rates; interest rates; and employment and the associated wages.

1.3 EASTERN CAPE MODEL CONCEPTUALISATIONThe Eastern Cape economy is not modelled explicitly due to data constraints, but is

inferred from the core target variables generated by the national macro-economic model.

The signs between the variables specify the nature of the expected relationship between

those variables, with the direction of the arrow representing the direction of causality

among variables.

The simple conceptualised model is structured taking into account that the province is part

of the national economy. For this reason, any national developments regarding interest

rates, exchange rates, imports and exports, and the growth rates of other provinces,

would have an impact on the Eastern Cape economy. The increase in the growth rate

and employment would lead to an increase in workers’ incomes, which would stimulate

provincial demand, investment and hence national economic growth. The continued rise

in employment, if sustainable, would reduce the unemployment rate, which in turn would

cumulatively reduce the poverty rate. The growth rate of the province’s output, if it was

more than the national average, would lead to an increase in the province’s contribution

to national output. The rise in Eastern Cape employment would lead to an increase in the

6

province’s contribution to national employment. However, the growth in Eastern Cape

demand, combined with the demand from other provinces, would push inflation up and

lead to an increase in interest rates owing to the inflation-targeting policy adopted by the

South African Reserve Bank. The impact of these rate increases could lower the national

growth rate, which in turn might affect growth in the Eastern Cape.

Figure 1 : Conceptual Model of the Eastern Cape Economy

EC GDPContribution

National PublicInvestment

National GrowthRate

Interest Rate Exchange Rate

Trade

EC GrowthRate

EC Demand

EC Employment Growth

Unemployment Rate

Poverty Rate

Inflation Rate

9

2 NATIONAL ECONOMIC PERFORMANCE AND OUTLOOK

2.1 OVERVIEW OF CURRENT SITUATIONIn the mid 2000s, South Africa experienced unprecedented levels of growth which were

buoyed by favorable external conditions, coupled with strong domestic demand and rapid

credit extension. These conducive conditions raised growth to 5 percent on average in

2004 to 2007 and lowered the rate of unemployment by approximately 5 percentage

points, while strengthening public finances, maintaining single-digit inflation and improving

the level of external reserves. The favorable economic climate is attributed to sound

macroeconomic policies, which were firmly supported by a consistent and transparent

policy framework.

Despite these positive results, South Africa still faces a number of medium-term

challenges. The country’s output growth has remained lower than in many Emerging

Market Economies (EMEs), even during the 2004 to 2007 economic expansion. Strong

employment growth over the same period failed to bring down a very high unemployment

rate of over 23 percent, even though labour-force participation remains lower than in

other emerging economies. Another challenge that plagued South Africa was increasing

income inequality, which, despite modest improvement, remains among the highest in

the world.

Output and demand remain weak in spite of the slow recovery in the economy. In the

fourth quarter of 2008, output contracted by 1.8 percent quarter-on-quarter (Q-on-Q) and

by more than 6.7 percent in the first quarter of 2009. The contraction in output can be

traced to the dramatic slump in manufacturing and mining output. Residential property

prices continued trending downwards. Consumer spending has fallen, and the short-

term outlook for South Africa remains constricted. Annual output for 2009 is expected to

contract further by about 2 percent, with a slow recovery towards the end of the year as

shown in Table 2.1. The recovery is supported by policy measures that government has

put in place aimed at countering the cycle of the crisis, which have also been dampened

by the weak recovery of demand in trading-partner countries. Projections show that by

2010, the country would again start seeing positive growth as global recovery improves

and commodity prices recede to levels last seen before the crisis. It would not be until

2011 before output starts to show strong signs of recovery, when it is expected to tread

10

SOUTH AFRICAN GROSS DOMESTIC PRODUCT FORECASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 5.1 3.1 -2.0 2.0 3.8 4.2 4.5

GDP* (Rm) 1,233,930 1,271,717 1,246,111 1,270,787 1,319,577 1,375,619 1,437,222

Source: Own Calculation and Quantec Research *Constant 2000 prices

at about 3.8 percent before climbing to about 4.2 percent in 2012 and 4.5 percent in 2013.

The potential output growth of about 4 percent is underpinned by private investment

(which is not expected to continue to grow at the rates seen from 2007 to 2008), weak

demand from trading-partner countries, commodity prices and diminished capital flows

for EMEs. While the projected growth may be disappointing in light of the experience of

the mid-2000s, it is still encouraging given the severity of external shocks. Medium-term

growth is sustained by large public infrastructure investment programs aimed at reducing

bottlenecks in transport infrastructure, electricity and ports, which are all likely to impede

growth as the economy recovers.

Table 2.1: South African Gross Domestic Product, 2007 - 2013

2.2 CONSUMER SPENDINGAgainst the backdrop of a sharply weakening economy, together with weak business-

and consumer-confidence indices, final household consumption expenditure has fallen

substantially in South Africa. The economy lost more than 200 000 jobs in early 2009,

leading to an increase in the unemployment rate of 23.5 percent in the first quarter of

2009, from a low of 21.9 percent in the fourth quarter of 2008. These job losses have

throttled household consumption expenditure.

Household expenditure also declined due to stringent extension of credit in light of the

National Credit Act (NCA) implemented in mid-2007, the global credit crisis, the high debt

burden, increased interest rates aimed at curtailing extreme spending, rising prices which

also corroded the disposable income of consumers and the national economic downturn.

Collectively, these events have dampened appetite for new debt, as shown in the growth

rate for credit, which declined from 23.1 percent in July 2007 to 4 percent in July 2009.

Spending decreased from 4 percent in 2008:Q1 to minus 1.7 percent in 2009:Q1, and

again from 3.4 percent in 2008:Q2 to minus 3.5 percent in 2009:Q2 as the impact of the

crisis began to be felt. Projections indicate that the figure would drop to about minus 3.9

percent in 2009:Q4 compared to about 0.5 percent in the 2008:Q4. Household expenditure

was projected to decrease to about 67.8 percent of GDP in 2009 and then pick up again by

not more 1 percent of GDP. Overall, household expenditure is expected to remain weak

throughout 2009, with some slight improvement envisaged in 2010, when the annual

consumption expenditure is expected to average 2.3 percent compared to minus 2.2

percent in 2009. In 2011, 2012 and 2013, consumer spending is projected to average, on

an annual basis, 3.8 percent, 4.2 percent and 4.5 percent respectively (see Table 2.2).

11

FINAL CONSUMPTION EXPENDITURE BY HOUSEHOLD FORECASTS

2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) -2.2 2.3 3.8 4.2 4.5

FCEH* (Rm) 864,346 845,330 864,773 897,633 935,335 977,426


Table 2.2: Final Consumption Expenditure by Household, 2008-2013

2.3 PUBLIC FIXED INVESTMENTSouth Africa faces a grave challenge because of the ageing of its key infrastructure such

as roads, water and sanitation, and electricity, all of which requires either rehabilitation,

replacement or both. Government has responded to the imminent infrastructure crisis

by investing R787 billion over a period of 3 years. Public Fixed Investment is expected

to increase to a peak of about 9.1 percent as a percentage of GDP in 2012 in order to

relieve critical bottlenecks in electricity and transportation; this includes the purchasing

of locomotives and the upgrading of the main airports and ports. It is then expected to

gradually slow down afterwards to about 8.7 percent as a share of GDP in 2013 with

longrun fiscal multipliers. The investment would help support output in the short term,

creating jobs in 2009 and 2010 while maintaining the confidence of both domestic and

external sources of finance that fund the Public Sector Borrowing Requirement (PSBR).

Spending could also be prioritised to speed up service delivery, given the widespread

protests against the sluggish delivery of basic services. This infrastructure investment

is made possible by fiscal policies adopted during the previous economic expansion that

resulted in a sharp decline of public debt and debt-servicing costs.

PUBLIC FIXED INVESTMENT FORECASTS

2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 7.1 5.7 7.5 7.8 -0.1

Gross Fixed Pub. Inv.* (Rm) 95,379 102,182 108,017 116,123 125,181 125,038


Table 2.3: Public Fixed Investment, 2008-2013

2.4 PRIVATE FIXED INVESTMENTPrivate Fixed Investment is expected to average about 15 percent of GDP from 2009 to

2013. This reflects the conservative stance adopted by the private sector, as well as risk-

aversion and low business confidence. The sluggish growth in private fixed investment

is largely due to the cancellation of a number of planned private-sector capital-investment

programmes; deteriorating demand faced by retailers, manufacturers, dealerships and

wholesalers; limited access to credit facilities; concerns over the supply of electricity; and

a weakening residential property market as inventories and production capacities adjust

to weak demand. Figures for the second quarter of 2009 show that loans and advances to

the private sector are expected to approach low levels last seen in the 1960s. The private

sector accounts for approximately 70 percent of total fixed investment in South Africa.

The pressure experienced by the private sector can also be seen in the deceleration of

bank loans and advances to the private sector, from 7.3 percent in March 2009 to 2.2

percent in June 2009.

12

PRIVATE FIXED INVESTMENT FORECASTS

2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 1.3 1.3 3.8 5.6 5.2

Gross Fixed Priv. Inv.* (Rm) 189,416 191,902 194,430 201,896 213,221 224,206


Table 2.4: Private Fixed Investment, 2008-2013

2.5 LABOUR-MARKET CONDITIONThe unemployment rate in South Africa, which was currently estimated to be 23 percent

and forecast to average about 24.9 percent at the end of 2009, still remains obstinately high

despite the low labourforce participation compared to other emerging-market economies.

Figures published by Statistics South Africa in the Labour Force Survey show that 267 000

jobs were lost between the first two quarters of 2009, with most of the losses recorded in

private households, followed by the informal sector. This decrease in employment did not

translate into an increase in unemployment. However, it increased the number of people

who are not economically active, the majority of whom were discouraged workseekers.

When comparing the employment figures of the second quarter of 2009 to those of

the same period in 2008, there is a decrease of about 2.6 percent in the employment

numbers: an 11 000 increase in the number of unemployed persons and a 724 000

increase in the number of persons who are not economically active, 60 percent of whom

were discouraged work-seekers. As outlined above, most of the job losses were in private

households (105 000), followed by trade (59 000) and agriculture (28 000). All industries

experienced job losses, with the exception of community and social services.

When looking at the year-on-year figures, data shows that trade accounted for 143 000 job

losses, followed by manufacturing (95 000) and agriculture (80 000). Despite the overall

decline in jobs between Q2:2008 and Q2:2009, there were sectors that experienced gains,

such as community and social services (29 000), finance (23 000) and private households

(9 000). The employment growth rate is forecast to average 1.1 percent by the end of

2009, which is very low when compared to the average employment growth rate of 10.2

percent in 2008. Projections reveal that this growth rate would drop to 0.4 percent in

2010, and start picking up in 2011 to slightly above 1 percent and move closer to 2 percent

towards the end of 2012 (see Table 2.5).

The annual nominal unit labour cost in the formal non-agricultural sector of the economy

has exceeded 10 percent since 2008. It averaged 11.5 percent in 2008 after decelerating

from a peak of 13.2 percent in Q3:2008. In the private sector, the average nominal unit

labour cost decreased to 10.5 percent in 2008 till the first quarter of 2009, and to 11.7

percent in the public sector. In the private sector, the annual nominal unit labour cost

exceeded the upper band of the inflation target. Forecasts indicate that the average

nominal labour cost would fall from 2009, in line with the economic downturn, reaching

levels of about 5.1 percent in 2013.

In response to the contraction in the economy, labour productivity is forecast to decrease

to an average of 0.6 percent in 2009 due to a sharp decline in external demand and a

slump in the prices of major export commodities brought about by the global economic

13

LABOUR MARKET (ANNUAL PERCENTAGE CHANGE)

FORECASTS

2007 2008 2009 2010 2011 2012 2013

Population Growth Rate 1.1 1.1 1.07 1.04 1.04 1.05 1.05

Unemployment Growth Rate 22.7 21.9 24.9 24.3 23.4 22.1 20.7

Employment Growth Rate 0 10.2 1.1 0.4 1.4 1.8 1.8

Nominal Unit Labour Cost 4.2 11.5 9.2 8.6 5.3 5.2 5.1(excluding agriculture sectors)

Labour Productivity 2.4 1.1 0.6 0.9 2.3 2.3 2.5(excluding agriculture sectors)

Source: Own Calculation and Quantec Research

Table 2.5: Labour Market, 2007-2013

Table 2.6: Price Inflation, 2007-2013

downturn. However, in 2011, labour productivity is expected to pick up as the global

economic recovery becomes more apparent, with the demand for goods and services

returning to pre-crash levels.

2.6 INFLATIONWeak economic activity and falling commodity prices have lowered inflation, albeit at a

much slower rate than anticipated. Inflation has been out of the 3-to-6 percent target band

set by the reserve bank since 2007, and picked up to an annual average of 11.5 percent

in 2008. Consumer Price Index (CPI) inflation is expected to return to an annual average

of 7.1 percent, the rate last observed in 2007, although it still remained sticky above the

inflation target. The slower rate of disinflation reflects a combination of factors, including

the larger-than-expected exchange-rate depreciation in the fourth quarter of 2008, the

strong oil-price increases in the first six months of 2009 and retrospective wage indexation

that resulted in substantial wage increases. In 2009, annual wages grew by more than

10 percent, with entities such as Eskom recording 23 percent. Among other things that

contributed to the stubborn inflation rate was price-fixing by some companies, as noted

by the Competition Commission. The electricity tariffs requested by Eskom contributed

significantly to the high rate of producer-price inflation, despite the significant decreases

in the overall Producer Price Index (PPI) inflation, as observed in Table 2.6 below.

PRICE (ANNUAL PERCENTAGE CHANGE) FORECASTS

2007 2008 2009 2010 2011 2012 2013

CPI inflation (annual average) 7.1 11.5 7.1 5.9 5.1 4.4 4.5

GDP deflator 9 10.8 7.5 8.6 7.1 6 5.9

PPI (annual average) 10.9 14.3 1.4 5.3 5.8 5.6 5.5


2.7 MONETARY POLICY AND INTEREST RATESOn 19th July 2009, the new Governor of the South African Reserve Bank (SARB),

Ms. Gill Marcus, was appointed. Marcus is no stranger to the SARB, as she served as

Deputy-Governor from 1999 to 2004 before her departure in 2004.

14

A number of changes on the monetary policy front were observed in 2009. The inflation

measure targeted by the SARB was changed from CPIX to a revamped CPI. The revamped

CPI was reweighted with the introduction of new expenditure weights and rebased so that

2008 = 100. It also marked the introduction of the Classification of Individual Consumption

by Purpose (COICOP), which replaces the International Trade Classification (ITC).

The feeble economic activity in 2009 hampered the global prices of commodities and

helped to lower inflation, albeit at a much slower pace. Despite remaining outside the

3-to-6 percent target band since 2007, inflation is projected to return to the band only in

2010, when the annual average is expected to hover around 5.9 percent. The currency

depreciation, the increases in the oil price and wage indexation created inflationary

pressures in 2008 and 2009. With all these challenges, the SARB responded by easing

monetary policy, which resulted in a 450-basis-point cut in the repo rate between December

2008 and June 2009. These policy rate cuts have yielded positive effects in reviving the

real economy. Further rate cuts were anticipated after June 2009, but the Monetary Policy

Committee (MPC) chose to tread carefully by leaving the repo rate unchanged, arguing

that inflation-related risks still remained.

There are still a number of challenges facing South Africa. First, the current-account deficit

is expected to widen from about minus 6 percent of GDP in 2009 to about minus 7.4

percent of GDP in 2014, which would lead to South Africa relying on portfolio inflows

to make up for the deficit2. Second, the inflation-targeting framework has come under

attack from labour unions, who argue that the mandate of the SARB should be expanded

to include growth and employment. As a result of some of these burning issues, the

Minister of Finance has extended an invitation to key stakeholders to deliberate on the

pros and cons of the inflation-targeting framework currently pursued by the SARB.

Table 2.7: Monetary Sector and Exchange Rate, 2007-2013

MONETARY SECTOR AND EXCHANGE RATE FORECASTS

2007 2008 2009 2010 2011 2012 2013

Repo Rate (year-end %) 11.0 11.5 7.0 8.5 7.0 7.0 7.0

USD / ZAR (annual average) 7.06 8.25 8.62 8.26 8.58 9.00 9.44

EUR / ZAR (annual average) 9.66 12.13 11.70 11.50 12.15 13.12 13.50


2.8 FISCAL POLICYThe fiscal policies pursued in previous years, which resulted in a surplus of R14.3 billion in

the 2008/09 financial year and a consistently exceeded tax-revenue target, have yielded a

number of benefits such as: a) a counter-cyclical impact on the economic downturn, b) a

sharp decline in debt-servicing costs and c) the creation of a fiscal space to invest in public

infrastructure and widen the social network. These investment plans, which were drafted

before the global economic downturn, would relieve critical infrastructure challenges with

long-run fiscal multipliers. They are also expected to support demand in the short-term.

However, the anticipated decline in revenue collection and the increase in government

2 IMF 2009

15

spending would result in an overall Public Sector Borrowing Requirement (PSBR) of R285

billion in 2009/10, compared to R89 billion in 2008/09. If the financing requirements of

entities such as Eskom and other state-owned enterprises and municipalities are excluded,

the consolidated budget deficit in 2009/10 would amount to R184 billion, or 7.5 percent of

GDP, before contracting to 5.3 percent of GDP in 2011/12. The budget deficit is expected

to increase to 3.9 percent of GDP before it decreases to minus 3.1 percent in 2010/11.

Projections show that debt-servicing costs as a percentage of GDP would decline slightly

over the medium term, from 2.4 percent in 2008/09 to 2.2 percent in 2009/10. The PSBR

has moved from minus 0.6 percent of GDP in 2008/09 to 3.9 percent of GDP in 2009/10.

Table 2.8: Public Finance, 2006/07-2011/12

PUBLIC FINANCE DATA

2006/7 2007/8 2008/9 2009/10 2010/11 2011/12

Feb 2008 Revised Actual Actual Budget Estimates Medium Term Estimates

National Government (Rb)

Deficit ( - ) / Surplus ( + ) +11 +18.3 +14.3 -22.8 -95.6 -83.3 -67.7

Revenue 481.2 559.8 625.4 611.1 643 7.9.1 781.2

Expenditure 470.2 541.5 611.1 633.9 738.6 792.4 849

As a percentage of GDP

Deficit ( - ) / Surplus ( + ) +0.6 +0.9 +0.6 -1 -3.9 -3.1 -2.3

State Debt Cost 2.9 2.6 2.2 2.4 2.2 2.2 2.3

Total Net Loan Debt 26.4 23.4 19.7 22.6 25.6 27.1 27.4

PSBR* -0.3 -0.6 1.2 3.9 7.5 6.5 5.3

Source: SARB

17

3 THE ECONOMY OF THE EASTERN CAPE

3.1 PROFILE OF THE EASTERN CAPE

3.1.1 OVERVIEW

The Eastern Cape is located in the south-eastern part of South Africa and is divided into six

district municipalities, namely: Alfred Nzo, Amatole, Cacadu, Chris Hani, O.R. Tambo, and

Ukhahlamba, and a metropolitan area called Nelson Mandela Bay. More than 60 percent

of the province is rural, a reversal of the national urban/rural average split of 63/37, and

comprises the former homelands of Transkei and Ciskei. It produces about 70 percent

of the world’s mohair. The Eastern Cape provides easy access to four other provinces in

South Africa, namely the Western Cape, the Northern Cape, the Free State and KwaZulu-

Natal, and also shares an international border with the Kingdom of Lesotho.

The province is richly endowed with farming land. The province boasts the deepest

container terminal port in Africa, the newly built deep-water port of Ngqura, adjacent to

the Coega Industrial Development Zone (IDZ), and two other harbours located in East

London River Port and Port Elizabeth. Of the ten principal airports in South Africa, two

are located in this province: one in Port Elizabeth and the other in East London. A total

of 48 582 kilometers of South Africa’s 596 234 kilometers of road, or 14 percent of the

country’s road network, is located in the Eastern Cape. According to the Visual Condition

Index (VCI), a measure used to assess road conditions, the Eastern Cape’s VCI was 51 in

2008, which is considered fair. The condition of road infrastructure has deteriorated over

the past 20 years, falling from an index point of 69 in 1988, which is considered as good,

to 51 in 2008. The rail infrastructure also requires attention as 37 percent of it has been

abandoned, 23 percent is still in good condition, 18 percent is considered fair and 22

percent is in poor condition.

Some of the challenges the province faces include the availability of electricity, water

and sanitation infrastructure. In 2009/10, government set aside a budget of R16.8 billion

for infrastructure development in the province, which would cover roads and logistics

infrastructure, healthcare facilities, education and other social infrastructure.

On the social infrastructure side, there are 23 school districts and further education and

training (FETs) institutions in the province under the Department of Education, making the

18

total number of both school and FETs 6 078. The province has four universities: Nelson

Mandela Metropolitan, Rhodes, Walter Sisulu, and Fort Hare. Access to comprehensive

public healthcare remains one of the key social-health policies of the current administration.

This commitment is reflected by the increasing expenditure on public health. Between

the 2005/06 and 2008/09 financial years, total public health expenditure increased by

16.7 percent annually. By 2009/10, public health spending made up 9 percent of non-

interest spending and 3.7 percent of GDP (PBER, 2009). These financial commitments are

expected to be sustained over the medium term. Despite these rising financial allocations

and progress made in the delivery of public health services, the public healthcare system

continues to face significant challenges, including the increasing burden of diseases

such as HIV and TB, and slow progress towards the achievement of the Millennium

Development Goals, particularly those pertaining to child- and maternal mortality.

In spite of employment challenges in the province, sectors employing the largest proportion

of the province’s population in the second quarter of 2009 were community, social and

personal services (31.7%); wholesale (23.6%); manufacturing (14.8%) and finance,

insurance and business services (9.5%). The province’s unemployment rate dropped from

27.4 percent in the third quarter of 2008 to 26.8 percent in the third quarter of 2009, while

the number of discouraged job-seekers increased over the same period.

Eastern Cape is one of the choice destinations in South Africa, with immaculate city

recreations. In the northern part of the province lie the Drakensberg Mountains, the Great

Karoo, beaches of the Sunshine Coast and the indigenous forests of the Wild Coast.

East London and Port Elizabeth are ideal for family holidays because of their size and

welcoming environment. The province boasts, among other things, the annual winter

cultural festival that takes place in Grahamstown, premier surf spots, South Africa’s only

ski resort, a museum dedicated to Nelson Mandela, a waterfall plunging into the sea and

the rock art of the San people.

3.1.2 POPULATION SIZE AND AgE DISTRIBUTION

The Eastern Cape is the second-largest province in South Africa, covering a total land area

of 169 056 km2, that is, 13.8 percent of South Africa. With its capital, Bisho, 70 kilometers

from East London, the province is home to approximately 6.6 million people, 87.6 percent

of whom are African, 7.5 percent Coloured, 4.7 percent White and 0.3 percent Asian.

Eastern Cape is the third-most populous province in the country (13.5 percent of the total

population3) after Gauteng and KwaZulu-Natal.

The proportional size (5.3 percent or 716 000) of the population in the 5-9 age cohort is

startlingly much smaller than the proportional size (5.6 percent or 734 000) in the 0-4 age

cohort. Though the population size increases progressively all the way from 10-14 to 15-19

age cohorts, it plunges in the 20-24 to 30-34 age groups and then starts to fall at marginal

rates throughout the rest of the other age cohorts. The age distribution indicates that

children in the 10 to 19 years age cohort constitute the largest proportion (24.3 percent or

approximately 1.6 million) of the provincial population.

Collectively children between the ages of 0 and 19 years constitute about 3.1 million, or

46.9 percent of the total population in the province. If this number is added to the 20-34

3 Mid-year population estimates, 2009

19

Figure 3.1 : Figure 3.1: Distribution of the Eastern Cape’s Population by Age and Gender, 2009

Male Female

Source: Own Calculations Derived from Stats SA, Medium Variant Mid-Year Population Estimates (2008)

year age group, the resulting outcome is about 4.7 million (or 71.2 percent), implying that

the provincial population is predominantly youth. The larger proportion of approximately

59.7 percent (4 million) of the provincial population falls within the 15-65 year age bracket,

which constitutes the economically active population (EAP). This means that 40.3 percent

(2.7 million) of the population is distributed between the age categories (0-15 and 65+

years) which translate to a dependency ratio4 of 67.4 percent.

3.1.3 HIV AND AIDS

The incidence of HIV infection is one of the most important indicators of the progression

of the epidemic that needs to be monitored, particularly in the more mature stages of

the epidemic. Hence, Millennium Development Goal (MDG) number 6 has identified HIV/

AIDS as one of the diseases that require particular attention.

The Eastern Cape has the third-largest number of HIV-positive people in South Africa

(Nathea Nicolay, Metropolitan, October 2008). The epidemic is still growing quickly,

with new infections double the number of AIDS-related deaths. The Actuarial Society

of South Africa’s model shows that 11 percent of the population and one in every five

adults are estimated to be HIV-positive, and an estimated 110 000 people were in need of

antiretroviral treatment in 2008, with about 44 percent having taken up treatment.

These figures suggest that the scourge of AIDS is still rampant in the province, and this

could compromise any developmental strategy the province might have. HIV/Aids has far-

reaching effects, as it leads to an increase in the number of orphans whilst at the same

time reducing human capital in the province.

4 The dependency ratio is an age-population ratio of those typically not in the labour force (the dependent part) to those typically in the labour force (the EAP). The dependent part usually includes those under the age of 15 and over the age of 64.

20

3.1.4 MIgRATION

According to the Stats SA Mid-Year Population Estimates for 2008 and 2009, the Eastern

Cape’s estimates and projections for the period 2006-2011 suggest the total number

of about 415 000 people estimated to have emigrated from the Eastern Cape Province

dropped to approximately 390 000 people in 2009. Over the same period, the estimated

number of immigrants declined slightly, from about 120 000 people to about 117 000

people. This implies that the province’s net loss to other provinces, estimated at about

295 000 people in the 2008 mid-year estimates, is projected to have dropped to

approximately 274 000 people in the 2009 mid-year estimates.

3.2 ECONOMIC OUTLOOK OF THE EASTERN CAPE

3.2.1 ECONOMIC PERfORMANCE AND PROJECTIONS

The Eastern Cape is the fourth-largest economy in South Africa after Gauteng, the Western

Cape and KwaZulu-Natal. It contributed about 7.8 percent to the total economy in 2008,

and showed a strong comovement in terms of growth in the national economy, except for

the period 2001 to 2003.

Figure 3.2: Gross Domestic Product of Eastern Cape

The Eastern Cape economy’s growth has been characterised by upward and downward

cycles from 1996 to 2009, resulting in a combination of possible poverty-level increases

and reductions. On average, the real growth rate for the period 1996 to 2009 was

estimated at 2.6 percent, an indication of a potential reduction in poverty levels during this

period. In line with the unprecedented level of growth in South Africa in the mid-2000s,

the Eastern Cape economy performed relatively well during 1998 to 2000 and 2002 to

2006, registering growth rates from minus 0.4 percent to 4.3 percent and 1.7 percent

to 5.2 percent respectively. This period was characterised by an upswing phase of the

cycle, indicating an increase in initiatives of poverty reduction, and in the income- and

consumption levels of the poor. Between 2000 and 2002, the province registered positive

growth and later on a downswing phase of the cycle, indicating a possible increase in

poverty levels, and a decrease in income- and consumption levels that are linked to a

significant decrease in living standards. From 2006 to 2009, growth in the economy of

the Eastern Cape plummeted from 5.2 percent to minus 2.4 percent owing to the world

21

GDP_R FORECASTS

2007 2008 2009 2010 2011 2012 2013


GDP_R* (Rm) 96,704 99,269 96,922 98,499 101,954 105,973 110,424


financial crisis and the provincial economy’s dominance by the automotive sector and its

vulnerability to external shocks.

Table 3.1 shows the outlook of the Eastern Cape economy from 2009 to 2013.

Table 3.1: Gross Domestic Product of the Eastern Cape, 2007-2013

The Eastern Cape output is expected to remain fragile in spite of the slow recovery in

the economy. The improvement of the provincial economy would be supported by the

policy measures put in place by the national and provincial governments, and an increase

in consumer confidence levels. Spending related to the 2010 FIFA World Cup, tourism

and tax income generated would support the recovery. Also, the medium-term growth

of the Eastern Cape would be sustained by the large public infrastructure investment

programmes by the national government. These are aimed at easing bottlenecks in

construction and transport infrastructure, electricity, ports and rural development, all of

which are likely to constrict growth as the provincial economy recovers.

The outlook in Table 3.1 shows that by 2010, the Eastern Cape Province would again start

seeing positive growth as global recovery and consumption levels improve to those last

seen before the financial crisis. The province’s output would start showing relative strong

signs of recovery by 2011, when it is expected to tread at about 3.5 percent before rising

to about 3.9 percent in 2012 and 4.2 percent in 2013.

3.2.2 TOTAL INVESTMENT IN THE EASTERN CAPE

The total gross domestic fixed investment (fixed capital formation) in the Eastern Cape

has been underpinned by two sectors, namely machinery and other equipment as well as

building and construction, with a joint contribution of about 83 percent.

The figure below depicts how the growth rate of investment in the Eastern Cape has

evolved from 1996 to 2008, and provides an outlook for 2009 to 2013.

Growth in fixed-investment spending contracted in 2009, registering an annual growth

rate of 2.04 percent, from 12.4 percent in 2007. This is in contrast with the strong and

sustained growth in fixed investment that occurred between 2003 and 2007, which

averaged about 11 percent. The aggressive growth in total fixed investment spending

between 2003 and 2007 is attributed to the combined growth of investment in building

and construction work, transport equipment, machinery and other equipment.

The following table highlights the outlook for the total investment in the Eastern Cape.

22

Figure 3.3: Growth Rate of Investment in the Eastern, 1996-2013

Table 3.2: Total Gross Domestic Fixed Investment of the Eastern Cape, 2007-2013

GROSS DOMESTIC FIXED INVESTMENT (GDFI) FORECASTS

2007 2008 2009 2010 2011 2012 2013

GDFI Growth Rate (%) 7.3 2.7 2 8.1 9.4 7.3

GDFI share of GDP (%) 19.2 20 21.1 21.1 22.1 23.3 24

GDFI (Rm) 18,521 19,879 20,408 20,825 22,521 24,646 26,453


Nevertheless, while the growth rates in fixed investment in assets remain relatively

strong, the trend is clearly declining. This is in part indicative of the fact that public-sector

fixed-investment spending is already on a downward trend until 2010. The inability of

some public-sector entities to spend allocated capital budgets efficiently may also affect

the total fixed investment of the province. However, the outlook for total fixed investment

is encouraging. While total fixed-investment spending for 2009 is

likely to disappoint, forecasts pronounce a rebound in 2011, 2012 and 2013 due to

government’s investment plan as outlined in the medium-term strategic framework, as

well as private investment flowing from Coega and the East London IDZ.

3.2.2.1 Machinery and other equipment

Machinery and other equipment has been the leading contributor to total gross domestic

fixed investment in the Eastern Cape, with an annual average contribution of about

44 percent between 1995 and 2008. For the years 1997 to 2004, the annual average

contribution of this sector to total gross domestic fixed investment was around 46 percent,

reaching a historical high of 48 percent in 2002, before reverting back to an annual average

contribution of 42 percent between 2005 and 2008. The annual average growth for the

period 1995 to 2000 was about 8.3 percent, with a growth rate of about

16 percent recorded in 1995, before declining to 4.2 percent in 2000. In 1998, the average

growth in machinery-and-other-equipment investment dropped to a record low of about

0.1 percent before rebounding in 1999 to about 4.2 percent. Overall, the growth of

investment in machinery and other equipment has been characterized by unsteadiness

23

and dropped, on average, by about 1 percent for the period 2005 to 2008 when compared

to the 8.3 percent growth seen during the period 1995 to 2000.

In line with the total investment in the Eastern Cape, machinery and other equipment

would contract in 2009 and bounce back from 2010 onwards due to the global economic

recovery and rising investment confidence.

Table 3.3: Machinery and Other Equipment Investment in the Eastern Cape, 2007-2013

GROSS DOMESTIC FIXED INVESTMENT: MACHINERY AND OTHER EqUIPMENT FORECASTS

2007 2008 2009 2010 2011 2012 2013


Machinery and Other Equipment (Rm) 7,418 8,157 8,137 8,852 9,696 10,637 11,696


3.2.2.2 Building and construction

Building and construction was the second-largest contributor to total gross domestic fixed

investment in the Eastern Cape for the past 14 years. Between 1995 and 1997, this sector

contributed an annual average of about 41 percent to gross domestic fixed investment

and started to decline from 1998 to 2005, when its annual contribution averaged 38

percent. However, between 2006 and 2008, the percentage contribution of building and

construction started picking up, reaching an annual average of 41 percent, a figure last seen

between 1995 and 1997. This sector has also shown vigorous growth over the past 13

years, from a low of 1.6 percent annual average growth between 1996 and 2000 to a high

of 12.6 percent annual average growth between 2006 and 2008, after an annual average of

about 7 percent between 2001 and 2005. This growth was attributed to the infrastructure

investments made in the province, such as the building of the deep-water port, the

IDZ, the stadium and road works, partly in preparation for the 2010 Soccer World Cup.

In 2009/10, a decline is expected as most construction projects would be nearing

completion for the 2010 World Cup. Given the rural-development priorities of national

government, investment in building and construction is expected to start showing signs of

improvement from 2011 and pick up further in 2012 before declining slightly in 2013.

Table 3.4: Building and Construction Investment in the Eastern Cape, 2007-2013

GROSS DOMESTIC FIXED INVESTMENT: BUILDING AND CONSTRUCTION FORECASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 11.4 3 -0.9 0.6 8.3 7.6

Buildings and Construction Works (Rm) 7,670 8,545 8,797 8,721 8,774 9,505 10,231


24

3.2.2.3 Transport equipment

The average annual contribution of transport equipment to total gross domestic fixed

investment was about 14 percent between 1995 and 2008. The annual contribution to

total gross domestic fixed investment fluctuated between 16 and 14 percent in 1995 and

2008, respectively. In 1996 this sector of fixed investment observed negative growth

of about minus 2 percent associated with the first South African currency crisis, before

an all-time low of approximately minus 27 percent in 1999, from 11 percent growth the

previous year. The negative growth observed in 1999 coincided with the East Asian crisis

of 1997/1998, when South Africa witnessed large-scale withdrawals by non-residents of

portfolio investments previously made in the country5. The growth in this sector was

also in response to the events of 11 September 2001, which led to growth slumping to

about minus 2 percent. Investment in this sector grew aggressively between 2002 and

2005, reaching 22 percent in 2005. Apart from the slight decline from the 2005 figures,

investment remained high until the 2008 financial crisis. The annual average growth

between 2001 and 2005 was slightly more than 10 percent. Growth started to slow down

in 2006 and 2007, and declined to about minus 4 percent in 2008 amidst the threat of a

global economic crisis. This sector proves susceptible to both domestic and international

macroeconomic shocks.

Projections show that investment in transport equipment would improve from 2009

onwards, with robust growth rates from 2010 to 2013.

Table 3.5: Transport Equipment Investment in the Eastern Cape, 2007-2013

GROSS DOMESTIC FIXED INVESTMENT: TRANSPORT EqUIPMENT FORECASTS

2007 2008 2009 2010 2011 2012 2013


Transport Equipment (Rm) 2,910 2,796 2,847 2,955 3,260 3,629 3,962


3.2.3 fINAL CONSUMPTION ExPENDITURE IN THE EASTERN CAPE

Eastern Cape is ranked fourth in terms of consumption expenditure after KwaZulu-Natal.

The total spending growth in the province declined from 8.9 percent in 2006 to 8.6

percent in 2007, before plummeting to 0.9 percent in 2008, and is expected to contract

by 0.7 percent in 2009 (see Table 3.6). Contraction in overall household consumption

expenditure in 2009 was due to outright reductions in household spending on durable

goods, non-durable goods and services. The largest decline was in household spending

on non-durable goods, which is expected to fall to minus 3.2 percent in terms of growth

compared to 2008 spending (see Figure 3.4).

The decline in household consumption expenditure in the Eastern Cape Province mirrors

the severe pressure on the financial health of South African households that built up in

the course of 2007 and 2008. This was due to a marked deterioration in the fundamental

drivers of household income. The surge in inflation during 2008, rising interest rates,

elevated levels of household indebtedness and negative wealth effects stemming from

the decline in asset prices (particularly residential property) and equity prices also had a

detrimental impact on real household disposable income. 5 Reserve Bank of South Africa: The South African Economy in a World of Volatile Financial Markets, BER,University of Stellenbosch, 1999.

25

Table 3.6: Final Consumption Expenditure by Households in the Eastern Cape, 2007-

2013

FINAL CONSUMPTION EXPENDITURE BY HOUSEHOLDS FORECASTS

2007 2008 2009 2010 2011 2012 2013


FCEH share of GDP (%) 76.4 75.1 76.4 78 79 80.1 81.4

FCEH (Rm) 73,887 74,544 74,058 76,797 80,576 84,931 89,845


On average, the contribution of durable goods to total household consumption from 1995

to 2009 was 10 percent. Non-durable goods was expected to contribute around 34.6

percent of total spending by households in 2009, a 3.2 percent fall in 2009 followed by a

decline of 1.01 percent the previous year.

Household spending on services, which represented about 35 percent of total household

expenditure, fell by 0.7 percent in 2008 and is expected to contract to 2.2 percent in

2009. In conclusion, movements in spending on non-durable goods and services have a

considerable pressure on total household spending, given their substantial weighting in

household budgets.

Table 3.6 shows the outlook of final consumption expenditure by households in the

Eastern Cape. Final consumption is expected to start improving from 2010, given the

easing in monetary policy and the improving economic climate.

Figure 3.4: Household Consumption in the Eastern Cape, 1996-2013

26

3.2.3.1 Consumption of durable goods in Eastern Cape

Figure 3.5: Growth Rate of Consumption of Durable Goods - Eastern Cape vs. S.A., 1996-2008

After a period of robust and sustained growth from 2003 to 2005, expenditure on durable

goods declined to 3.7 percent in 2008, as outlined in Table 3.7. Figure 3.5 shows that

growth rate on durable goods in the Eastern Cape and South Africa. In the Eastern Cape,

growth rose from almost zero in 2002 to as high as 20.3 percent in 2005 before declining

to 16.8 percent in 2006. Nationally, spending had reached 65.2 percent in 2006, before

dropping to 22 percent in 2007, in response to tighter lending criteria. This sector is very

vulnerable to tight monetary policy. The decline in spending on durable goods coincided

with the increase in the repo rate that started in 2007. From 2007 to 2009, lenders became

more risk-averse, and households were also more averse to debt.

Table 3.7 captures the outlook for durable goods in the Eastern Cape from 2009 to 2013.

Projections highlight that consumption of durable goods would start improving in 2010,

before easing from 2011 onwards. The forecast is characterised by fluctuations.

Table 3.7: Consumption of Durable Goods in the Eastern Cape, 2007-2013

DURABLE GOODS: TOTAL FORECASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 3.7 1.1 7.2 5.4 4.5 3.1

Durable Goods (Rm) 8,584 8,903 8,999 9,645 10,168 10,621 10,949


27

3.2.3.2 Consumption of non-durable goods in Eastern Cape

Figure 3.6: Growth Rate of Consumption of Non-durable Goods - Eastern Cape vs. S.A., 1996-2008

Figure 3.6 contrasts the growth rate of consumption of non-durable goods in the Eastern

Cape with that of South Africa as a whole. Between 1996 and 2005, consumption of

non-durable goods in the Eastern Cape, which accounts for more than 30 percent of total

consumption, exhibited the same growth trajectory as national non-durable consumption.

After recovering from the decline of 1999, it continued growing steadily at a rate of

approximately 5 percent per annum, tracking national consumption patterns, until it

started decelerating towards the end of 2008. Post-2005, growth in national consumption

of non-durable goods started to rise aggressively, resulting in a striking deviation between

provincial and national growth. This was a period associated with economic stimulation

in South Africa boosted by low interest rates, ease of access to credit and robust world

economic growth. Spending on this sector accounted for 36 percent of consumption in

the Eastern Cape between 2005 and 2009, compared to 35 percent nationally.

The outlook for consumption of non-durable goods in the Eastern Cape (Table 3.8) shows

that consumption would contract by slightly more than 3 percent in 2009 before improving

to about 3 percent annual growth rate in 2010. From 2011, the forecasts show that the

annual growth rate would improve by about 1 percentage point until 2013.

Table 3.8: Consumption of Non-durable Goods in the Eastern Cape, 2007-2013

NON-DURABLE GOODS: TOTAL FORECASTS

2007 2008 2009 2010 2011 2012 2013

Annual Growth Rate (%) 1 -3.2 2.9 4.2 5.2 6.2

Non-durable Goods (Rm) 26,211 26,477 25,631 26,376 27,486 28,917 30,700

28

3.2.3.3 Consumption of semi-durable goods in Eastern Cape

Figure 3.7: Growth Rate of Consumption of Semi-durable Goods - Eastern Cape vs. S.A., 1996-2008

The growth rate in consumption of semi-durable goods in the Eastern Cape has also been

co-moving with the national growth rate, except in 2000. This trend persisted until 2006,

when the country’s growth rate of spending on semi-durables shot to 73.1 percent before

reaching the pronounced decline of 14.3 percent in 2009. Spending on semi-durable

goods in the Eastern Cape could not keep pace with the national growth rate despite the

continued growth, registering a growth rate of 20.6 percent in 2006, before declining to

minus 0.8 percent in 2008. Consumption of semi-durable goods is expected to pick up

again in 2009 with the easing monetary policy and the recovery of the economy. The same

growth trajectory is expected from 2010 until 2013.

Table 3.9 outlines the consumption outlook for semi-durable goods in the Eastern Cape.

Table 3.9: Consumption of Semi-durable Goods in the Eastern Cape, 2007-2013

SEMI-DURABLE GOODS: TOTAL FORECASTS

2007 2008 2009 2010 2011 2012 2013


Semi-durable Goods (Rm) 13,270 13,162 14,002 14,925 15,977 17,510 19,214


29

3.2.3.4 Consumption of Services in Eastern Cape

Figure 3.8: Growth Rate of Consumption of Services - Eastern Cape vs. S.A., 1996-2008

Expenditure on services between 2005 and 2009 constituted 35.2 percent of total

household consumption in the Eastern Cape, a decrease from 39 percent for the years

2000 to 2004. The average growth rate of consumption on services between 2006 and

2009 was 4.6 percent, an increase of 0.2 percent from the average growth rate between

2000 and 2004. Growth declined to less than 1 percent in 2008, and projections show a

further decline in 2009 before recovering in 2010. Furthermore, consumption of services

would grow by not less than 3 percent between 2011 and 2013 (see Table 3.10).

Table 3.10: Consumption of Services in the Eastern Cape, 2007-2013

SERVICES: TOTAL FORECASTS

2007 2008 2009 2010 2011 2012 2013


Services (Rm) 25,822 26,001 25,425 25,852 26,944 27,883 28,982


3.2.4 HOUSEHOLD CONSUMPTION, SAVINg AND DEBT

As the growth rate of disposable income of households increased in the Eastern Cape, so

did the propensity to consume, as shown in Figure 3.9. From 1995 to 2004, the propensity

to consume in the Eastern Cape averaged 98.4 and started to exceed 100 percent in 2006.

With the increase in consumption came a decline in the propensity to save, which reached

negative territory in 2006 when consumption exceeded income as households sank deep

into debt (Figure 3.10). A similar trend was observed countrywide. The rising consumption

corroded the ability to save in the entire country.

30

Figure 3.9: Propensity to Consume, Eastern Cape vs. S.A., 1995-2009

Figure 3.10: Propensity to Save Eastern Cape vs. S.A., 1995-2009

3.2.5 TRADE POSITION

3.2.5.1 Exports of Goods

The Eastern Cape has contributed an average of only 6.3 percent to national exports of

goods since 1995. The province’s contribution exhibited fluctuations between 1995 and

2008, with a jump from 3 percent in 1995 to 11.1 percent in 2000 before experiencing a

long downward trend between 2000 and 2007. In 2008, the province’s contribution was

6.9 percent, and approximately the same contribution is expected in 2009.

Motor vehicles, parts and accessories; machinery and equipment; textile; food; and

agriculture, forestry and fishing constitute the top five export-contributing industries in the

Eastern Cape. The composition of exports in the province has changed over the last 14

years, with motor vehicles, parts and accessories taking the lead. This industry increased

its share of total exports from 20.6 percent in 1995 to 60.2 percent in 2005, and 57.6

percent in 2008. Machinery and equipment represents the second-largest contributor

since 2000, with an increase in contribution for the past 9 years, from 9.5 percent in 2000

to 26.4 percent in 2008. Textiles, food and rubber products have shown a declining trend

31

from 1995 to 2009. In 1995, these industries’ contribution was about 38 percent, while in

2008 it was only 6.4 percent of the province’s total exports.

Agriculture, forestry and fishing industry contributed 9.3 percent and 10.1 percent in 1995

and 1996, respectively. Since 1996, the industry’s contribution to the province’s exports

started declining, reaching 1.6 percent in 2000 and 2.8 percent in 2001. The contribution

of agriculture, forestry and fishing fluctuated between 3 and 4.8 percent in the last 4 years.

This industry is far behind the contribution of motor vehicles, parts and accessories, and

machinery and equipment. Table 3.11 illustrates the contribution of the top five sectors in

the Eastern Cape Province.

Table 3.11: Consistent Major Export Contributors in the Eastern Cape, 1995-2008

MAJOR EXPORT CONTRIBUTORS (%)

1995 2000 2005 2008

Motor vehicles, parts and accessories 20.6 21.8 60.2 57.6

Machinery and equipment 12.8 9.5 17.9 26.4

Agriculture, forestry and fishing 9.3 1.6 3 4

Food 15.2 2.4 3.5 2.5

Textiles 17.3 3.7 3.3 2

Rubber products 5.5 2.6 3.2 1.9


3.2.5.2 Imports of Goods

The weighting of the Eastern Cape’s imports relative to national imports was about 5.9

percent in 2008, while its contribution between 1995 and 2008 was an average of 7.4

percent. Over the period 1996 to 2008, the growth rate of imports in the Eastern Cape was

characterised by moments of sharp decline, which were accompanied by a contraction in

growth between 1997 and 2003.

Manufacturing is the dominant industry in the Eastern Cape. Within this sector, motor

vehicles, parts and accessories represents the highest contributor to imports in the

province. This industry contributed about 63.2 percent to provincial imports in 1995, 56.4

percent in 2000 and reached 72.7 percent in 2005 before declining to 67.2 percent in 2008.

The contribution of machinery and equipment imports to provincial imports fluctuated

between 7.1 percent and 5.1 percent from 1995 to 2009. Also, agriculture, forestry and

fishing; and chemicals and electrical machinery, which are part of the top ten industries

contributing the most in the Eastern Cape, had a collective contribution of 14.1 percent in

1995. The contribution of the former sectors was 9.7 percent in 2000, 8.5 percent in 2005

and 11.1 percent in 2008. Table 3.12 illustrates the contribution of the top five sectors in

the Eastern Cape.

32

Table 3.12: Consistent Major Import Contributors in the Eastern Cape, 1995-2009

MAJOR IMPORT CONTRIBUTORS (%)

1995 2000 2005 2008

Motor vehicles, parts and accessories 63.2 56.4 72.7 67.2

Machinery and equipment 7.1 8.8 4.4 5.1

Basic chemicals 4 3.2 3 4.1

Agriculture, forestry and fishing 3.8 2.1 1.1 2.4

Other chemicals and man-made fibres 3.7 2.8 2.2 2.9

Food 1.7 1.9 1.3 1.5


3.2.5.3 Balance of Trade

Figure 3.11: Balance of Trade of the Eastern Cape, 1995-2008

Figure 3.11 shows that the Eastern Cape trade deficit reached R5.1 billion in 1995 and

increased to R5.6 billion in 1996. The trade deficit shrank to a low level of R0.2 billion in

2004, and grew again to a record high of R6 billion in 2006. The trade deficit of 1996 and

2006 was due to the fact that the growth rate of automotive vehicles exported declined

to minus 13.6 and minus 11.6 respectively, while parts and accessories grew rapidly

between 1995 and 2008. The positive trade balance observed in 2008 could be due to

the decrease in the machinery and equipment imported by the province. In general, the

movement of the provincial trade balance is characterised by the gap between the imports

and exports of the automotive sector. Even if the current trade deficit is a concern, an

acknowledgement needs to be made that, on average, between 1995 and 2008, exports

by motor vehicles, parts and accessories; and machinery and equipment grew by 43.2 and

33.4 percent respectively, while the growth rate of imports was on average 15.3 and 12.7

percent, respectively.

Most academic research has shown that immediate concerns regarding the trade deficit

have been compounded by more long-term anxieties that the deficit is “unsustainable.”

However, continual and rising deficits would burden future generations with a crushing

“foreign debt,” leave the province vulnerable to foreign pressure, undermine foreign-

investor confidence, and produce a “hard landing” for the provincial economy.

33

3.3 SECTORAL ANALYSIS OF THE EASTERN CAPE

3.3.1 SECTORAL CONTRIBUTION ANALySIS IN EASTERN CAPE

This section highlights the share of each industry in the Eastern Cape’s total output from

1995 to 2008. The visual analysis of the figure below shows that manufacturing, wholesale

and retail trade, finance and general government industries contribute the most to the

Eastern Cape’s output. General government services has decreased while the finance

industry has increased since 1995. Manufacturing, wholesale and retail trade have been

more stable and constant, on average, since 1995.

Figure 3.12: Industry Share of the Eastern Cape Output, 1995-2008

3.3.1.1 Primary Sector

The primary sector is the worst-performing of the three sectors. Its contribution to the

provincial total output has been on a decline since 1995. On average, from 1995 to 2008,

the primary sector’s contribution to the Eastern Cape’s total output was 2.7 percent,

which is higher than the 2008 contribution. Compared to the national primary sector, it

contributed only 2.1 percent on average. Agriculture, forestry and fishing is the dominant

industry in the sector, representing an average 94.6 percent of the primary sector, while

contributing only an average of 6.8 percent to the same industry at a national level.

The districts that contributed the most to agriculture, forestry and fishing industry were

Cacadu, Amatole and Chris Hani. Cacadu and Amatole districts’ contribution to agriculture,

forestry and fishing has been decreasing, whereas in the Chris Hani district, there has

been an increase in contribution of about 1.3 percent, on average, since 1995. On the

other hand, Alfred Nzo and OR Tambo, being the lowest-contributing districts in the

industry, have seen an increase in contribution.

34

3.3.1.2 Secondary Sector

In the Eastern Cape, the secondary sector has contributed an average of 21.5 percent to

the total economy, while its share in this sector at the national level has remained flat at

around 7.2 percent since 1995. Manufacturing has dominated the sector over the past

14 years, representing 83.4 percent of the secondary sector in the province, but only

contributing an average of 7.8 percent to the national manufacturing industry. Electricity,

gas and water is the least contributor, with 6.5 percent of the secondary sector, while

construction industry accounts for 10.1 percent.

Approximately 86.1 percent of the manufacturing industry in the Eastern Cape comes

from the Nelson Mandela Bay Metro and the Amatole districts. Even though their share

in the provincial manufacturing industry has declined over the years, they’re still playing

a leading role in this industry. Alfred Nzo and UKhahlamba are the lowest-contributing

districts in electricity, gas and water since 1995.

3.3.1.3 Tertiary Sector

The tertiary sector in the Eastern Cape had an average contribution of 75.8 percent to the

total economy, while contributing 9.3 percent to the national tertiary sector. From 1995 to

2003, general government services (29.4 percent) has played a leading role, followed by

finance, insurance, real estate and business services (25.6 percent), and wholesale and

retail (20.7 percent) in third place. Since 2004, there is a shift in the ranking, with finance,

insurance, real estate and business services (28.0 percent) taking the top position, followed

by general government services (25.9 percent). Transport, storage and communication

has lagged behind, with only 11.9 percent over the period 1995-2008.

Amatole district and Nelson Mandela Bay Metro represent 63.7 percent of the total

sector in the province. However, their contribution has declined over the years. General

government industry dominates in Amatole district, while finance, insurance, real estate

and business services leads in Nelson Mandela Metro. Alfred Nzo and Ukhahlamba

contributed less to this sector on average.

3.3.2 SECTORAL gROWTH ANALySIS IN THE EASTERN CAPE

3.3.2.1 Primary Sector

During the period 1995 to 2008, the primary sector in the Eastern Cape contributed less than

3 percent to the economy. Equally, growth in this sector, both nationally and provincially,

has been disappointing for the past 14 years. In fact, there is a dive in the sector’s growth,

from 1.3 percent in 1995-2000 to 0.9 percent in 2001-2008. Provincially, the sector grew

from an average rate of minus 0.2 percent in 1995-2000 to an average of 3.1 percent in

2001-2005. In light of the negative growth experienced provincially throughout 1995-2000

and the sluggish growth nationally, the average growth of 1.7 percent for 2001- 2008 may

not seem so bad as it also exceeds the national level for that period. Agriculture, forestry

and fishing (AFF) has exhibited the same characteristics of growth shown by the provincial

primary sector. This is not surprising given that agriculture, forestry and fishing contributed

more than 95 percent to the primary sector’s output for the Eastern Cape.

35

Table 3.13: Eastern Cape Primary Sector Average Growth, 1995-2008

AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

SA 1.3 1.6 0.9

PRIMARY SEC. EC -0.2 3.1 1.7

SA 6.1 1.6 2.8

AFF EC -0.2 3.1 1.8


3.3.2.2 Secondary Sector

The secondary sector in the Eastern Cape has been dominated by the manufacturing

industry, followed by the construction industry. Jointly, these two industries contributed

more than 90 percent of the sector’s output, with manufacturing contributing more than

80 percent of this figure. The growth rate of manufacturing has been slightly below that of

South Africa during the two latter periods investigated, that is, 2001-2005 and 2001-2008.

Oddly, the period 2001-2005, which was typified by sustained economic growth in the

country, shows no improvement in the growth rate of manufacturing sector.

The growth of manufacturing in the Eastern Cape appears to lag the national growth for

all periods considered. Turning to the construction industry, growth rose to a remarkable

11.1 percent in 2001- 2005 from 2.1 percent in 1995-2000, and has remained above the

11-percent margin for the period spanning 2001 and 2008. This increased growth level

in the construction industry, although above the national growth, has been aligned to the

national growth rate.

Table 3.14: Eastern Cape Secondary Sector Average Growth, 1995-2008

AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

SA 2.3 3.2 3.9

SECONDARY SEC. EC 2.6 2.8 3.2

SA 2.5 2.8 3.1

MAN EC 2.6 2.4 2.6

SA 0.8 8.2 10.7

CON EC 2.1 11.1 11.4


3.3.2.3 Tertiary Sector

The tertiary sector in the Eastern Cape contributes more than 75 percent to the economy

of the province. The periods investigated in Table 3.15 show that, both provincially and

nationally, the sector has experienced steady growth. This is despite the fact that provincial

growth has always lagged national growth, by almost 1 percent. Industries that have

driven growth in this sector were finance, insurance, real estate and business services

(FIBS); community, social and personal services (CSPS); and transport, storage and

communications (TSC), despite showing signs of decline during the period 2001 to 2005

with the exception of finance, insurance, real estate and business services. Provincial

36

growth in finance, real estate and business services has shown enormous growth after

jumping from an average growth of a mere 1.2 percent in 1995-2000 to 6.3 percent in

2001-2005, and has remained within the 6-percent level of growth for the period 2001-

2008. Growth in this industry has closely tracked national growth. Analysis shows that

average growth in community, social and personal services in the province experienced a

marginal decline in 2001-2005, but overall has been growing at an almost similar rate with

the national level. The transport, storage and communications industry declined slightly in

growth in 2001-2005 to 4.8 percent when compared to the growth level of 1995- 2000,

which averaged 5.1 percent. The decline in growth is even more apparent when comparing

the average growth rate in 2001- 2008 with that of 1995-2000.

Table 3.15: Eastern Cape Tertiary Sector Average Growth, 1995-2008

AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

SA 3.4 4.5 4.7

TERTIARY SEC. EC 2.5 3.3 3.6

SA 4.4 6.3 6.3

FIBS EC 1.2 6.3 6

SA 3.5 3.1 3.6

CSPS EC 3.6 2.9 3.5

SA 6.5 6.2 5.9

TSC EC 5.1 4.8 4.6


3.4 LABOUR-MARKET ANALYSIS IN EASTERN CAPEThe main objective of any government (provincial or national) is to develop a policy which

can assure both economic growth and job creation. However, since 1990, South Africa’s

rate of job creation in general, and the Eastern Cape’s in particular, have risen more slowly

than economic growth during economic upturns, and fallen more rapidly during downturns.

This puzzle of economic growth and job creation has given rise to a phenomenon that

some economists term “jobless growth”. Over the years, economic growth has largely

benefited sectors that rely more on relatively educated labour, while the sectors which can

absorb significant unskilled labour have declined, creating a loss in semi- and unskilled jobs.

This section provides vital information about the labour-force data in the Eastern Cape.

The analyses are based on the official definition of labour force and unemployment used

by StatsSA.

37

UNEMPLOYMENT AND EMPLOYMENT (000)

YEAR ON YEAR qUARTER TO qUARTER

1Q2009 - 1Q2009 - 2Q2009 - 3Q2009 - 1Q2009 - 2Q2009 - 3Q2009 - 1Q2000 1Q2008 2Q2008 3Q2008 4Q2008 1Q2009 2Q2009

EC (Total)

Formal sector -1.2 2.4 3.5 -3.3 -2.2 3.3 -7.5

Informal sector -48.2 -9.5 -13.7 -8.0 -7.8 2.5 -5.3

Employed -17.2 -0.4 -0.7 -4.3 -3.5 3.2 -7.0

Unemployed -13.1 1.3 16.6 -7.3 13.8 0.6 -12.3

Economically active -16.1 0.1 3.6 -5.1 0.9 2.4 -8.5

Discouraged job seekers -0.1 2.1 33.2 39.6 -1.5 18.3 7.8

Economically not active 51.9 2.3 -4.8 2.6 0.0 -4.2 8.1

Population (15-64) 8.0 1.2 1.2 1.2 0.3 0.3 0.3

SA (Total)

Formal sector 22.8 1.6 -1.1 -5.1 -1.0 -2.0 -3.3

Informal sector -15.1 -7.3 -9.9 -8.4 -4.2 -1.9 -5.5

Employed 14.7 0.1 -2.6 -5.6 -1.5 -2.0 -3.6

Unemployed -14.1 -0.2 0.3 1.7 8.0 -1.4 1.6

Economically active 6.3 0.0 -2.0 -3.9 0.6 -1.8 -2.4

Discouraged job seekers -33.6 3.3 40.7 52.3 4.0 24.9 7.5

Economically not active 54.5 2.9 2.4 4.3 -0.5 1.0 3.3

Population (15-64) 17.7 1.2 1.2 1.2 0.3 0.3 0.3

Source: Stats SA and Own Calculations

Table 3.16: Key Labour Market Indicators in Eastern Cape, 3Q2008 – 3Q2009

UNEMPLOYMENT AND EMPLOYMENT (000)

3Q2008 4Q2008 2Q2009 2Q2009 3Q2009

Employed Formal sector 1019 1055 1031 1066 986

Employed Informal sector 296 304 280 287 272

Total Employed 315 1358 1311 1353 1258

Total Unemployed 496 458 521 524 460

Discouraged job seekers 234 260 256 302 326

Unemployment rate (%) 27.4 25.2 28.4 27.9 26.8

Source: Stats SA

From the table above, the Eastern Cape had a total of 1 258 000 people employed in the

third quarter of the year 2009, which represented 31.4 percent of the total working age

group6. The province lost about 57 000 jobs between the third quarter of 2009 and the

third quarter of 2008, while the unemployment rate over the same period declined from

27.4 percent to 26.8 percent. The immediate explanation can be attributed to the narrow

definition of unemployment which is officially adopted by the South African government.

The table also shows that the decline in the total number of people employed did not

translate into an increase in the number of unemployed, but rather an increase in the

number of discouraged job-seekers over the same period. The number of discouraged

job-seekers showed an increase of 92 000 between 3Q2008 and 3Q2009.

Table 3.17: Eastern Cape and South Africa Labour Market, 1Q2000 – 3Q2009

6 Working age population is defined as the working population between the ages of 15 and 64

38

Table 3.17 provides a comparative outline of labour-market growth in the Eastern Cape

and South Africa from the first quarter of 2000 to the third quarter of 2009. Between 2000

and 2009, formal employment in the Eastern Cape decreased by 1.2 percent, while the

country’s formal employment grew by 22.8 percent. Over the same period, the number

of unemployed people has declined by 13.1 percent in the Eastern Cape, lower than the

national decline of 14.1 percent. Also, the population age group 15-64 years increased

more at a national level than at a provincial level. In general, the performance of the

Eastern Cape labour market is poorer than that of the South African labour market as a

whole.

3.4.1 EMPLOyMENT

This section highlights the performance of employment in the Eastern Cape.

3.4.1.1 Employment by province

The table below shows the number of people employed from the third quarter of 2008 to

the third quarter of 2009 per province.

Table 3.18: Employment by Province, 3Q2008 – 3Q2009

EMPLOYMENT BY PROVINCE (000)

3Q09 - 2Q09 3Q09 - 3Q08 3Q2008 4Q2008 1Q2009 2Q2009 3Q2009 change change

Western Cape 1865 1931 1965 1898 1868 -30 3

Eastern Cape 1315 1358 1311 1353 1258 -95 -57

Northern Cape 308 316 278 283 255 -28 -53

Free State 842 829 812 773 758 -15 -84

KwaZulu-Natal 2583 2631 2514 2457 2458 1 -125

North West 868 895 885 849 789 -60 -79

Gauteng 4063 4079 4030 3953 3719 -234 -344

Mpumalanga 924 934 936 897 881 -16 -43

Limpopo 888 870 905 906 899 -7 11

Total 13655 13844 13636 13369 12885 -484 -770


Between the third quarter of 2008 and the third quarter of 2009, a period characterised

by the economic downturn in the country, there was an annual decrease of 4.3 percent

(equivalent to 57 000 jobs lost) in the number of employed people in the province. This

figure ranked the province in fifth position, with Limpopo first and Western Cape second,

with positive growth of 1.2 percent (11 000 jobs gained) and 0.2 percent (3 000 jobs

gained), respectively. Gauteng, with an 8.5-percent decrease in the number of employed

people (344 000 jobs lost) and KwaZulu-Natal with minus 4.8 percent (125 000 jobs

lost) constituted the most-affected provinces during the period. Between the second

and third quarter of 2009, except for KwaZulu-Natal, all provinces experienced a loss in

employment, with Gauteng taking the lead.

39

3.4.1.2 Employment Sectoral Analysis

3.4.1.2.1 Employment Industry’s Contribution to Total Employment

The figure below illustrates the change in the contribution of employment (formal and

informal) of different industries to the total employment in the Eastern Cape between

2000 and 2009.

Figure 3.13: Industry Employment share of the total in Eastern Cape, 1Q2000 – 2Q2009

From this figure, it appears that agriculture, forestry and fishing has lost momentum over

the period 2000 to 2009. Its contribution to the total (formal and informal) employment in

the province was about 40.9 percent in the first quarter 2000, while it only represented 6.4

percent in the same quarter of 2009. Over the same period, manufacturing and finance,

insurance and business services have doubled their employment contribution. Wholesale

and retail trade; catering and accommodation saw an increase in contribution from 16.5

percent in the first quarter of 2000 to 23.6 percent in the same quarter in 2009. Also, the

contributions from construction, transport and community, social and general government

saw a slight increase over the period, while those of mining and electricity decreased.

3.4.1.2.2 Formal Employment by Industry

Between the first quarter of both 2000 and 2005, agriculture, forestry and fishing lost

around 271 000 employed people, while gaining only 20 000, between the first quarter

of both 2005 and 2009. The industry employed around 333 000 people in the first quarter

of 2000; this number dropped to 86 000 in the second quarter of 2009. The industry has

lost a number of employed persons over the years; consequently, its contribution to the

provincial economy has plummeted. Mining activity in the Eastern Cape is marginal. The

number of people employed in the mining industry dropped from 1900 in 1Q2000 to 1500

in 2Q2009.

Employment Contribution

40

FORMAL EMPLOYMENT BY INDUSTRY (NUMBER)

YEAR ON YEAR qUARTER TO qUARTER CHANGE CHANGE

1Q2005 - 1Q2009 - 1Q2000 1Q2005 1Q2006 - 1Q2007 - 1Q2008 - 1Q2009 - 1Q2009 - 2Q2009 - change change 1Q2005 1Q2006 1Q2007 1Q2008 4Q2008 1Q2009

AFF -271406 20165 23622 -23215 13058 6700 3288 4347

MQ 3256 -2250 -4305 2077 -1121 1099 173 -1471

MAN 36173 55167 15852 1579 12991 24745 -9425 1153

EGW -5947 -2568 1074 -1925 -757 -960 1399 -182

CON 19528 15184 21033 9334 -2158 -13025 -8928 16976

WRTCA 48933 12095 2982 19969 -10443 -413 -23268 8988

TSC 642 4374 3115 5890 -7185 2554 46 5947

FIBS 27100 10820 -11137 18669 11481 -8193 7485 22303

CSPS -31045 47302 7854 -22661 49970 12139 5890 -23585

Total -172766 160289 60090 9717 65836 24646 -23340 34476


Table 3.19: Formal Employment by Industry in Eastern Cape, 1Q2000 – 2Q2009

The manufacturing industry showed an ascending trend in people employed since

2000. This industry is the major contributor in the secondary sector and represents, on

average, 17.9 percent of provincial output. The average growth rate of employment in

this industry is higher than the national growth rate. In 1Q2000, the industry employed

84 200 people, a figure that increased to 176 700 in 2Q2009. Between 1Q2000 and

1Q2005, the manufacturing industry has expanded by 36 100 jobs, representing an annual

average growth of 6.9 percent. Manufacturing industry lost around 9 400 jobs between

the first and second quarter, and gained 1 100 jobs between the second and third quarter

of 2009. This situation, which reflects a loss of employment in the industry, is a result of

the challenges it faces due to the global economic downturn. Like the mining industry,

the average contribution of electricity and water to the secondary sector is minor, and

represents about 6.5 percent of the total. The number of jobs created by this industry is

correlated to its contribution. In 1Q2000, the industry created 12 300 jobs in the province,

a figure that has since decreased to 3 600 jobs in 2Q2009. The construction industry’s

employment has shown a soft volatility pattern over the years. It grew from 21 000 in the

first quarter of 2000 to 73 000 employed people in the second quarter of 2009. Since the

first quarter of 2006, the number of people employed in the industry was consistently

above 50 000 owing to major construction for the 2010 World Cup. The outlook for

employed people in the construction industry would remain the focus due to the fact

that major construction projects for the World Cup are reaching their final stage. Also, the

development of the Coega IDZ and Deepwater Port is an opportunity for the province to

add value in terms of job creation in this industry.

Wholesale and retail trade, catering and accommodation is part of the industries in the

tertiary sector with a good employment contribution to the provincial economy. The

industry has shown an upward trend since 2000. The number of jobs created in the first

quarter of 2000 was about 106 000 and has since increased to 176 000 in the second

quarter of 2009. The transport and communication industry is a minor contributor in terms

of employment in the tertiary sector. The number of jobs created in 2000 was around

32 000, and has smoothly increased to 43 000 in the second quarter of 2009. Since 2000,

41

annual growth has shown some volatility, reaching minus 17.1 percent in the first quarter

of 2008. Finance, insurance and business services has grown rapidly in the province.

The industry’s employed number has doubled between 2000 and 2009, with an annual

average growth of 8.1 percent. This industry has become one of the major employment

contributors in the Eastern Cape. In the second quarter of 2008, the industry created

114 000 jobs, about 60 000 jobs more than in the first quarter of 2000.

Community, social and other personal services represents the major contributor in terms

of employment in the province. Its contribution was flat over the year, with on average

annual growth rate of 1.7 percent between 2000 and 2009. Employment growth in the

community, social and other personal services industry followed the national trend and

showed an upward and downward trend between 2000 and 2009. Between the third

quarter 2008 and the second quarter 2009, the number of jobs in this industry dropped

from 422 000 to 392 000.

3.4.1.2.3 Informal Employment by Industry

Informal employment has been discussed since the early seventies, initiated by the launch

of the International Labour Office (ILO) World Employment Programme, the publication of

the Kenya Report (1972) and the seminal contribution by Hart (1973).

The ILO represents the key international body providing official definitions of informal

employment. The first definition of informal employment agreed upon in 1993 was

reviewed in 2003 due to findings that the definition left out important segments of informal

employment. The broad-understanding explanation adopted in 2003 defined informal

employment as the “total number of informal jobs, whether carried out in formal sector

enterprises, informal sector enterprises, or households” (ILO 2002b). Also, informal jobs

are characterised by poor working conditions, low earnings and are not subject to labour

legislation, social protection, taxes or employment benefits. Statistics South Africa doesn’t

depart from this broad definition in general. The table below gives the broad picture for

informal employment by industry in the Eastern Cape.

Table 3.20: Informal Employment by Industry in Eastern Cape, 1Q2000 – 2Q2009

INFORMAL EMPLOYMENT BY INDUSTRY (NUMBER)

YEAR ON YEAR qUARTER TO qUARTER CHANGE CHANGE

1Q2005 - 1Q2009 - 1Q2000 1Q2005 1Q2006 - 1Q2007 - 1Q2008 - 1Q2009 - 1Q2009 - 2Q2009 - change change 1Q2005 1Q2006 1Q2007 1Q2008 4Q2008 1Q2009

AFF -82631 123054 -205406

MAN 21808 -13042 -2722 5443 -13068 -2695 -2355 3173

CON 320383 -12706 12437 -13419 10509 -22233 -12263 3282

WRTCA -2442 -4665 25747 -33167 -10737 13492 -2407 -4278

TSC 888 14305 4188 -235 13785 -3433 3679 3779

FIBS -893 10661 5501 -6909 17410 -5341 -3039 -674

CSPS 11653 8427 3183 25308 -11582 -8482 -7339 1758

Total -30069 -230365 169581 -228385 -141996 -29565 -23724 7040


42

EMPLOYMENT BY OCCUPATION

2004 2008

Number Share (%) Male (%) Female (%) Number Share (%) Male (%) Female (%)

Legislators, senior officialsand managers 58492 5.1 75.3 24.7 71417 4.6 70.9 29.1

Professionals 49089 4.3 51.2 48.8 61005 4.0 37.9 62.1

Technicians and associateprofessionals 117889 10.4 37.4 62.6 145189 9.4 33.3 66.7

Clerks 77888 6.8 23.5 76.5 122374 7.9 23.1 76.9

Service workers and shopand market sales workers 139968 12.3 51.0 49.0 184566 12.0 53.5 46.5

Skilled agricultural andfishery workers 152331 13.4 69.9 30.1 152449 9.9 61.1 38.9

Craft and related tradesworkers 167158 14.7 66.1 33.9 234942 15.2 69.5 30.5

Plant and machinery operators and assemblers 61563 5.4 90.9 9.1 110761 7.2 84.2 15.8

Elementary occupations 312830 27.5 58.1 41.9 460684 29.8 43.5 56.5

Total 1137208 100.0 57.8 42.2 1543387 100.0 51.8 48.2


Agriculture, forestry and fishing industry has played a leading role in the informal

employment sector. But due to lack of data since 2008, its current contribution cannot be

measured accurately. Wholesale, retail trade, hotels and restaurants is ranked in second

position and plays an important role in informal sector employment. Its contribution was

flat over the years, with an annual average growth of 2.8 percent. Community and social

services; transport and communication; construction and manufacturing have also created

an important number of informal jobs in the Eastern Cape.

3.4.1.3 Employment by Occupation and by Skills

An analysis of employment by occupation is essential in the determination of skill groupings

such as highly skilled, skilled, semi- and unskilled. This section examines employment by

occupation between 2004 and 2008 in the Eastern Cape.

3.4.1.3.1 Employment by Occupation

The table below provides employment by occupation and by gender in 2004 and 2008.

Table 3.21: Employment by Occupation in the Easter Cape, 2004-2008

Elementary occupations represent the largest share of employed people from 2004 to

2008, followed by craft and related trades workers. The proportion of legislators, senior

officials and managers decreased over the period, while the share of females increased.

For both years, the smallest number of people employed in the province came from the

professional sector.

43

3.4.1.3.2 Employment by Skills

The graph below provides employment by skills in the Eastern Cape in 2004 and 2008.

Figure 3.14: Composition of Employment by Skills, 2004 and 2008

The figure above reveals a decline of two percentage points within the semi- and unskilled

category over the period of 2004 and 2008. During the same period, skilled jobs in the

formal sector gained the same number of percentage points as those lost in the semi- and

unskilled category, while the number of highly skilled jobs remained unchanged.

Given the state of the distribution of skills in the province, the Eastern Cape government’s

objectives of job creation and poverty reduction would depend on the success of policies

that promote the employment of skilled, semi- and unskilled labour.

3.4.2 UNEMPLOyMENT RATE IN EASTERN CAPE

Unemployment is regarded as one of the most challenging economic problems facing the

Eastern Cape. The structure of the provincial economy is dominated by the tertiary sector,

which requires highly skilled labour, while the Eastern Cape is dominated by skilled, semi-

skilled and unskilled labour. The table below provides a comparison of unemployment

statistics across provinces between the third quarter of 2008 and 2009.

From Table 3.22, the Western Cape and KwaZulu-Natal have managed to keep their

unemployment rates below the national rate since the third quarter of 2008. Estimates

obtained from labour-force surveys indicate that between the third quarter 2008 and 2009,

the unemployment rate in the Eastern Cape ranged from a minimum of 25.2 to a maximum

of 28.4 percent. The Eastern Cape Province’s unemployment rate has always been above

South Africa’s rate, decreasing to 26.8 percent in the third quarter of 2009. Also, the

province had the highest unemployment rate after Limpopo in the third quarter of 2008, and

was in first position in terms of its high unemployment rate in the second quarter of 2009.

The high level of unemployment is due to the fact that the province is mostly rural, while

the growth of its economy is driven by the tertiary sector. The enhancement of the primary

and secondary sectors is needed to dilute the high rate of unemployment in the province.

44

UNEMPLOYMENT

3Q2008 4Q2008 1Q2009 2Q2009 3Q2009

SOUTH AFRICA Unemployment (‘000) 4122 3873 4184 4125 4192

Rate (%) 23.2 21.9 23.5 26.6 24.5

WESTERN CAPE Unemployment (‘000) 457 392 442 490 542

Rate (%) 19.7 16.9 8.4 20.5 22.5

EASTERN CAPE Unemployment (‘000) 496 458 521 524 460

Rate (%) 27.4 25.2 28.4 27.9 26.8

NORTHERN CAPE Unemployment (‘000) 90 87 105 102 109

Rate (%) 22.6 21.6 27.4 26.5 29.9

FREE STATE Unemployment (‘000) 250 242 227 285 304

Rate (%) 22.9 22.6 25.4 26.9 28.6

KWAZULU-NATAL Unemployment (‘000) 729 690 733 586 566

Rate (%) 22 20.8 22.6 19.3 18.7

NORTH WEST Unemployment (‘000) 317 310 326 325 306

Rate (%) 26.8 25.7 26.9 27.7 27.9

GAUTENG Unemployment (‘000) 1131 1062 1119 1188 1294

Rate (%) 21.8 20.7 21.7 23.1 25.8

MPUMALANGA Unemployment (‘000) 279 280 307 324 304

Rate (%) 23.2 23.1 24.7 26.5 25.7

LIMPOPO Unemployment (‘000) 372 353 354 300 308

Rate (%) 29.5 31.7 28.1 24.9 25.5


Table 3.22: Unemployment Rate and Number by province, 3Q2008 – 3Q2009

3.4.3 LABOUR REMUNERATION

Labour remuneration is defined as the sum of wages, salaries, and fringe benefits paid to

workers. In this section, an analysis of the remuneration of labour (by gender, age group

and sector), divided into different income categories, will be conducted. The following

figure shows the monthly labour remuneration by income group in the Eastern Cape

between 1995 and 2009.

Figure 3.15: Labour Remuneration per Income Category in the Eastern Cape, 1995 – 2009

45

INCOME BY GENDER (%)

Monthly Less orLabour No equal to R501 - R2 501 - R6 001 - R11 001 - R30 001 TotalRemuneration Income R500 R2 500 R6 000 R11 000 R30 000 or more

Male 36.1 4.2 14.4 19.3 13.2 12.3 0.4 100.0

Female 39.8 6.0 8.9 21.0 18.5 5.9 100.0

Total 37.9 5.1 11.7 20.1 15.8 9.2 0.2 100.0


Figure 3.15 shows that there was an improvement in terms of income redistribution

over the past 14 years. High-income levels have increased, while the distribution of low

incomes has decreased. However, there is an increase in the number of people earning

no income.

For the gender, age group and sector-income distribution analysis, the focus was on the

2008 household survey, and included no-income earning group as a means of figuring out

the impact of this category on income distribution in the province. According to the 2008

household survey in Table 3.23, the Eastern Cape features different income categories,

with the largest portion of employed people earning no income, R500 or less per month

(43 percent). The percentage of employed people falling in the range of R11 001 or more

per month represented approximately only 9.4 percent of the total number of employed

people in the province.

3.4.3.1 Remuneration by Gender

The table below highlights the monthly labour remuneration in 2008, by gender, in the

Eastern Cape.

Table 3.23: Labour Remuneration by Gender in Eastern Cape, 2008

Women are more vulnerable in terms of income redistribution in the province, according

to the 2008 household survey. The results from the above table show that about 39.8

percent of women and 36.1 percent of men have no income. The survey shows the

dominance of women in the R2 501 and R11 000 income categories, while their proportion

has been reduced in income exceeding R11 000.

46

INCOME BY SECTOR (%)


Private Households 27.3 40.5 12.5 19.8 0.0 0.0 0.0 100.0

AFF 94.8 0.8 2.5 0.3 0.0 1.5 0.0 100.0

MQ 0.0 0.0 33.5 66.5 0.0 0.0 0.0 100.0

MAN 13.3 2.6 11.0 33.8 24.2 15.2 0.0 100.0

EGW 0.0 0.0 0.0 100.0 0.0 0.0 0.0 100.0

CON 60.9 4.2 14.5 10.3 5.9 4.2 0.0 100.0

WRTCA 11.5 10.5 23.5 28.8 16.6 9.2 0.0 100.0

TSC 0.0 19.2 24.5 25.6 20.6 10.1 0.0 100.0

FIBS 0.0 2.8 17.2 33.7 23.1 23.3 0.0 100.0

CSPS 3.1 3.7 9.3 31.3 36.4 15.2 1.0 100.0

Total 37.9 5.01 11.7 20.1 15.8 9.2 0.2 100.0


INCOME BY AGE (%)


15 - 29 years 49.4 4.3 13.7 21.1 10.1 1.5 0.0 100.0

30 - 44 years 23.8 6.3 11.4 26.0 20.3 11.9 0.4 100.0

45 - 54 years 20.3 4.3 15.2 20.5 21.7 17.7 0.4 100.0

55 - 64 years 48.6 6.5 7.4 9.2 16.4 12.0 0.0 100.0

More than 64 90.8 1.4 1.3 4.1 0.2 2.2 0.0 100.0

Total 37.9 5.0 11.7 20.2 15.8 9.2 0.2 100.0


3.4.3.2 Remuneration by Age Group

The table below shows the monthly labour remuneration by age group in the province of

Eastern Cape.

Table 3.24: Labour Remuneration by Age Group in Eastern Cape, 2008

The result of the 2008 household survey shows that the age groups 15-29 years and 54+

years lead the category of no-income earners. The age group of people between 30 and

54 years old represents the most earning group in the province and this result is expected

given their experience and history at the work place.

3.4.3.3 Remuneration by sector

The table below shows the monthly labour remuneration by industry in the province of

Eastern Cape.

Table 3.25: Labour Remuneration by Industry in Eastern Cape, 2008

47

The agriculture, forestry and fishing industry, followed by the private households and

construction industries, employed the largest share of people earning no income, R500

or less per month in the province. High-skill employment such as financial and business

services lead in the category of people earning between R11 001 and R30 000 per month,

followed by community, social and personal services. Monthly income earnings in the

wholesale and retail trade are proportionally distributed, given the nature of this industry

in terms of skill distribution.

49

4 ECONOMIC PERFORMANCE OF DISTRICT MUNICIPALITIES

The highest concentration of population is located in OR Tambo and Amatole. Collectively,

these two districts constitute slightly more than half of the total provincial population.

Despite its metropolitan status, the Nelson Mandela Metropolitan district is only the third-

largest in terms of population size, with the highest population density per square kilometre.

Although Cacadu is the largest district in the Eastern Cape in terms of geographic spread,

it has the lowest population concentration. UKhahlamba has a much smaller and less

concentrated percentage of the provincial population, when compared with the other six

district municipalities.

4.1 AMATOLE DISTRICT MUNICIPALITYThe Amatole District Municipality (DM) is situated on the Eastern Cape coast side of

South Africa. The district has eight local municipalities, each containing at least one urban

service centre. It also shares a border with the following district municipalities: Chris Hani,

O.R. Tambo and Cacadu. The district covers 23 594 square kilometers. About 60 percent

of the district is urban, while 40 percent is rural.

In 2009, the Amatole DM had an estimated population of 1.7 million people. About 93

percent of the population was African, 3.4 percent Coloured, 0.3 percent Asian and 3.5

percent White. Males constituted 46 percent of the population, while females were 54

percent.

The total Gross Value Added (GVA) for 2008 was R22.4 billion in 2008, a growth rate of 1

percentage from 2007. Amatole DM is the second largest economy in the province. Of

the total GVA, the sectors which contributed the most to the economy of the district were

general government service and finance and insurance, with 23 percent apiece, followed

by manufacturing with 15 percent, and wholesale retail and trade with 14 percent. Jointly,

these sectors contributed about 75 percent to the GVA of the district.

4.1.1 TOTAL POPULATION By AgE gROUP

The population of Amatole has more people between the ages of 45 and above. All

age groups showed negative growth in the period 2001 to 2005, but recovered slightly

afterwards.

50

NUMBER OF PEOPLE AFFECTED BY HIV (THOUSAND)

2000 2005 2007

Total population affected by HIV 114.0 174.0 183.0

Black African 113.0 170.0 179.0

Coloured 1.0 3.0 3.0

Indian or Asian 0.0 0.1 0.1

White 0.2 0.3 0.8

Age Group (0 - 4) 3.0 6.0 6.0

Age Group (15 - 44) 100.3 146.1 151.1

Total population 1654.0 1654.0 1635.0

Source: ECSECC and Own Calculations

POPULATION BY AGE GROUP

1995 (Number) 1995 - 2000 (%) 2001 - 2005 (%) 2006 (Number) 2009 (Number) 2006 - 2009 (%)

Total 1,576,593 4.9 -0.3 1,646,156 1,647,889 0.1

0 - 14 566,649 0.4 -0.4 599,745 557,095 -0.5

15 - 44 708,921 6.0 0.2 754,441 758,448 0.5

45 - 65+ 301,023 11.0 -1.3 331,970 332,346 0.1


Table 4.1.1: Population by Age Group in Amatole, 1995-2009

4.1.2 TOTAL POPULATION AffECTED By HIV

The table below analyses the population affected by HIV. Out of the races listed, Africans

are the most affected, forming about 98 percent of the affected population. However, this

affected population only constitutes 11 percent of the entire African population.

The number of children affected increased over the years. About 82 percent of the affected

population belonged to the age group 15-44 in 2007.

Table 4.1.2: People affected by HIV in Amatole, 2000-2007

4.1.3 HOUSEHOLD INCOME AND ExPENDITURE

Consumption expenditure by household has been rising in the district. Most consumption

expenditure comes from the consumption of non-durable goods and semi-durable goods.

Between 2000 and 2005, there was a drastic increase in consumption expenditure for all

goods, and this was coupled with negative savings. Consumption expenditure dropped

between 2006 and 2009, and the saving rate more than doubled.

AM

ATO

LE D

ISTR

ICT

MU

NIC

IPALIT

Y

51

HOUSEHOLD INCOME AND EXPENDITURE

1995 1995 - 2001 - 2006 2009 2006 - (Number) 2000 (%) 2005 (%) (Number) (Number) 2009 (%)

Final consumption expenditure by households 11,316,749 14.1 23.2 17,932,607 20,756,075 15.7

Durable goods 1,112,020 5.7 49.8 2,134,990 2,515,147 17.8

Semi-durable goods 1,129,919 29.7 63.4 3,055,131 4,043,166 32.3

Non-durable goods 5,186,352 3.1 15.0 6,546,488 7,245,846 10.7

Services 3,888,459 26.6 13.4 1,049,813 6,951,917 12.2

Current income 13,075,326 14.5 20.6 19,958,141 20,462,394 15.0

Disposable income 11,479,061 14.3 21.7 17,799,953 20,462,394 15.0

Saving by households 162,312 31.7 -79.3 -132,654 -293,681 121.4


Table 4.1.3: Household Income and Expenditure in Amatole, 1995-2009

4.1.4 SECTORAL CONTRIBUTION ANALySIS

Amatole is the second-largest contributor to the primary sector, with only 18.3 percent of

the total sector in the province. The primary sector’s contribution in the district economy has

been less than 2 percent on average, with agriculture holding an average of 94.2 percent

of this sector in the entire district. The share of both primary and secondary sectors in the

district economy has decreased over the years. The secondary sector has represented an

average of 21.5 percent of the district production and 27.6 percent of the total sector in the

province. On average, the manufacturing industry alone has contributed 81.3 percent to

the sector, representing 26.9 percent of the provincial manufacturing industry. The tertiary

sector is the dominant sector in the region, with 76.7 percent of the total production.

Finance, insurance, real estate and business services, general government and wholesale

industry are the major industries, with 80 percent of the tertiary sector.

Figure 4.1: GVA Contribution for Amatole, 1995-2008

52

4.1.5 SECTORAL gROWTH ANALySIS

The primary sector contributed less than 2 percent to the economy of the Amatole District

Municipality (DM) in 2008. More than 95 percent of this contribution was derived from the

agriculture, forestry and fishing industry. Between 1995 and 2000, growth in this sector

was negative, in line with provincial growth, and started growing around 2001 and 2005

before declining, although it still remained slightly above 1 percent. The growth rate of the

DM has always been less than that of the province. The growth of agriculture, forestry

and fishing, which has been a mirror image of the entire primary sector in the Amatole DM

despite showing the same trend as the national growth, has always lagged the provincial

growth rate.

The secondary sector, which is the second-largest contributor to the district’s economy,

has experienced negative since 2001. This growth rate is contrary to the one showed by

the province, which has been both positive and growing steadily. This pattern of growth has

been mimicked by manufacturing, which is the leading industry within this sector. There

has been a gradual contraction in the growth of the secondary sector in the Amatole DM.

The tertiary sector, which is the dominant sector in the Amatole DM, is the only one that

has shown resilience from 1995 to 2008. It recorded positive growth, which has been

increasing steadily over the period under review. This has been the case for the provincial

growth of the sector. Out of the three leading industries in terms of growth, only two

have grown without decreasing in momentum, namely, finance, insurance, real estate and

business services and general government services. Growth in finance, insurance, real

estate and business services increased between 2001 and 2005, and also between 2001

and 2008, compared to the growth between 1995 and 2000. General government services

has grown gradually despite the difference in growth recorded between the district and

provincial growth in 2001 and 2006, when provincial growth shot up to just above 6

percent, while the district trailed with 1 percent. Wholesale and retail trade, catering and

accommodation has declined in growth when compared to the period 1995 to 2000. Also,

in relation to the provincial growth, this industry’s growth has dropped.

AM

ATO

LE D

ISTR

ICT

MU

NIC

IPALIT

Y

53

ACCESS TO WATER (%)

Average 1995 - 2005 2006 2007 2008 2009

Piped water inside dwelling 21.1 19.0 18.9 18.6 18.3

Piped water inside yard 15.0 17.2 17.3 17.6 17.8

Piped water on community stand:distance less than 200m from dwelling 20.8 18.6 18.5 18.2 18.0

Piped water on community stand:distance greater than 200m from dwell. 9.7 13.7 13.8 14.4 14.9

Borehole/rain-water tank/well 3.8 4.1 4.1 4.1 4.2

Dam/river/stream/spring 27.3 25.3 25.3 25.0 24.7

Water carrier/tanker/water vendor 0.7 0.4 0.4 0.3 0.3

Other/unspecified/dummy 1.6 1.8 1.8 1.8 1.8


AMATOLE DM: AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

EC -0.2% 3.1% 1.7%

PRIMARY SEC. Amatole DM -1.7% 2.4% 1.1%

EC -0.2% 3.1% 1.8%

AFF Amatole DM -1.4% 2.6% 1.3%

EC 2.6% 2.8% 3.2%

SECONDARY SEC. Amatole DM 1.2% -0.7% -1.1%

EC -0.2% 3.1% 1.8%

MAN Amatole DM 0.6% -0.8% -1.2%

EC 2.5% 3.3% 3.6%

TERTIARY SEC. Amatole DM 1.8% 2.4% 2.7%

EC 1.2% 6.3% 6.0%

FIBS Amatole DM 1.3% 5.8% 5.3%

EC 1.3% 6.3% 2.0%

GGS Amatole DM 0.9% 1.0% 1.7%

EC 3.7% 2.2% 2.7%

WRTCA Amatole DM 2.4% 0.1% 0.2%


Table 4.1.4: Sectoral Growth for Amatole, 1995-2008

4.1.6 ACCESS TO SERVICES

4.1.6.1 Access to Water

The number of households with access to piped water inside their dwellings has increased

from 66.6 percent between 1995 and 2005 to 69.0 percent in 2009. About 31 percent of

households continue to access water through boreholes, dams, rivers, streams and other

modes.

Table 4.1.5: Access to Water in Amatole, 1995-2009

54

4.1.6.2 Access to Energy

The majority of the population uses electricity and paraffin for lighting. Households using

electricity for lighting have increased by approximately 6 percent, which was coupled with

a decrease of about 6 percent in households using paraffin. The number of people using

candles for lighting grew by less than half a percentage point.

Table 4.1.6: Access to Energy in Amatole, 1995-2009

ACCESS TO ENERGY (%)

Average 1995 - 2005 2006 2007 2008 2009

Solar/other/unspecified 0.8 0.6 0.6 0.6 0.6

Electricity 44.9 49.9 49.8 50.5 51.2

Gas 0.6 0.5 0.5 0.4 0.4

Paraffin 47.1 42.0 42.1 41.4 40.7

Candles 6.6 7.0 7.1 7.1 7.2


4.1.6.3 Access to Sanitation

A growth rate of less than 1 percentage point was observed in the number of households

using flushed or chemical toilets between 2005 and 2009. Households using pit latrines,

bucket latrines, and other forms of waste removal have remained above 60 percent since

the period 1995 and 2005.

Table 4.1.7: Access to Sanitation in Amatole, 1995-2009

ACCESS TO SANITATION (%)

Average 1995 - 2005 2006 2007 2008 2009

Flush or chemical toilet 37.8 38.4 38.4 38.5 38.6

Pit latrine 31.0 29.7 29.6 29.4 29.2

Bucket latrine 2.8 2.7 2.6 2.6 2.6

None of the above 28.3 29.2 29.4 29.5 29.6

Unspecified/dummy 0.2 0.1 0.1 0.1 0.1


AM

ATO

LE D

ISTR

ICT

MU

NIC

IPALIT

Y

55

4.1.6.4 Access to Telephones

The number of households that have access to phones, either through cellphones or

nearby public phones, grew from about 60 percent between 1995 and 2005 to slightly

above 67 percent in 2009. This was accompanied by a decline of about 9 percentage

points in the number of households without access to phones.

ACCESS TO TELEPHONE (%)

Average 1995 - 2005 2006 2007 2008 2009

In this dwelling and/or cellular phone 24.3 27.6 27.6 28.0 28.4

At a public telephone nearby 35.5 38.0 38.2 38.5 38.8

At a neigbour nearby 8.8 9.5 9.5 9.5 9.6

At another location, not nearby 5.7 5.7 5.8 5.8 5.8

At another location nearby 3.8 4.1 4.2 4.2 4.2

NA (institution)/unspecified/none 21.9 15.1 14.9 14.0 13.1


ACCESS TO REFUSE (%)

Average 1995 - 2005 2006 2007 2008 2009

Unspecified/other 0.6 0.2 0.2 0.2 0.1

Removed by local authority at least once a week 38.1 39.8 39.8 40.0 40.3

Removed by local authority less often 2.0 1.5 1.5 1.4 1.4

Communal refuse dump 1.4 1.2 1.2 1.1 1.1

Own refuse dump 40.5 41.3 41.3 41.4 41.4

No rubbish disposal 17.3 16.0 16.0 15.9 15.7


Table 4.1.8: Access to Telephone in Amatole, 1995-2008

4.1.6.5 Access to Refuse

Generally, the number of households with access to refuse-removal by local authorities has

changed by less than 2 percentage points since 2005. Almost 60 percent of households

either use their own means of dumping or have no access to disposal of refuse.

Table 4.1.9: Access to Refuse in Amatole, 1995-2009

56

4.1.7 TyPES Of DWELLINg

The numbers of people with access to a house on a separate stand or a traditional

dwelling, are the highest in the district. They account for approximately 70 percent of the

population. But there has not been much change in growth since 1995.

Table 4.1.10: Access to Housing in Amatole, 1995-2009

TYPES OF DWELLING (%)

Average 1995 - 2005 2006 2007 2008 2009

House or brick structure on a separate stand or yard 41.2 41.2 41.1 41.1 41.1

Traditional dwelling/hut/structure madeof traditional materials 33.2 31.9 31.8 31.6 31.5

Flat in a block of flats 3.2 3.4 3.5 3.5 3.5

Town/cluster/semi-detached house(simplex, duplex or triplex) 1.2 1.1 1.1 1.1 1.1

House/flat/room in backyard 2.4 2.2 2.2 2.2 2.1

Informal dwelling/shack in backyard 3.5 3.4 3.4 3.4 3.4

Informal dwelling/shack, NOT in back-yard, e.g. in an informal settlement 12.5 14.0 14.2 14.5 14.7

Room/flatlet not in backyard, but ona shared property 1.0 0.8 0.8 0.8 0.7

Other/unspecified/NA 1.8 1.9 1.9 1.9 1.9


4.1.8 fORMAL EMPLOyMENT By SECTOR

Economic sectors were dealt a huge blow between 1995 and 2000, with nine of the

11 sectors recording job losses. There were slight improvements between 2001 and

2005, with five sectors shedding jobs. About 80 percent of the people in the district

were employed in the tertiary sector, followed by the secondary and primary sectors.

The propensity to shed jobs has continued to plague the sectors in this district, with the

highest rate of losses registered in agriculture, forestry and fishing.

Table 4.1.11: Formal employment by sector in Amatole, 1995-2008

NUMBER OF PEOPLE EMPLOYED BY SECTOR


AFF 12,973 -14.9 14.0 9,006 8,064 -10.5

MQ 2,086 -53.4 -17.1 747 716 -4.1

MAN 46,800 -15.0 -6.2 36,143 34,695 -4.0

EGW 1,095 -10.5 -3.2 931 918 -1.4

CON 14,496 -28.0 0.8 11,036 10,638 -3.6

WRTCA 26,014 13.4 9.9 33,756 35,214 4.3

TSC 8,714 -28.1 0.4 6,175 6,072 -1.7

FIBS 17,534 41.2 20.2 33,451 36,596 9.4

CSPS 90,753 -6.2 -2.4 81,834 81,412 -0.5

OGSS 33,446 2.0 -3.6 32,241 32,064 -0.5

GSS 57,307 -10.9 -1.6 49,593 49,348 0.5


AM

ATO

LE D

ISTR

ICT

MU

NIC

IPALIT

Y

57

DEPENDENCY RATIO

1995 (%) 2000 (%) 2005 (%) 2009 (%)

73.3 69.3 67.9 67.1


NUMBER OF PEOPLE EMPLOYED BY SKILL


Formal and informal

employment 269,885 -3.1 -1.0 259,003 256,709 -0.9

Informal employment 49,419 6.3 -10.0 45,925 42,384 -7.7

Formal employment 220,466 -5.3 1.1 213,078 214,326 0.6

High skilled 33,738 -11.3 -1.5 29,135 28,726 -1.4

Skilled 86,304 1.7 5.9 95,854 98,548 2.8

Semi- and unskilled 100,424 -9.2 -2.7 88,089 87,052 -1.2

Source: Quantec Research and Own Calculations

4.1.9 INfORMAL AND fORMAL EMPLOyMENT By SkILL

Since 2001, the informal sector has been shedding jobs, with about 10 percent of losses

recorded between 2001 and 2005. Most people are employed in the formal sector, with

most jobs being shed in those industries requiring highly skilled individuals.

Table 4.1.12: Informal and Formal Employment by Skill in Amatole, 1995-2008

4.1.10 DEPENDENCy RATIO

The dependency ratio in the district has been decreasing since 1995, and was 67.1 percent

in 2009. This is indicative of the fact that the number of people who are not economically

active has decreased proportionally. However, more than half of the households still earn

not more than R3 500 per month.

Table 4.1.13: Dependency Ratio in Amatole, 1995-2009

4.1.11 LEVEL Of EDUCATION

The majority of the people in the district have no education, and the rate of growth

still remains negative. Since 2000, there has been no significant change in the number

of people with Grade 12. Between 1995 and 2000, the number of pupils that have a

Bachelor’s degree and a diploma doubled, and there was 85% growth in the number of

people who pursued an Honours degree. On the other hand, the growth rate of illiterate

pupils has also been negative.

58

LEVEL OF EDUCATION


Total 1,576,593 4.9 -0.3 1,646,156 1,647,889 0.1

Grade 0 / No schooling 313,490 -11.9 -1.0 259,528 252,083 -2.9

Grade 12 111,420 22.8 0.2 142,141 145,270 2.2

Less than matric and certificate / diploma 11,211 -29.2 -3.2 6,754 6,182 -8.5

Certificate with Grade 12 6,906 56.0 1.4 11,835 12,402 4.8

Diploma with Grade 12 26,137 21.8 0.9 33,231 34,043 2.4

Bachelor’s Degree 8,446 6.8 1.2 9,159 9,255 1.1

Bachelor’s Degree and Diploma 1,626 104.7 2.3 3,825 4,087 6.8

Honours Degree 1,030 84.1 2.5 2,158 2,295 6.4

Higher Degree (Master’s /

Doctorate) 1,178 60.1 1.2 2,073 2,172 4.8

Other (unspecified) 191,186 -12.1 -0.5 158,957 154,943 -2.5


LITERATE / ILLITERATE


Illiterate 313,490 -11.9 -1.0 259,528 252,083 -2.9

Literate 540,933 11.4 -0.3 609,129 614,448 0.9


Table 4.1.14: Level of Education in Amatole, 1995-2009

AM

ATO

LE D

ISTR

ICT

MU

NIC

IPALIT

Y

Table 4.1.15: Literacy level in Amatole, 1995-2009

4.1.12 NUMBER Of PEOPLE IN POVERTy

The district saw a rise in the number of people living in poverty between 1995 and 2005,

before it fell to 55.1 percent of the total population in 2009. Despite this decrease, more

than half of the population is still categorised as poor in the second-largest economy in

the province.

Table 4.1.16: People in Poverty in Amatole, 1995-2009

NUMBER OF PEOPLE IN POVERTY

1995 (%) 2000 (%) 2005 (%) 2009 (%)

53.1 63.0 64.98 55.1


4.1.13 DISTRIBUTION Of HOUSEHOLDS By INCOME

The household distribution per income group indicates that the income levels of the

population have improved over the years, with most households earning between R1 001

and R3 500. There has been a reduction in the number of households earning less than

R3 500, from about 78 percent in 1995 to about 52 percent in 2009. This was coupled with

an increase in households earning more than R3 500, from approximately 22 percent in

1995 to almost 48 percent in 2009.

59

HOUSEHOLD DISTRIBUTION BY INCOME

1995 (%) 2000 (%) 2005 (%) 2009 (%)

< R500 per month 10.6 11.5 5.7 2.1

R500 - R1000 22.9 17.4 15.7 10

R1001 - R3500 44.1 45.6 44.9 40.3

R3501 - R6000 9.4 10.5 13.8 18.4

R6001 - R11000 6.7 6.5 8.3 12.3

R11001 - R16000 3.5 3.4 3.7 5.1

R16001 - R30000 1.9 3.2 4.8 6.4

R30001 - R50000 0.7 1.5 2.1 3.3

R50000+ 0.2 0.4 1.1 2.3


URBANIZATION (%)

1995 (%) 2000 (%) 2005 (%) 2009 (%)

People living in rural areas 61.4 60.0 60.0 59.9

People living in urban areas 38.6 40.0 40.0 40.1


Table 4.1.17: Distribution of Households by Income in Amatole, 1995-2009

4.1.14 HUMAN DEVELOPMENT INDICATOR (HDI)

The Human Development Indicator (HDI) was developed by the United Nations to assess

comparative levels of development in terms of literacy, life expectancy and purchasing

power. The more the HDI is close to one, the better is the human development in the

area. The HDI in Amatole increased from 0.48 in 1995 to 0.53 in 2005, and remained at

0.53 until 2009.

Table 4.1.18: Human Development Indicator in Amatole, 1995-2009

HUMAN DEVELOPMENT INDICATOR

1995 (%) 2000 (%) 2005 (%) 2009 (%)

0.48 0.51 0.53 0.53


4.1.15 URBANISATION

The majority of the people in the district reside in rural areas.

Table 4.1.19: Urbanisation in Amatole, 1995-2009

60

4.2 ALFRED NZO DISTRICT MUNICIPALITYThe Alfred Nzo District Municipality consists of two local municipalities, namely, Umzimkulu

and Umzimvubu local municipalities. It is situated on the north-eastern part of the Eastern

Cape and shares a border with KwaZulu-Natal, the Kingdom of Lesotho and the two other

Eastern Cape district municipalities, namely, Ukhahlamba and O.R. Tambo. The district

covers 6 859 square meters. The Umzimkulu local municipality is an island within KwaZulu-

Natal. About 94 percent of the population is rural, while 6 percent is urban.

The total population of the Alfred Nzo District Municipality consisted of 437 707 people in

2008 and increased to 442 050 in 2009. These figures were made up of 98.9 percent of

Africans, 0.7 percent of Coloureds, 0.1 percent of Asians and only 0.3 percent of Whites.

About 45 percent of the 2009 total population was male and the remaining 55 percent was

female. There has been no increase in gender proportion since 2008.

The total Gross Value Added for 2008 was R2.1 billion, a growth of almost 3 percent.

Alfred Nzo is the smallest economy in the province. Of this total, the sectors which

contributed the most to the economy of the district were general government services

with 35 percent, followed by community, social and personal services with 18 percent,

finance and insurance with 14 percent, and wholesale and retail trade with 11 percent.


The age group of people between 45 years and above has been growing faster than any

other age group in the district municipality. However, all population groups recorded a

decline in growth for the period 2006 to 2009 compared to the previous two periods. The

total population growth declined to just above 4 percent in 2006 to 2009.

Table 4.2.1: Population by Age Group in Alfred Nzo, 1995-2009

ALF

RED

NZO

DIS

TRIC

T M

UN

ICIPA

LITY



Total 367,880 5.0 6.5 424,580 442,050 4.1

0 - 14 165,582 1.9 6.2 183,484 190,120 3.6

15 - 44 139,170 7.0 6.9 165,231 172,678 4.5

45 - 65+ 63,128 8.5 6.6 75,865 79,252 4.5



The total number of people affected by HIV increased from about 23 000 to 41 000 between

2000 and 2007. Africans are the most affected, forming an alarming 99 percent of the

total affected population, followed by Coloureds, Whites and Asians. Despite a decrease

from about 87 percent to about 77 percent in the number of people aged between 15 and

44 years who were affected by HIV, this group remains the most affected.

61

Table 4.2.2: People affected by HIV in Alfred Nzo, 2000-2007




Durable goods 258,920 4.5 49.6 494,897 604,838 22.2

Semi-durable goods 288,728 27.2 69.4 810,156 1,150,961 42.1


Services 680,005 30.7 19.0 1,212,508 1,480,063 22.1

Current income 2,423,305 15.6 24.0 4,445,678 5,318,274 19.6


Saving by households 13,946 265.0 -80.1 -32,527 -108,379 233.2



2000 2005 2007


Black African 22.5 36.9 40.9



White 0.0 0.0 0.0

Age Group (0 - 4) 1.0 2.0 2.1

Age Group (15 - 44) 19.6 29.5 31.8




Final consumption in Alfred Nzo grew by slightly more than 22 percent between 2006 and

2008 from about 14 percent between 1995 and 2000, while disposable income recorded

a growth of about 21 percent and household savings recorded growth of more than 200

percent. Sectors that recorded most growth are semi-durables, durables, non-durables

and services, respectively. Non-durables have continued to hold the largest share of

consumption in the district. The rate of savings has been plummeting since 2000.

Table 4.2.3: Household Income and Expenditure in Alfred Nzo, 1995-2009


The tertiary sector has contributed an average 88.1 percent to the district output,

representing only 2.7 percent of the sector at the provincial level. The secondary sector,

with 7.5 percent of district output, represents less than 1.0 percent of the Eastern

Cape secondary sector. The district economy is dominated by tertiary industries, with

general government playing the leading role. Wholesale and retail trade, catering and

accommodation is the second major contributing industry. Community, social and personal

services is third, and finance fourth, with 11.8 percent of total output.

62

Figure 4.2: GVA Contribution for Alfred Nzo, 1995-2008


The primary sector has experienced a trend reversal in its growth figures, in spite of the

positive growth displayed from 2001 to 2005. Growth in the Alfred Nzo District Municipality

has been driven by the agriculture, forestry and fishing industry, which accounted for

most of the provincial growth between 1995 and 2000.

The secondary sector shows a decline in the sector’s growth when comparing the years

1995 to 2000 against the years 2001to 2008, while at a provincial level, a steady incline

was observed in this sector’s growth. Growth in the district’s secondary sector has

been driven by growth experienced in the manufacturing industry, which consistently

outperformed that of the province, despite showing signs of falling during the period 2001

to 2008, when contrasted against the growth rate seen between 1995 and 2000.

The tertiary sector’s growth has been moving in the opposite direction to that of the

province, which showed an improvement in growth. Industries that have been at the

forefront of growth in this sector are general government services, with growth levels

that surpassed the province’s from 1995 to 2008.

ALF

RED

NZO

DIS

TRIC

T M

UN

ICIPA

LITY

63

ALFRED NZO DM: AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

EC -0.2% 3.1% 1.7%

PRIMARY SEC. Alfred Nzo DM 2.0% 2.4% 0.8%

EC -0.2% 3.1% 1.8%

AFF Alfred Nzo DM 2.1% 3.1% 1.6%

EC 2.6% 2.8% 3.2%

SECONDARY SEC. Alfred Nzo DM 7.3% 4.8% 4.9%

EC -0.2% 3.1% 1.8%

MAN Alfred Nzo DM 8.4% 4.4% 4.3%

EC 2.5% 3.3% 3.6%

TERTIARY SEC. Alfred Nzo DM 4.5% 2.5% 2.7%

EC 1.3% 1.0% 3.0%

GGS Alfred Nzo DM 3.7% 2.3% 4.3%


Table 4.2.4: Sectoral Growth for Alfred Nzo, 1995-2008



In 2009, about 43 percent of households had access to piped water, an increase of more

than 6 percent from the 1995 to 2005 figure. This was accompanied by a decrease of

about 7 percent in the number of people who drew water from dams, streams, springs,

tankers and other modes.

Table 4.2.5: Access to Water in Alfred Nzo, 1995-2009

ACCESS TO WATER (%)

Average 1995 - 2005 2006 2007 2008 2009











The majority of households in Alfred Nzo use candles for energy. The number of households

that have access to electricity increased from 16.1 percent between 1995 and 2005 to

19.8 percent in 2009. This increase was associated with a decrease of about 3 percent in

households using candles for energy.

64

ALF

RED

NZO

DIS

TRIC

T M

UN

ICIPA

LITY

Table 4.2.6: Access to Energy in Alfred Nzo, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Electricity 16.1 19.1 19.0 19.4 19.8

Gas 0.3 0.2 0.2 0.2 0.2

Paraffin 12.5 12.1 12.2 12.1 12.0

Candles 69.7 67.2 67.2 66.9 66.5



Average 1995 - 2005 2006 2007 2008 2009


Pit latrine 59.2 56.5 56.3 56.0 55.7

Bucket latrine 1.3 1.1 1.0 1.0 1.0

None of the above 30.6 31.0 31.1 31.1 31.1




Average 1995 - 2005 2006 2007 2008 2009









Households with access to flushed or chemical toilets increased by 3.5 percentage points

from 1995 to 2005. This was associated with an identical decrease in the number of

households who used pit latrines, bucket latrines, or other modes of waste disposal.

However, the number of people using these forms of waste management still remains

high, at almost 90 percent since 2005.

Table 4.2.7: Access to Sanitation in Alfred Nzo, 1995-2009


Access to phones increased from about 50 percent in 2005 to almost 61 percent in 2009,

with the majority of households using public phones, followed by cellular phones. This

leaves a staggering 39 percent of households with no access at all to phones.

Table 4.2.8: Access to Telephone in Alfred Nzo, 1995-2008

65


Average 1995 - 2005 2006 2007 2008 2009





Own refuse dump 68.8 67.6 67.5 67.4 67.2




About 90 percent of households in the district municipality either use their own dumping

or have no means of disposing of their rubbish. The number of households that have

access to removal of refuse by local authorities increased by more than 2 percentage

points since 2005.

Table 4.2.9: Access to Refuse in Alfred Nzo, 1995-2009


Traditional dwellings are the most common type of dwelling in the district, with an average

growth of 65.9 percent between 1995 and 2005, and 62.58 percent in 2009. There was

a 3 percent increase over 14 years in the number of households that gained access to

houses or brick structures. Overall, about 96 percent of households have access to some

form of housing.

Table 4.2.10: Access to Housing in Alfred Nzo, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009





House/flat/room in backyard 40 3.3 3.2 3.2 3.1






66

ALF

RED

NZO

DIS

TRIC

T M

UN

ICIPA

LITY


Growth in formal employment in Alfred Nzo has improved over the past fourteen years,

with some industries ceasing to shed jobs. Between 2001 and 2005, manufacturing,

construction, wholesale and retail, transport and communication, and general government

services all recorded an increase in job creation.

Table 4.2.11: Formal employment by sector in Alfred Nzo, 1995-2008



AFF 1,808 -12.2 -5.3 1,573 1,521 -3.3

MQ 325 -45.2 -1.8 168 172 2.4

MAN 910 -7.4 3.5 893 891 -0.2

EGW 132 -6.8 -5.9 118 116 -1.3

CON 931 -16.7 8.2 946 975 3.1

WRTCA 2,716 10.8 4.9 3,142 3,211 2.2

TSC 552 -16.5 10.7 525 541 3.0

FIBS 979 44.0 20.0 1,905 2,075 9.0

CSPS 8,870 0.5 3.6 9,317 9,509 2.1

OGSS 3,287 8.8 1.2 3,625 3,703 2.2

GSS 5,583 -4.5 5.4 5,692 5,806 2.0



The formal and informal sectors of employment in the district created jobs, even though

the growth rate has been declining. Formal employment by skill recorded a growth in the

number of jobs created. The one sector to experience significant growth between 2001

and 2005 was the skilled sector, followed by the semi- and unskilled sectors. However, job

losses continued to be observed afterwards among skilled workers. The entire informal

employment sector has decreased since 2000 and has been shedding jobs.

Table 4.2.12: Informal and Formal Employment by Skill in Alfred Nzo, 1995-2008



Formal and informal

employment 21,781 1.6 1.9 22,988 23,144 0.7


Formal employment 17,223 0.4 4.7 18,587 19,013 2.3

High skilled 2,781 -9.9 -0.3 2,460 2,425 -1.4

Skilled 6,623 7.0 9.4 8,081 8,403 4.0

Semi- and unskilled 7,819 -1.5 2.0 8,046 8,185 1.7


67

DEPENDENCY RATIO

1995 (%) 2000 (%) 2005 (%) 2009 (%)

73.3 69.3 67.9 67.1



The dependency ratio in Alfred Nzo has been decreasing over the years, but still remains

high at almost 98 percent. The number of households earning not more than R3 500

remains high at about 72 percent.

Table 4.2.13: Dependency Ratio in Alfred Nzo, 1995-2009


The number of persons with no schooling increased between 2001 and 2008, when

compared to the number recorded between 1995 and 2000. Figures show growth in

the number of people who completed matric since 2000. Between 1995 and 2000, the

number of people with Honours degrees recorded the highest growth, followed by those

with Bachelor’s degrees and diplomas, and those with higher degrees. The number

of people attaining post-matric qualifications has been growing since 2000, with most

candidates being awarded diplomas, Bachelor’s degrees and post-graduate degrees. The

illiteracy rate has still been growing, although at a much slower rate.

Table 4.2.14: Level of Education in Alfred Nzo, 1995-2009

LEVEL OF EDUCATION


Total 367,880 5.0 6.5 424,580 442,050 4.1

Grade 0 / No schooling 69,260 -7.1 4.3 66,390 67,254 1.3

Grade 12 10,524 26.7 7.9 15,189 16,509 6.6

Less than matric and certificate / diploma 1,689 -23.8 3.7 1,235 1,206 -2.4

Certificate with Grade 12 550 61.0 4.1 1,016 1,081 6.5


Bachelor’s Degree 466 44.1 3.3 744 700 4.9

Bachelor’s Degree and Diploma 126 162.0 6.3 412 452 9.8

Honours Degree 47 167.0 10.8 167 186 11.7


Doctorate) 53 95.9 11.6 134 150 11.3

Other (unspecified) 56,284 -8.5 5.5 53.989 55,014 1.9




Illiterate 69,268 -7.1 4.3 66,390 67,254 1.3

Literate 89,874 8.5 7.2 109,021 114,295 4.8


Table 4.2.15: Literacy level in Alfred Nzo, 1995-2009

68

ALF

RED

NZO

DIS

TRIC

T M

UN

ICIPA

LITY


The number of people living in poverty in the Alfred Nzo district grew by 5.3 percentage

points between 1995 and 2009. In 2009, more than two-thirds of the district population

is categorised as poor.

Table 4.2.16: People in Poverty in Alfred Nzo, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

67.4 81.9 88.9 72.7



In 1995, about 92 percent of households were earning not more than R3 500, with almost

52 percent of this group earning not more than R1 000. The majority of households in this

district municipality were earning slightly above R1 000, and not more than R3 500. This

income group grew from just below 40 percent in 1995 to slightly more than 51 percent

in 2009. The group earning between R3 501 and R11 000 increased from 7 percent in

2000 to about 22 percent in 2009. This was accompanied by a decline since 2005 in

those groups earning less than R500; and more than R500 but not more than R1 000.

Households earning more than R11 000 recorded growth of about 4 percent from 1995

to 2009.

Table 4.2.17: Distribution of Households by Income in Alfred Nzo, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

< R500 per month 16.7 19 9.8 4.2

R500 - R1000 34.9 27.9 25.4 16.5

R1001 - R3500 39.5 43.2 49.4 51.1

R3501 - R6000 4.5 5.1 8.5 15.4

R6001 - R11000 2.4 5.1 8.5 15.4

R11001 - R16000 1.5 0.9 1.3 2.2

R16001 - R30000 0.3 0.7 1.3 2.2

R30001 - R50000 0.1 0.3 0.5 0.9

R50000+ 0.0 0.1 0.2 0.4



The Human Development Indicator (HDI) in Alfred Nzo has shown minimal growth over

the past 14 years. HDI increased from 0.40 in 1995 to 0.42 in 2009, indicating that the

district remains underdeveloped with low life expectancy, high illiteracy rates and low

standards of living.

69

URBANIZATION (%)

1995 (%) 2000 (%) 2005 (%) 2009 (%)





1995 (%) 2000 (%) 2005 (%) 2009 (%)

0.4 0.41 0.43 0.42


Table 4.2.18: Human Development Indicator in Alfred Nzo, 1995-2009

4.2.15 URBANISATION

The majority of people in the district remain rural.

Table 4.2.19: Urbanisation in Alfred Nzo, 1995-2009

707 Ratio is defined as the number of affected people in each race group divided by the total affected population

4.3 CACADU DISTRICT MUNICIPALITYThe Cacadu District Municipality consists of nine local municipalities. It shares a border

with the Western Cape and the Northern Cape, as well as two Eastern Cape district

municipalities, namely, Chris Hani and Amatole. Cacadu covers an area of 58 272 square

meters. About 71 percent of its population is rural, while 29 percent is urban.

Total population increased from 385 019 in 2008 to 386 875 in 2009. These totals constitute

53.2 percent of Africans, 36 percent of Coloureds, 0.2 percent of Asians and 10.6 percent

of Whites. About 48 percent of the population is male and 52 percent is female.

The Gross Value Added was R11.4 billion in 2008, making it the third-largest economy

in the region. The 2008 GVA saw growth of 8 percent from 2007. Finance and insurance

contributed 24 percent, wholesale and retail trade 18 percent, general government 16

percent and manufacturing 12 percent.


The table below shows the population by age group in the Cacadu DM from 1995 to

2009.

Table 4.3.1: Population by Age Group in Cacadu, 1995-2009

CA

CA

DU

DIS

TRIC

T M

UN

ICIPA

LITY



Total 364,710 5.7 0.0 385,448 386,875 0.4

0 - 14 112,157 0.9 -0.1 111,776 111,480 -0.3

15 - 44 175,033 6.3 0.1 186,705 187,646 0.5

45 - 65+ 77,521 11.1 0.0 86,967 87,749 0.9


The total population in Cacadu DM has been growing since 1995, although at a much

slower rate. The group that recorded the highest growth is the 45 years-and-above, while

the age group 0 to 14 has been declining since 2001. The population weight composition

has not changed that much since 1995, with the age group 15 to 44 accounting for almost

50 percent of the population and the age group 0 to 14 accounting for 29 percent of the

population.

4.3.2 TOTAL POPULATION By AffECTED HIV

There has been an increase in the number of people affected by HIV, with the percentage

composition of the population affected showing variation with time. The growth rate

between 2000 and 2007 was 61 percent among Africans, with the ratio7 exceeding 80

percent in 2000 and 2007. People in the age group 15 to 44 were the most affected,

contributing about 86 percent in 2007 to the total affected. This was a growth rate of

about 72 percent between 2000 and 2007.

71

Table 4.3.2: People affected by HIV in Cacadu, 2000-2007




Durable goods 316,398 9.1 59.8 682,654 859,668 25.9

Semi-durable goods 297,710 34.2 65.1 828,130 1,127,620 36.2


Services 1,435,709 28.4 16.2 2,363,210 2,748,439 16.3

Current income 4,309,899 17.1 23.6 6,912,485 8,139,737 17.8

Disposable income 3,789.357 17.0 24.8 6,189,450 7,342,571 18.6

Saving by households 92,233 11.6 6.9 84,327 63,656 -24.5



2000 2005 2007





White 0.1 0.3 0.4

Age Group (0 - 4) 0.4 1.0 1.0

Age Group (15 - 44) 18.0 30.0 31.0




Between 2006 and 2009, final consumption of goods decreased to 19.2 percent from

25.2 percent between 2001 and 2005. Semi-durable goods recorded the highest growth

between 2006 and 2009, followed by durable goods, service and non-durable goods. In

2009, the services sector was the highest contributor to consumption with 38 percent,

followed by non-durable goods (35 percent), semi-durable and durable goods. This is a shift

from 1995, when non-durable goods was the highest contributor to final consumption,

followed by services.

Table 4.3.3: Household Income and Expenditure in Cacadu, 1995-2009


The primary sector percentage of the district total output has declined from 14.4 percent

in 1995 to 6.1 percent in 2008, and this trend is not about to stop. Over that period, it

contributed an average of 10.6 percent to the district’s economy, with agriculture being

the dominant industry in the sector. Cacadu is the largest contributor to the primary sector,

with 35.6 percent of the total. The secondary sector contribution to the district’s economy

grew from 13.8 percent in 1995 to 20.1 percent in 2008, with manufacturing leading

the sector. The construction industry’s share of the secondary sector increased slightly

72

over the years, and was at 28.3 percent in 2008. The tertiary sector is the dominant

sector in the district, representing 73.6 percent of its output. The contribution of finance,

insurance, real estate and business services has increased over the years, and since 2003

has become the leading industry in the sector. Transport, storage and communications is

the lowest-contributing industry in the sector.

Figure 4.3: GVA Contribution for Cacadu, 1995-2008

CA

CA

DU

DIS

TRIC

T M

UN

ICIPA

LITY


Similar to the province’s primary sector’s growth, the primary sector in the Cacadu District

Municipality rose from negative territory between 1995 to 2000 to an average growth

of almost 3 percent in 2001 and 2008. Overall, the period 2001 to 2008 experienced

positive growth in the district, with a trend that closely resembled that of the province.

This sector’s growth was driven by agriculture, forestry and fishing, with almost similar

growth rates. Despite lagging provincial growth, this industry’s growth has tracked the

provincial trend.

The secondary sector showed impressive growth from 1995 to 2008, with growth

levels rising from about 6 percent between the years 1995 to 2000 to almost 14 percent

between 2001 and 2008. This growth rate was fuelled by the construction industry

and manufacturing, which recorded growth figures of about 19 percent and 12 percent

respectively between 2001 and 2008. These growth figures were above those of the

province by a considerable margin.

The tertiary sector has shown resilient growth since 1995, and continued to grow between

2001 and 2008, reaching almost 9 percent, compared to 4 percent for the province.

Industries that have been the cornerstone of this growth were transport, storage and

communication; finance, insurance and business services; and wholesale and retail trade,

catering and accommodation. These industries, at a provincial level, have failed to keep

pace with the district’s growth levels, and in some instances even recorded a decline in

growth, as can be observed in wholesale and retail trade, catering and accommodation;

and transport, storage and communication.

73

Table 4.3.4: Sectoral Growth for Cacadu, 1995-2008

CACADU DM: AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

EC -0.24% 3.10% 1.68%

PRIMARY SEC. Cacadu DM -0.4% 2.7% 1.2%

EC -0.2% 3.1% 1.8%

AFF Cacadu DM -0.4% 2.7% 1.2%

EC 2.6% 2.8% 3.2%

SECONDARY SEC. Cacadu DM 5.7% 12.5% 13.3%

EC 2.1% 11.1% 11.4%

CON Cacadu DM 4.9% 18.5% 19.2%

EC 3.2% -1.9% -0.5%

EGW Cacadu DM 9.5% 9.9% 13.3%

EC 2.6% 2.4% 2.6%

MAN Cacadu DM 5.5% 11.3% 11.5%

EC 2.5% 3.3% 3.6%

TERTIARY SEC. Cacadu DM 5.3% 8.0% 8.7%

EC 3.7% 2.2% 2.7%

WRTCA Cacadu DM 6.7% 8.0% 8.8%

EC 5.1% 4.8% 4.6%

TSC Cacadu DM 8.2% 13.3% 13.5%

EC 1.2% 6.3% 6.0%

FIRBS Cacadu DM 5.4% 13.3% 13.2%


ACCESS TO WATER (%)

Average 1995 - 2005 2006 2007 2008 2009












Between 1995 and 2000, about 91 percent of the population in the district had access to

piped water, with the number increasing to 93 percent in 2009. This was complemented

by a 2 percent decrease in the number of households that access water through other

modes such as dams, tanks and water vendors, etc.

Table 4.3.5: Access to Water in Cacadu, 1995-2009

74

CA

CA

DU

DIS

TRIC

T M

UN

ICIPA

LITY


In 2009, about 71 percent of households had access to electricity for energy, which is an

increase of about 2 percentage points over the average figure recorded between 1995

and 2005. This was coupled with a decrease in the number of households using other

modes of energy such as candles, gas and paraffin, from an average of almost 30 percent

between 1995 to 2005, to 25 percent in 2009.

Table 4.3.6: Access to Energy in Cacadu, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Electricity 68.9 70.4 70.4 70.6 70.8

Gas 0.5 0.4 0.4 0.4 0.4

Paraffin 21.5 20.4 20.5 20.3 20.2

Candles 8.2 8.0 8.1 8.0 8.0



Average 1995 - 2005 2006 2007 2008 2009


Pit latrine 23.9 21.9 21.7 21.4 21.2

Bucket latrine 17.1 15.0 14.8 14.6 14.3

None of the above 10.9 11.1 11.1 11.1 11.1




The 2009 figures show that about 36 percent of households in the district used pit-and-

bucket latrines, which is a decline when compared to the 41 percent recorded between

1995 and 2005, on average. About 53 percent of households in the district had access to

flushed or chemical toilets.

Table 4.3.7: Access to Sanitation in Cacadu, 1995-2009


About 59 percent of households in the district had access to public telephones as a way

of telecommunication. The number of households with access to cellular phones was

recorded at 37 percent in 2009, which is an increase of about 1.3 percentage points when

compared to the figure recorded between 1995 and 2005.

75


Average 1995 - 2005 2006 2007 2008 2009









Average 1995 - 2005 2006 2007 2008 2009





Own refuse dump 24.1 21.9 21.4 21.1 20.8



Table 4.3.8: Access to Telephones in Cacadu, 1995-2008


In 2009, slightly more than 70 percent of households in the district had access to removal

of refuse by the local authority. This is an increase of about 4 percent when compared to

the figure that was recorded between 1995 and 2005. This was coupled with a decrease in

the number of households using their own refuse dumping and those with no conventional

rubbish disposal.

Table 4.3.9: Access to Refuse in Cacadu, 1995-2009

4.3.7 Type of Dwelling

About 82 percent of households had access to housing in 2009. The number of households

that had access to houses or brick structures recorded an increase of about 2 percent from

the figure that was recorded between 1995 and 2005. About 13 percent of households in

the district still have no access to housing.

76

Table 4.3.10: Access to Housing in Cacadu, 1995-2009

CA

CA

DU

DIS

TRIC

T M

UN

ICIPA

LITY


Average 1995 - 2005 2006 2007 2008 2009












Formal employment in the district improved from 2001, as most of the sectors eased up

on shedding jobs. The sector providing most jobs is the tertiary sector, with a 68-percent

contribution in 2008, up from 58 percent in 1995. This was followed by the primary

sector with 21 percent, down from 29 percent in 1995; and the secondary sector with 12

percent, a percentage-point decrease from 1995. Finance and business services, mining

and wholesale had the highest growth rates in 2009, which represent a decline from

those seen between 2001 and 2005.

Table 4.3.11: Formal employment by sector in Cacadu, 1995-2008



AFF 34,730 -11.5 -10.6 26,181 24,058 -8.1

MQ 298 -16.4 14.1 290 312 7.6

MAN 8,045 -11.6 -0.9 6,945 6,814 -1.9

EGW 448 -9.4 1.0 415 422 1.8

CON 7,121 -21.1 7.9 6,443 6,486 0.7

WRTCA 8,560 15.9 13.4 11,826 12,464 5.4

TSC 1,962 -27.2 1.3 1,427 1,407 -1.4

FIBS 4,003 46.7 22.9 8,305 9,146 10.1

CSPS 27,428 1.3 1.0 28,297 28,709 1.5

OGSS 11,829 13.5 3.6 14,092 14,577 3.4

GSS 15,599 -8.0 -1.5 14,205 14,132 -0.5


77


The formal and informal sectors of employment in Cacadu DM have been shedding

jobs since 1995. The trend continued up to 2008, with informal employment recording

the highest rate of job losses in 2008. The formal-employment sector lost more jobs in

the semi-, unskilled and highly skilled categories. Formal employment accounted for 76

percent of employment in 2008, an increase of 5 percentage points from the 1995 figure.

This was accompanied by a 5 percentage point decrease in informal employment. Within

formal employment, the semi- and unskilled sector accounted for 52 percent of jobs,

followed by the skilled with 38 percent. About 10 percent of jobs were lost in the formal

and informal sector between 1995 and 2008.

Table 4.3.12: Informal and Formal Employment by Skill in Cacadu, 1995-2008



Formal and informal

employment 130,370 -3.1 -2.9 122,120 118,834 -2.7

Informal employment 37,781 -1.6 -11.1 31,992 29,017 -9.3

Formal employment 92,589 -3.8 0.4 90,128 89,817 -0.3

High skilled 9,698 -8.0 0.0 8,937 8,851 -1.0

Skilled 32,519 -1.2 3.0 33,743 34,046 0.9




The dependency ratio in the Cacadu district decreased slightly between 1995. This was

due to an increase in the district’s labour force.

Table 4.3.13: Dependency Ratio in Cacadu, 1995-2009

DEPENDENCY RATIO

1995 (%) 2000 (%) 2005 (%) 2009 (%)

59.3 56.3 55.4 55.0


78

LEVEL OF EDUCATION


Total 364,710 5.7 0.0 385,448 386,875 0.4


Grade 12 28,239 23.6 -0.2 36,047 36,775 2.0


Certificate with Grade 12 1,473 54.7 -0.2 2,446 2,539 3.8

Diploma with Grade 12 6,188 11.3 -2.0 6,782 6,761 -0.3

Bachelor’s Degree 2,935 -0.2 -5.4 2,687 2,581 -4.0

Bachelor’s Degree and Diploma 644 72.7 -3.1 1,162 1,195 2.8

Honours Degree 497 52.2 -5.0 759 765 0.9


Doctorate) 663 26.2 -6.5 798 783 -1.9





Illiterate 58,389 -7.5 -0.6 51,797 50,856 -1.8

Literate 123,465 14.7 0.3 144,829 146,986 1.5


Table 4.3.15: Literacy level in Cacadu, 1995-2009

CA

CA

DU

DIS

TRIC

T M

UN

ICIPA

LITY


There has been a decline in the number of people with no schooling, while the number

of people with matric grew. The recipients of Bachelor’s degrees and diplomas, and

Honours degrees, increased between 2006 and 2009, with the number of higher degree

decreasing. However, the growth rate in the improvement of education standards has

slowed down compared to the 1995 and 2000 growth rate.

The illiteracy level has been decreasing over the year.

Table 4.3.14: Level of Education in Cacadu, 1995-2009


The district saw a rise in the number of people living in poverty between 2000 and 2005,

before dropping to 38.5 percent in 2009.

Table 4.3.16: People in Poverty in Cacadu, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

38.7 45.4 45.4 38.5


79


In 1995, almost 72 percent of households earned R3 500 or less, with approximately

28 percent of this group earning not more than R1 000. This changed in 2009, with 42

percent of households earning R3 500 or less. Households earning more than R3 500

increased from 28 percent in 1995 to 58 percent in 2009.

Table 4.3.17: Distribution of Households by Income in Cacadu, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

< R500 per month 4 5.5 3.3 1.6

R500 - R1000 16.5 12.9 11.4 7.8

R1001 - R3500 51.6 49.8 42 32.7

R3501 - R6000 12 14 17.2 19.6

R6001 - R11000 8.3 7.6 11.4 16.4

R11001 - R16000 4.1 4.2 4.9 7

R16001 - R30000 2.3 3.8 6 8

R30001 - R50000 0.8 1.7 2.4 4

R50000+ 0.4 0.5 1.4 2.9



The Human Development Indicator (HDI) in the Cacadu district has experienced slow

growth, recording a 7-percent increase between 1995 and 2009. The low increase in

HDI reflects a slow growth rate in the access of households to services, education and

adequate income distribution in the district.

Table 4.3.18: Human Development Indicator in Cacadu, 1995-2009

URBANIZATION (%)

1995 (%) 2000 (%) 2005 (%) 2009 (%)





1995 (%) 2000 (%) 2005 (%) 2009 (%)

0.5 0.54 0.56 0.57


4.3.15 URBANISATION

There has been a slow increase in the number of people moving to urban areas in the

district, with 70 percent of households in this district urbanised.

Table 4.3.19: Urbanisation in Cacadu, 1995-2009

80

CH

RIS

HA

NI D

ISTR

ICT

MU

NIC

IPALIT

Y

4.4 CHRIS HANI DISTRICT MUNICIPALITYThe Chris Hani district municipality lies in the heart of Eastern Cape, between the

coastline and the Drakensberg mountains, and consists of eight local municipalities. Chris

Hani is surrounded by Amatole DM, O.R Tambo DM, Ukhahlamba DM and Cacadu DM.

The district covers 37 294 square kilometers of land, 67 percent of which is rural and

33 percent urban.

The total population of Chris Hani was 783 652 in 2008, and increased to 786 637. Africans

were 94.2 percent of the total, Coloureds 3.9 percent, Whites 1.8 percent and Asians 0.1

percent. About 46 percent of this population was male and 54 percent female.

The total Gross Value Added for 2008 was about R8 billion, a growth rate of 6 percent.

Chris Hani DM is the fifth-largest economy in the region. Industries in the tertiary sector

contributed the most, totaling 84 percent of GVA. These were general government with

28 percent, finance and insurance with 17 percent, wholesale and trade with 16 percent,

community services with 13 percent and transport and communications with 10 percent.


The population of the Chris Hani District Municipality has been decreasing since the period

2001 and 2005, with the age group 0 to 14 recording the second-largest decline. The age

group 15 to 44 contributed 40 percent to the population, followed by the age group 0 to

14, with 39 percent in 2009 — a decline of about 2 percentage points on 1995.

Table 4.4.1: Population by Age Group in Chris Hani, 1995-2009



Total 766,773 5.0 -1.2 789,954 786,637 -0.4

0 - 14 317,699 1.0 -1.3 311,637 308,655 -1.0

15 - 44 305,483 6.0 -1.1 319,129 318,480 -0.2

45 - 65+ 143,591 11.6 -1.4 159,187 159,503 0.2



There has been an increase in the number of people affected by HIV. The growth rate

between 2000 and 2007 was 57 percent among Africans, with the ratio exceeding 95

percent in both 2000 and 2007. People in the age group 15 to 44 were the most affected,

contributing about 78 percent in 2007 to the total. This was a growth rate of 38 percent


81

Table 4.4.2: People affected by HIV in Chris Hani, 2000-2007




Durable goods 367,703 4.8 49.0 695,639 833,903 19.9

Semi-durable goods 403,239 27.8 63.5 1,067,406 1,445,155 35.4


Services 1,170,273 28.4 14.8 1,916,114 2,210,028 15.3

Current income 4,326,827 14.9 20.9 6,622,950 7,637,866 15.3


Saving by households 66,973 77.2 -39.4 16,545 -71,402 -531.6



2000 2005 2007





White 0.0 0.1 0.2

Age Group (0 - 4) 2.0 3.0 3.0

Age Group (15 - 44) 42.2 60.1 58.2




Final consumption grew by almost 18 percent in 2006 and 2009, with the highest growth

of about 24 percent recorded in 2001 and 2005. This was supported by growing current

incomes and disposable incomes. This growth in consumption came with increasing

levels of debt as savings plummeted into negative territory, and was at its worst in 2009.

Consumption of semi-durable goods in 2009 showed the highest growth rate, followed

by durable goods, services and non-durable goods. Despite dropping from 49 percent to

36 percent respectively in 1995 and 2009, consumption of non-durable goods had the

highest consumption weight of 36 percent, followed by services with 32 percent, semi-

durable goods with 21 percent (an increase of 11 percent from 1995) and lastly durable

goods, with a contribution of about 12 percent in 2009.

Table 4.4.3: Household Income and Expenditure in Chris Hani, 1995-2009

82

CH

RIS

HA

NI D

ISTR

ICT

MU

NIC

IPALIT

Y


The tertiary sector, with an average of 84.6 percent of the total production in the district,

is the highest contributor, followed by the secondary sector, with only 10.8 percent of

district output. General government is the most dominant industry, representing 31.9

percent of district output. Wholesale and retail trade, catering and accommodation is the

second-largest, followed by finance and business services with 14.4 percent of district

output. Since 1995, finance has increased, while wholesale and retail trade has stagnated.

As a consequence, in 2008, finance was the second-largest contributor to the district.

The primary sector, which represents less than 5 percent of district output, has been on

a downward trend since 2001.

Figure 4.4: GVA Contribution for Chris Hani, 1995-2008

Chris Hani - GVA Contribution


Growth in the Chris Hani District Municipality’s primary sector has improved when

compared to the declines experienced during the period 1995 to 2000, recording an

average of about 5 percent between 2001 and 2008, while the province registered growth

of 1.7 percent for the same period. This growth has been supported by growth in the

agriculture, forestry and fishing industry.

The secondary sector has shown sustained growth levels that have marginally exceeded

the province’s growth rate, moving from 2.6 percent during 1995 and 2000 to 8.1 percent

between 2001 and 2008, while the province itself rose from 2.6 percent between 1995 and

2000 to 3.2 percent in 2001 and 2008. The backbone of this sector is manufacturing and

construction. However, growth in the construction industry has followed the province’s

growth for the periods discussed.

Growth in the tertiary sector exceeded provincial growth from 1995 to 2008. This growth

was underpinned by three industries, namely, general government services, wholesale

and retail trade, catering and accommodation; and finance, insurance, and business

services.

83

Table 4.4.4: Sectoral Growth for Chris Hani, 1995-2008

CHRIS HANI DM: AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

EC -0.2% 3.1% 1.7%

PRIMARY SEC. Chris Hani DM -0.5% 5.7% 4.6%

EC -0.2% 3.1% 1.8%

AFF Chris Hani DM -0.6% 5.3% 4.1%

EC 2.6% 2.8% 3.2%

SECONDARY SEC. Chris Hani DM 2.6% 7.1% 8.1%

EC 2.6% 2.4% 2.6%

MAN Chris Hani DM 3.2% 7.9% 8.5%

EC 2.1% 11.1% 11.4%

CON Chris Hani DM 1.3% 10.4% 10.7%

EC 2.5% 3.3% 3.6%

TERTIARY SEC. Chris Hani DM 2.3% 4.8% 5.6%

EC 1.3% 1.0% 3.0%

GGS Chris Hani DM 0.8% 2.2% 3.3%

EC 3.7% 2.2% 2.7%

WRTCA Chris Hani DM 3.5% 4.1% 4.8%

EC 1.2% 6.3% 6.0%

FIRBS Cacadu DM 3.6% 9.3% 9.2%




The number of households with access to piped water grew from about 57 percent in

1995 to 61 percent in 2009, resulting in about 39 percent of households still accessing

water from sources such as dams, streams and boreholes.

Table 4.4.5: Access to Water in Chris Hani, 1995-2009

ACCESS TO WATER (%)

Average 1995 - 2005 2006 2007 2008 2009










84

CH

RIS

HA

NI D

ISTR

ICT

MU

NIC

IPALIT

Y


Slightly more than 50 percent of households used candles and paraffin in 2009, compared

to 57 percent in 1995 and 2005. This was complemented by an increase of about 6 percent

in households using electricity in the district.

Table 4.4.6: Access to Energy in Chris Hani, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Electricity 42.0 46.5 64.4 47.0 47.6

Gas 0.4 0.4 0.4 0.3 0.3

Paraffin 36.5 31.7 31.8 31.1 30.5

Candles 20.2 20.7 20.8 20.8 20.9



Average 1995 - 2005 2006 2007 2008 2009


Pit latrine 29.1 27.7 27.6 27.4 27.2

Bucket latrine 5.9 5.4 5.4 5.4 5.3

None of the above 42.0 43.0 43.1 43.2 43.4




Average 1995 - 2005 2006 2007 2008 2009









Slightly more than 75 percent of households continued to use pit-and bucket-latrines,

and other unspecified ways of waste management. Of this 75 percent, 43 percent used

pit-and-bucket latrines, while 57 percent used unspecified waste-management methods.

Conditions have not improved since the period 1995 to 2005.

Table 4.4.7: Access to Sanitation in Chris Hani, 1995-2009


More than 80 percent of households had access to phones in 2009, 27 percent of whom

were using cellular phones compared to 25 percent between 1995 and 2005.

Table 4.4.8: Access to Telephone in Chris Hani, 1995-2008

85


Average 1995 - 2005 2006 2007 2008 2009





Own refuse dump 42.0 43.8 43.8 44.0 44.3




In 2009, about 72 percent of households used either their own refuse dump or could

not dispose of their refuse. This has been the case since the period 1995 and 2005,

when approximately 27 percent of households had their refuse disposed of by the local

authority.

Table 4.4.9: Access to Refuse in Chris Hani, 1995-2009


About 93 percent of households had access to housing in 2009, a figure similar to that

of the period 1995 and 2005. However, 45 percent of this population still used traditional

dwellings. Only 5.4 percent of the population used informal dwellings such as shacks.

Table 4.4.10: Access to Housing in Chris Hani, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009












The tertiary sector is the biggest employer in the district, with a contribution of about 83

percent in 2008 compared to 78 percent in 1995. This was followed by the secondary

sector, with a 9-percent contribution from 11 percent in 1995, and the primary sector with

an 8-percent contribution from 11 percent in 1995. These sectors were not without job

losses. The sector with the highest job losses was the primary sector, with a 27-percent

86

CH

RIS

HA

NI D

ISTR

ICT

MU

NIC

IPALIT

Y



AFF 9,854 -8.2 -7.9 7,932 7,462 -5.9

MQ 1,153 -47.1 -5.9 556 563 1.3

MAN 4,777 -13.1 -1.3 4,046 3,958 -2.2

EGW 292 -4.5 0.4 283 284 0.5

CON 5,543 -28.4 0.6 4,167 4,027 -3.4

WRTCA 7,667 14.7 10.2 10,097 10.554 4.5

TSC 1,866 -20.6 10.5 1,658 1,699 2.5

FIBS 4,257 43.2 20.0 8,234 8,993 9.2

CSPS 31,229 -5.0 -0.7 29,251 29,314 0.2

OGSS 11,097 2.1 -3.7 10,731 10,675 -0.5

GSS 20,132 -0.9 1.2 18,520 18,639 0.6




Formal and informal

employment 85,415 -2.4 -0.4 83,137 82,472 -0.8



High skilled 10,180 -12.5 -1.8 8,594 8,437 -1.8

Skilled 24,911 3.6 7.3 28,630 29,565 3.3




The number of people in formal and informal employment has been dropping over the

years, with job losses of about 3 percent between 1995 and 2008. Informal employment

accounted for most job losses, with about 17 percent of jobs lost between 1995 and

2008. In total, informal employment contributed 19 percent to employment in 2008 from

22 percent in 1995, while formal employment contributed 81 percent in 2008 from 78

percent in 1995. Within formal employment, the highly skilled sector shed jobs faster

than any other sector between 1995 and 2008, recording a decline of slightly more than

17 percent, with an average of about 2 percent between 2006 and 2008. The only sector

that experienced growth was the skilled sector, with total growth of about 19 percent

between 1995 and 2008, while growing by 3.3 percent between 2006 and 2008.

Table 4.4.12: Informal and Formal Employment by Skill in Chris Hani, 1995-2008

loss between 1995 and 2008, followed by the secondary sector, with a 22-percent loss

between 1995 and 2008. Construction was the hardest-hit industry in the secondary

sector. The only sector that experienced growth was the tertiary sector, with 5 percent

between 1995 and 2008. Wholesale and finance experienced the highest growth.

Table 4.4.11: Formal employment by sector in Chris Hani, 1995-2008

87

DEPENDENCY RATIO

1995 (%) 2000 (%) 2005 (%) 2009 (%)

93.1 88.5 87.2 86.5



The dependency ratio declined from 93.1 percent in 1995 to about 87 percent in 2009.

However, the low income distribution, together with the relatively high dependency ratio,

explains the high percentage of people living in poverty.

Table 4.4.13: Dependency Ratio in Chris Hani, 1995-2009


The number of people with no schooling has been dropping over the years, with more

people completing matric, and obtaining diplomas and degrees. Growth in people who

received Honours degrees was highest in 2006 and 2009, followed by Master’s and

doctoral degrees. This was also evident in the 19-percent reduction in illiteracy rates


Table 4.4.14: Level of Education in Chris Hani, 1995-2009

LEVEL OF EDUCATION


Total 766,773 5.0 -1.2 789,954 786,637 -0.4


Grade 12 32,415 13.1 -1.9 36,328 36,353 0.1



Diploma with Grade 12 10,002 18.9 -1.6 11,921 12,001 0.5

Bachelor’s Degree 1,724 16.7 -1.6 2,018 2,029 0.5

Bachelor’s Degree and Diploma 464 135.7 -1.7 1,207 1,270 5.2

Honours Degree 210 124.1 3.2 555 599 7.9


Doctorate) 244 77.0 1.9 488 518 6.0



Table 4.4.15: Literacy level in Chris Hani, 1995-2009



Illiterate 187,847 -10.0 -2.0 157,504 152,539 -3.2

Literate 192,970 9.0 -1.6 207,299 206,918 -0.2


88

CH

RIS

HA

NI D

ISTR

ICT

MU

NIC

IPALIT

Y


The number of people in poverty grew by about 12 percentage points between 1995

and 2009 to slightly more than 74 percent of the total population in the district.

Table 4.4.16: People in Poverty in Chris Hani, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

62.0 75.8 82.0 74.3



1995 (%) 2000 (%) 2005 (%) 2009 (%)

< R500 per month 13.5 14.5 7.2 2.7

R500 - R1000 28.9 22.3 19.3 12.2

R1001 - R3500 43.2 46.6 48.2 45.1

R3501 - R6000 6.5 7.6 12 18.1

R6001 - R11000 4.3 4.4 6.2 10.4

R11001 - R16000 2.3 2 2.9 4.3

R16001 - R30000 0.9 1.7 2.9 4.3

R30001 - R50000 0.4 0.8 1.2 2.1

R50000+ 0.1 0.2 0.6 1.3



1995 (%) 2000 (%) 2005 (%) 2009 (%)

0.41 0.43 0.45 0.45


4.4.13 INCOME DISTRIBUTION

Households earning not more than R3 500 dropped from 86 percent in 1995 to 60 percent

in 2009, while households earning between R3 501 and R11 000 rose from about 11

percent in 1995 to 29 percent in 2009.

Table 4.4.17: Distribution of Households by Income in Chris Hani, 1995-2009


The Human Development Indicator grew from 0.41 to 0.45 between 1995 and 2009,

denoting little improvement in standards of living, and high levels of poverty and

underdevelopment.

Table 4.4.18: Human Development Indicator in Chris Hani, 1995-2009

89

URBANIZATION (%)

1995 (%) 2000 (%) 2005 (%) 2009 (%)




4.4.15 URBANISATION

In 2009, about 68 percent of the people still remained rural.

Table 4.4.19: Urbanisation in Chris Hani, 1995-2009

90

NEL

SON

MA

ND

ELA

BAY

MET

ROPO

LITA

N M

UN

ICIPA

LITY

4.5 NELSON MANDELA BAY METROPOLITANThe Nelson Mandela Bay is one of the six metropolitan areas in South Africa. It is located

on the shores of Algoa Bay in the Eastern Cape and comprises the city of Port Elizabeth,

along with other smaller towns, namely, Uitenhage and Despatch. It covers a total area of

1 845 square kilometres, 4 percent of which is rural.

The total population of Nelson Mandela Bay Municipality was 945 479 in 2009, 61 percent

of whom were African, 22 percent Coloured and 15 percent White. Females were the

largest proportion of the population, at 52 percent, while males were only 48 percent.

In 2008, the Gross Value Added was R31.6 billion, a growth rate of 0.04 percent from

2007. Nelson Mandela Bay Metropolitan is the largest economy in the province. Sectors

that contributed the most were manufacturing with 28 percent, finance and insurance

with 24 percent, wholesale and retail trade with 16 percent and general government with

12 percent. These industries had a joint contribution of about 80 percent to the GVA.


Since 2001, the population of Nelson Mandela Bay Metro has been decreasing. The group

that recorded the highest decrease is the 0-14, with slightly more than 2 percent in 2006

and 2009. The population weight composition has not changed much from 1995, with the

age group 15-44 accounting for 53 percent of the population, followed by the 0-14 age

group with 27 percent.

Table 4.5.1: Population by Age Group in Nelson Mandela Bay Metro, 1995-2009



Total 960,267 4.5 -2.8 960,519 945,479 -1.6

0 - 14 268,861 0.4 -2.8 255,946 250,668 -2.1

15 - 44 503,549 4.2 -2.4 504,577 497,841 -1.3

45 - 65+ 187,857 11.2 -3.9 199,996 196,970 -1.5



Despite Africans representing the largest proportion of those affected, the growth rate

was highest among Asians (39 percent), followed by Whites (29 percent), Coloureds (14

percent) and Africans (3 percent). About 83 percent of people affected in 2007 were

between the ages 15 and 44, which is a 7 percent decrease on the figure recorded in

2000.

91

Table 4.5.2: People affected by HIV in Nelson Mandela Bay Metro, 2000-2007


2000 2005 2007



Coloured 4.4 10.7 12.2


White 0.4 1.5 1.9

Age Group (0 - 4) 1.1 2.1 2.1

Age Group (15 - 44) 55.9 81.6 84.0




Since 1995, consumption expenditure has been increasing, with the highest increase

recorded in the period 2001 and 2005. Consumption of semi-durable goods had the

highest growth, followed by durable goods, non-durable goods and services. However,

the sector with the highest consumption weight was services with 38 percent, followed

by non-durable goods, semi-durable goods and durable goods. The saving rate has been

decreasing from minus 5.7 percent in 1995 to minus 18.5 percent between 2006 and

2009. The growth rates in income and disposable income were highest in 2001 and 2005,

and started easing between 2006 and 2009.

Table 4.5.3: Household Income and Expenditure in Nelson Mandela Bay Metro, 1995-

2009




Durable goods 1,108,056 9.1 54.8 2,281,461 2,752,824 20.7

Semi-durable goods 1,022,702 34.0 59.7 2,735,878 3,581,933 30.9


Services 5,186,196 26.6 12.3 8,041,106 8,907,623 10.8

Current income 15,094,161 15.6 20.0 22,927,955 26,101,190 13.8


Saving by households 398,641 -5.7 -9.5 205,693 167,610 -18.5


92


The major driver of the metro’s economy since 1995 has been the tertiary sector, with an

average contribution of 69.7 percent of total district output, representing 36 percent of

the tertiary sector in the province. The secondary sector followed, with 29.8 percent of

the district’s output, which is approximately 54.1 percent of the secondary sectors in the

province.

The manufacturing industry has been playing a leading role in the province since 1995,

with an average contribution of about 27.1 percent to the province’s economy. In second

position is the finance, insurance, real-estate and business services industry, with a

contribution of about 23.9 percent, followed by wholesale and retail trade, catering and

accommodation, with 15.2 percent. The primary sector is the lowest contributor, with less

than 1 percent of provincial output.

Figure 4.5: GVA Contribution for Nelson Mandela, 1995-2008

NEL

SON

MA

ND

ELA

BAY

MET

ROPO

LITA

N M

UN

ICIPA

LITY


The Nelson Mandela Bay Metropolitan has the largest share of the economy of the

Eastern Cape. Its primary sector’s growth rose from 3.4 percent between 1995 and 2000

to 5 percent in 2001 and 2008, and has consistently outperformed the province’s primary

sector’s growth. Agriculture, forestry and fishing was the industry that propelled growth

in this sector from 1995 to 2008.

Growth in the secondary sector for the period 2001 to 2008 has shown a decline

compared to the growth rate observed between 1995 and 2000. This is contrary to the

province’s growth rate, which has increased steadily from 1995 to 2008. Construction

and manufacturing are the industries that have supported growth in the metro. Between

2001 and 2008, the construction industry showed an increase of more than 9 percent in

the rate of growth, from 1.7 percent during the period 1995 to 2000. When comparing

these figures to the province’s growth figures, there is an apparent lag. The manufacturing

industry’s growth declined from 3.3 percent during the first period considered, to 1.8

percent between 2001 and 2008.

NMBM - gVA Contribution

93

The tertiary sector, which is the main driver of the economy in the metro, experienced

a decline in growth from 2.2 percent between 1995 and 2000 to 1.5 percent between

2001 and 2008. At a provincial level, an increase of 1.2 percent in growth was observed

between 2001 and 2008. The decline observed in 2001 and 2008 was indicative of the

slump in growth from 2006 to 2008, as growth was around 1.8 percent between 2001

and 2005. Between 1995 and 2000, finance, insurance, real estate and business services

registered negative growth in the metro, while the province had a positive growth rate

of just above 1 percent. Conditions improved between 2001 and 2005, when the metro

had a growth rate of almost 4 percent while the province registered a figure of more than

6 percent. From 2001 to 2008, wholesale and retail trade, catering and accommodation;

and general government services experienced a slump in growth of 2.2 percent and 1.4

percent respectively after the period 1995 and 2000, in line with provincial growth.

Table 4.5.4: Sectoral Growth for Nelson Mandela Bay Metro, 1995-2008

NELSON MANDELA BAY METRO: AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

EC -0.2% 3.1% 1.7%

PRIMARY SEC. NMBM 3.4% 6.4% 5.0%

EC -0.2% 3.1% 1.8%

AFF NMBM 3.7% 6.5% 5.3%

EC 2.6% 2.8% 3.2%

SECONDARY SEC. NMBM 2.8% 1.9% 2.0%

EC 2.1% 11.1% 11.4%

CON NMBM 1.7% 9.6% 9.5%

EC 2.6% 2.4% 2.6%

MAN NMBM 3.3% 1.8% 1.8%

EC 2.5% 3.3% 3.6%

TERTIARY SEC. NMBM 2.2% 1.8% 1.5%

EC 1.2% 6.3% 6.0%

FIBS NMBM -0.1% 3.9% 3.2%

EC 3.7% 2.2% 2.7%

WRTCA NMBM 3.8% 1.4% 1.6%

EC 1.3% 1.0% 3.0%

GGS NMBM 1.1% -0.9% -0.3%


94

ACCESS TO WATER (%)

Average 1995 - 2005 2006 2007 2008 2009











4.5.6.1 Access to water

Since the period 1995 and 2000, slightly more than 98 percent of households had

access to piped water.

Table 4.5.5: Access to Water in Nelson Mandela Bay Metro, 1995-2009

NEL

SON

MA

ND

ELA

BAY

MET

ROPO

LITA

N M

UN

ICIPA

LITY


Electricity is the most common source of energy, with more than 70 percent of households

using it. Households relying on paraffin and candles increased to 2 percent in 2009 when

compared to the figure recorded between 1995 and 2005.

Table 4.5.6: Access to Energy in Nelson Mandela Bay Metro, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Electricity 72.2 71.8 71.2 71.1 71.1

Gas 0.3 0.4 0.4 0.4 0.4

Paraffin 25.1 25.6 26.2 26.2 26.3

Candles 1.7 1.9 1.9 2.0 2.0



The number of households with access to flushing or chemical toilets decreased from

an average of 80.3 percent between 1995 and 2005 to 75.8 percent in 2009. This was

coupled with an increase in the number of people using pit- and bucket latrines, and

unspecified forms of waste management.

95


Average 1995 - 2005 2006 2007 2008 2009


Pit latrine 2.5 3.5 3.8 3.9 4.0

Bucket latrine 13.3 14.2 14.3 14.5 14.6

None of the above 3.8 5.0 5.3 5.4 5.5




Average 1995 - 2005 2006 2007 2008 2009









Average 1995 - 2005 2006 2007 2008 2009





Own refuse dump 4.9 6.1 6.3 6.4 6.6



Table 4.5.7: Access to Sanitation in Nelson Mandela Bay Metro, 1995-2009

4.5.6.4 Access to telephones

About 97 percent of households had access to telephones, a figure similar to the

average of 1995 and 2005.

Table 4.5.8: Access to Telephones in Nelson Mandela Bay Metro, 1995-2008


More than 80 percent of households had access to refuse removal, with about 11 percent

either using their own dumping or having no standard way of dumping.

Table 4.5.9: Access to Refuse in Nelson Mandela Bay Metro, 1995-2009

96


Average 1995 - 2005 2006 2007 2008 2009











NEL

SON

MA

ND

ELA

BAY

MET

ROPO

LITA

N M

UN

ICIPA

LITY

4.5.7 TyPE Of DWELLINg

More than 70 percent of households had access to housing in 2009, 2 percent of which

were using traditional dwellings. About 26 percent of the population live in informal

settlements. There is no departure from the figures recorded between 1995 and 2005.

Table 4.5.10: Access to Housing in Nelson Mandela Bay Metro, 1995-2009


The biggest employer in the district is the tertiary sector, providing 78 percent of employment,

with the secondary sector contributing 21 percent to employment and the primary sector 1

percent. Sixty percent of employment in the tertiary sector comes from community, social and

other services, government and social services, and other government and social services.

Besides most industries recording job losses, mining, finance, and wholesale recorded the

highest growth rates.

Table 4.5.11: Formal employment by sector in Nelson Mandela Bay Metro, 1995-2008



AFF 6,564 -0.5 -4.5 3,298 3,170 -3.9

MQ 476 -26.7 9.2 377 400 6.1

MAN 71,670 -13.8 -5.1 57,122 55,395 -3.0

EGW 1,009 -9.0 -4.2 860 848 -1.4

CON 10,086 -25.8 3.5 8,133 7,971 -2.0

WRTCA 27,466 18.1 13.7 38,941 41,167 5.7

TSC 11,524 -19.5 7.5 10,008 10,158 1.5

FIBS 26,406 29.1 12.3 41,220 43,721 6.1

CSPS 75,741 -4.7 -0.6 71,039 71,193 0.2

OGSS 34,374 -0.5 -5.3 31,510 31,015 -1.6

GSS 41.367 -8.1 3.8 39,529 40,178 1.6


97



Formal and informal

employment 268,501 0.5 1.5 275,852 276,920 0.4

Informal employment 40.558 17.4 -4.7 44,854 42,900 -4.4


High skilled 33,902 -5.5 1.9 32,656 32,710 0.2

Skilled 91,390 4.0 7.4 105,413 108,829 3.2



DEPENDENCY RATIO

1995 (%) 2000 (%) 2005 (%) 2009 (%)

49.2 47.1 46.4 45.9



Formal and informal employment in the district has been growing slightly. About 85 percent

of people work in formal employment by skill, with 47 percent in the skilled sector. Informal

employment grew aggressively between 1995 and 2000 before it started shedding jobs

in 2001, with the semi-skilled and unskilled sector shedding jobs consistently. Between

2005 and 2008, most job losses were recorded in the informal sector.

Table 4.5.12: Informal and Formal Employment by Skill in Nelson Mandela Bay

Metro, 1995-2008


The dependency ratio dropped by about 3 percent between 1995 and 2009. This is the

region with the lowest dependency ratio in the province.

Table 4.5.13: Dependency Ratio in Nelson Mandela Bay Metro, 1995-2009


There has been a reduction in the number of people with no schooling, translating into more

people receiving matriculation certificates, post-matric certificates, diplomas, degrees and

post-graduate degrees. The number of people who became literate increased despite the

reductions in gains observed between 1995 and 2000.

98

Table 4.5.14: Level of Education in Nelson Mandela Bay Metro, 1995-2009

NEL

SON

MA

ND

ELA

BAY

MET

ROPO

LITA

N M

UN

ICIPA

LITY

LEVEL OF EDUCATION


Total 960,267 4.5 -2.8 960,519 945,479 -1.6


Grade 12 121,173 24.6 -2.0 151,960 153,448 1.0



Diploma with Grade 12 23,361 10.6 -3.4 24,862 24,558 -1.2

Bachelor’s Degree 10,052 -0.3 -3.3 9,435 9,206 -2.4

Bachelor’s Degree and Diploma 1,520 90.3 -2.4 3,083 3,201 3.8

Honours Degree 210 124.1 3.2 555 599 7.9


Doctorate) 1,854 37.6 -3.2 2,566 2,590 0.9



Table 4.5.15: Literacy level in Nelson Mandela Bay Metro, 1995-2009



Illiterate 90,952 -10.5 -3.1 74,262 71,193 -4.1

Literate 482,744 10.4 -2.9 516,948 512,423 -0.9



The percentage of the population in the Nelson Mandela Bay Metro living in poverty grew

by almost 2 percent in 2009 when compared to the 1995 figure. However, the number

decreased when compared to the 2005 figure.

Table 4.5.16: People in Poverty in Nelson Mandela Bay Metro, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

31.3 36.9 37.4 33.0



From 1995 to 2009, households earning not more than R3 500 decreased from 57 percent

to 35 percent, while households earning between R16 001 and R30 000 increased from

almost 5 percent to about 13 percent in 2009.

99


1995 (%) 2000 (%) 2005 (%) 2009 (%)

< R500 per month 5.8 7.3 3.8 1.5

R500 - R1000 14 10.5 10 6.9

R1001 - R3500 37.1 35.9 31.9 26.9

R3501 - R6000 16.2 15.7 15.2 15.6

R6001 - R11000 13.3 11.8 13.9 16

R11001 - R16000 7 7.5 7.6 9

R16001 - R30000 4.6 7.3 10.9 12.7

R30001 - R50000 1.5 3.2 4.4 6.7

R50000+ 0.4 0.8 2.3 4.7


URBANIZATION (%)

1995 (%) 2000 (%) 2005 (%) 2009 (%)





1995 (%) 2000 (%) 2005 (%) 2009 (%)

0.63 0.65 0.67 0.68


Table 4.5.17: Distribution of Households by Income in Nelson Mandela Bay Metro,

1995-2009


The Human Development Indicator in the metro grew from 0.6 to 0.7 between 1995 and

2009, signaling better standards of living, relatively low levels of poverty and improving

life expectancy in the region.

Table 4.5.18: Human Development Indicator in Nelson Mandela Bay Metro,

1995-2009

4.5.15 URBANISATION

Despite the fact that the majority of the population in the district live in urban areas, there

has been a slight increase in the number of people moving to rural areas.

Table 4.5.19: Urbanisation in Nelson Mandela Bay Metro, 1995-2009

100

O.R

. TA

MBO

DIS

TRIC

T M

UN

ICIPA

LITY

4.6 O.R. TAMBO DISTRICT MUNICIPALITYThe O.R. Tambo district municipality is located in the former Transkei homeland area of

the Eastern Cape. It is within the well-known Wild Coast and located on the north-eastern

side of the Eastern Cape. It has seven local municipalities, namely, Qaukeni, King Sabata

Dalindyebo, Mbizana, Mhlontlo, Ntabankulu, Nyandeni and Port St. Johns, and covers

both the Wild Coast and the Pondo land. The district has a land area of 15 535 square

kilometers, 92 percent of which is rural and 8 percent urban.

The population in the O.R. Tambo DM increased from an estimated 1 751 820 people in

2008 to 1 771 788 in 2009. Africans constituted 93.3 percent of the total, Coloureds 0.4

percent, Asians 0.1 percent and Whites 0.2 percent. There has been no change in terms

of gender proportion since 2008; in 2009, 45 percent of the population was male and 55

percent was female.

The Gross Value Added for 2008 was R9 billion, a growth rate of 3 percent from 2007.

This made O.R. Tambo the fourth-largest economy in the province. About 84 percent

of GVA came from the tertiary sector. Sectors that contributed the most were general

government with 29 percent, community services with 16 percent, finance and insurance

with 15 percent, wholesale and retail with 13 percent, and transport and communication

with 11 percent.


In the past 14 years, growth in all age groups has been decreasing. Between 1995 and

2000, the total population grew by 8.6 percent, with the age group 45–65+ experiencing

the highest growth of 11.29 percent, while the age group 0–14 had the lowest growth,

with 6.27 percent. However, all age groups have experienced a decline in growth over the

last two periods considered on the table below.

Table 4.6.1: Population by Age Group in O.R. Tambo, 1995-2009



Total 1,523,133 8.6 3 1,728,596 1,771,778 2.5

0 - 14 707,147 6.3 2.9 780,067 797,025 2.2

15 - 44 586,713 10.2 3.3 681,315 700,560 2.8

45 - 65+ 229,273 11.3 2.7 267,214 274,192 2.6



The total number of people affected by HIV in the O.R. Tambo DM grew from 6 percent

of the population to almost 10 percent from 1995 to 2007. The most-affected group

was African, with a startling figure of more than 99 percent of the affected population,

followed by the Coloureds, Whites and Indians. People in the age group 15 to 44 are the

most affected, despite the decline in their contribution from 89 percent in 2000 to 79

percent in 2007.

101

Table 4.6.2: People affected by HIV in O.R. Tambo, 2000-2007


2000 2005 2007





White 0.0 0.0 0.0

Age Group (0 - 4) 5.0 9.0 9.0

Age Group (15 - 44) 88.0 126.0 131.0




Final consumption in O.R. Tambo grew by almost 18 percent between 1995 and 2000,

while disposable income grew by almost 19 percent. During this period, households

became more indebted as savings plummeted by more than 200 percent. Consumption of

services grew the most, followed by semidurable goods and durable goods. However, the

tide appeared to turn in subsequent years, when the growth rate of semi-durable goods

took the lead, followed by durable goods. Growth in consumption of non-durable goods

has continued to lag that of durable and semi-durable goods. The pace of the growth rate

in the consumption of services became slower after 2000. Income and disposable income

grew at almost the same pace. Consumption growth was accompanied by a sharp decline

in savings, which fell by more than 400 percent between 2001 and 2005. This has since

improved during 2006 and 2009.

Table 4.6.3: Household Income and Expenditure in O.R. Tambo, 1995-2009




Durable goods 707,411 7.7 52.7 1,439,655 1,824,756 26.8

Semi-durable goods 754,238 31.1 70.6 2,198,401 3,233,298 47.1


Services 1,839,815 34.8 19.8 3,403,487 4,257,749 25.1

Current income 7,165,527 18.9 26.0 12,181,086 14,768,232 21.2


Saving by households -25,067 -202.3 -432.8 -255,179 -676,041 164.9


102

O.R

. TA

MBO

DIS

TRIC

T M

UN

ICIPA

LITY


The contribution of the primary sector to O.R Tambo district output has stagnated over

the last 14 years, with an average percentage of 2.5. The tertiary sector contributes most

to the district economy with an average percentage of 88, followed by the secondary

sector with 9.5 percent. Although the tertiary sector dominates the district economy,

it represented only 11.1 percent of the tertiary sector of the province in 2008, a decline

of 12.4 percent on 1995. The tertiary industries are the most dominant in the district

economy, with general government in front, followed by community and social services,

with industry and finance in third position.

Figure 4.6: GVA Contribution for O.R. Tambo, 1995-2008

OR Tambo - gVA Contribution


The primary sector in the O.R. Tambo District Municipality experienced growth levels

that exceeded 4 percent after emerging from negative territory between 1995 and 2000.

Growth in this sector was driven by agriculture forestry and fishing, which rose above 5

percent between 2001 and 2005 before declining between 2001 and 2008.

The secondary sector in the DM has grown robustly, from less than 3 percent between

1995 and 2000 to more than 10 percent between 2001 and 2008. This is significantly

higher than the province’s growth. Manufacturing and construction contributed the most

in terms of growth, both registering more than 10 percent, while electricity, gas and water

declined from 2001 to 2008. This industry was growing at almost 9 percent between 1995

and 2000, and has since dropped to minus 2 percent. The negative growth level in the

electricity, gas and water industry prevailed at provincial level as well.

Growth in the tertiary sector, in line with the provincial tertiary sector, has been steady since

1995. Industries that recorded positive growth in this district were general government

services; community, social and personal services; wholesale, retail and trade, catering

and accommodation; and finance, insurance and business services. Growth in the

community, social and personal-services industry has been around 3 percent since 1995,

103

while finance, insurance and business services grew from less than 1 percent during the

years 1995 to 2000 to almost 5 percent between 2001 and 2008. General government

services experienced gradual growth towards the latter part of the period 2001 and 2008,

while wholesale, retail and trade, catering and accommodation experienced a decline in

growth compared with 1995 to 2000.

Table 4.6.4: Sectoral Growth for O.R. Tambo, 1995-2008

O.R. TAMBO: AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

EC -0.2% 3.1% 1.7%

PRIMARY SEC. O.R Tambo -1.4% 4.3% 3.3%

EC -0.2% 3.1% 1.8%

AFF O.R Tambo 1.9% 5.7% 4.9%

EC 2.6% 2.8% 3.2%

SECONDARY SEC. O.R Tambo 2.7% 9.8% 10.5%

EC 2.6% 2.4% 2.6%

MAN O.R Tambo 0.8% 13.0% 13.3%

EC 2.1% 11.1% 11.4%

CON O.R Tambo 3.2% 11.1% 11.2%

EC 3.2% -1.9% -0.5%

EGW O.R Tambo 8.9% -2.2% -2.0%

EC 2.5% 3.3% 3.6%

TERTIARY SEC. O.R Tambo 1.7% 2.2% 2.6%

EC 1.3% 1.0% 3.0%

GGS O.R Tambo 0.5% 0.5% 1.5%

EC 3.6% 2.9% 3.5%

CSPS O.R Tambo 3.2% 2.7% 3.3%

EC 3.7% 2.2% 2.7%

WRTCA O.R Tambo 2.0% 0.3% 0.5%

EC 1.2% 6.3% 6.0%

FIBS O.R Tambo 0.9% 5.2% 4.7%




In 2009, about 65 percent of households in the O.R Tambo DM had access to water through

dams, rivers, streams or springs. Despite the decline in the number of such households,

the figure remains stubbornly high. Approximately 9 percent of households have access

to water inside their dwellings. Since 1995, there has been very little improvement in

bringing water closer to households.

104

O.R

. TA

MBO

DIS

TRIC

T M

UN

ICIPA

LITY

Table 4.6.5: Access to Water in O.R. Tambo, 1995-2009

ACCESS TO WATER (%)

Average 1995 - 2005 2006 2007 2008 2009











In 2009, about 25 percent of households use electricity as a form of energy, representing

an increase of 5 percentage points when compared to the years 1995 to 2005. The

majority of households in the district municipality still rely on candles for lighting, and this

has been the case since 1995.

Table 4.6.6: Access to Energy in O.R. Tambo, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Electricity 20.3 24.1 24.0 24.5 25.0

Gas 0.4 0.3 0.3 0.3 0.3

Paraffin 18.1 15.1 15.2 14.8 14.4

Candles 59.8 59.2 59.2 59.1 59.1



The average percentage of households that use pit latrines, bucket latrines and other

forms of waste management has never changed, remaining above 90 percent since 1995.

Less than 10 percent of households have access to flush- or chemical toilets.

Table 4.6.7: Access to Sanitation in O.R. Tambo, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Pit latrine 38.6 37.3 37.3 37.1 37.0

Bucket latrine 1.8 1.4 1.4 1.3 1.3

None of the above 50.2 21.4 51.6 51.7 51.9



105


Access to telephones by households in O.R Tambo has been increasing since 1995, with

public telephones taking the lead. This type of access to telephones by households has

increased from an average of 25.9 percent between 1995 and 2005 to 32.7 percent in

2009. The percentage of households using cellular phones in O.R Tambo increased from

an average of 12.15 percent during the years 1995 to 2005, to 17.13 percent in 2009,

despite the less-than-1-percentage point increase from 2006.

Overall, the number of households that have access to telephones increased from an

average of 58.8 between 1995 and 2005 to 76.9 in 2009.

Table 4.6.8: Access to Telephone in O.R. Tambo, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009









More than 90 percent of households in the district either use their own refuse dumps or

have no means of disposing of refuse. Households appeared to adopt their own refuse

dumps, as the removal of refuse by local authorities declined since 1995.

Table 4.6.9: Access to Refuse in O.R. Tambo, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009





Own refuse dump 60.6 63.6 63.8 64.2 64.5




Traditional dwellings have been the most common type of accommodation for households

in the O.R Tambo district, representing an average of 68 percent of the total for the years

1995 to 2005 and 66.8 percent in 2009, followed by houses or brick structures, which

represented an average of 16.9 percent of all dwellings between 1995 and 2005, and 18.0

percent in 2009. Informal dwellings or shacks in backyards have been the least common

106

type of accommodation, accounting for less than 1 percent of

the total.

Table 4.6.10: Access to Housing in O.R. Tambo, 1995-2009

O.R

. TA

MBO

DIS

TRIC

T M

UN

ICIPA

LITY


Average 1995 - 2005 2006 2007 2008 2009












Formal employment in O.R Tambo DM has improved in growth over the past 14 years.

Some industries have improved from high negative growth rates to positive rates.

Industries that have grown in terms of formal employment include finance and business

services; wholesale and retail trade, catering and accommodation; and general government

services. The primary sector, which includes agriculture, forestry and fishing, and mining,

has been shedding jobs heavily since 1995. The same trend prevailed in the secondary

sector for the period 1995 to 2000, with some slight improvements in electricity gas and

water. Although the tertiary sector has been able to create jobs during the periods 2001 to

2005 and 2006 to 2008, there were job losses during the period 1995 to 2000 in industries

such as transport, storage and communication and general government services.

107



AFF 4,454 -18.4 -17.5 2,835 2,457 -13.4

MQ 2,779 -52.0 -11.0 1,133 1,122 -0.9

MAN 6,252 -19.7 -9.0 4,344 4,056 -6.6

EGW 372 -10.5 0.9 345 351 1.7

CON 5,104 -21.9 6.8 4,484 4,478 -0.1

WRTCA 11,345 4.8 7.1 13,033 13,421 3.0

TSC 2,539 -22.2 9.0 2,164 2,203 1.8

FIBS 5,914 43.1 20.0 11,503 12,591 9.5

CSPS 46,757 -2.9 3.1 47,148 48,058 1.9

OGSS 16,873 8.2 0.4 18,312 18,634 1.8

GSS 29,884 -9.1 4.9 28,836 29,425 2.0


Table 4.6.11: Formal employment by sector in O.R. Tambo, 1995-2008


The formal and informal sectors of employment in the O.R. Tambo DM lost jobs during

1995 and 2001. From 2001, there was only a spark of growth. More jobs were shed in

formal employment, especially in the highly skilled sector. The propensity to shed jobs

in the highly skilled sector continued despite the overall growth in formal employment

from 2001. The one sector that experienced growth was the skilled sector, especially

between 2001 and 2005. Informal employment shrunk after 2000, and has continued to

shed jobs.

Table 4.6.12: Informal and Formal Employment by Skill in O.R. Tambo, 1995-2008



Formal and informal

employment 102,943 -2.2 1.7 103,424 103,956 0.5



High skilled 14,708 -14.5 -1.2 12,216 12,011 -1.7

Skilled 32,981 2.7 9.7 38,829 40,456 4.2




The dependency ratio in the O.R Tambo district decreased by about 5 percentage points

between 1995 and 2009. Be that as it may, this district still has the highest dependency

ratio in the entire province, implying that the number of people who are not economically

active exceeds those who are active by a great margin. The high dependency ratio, with

about 67 percent of households earning not more than R3 500 per month, contributes to

the high poverty levels in the district.

108

O.R

. TA

MBO

DIS

TRIC

T M

UN

ICIPA

LITY

Table 4.6.13: Dependency Ratio in O.R. Tambo, 1995-2009


The number of persons with no schooling has been declining since 1995 and 2000 –

despite an uptick in the years 2001 and 2006 which later dropped – resulting in an increase

in the number of people with Grade 12. This translated into almost 80 percent growth in

the number of people with matric. At postmatric level, the number of people awarded

certificates, diplomas and degrees grew significantly with those awarded Bachelor’s

degrees and diplomas taking the lead, followed by Honours degrees, certificates,

Master’s and doctoral degrees. However, post-1995 and 2000 there has been a decline

in the growth rates of the graduates. The total level of education in O.R Tambo grew by

8.6 percent between 1995 and 2000 and declined between 2001 and 2005, and 2006 and

2009. Growth in illiteracy levels dropped has been declining since 1995.

Table 4.6.14: Level of Education in O.R. Tambo, 1995-2009

DEPENDENCY RATIO

1995 (%) 2000 (%) 2005 (%) 2009 (%)

107.1 103.8 102.7 102.1


LEVEL OF EDUCATION


Total 1,523,133 8.5 3.0 1,728,596 1,771,778 2.5

Grade 0 / No schooling 461,519 -8.4 2.2 417,690 416,357 -0.3

Grade 12 52,032 21.2 3.3 68,023 70,652 3.9

Less than matric and certificate / diploma 5,091 -20.8 0.2 3,714 3,570 -3.9

Certificate with Grade 12 2,698 78.9 7.2 5,842 6,350 8.7


Bachelor’s Degree 3,875 12.6 1.9 4,540 4,642 2.3

Bachelor’s Degree and Diploma 751 125.8 5.2 2,052 2,236 9.0

Honours Degree 391 119.2 -5.1 1,035 1,126 8.8


Doctorate) 570 43.9 1.4 890 928 4.2

Other (unspecified) 247.236 -5.0 2.5 235,726 236,814 0.5


Table 4.6.15: Literacy level in O.R. Tambo, 1995-2009



Illiterate 461,519 -8.4 2.2 417,690 416,357 -0.3

Literate 317,439 13.0 3.1 378,883 390,193 3.0


109


The share of the population living in poverty in the O.R Tambo district increased from

65.5 percent to 76.1 percent between 1995 and 2005, before declining to 63.3 percent

in 2009.

Table 4.6.16: People in Poverty in O.R. Tambo, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

65.5 74.1 76.1 63.3



From 1995, almost 90 percent of households earned R3 500 or less, with approximately

50 percent of this group earning not more than R1 000. The majority of households in

this district municipality were earning slightly above R1 000 and not more than R3 500.

This income group has grown from just below 40 percent in 1995 to almost 50 percent in

2009. In 2005, the group earning between R3 501 and R11 000 increased from less than

10 percent in 2000 to almost 15 percent, and increased to just under 25 percent in 2009.

This was accompanied by a decline in those groups earning less than R500 and more than

R500, but not more than R1 000, since 2005. Households earning more than R11 000

grew from about 3 percent in 1995 to almost 10 percent in 2009.

Table 4.6.17: Distribution of Households by Income in O.R. Tambo, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

< R500 per month 16.3 17.2 8.4 3.2

R500 - R1000 32.2 25.1 22.9 14.8

R1001 - R3500 39.7 44.0 48.4 48.6

R3501 - R6000 5.3 6.3 9.9 16.3

R6001 - R11000 3.4 3.6 4.8 8.2

R11001 - R16000 2.0 1.5 1.9 2.9

R16001 - R30000 0.8 1.4 2.3 3.5

R30001 - R50000 0.3 0.7 0.9 1.6

R50000+ 0.1 0.2 0.5 0.9



The Human Development Indicator (HDI) in the O.R Tambo district has shown no

remarkable improvement over the past 14 years. HDI increased from 0.38 in 1995 to 0.40

in 2009. The low increase of HDI is a concern as it reflects low access to services, high

illiteracy and low standards of living. The measurement of the average achievements in

the O.R Tambo district in the three basic dimensions of human development (a long and

healthy life, knowledge and a decent standard of living) showed no significant change,

with a very low rate for the development of people.

110

Table 4.6.18: Human Development Indicator in O.R. Tambo, 1995-2009

O.R

. TA

MBO

DIS

TRIC

T M

UN

ICIPA

LITY


1995 (%) 2000 (%) 2005 (%) 2009 (%)

0.38 0.39 0.41 0.40


4.6.15 URBANISATION

Despite the slight increase in the number of people in the O.R. Tambo DM migrating to

urban areas, the majority of them are still rural.

Table 4.6.19: Urbanisation in O.R. Tambo, 1995-2009

URBANIZATION (%)

1995 (%) 2000 (%) 2005 (%) 2009 (%)




111

4.7 UKHAHLAMBA DISTRICTThe Ukhahlamba District Municipality lies in the northern part of the Eastern Cape and

shares a border with the Free State, the Northern Cape and the Kingdom of Lesotho.

The surrounding district municipalities are Chris Hani, O.R. Tambo and Alfred Nzo. It has

four local municipalities, namely, Elindini, Gariep, Maletswai and Senqu Municipality.

Ukhahlamba has a land area of 26 518 square kilometers, 70 percent of which is rural and

30 percent urban.

The total population of the Ukhahlamba region consisted of 349 783 people in 2008, and

increased to 352 319 in 2009. Of these, 94.2 percent were black African, 3.5 percent

Coloured, 0.1 percent Asian and 2.3 percent White. About 55 percent of the 2009

population was female and 45 percent was male.

The total Gross Value Added for the Ukhahlamba region was R5.7 billion in 2008, a growth

rate of 11 percent from 2007. Out of the seven districts in the Eastern Cape, Ukhahlamba

is ranked sixth in terms of its contribution to the economy of the province. Alfred Nzo,

in seventh place, was the smallest economy in the province in 2008. The sectors that

contributed the most to GVA were from the tertiary sector, with a contribution of about 82

percent. They were general government services with 23 percent, finance and insurance

with 20 percent, wholesale and retail trade with 18 percent, transport and communication

with 11 percent and community services also with 11 percent.


About 42 percent of the population falls within the age group 15 to 44, a 2 percent increase

from the figure recorded in 1995. Despite contributing 39 percent to the population, the

age group 0 to 14 has experienced a decline of 3 percent since 1995. The age groups 15

to 44, and 45 and above, recorded the highest growth rates between 2006 and 2009.

Table 4.7.1: Population by Age Group in Ukhahlamba, 1995-2009


The African population was most affected by HIV, accounting for about 99 percent of

affected population in 2007. However, this translated into only 10 percent of the African

population being affected by the virus. The most-affected age group was the 15-to-44,

with a contribution of 79 percent, up from 87 percent in 2000, despite a drop of about 18

percent between 2005 and 2007.



Total 319,215 6.1 1.7 347,202 352,314 1.5

0 - 14 133,182 1.2 1.6 136,611 137,798 0.9

15 - 44 127,811 8.5 2.1 143,447 146,215 1.9

45 - 65+ 58,222 11.9 1.1 67,144 68,301 1.7


112

UKH

AH

LAM

BA D

ISTR

ICT

MU

NIC

IPALIT

Y

Table 4.7.2: People affected by HIV in Ukhahlamba, 2000-2007


2000 2005 2007



Coloured 0.2 0.5 33.0


White 0.0 0.0 0.0

Age Group (0 - 4) 0.8 1.4 1.4

Age Group (15 - 44) 17.4 30.3 26.3




Current income and disposable income have been growing since 1995 and 2000, reaching

the highest growth rate in the period 2001 and 2005. These increases in income were

accompanied by growth in final consumption which recorded the highest growth rate

between 2001 and 2005. With this came a huge slump in the rate of savings, from 74

percent in 1995 and 2000 to about minus 70 percent in 2006 and 2009. Consumption of

semi-durable goods recorded the highest growth rate between 2006 and 2009, followed

by durable goods, services and non-durable goods. However, expenditure on nondurable

goods had the highest weighting with 36 percent, followed by services with 33 percent,

semidurable goods with 19 percent and durable goods with 12 percent.

Table 4.7.3: Household Income and Expenditure in Ukhahlamba, 1995-2009




Durable goods 152,523 8.9 52.5 305,933 363,458 18.8

Semi-durable goods 161,042 32.5 66.3 445,502 596,067 33.8

Non-durable goods 727,089 6.8 18.2 984,969 1,112,007 12.9

Services 497,651 33.6 17.7 868,370 1,004,580 15.7

Current income 1,770,578 19.3 24.6 2,925,640 3,423,897 17.0


Saving by households 21,734 73.8 -37.7 11,300 3,404 -69.9



In the Ukhahlamba district, the primary and secondary sectors have lagged the tertiary

sector. It occupied an average 80.5 percent of the total production of the district, followed

by the secondary sector with 10.6 percent and the primary sector with 8.9 percent. While

the tertiary sector is the largest contributor in the district, it only represents an average 4

percent of the tertiary sector at the provincial level. Also, the primary sector’s contribution

113

to district output declined from 13.7 percent in

1995 to 2.5 percent in 2008, making the area more vulnerable to any external shock.

Figure 4.7: GVA Contribution for Ukhahlamba, 1995-2008

Ukhahlamba - gVA Contribution


Ukhahlamba District Municipality is the only district that experienced negative growth in

the primary sector, from 0.5 percent between 1995 and 2000 to minus 4 percent between

2001 and 2008. The industry that drives growth in this sector is agriculture, forestry and

fishing, as it holds the largest share of output in this sector. Growth in this industry dropped

to almost minus 5 percent during 2001 and 2008.

The secondary sector grew from less than 8 percent between 1995 and 2000 to over

20 percent between 2001 and 2008. Manufacturing; construction; and electricity, gas

and water all achieved growth in excess of 20 percent from 2001 to 2008. Provincially,

growth in manufacturing has remained constant, with construction growing aggressively.

However, electricity, gas and water contracted to minus 0.5 percent.

The tertiary sector in the district has grown aggressively, from almost 7 percent between

1995 and 2000 to 14 percent between 2001 and 2008. The industries that contributed

to growth were general government services; wholesale, retail and trade, catering and

accommodation and community, social and personal services, which all had positive

growth. Provincially, wholesale, retail and trade, catering and accommodation experienced

a decline in growth.

114

Table 4.7.4: Sectoral Growth for Ukhahlamba, 1995-2008

UKH

AH

LAM

BA D

ISTR

ICT

MU

NIC

IPALIT

Y

UKHAHLAMBA: AVERAGE GROWTH (%)

1995 - 2000 2001 - 2005 2001 - 2008

EC -0.2% 3.1% 1.7%

PRIMARY SEC. Ukhahlamba 0.5% -1.2% -4.0%

EC -0.2% 3.1% 1.8%

AFF Ukhahlamba 0.6% -1.6% -4.6%

EC 2.6% 2.8% 3.2%

SECONDARY SEC. Ukhahlamba 7.9% 22.2% 21.5%

EC 2.6% 2.4% 2.6%

MAN Ukhahlamba 7.4% 22.0% 20.3%

EC 2.1% 11.1% 11.4%

CON Ukhahlamba 6.0% 25.0% 25.1%

EC 3.2% -1.9% -0.5%

EGW Ukhahlamba 14.1% 20.5% 22.5%

EC 2.5% 3.3% 3.6%

TERTIARY SEC. Ukhahlamba 6.8% 13.4% 14.0%

EC 1.3% 1.0% 3.0%

GGS Ukhahlamba 4.3% 7.0% 8.1%

EC 3.6% 2.9% 3.5%

CSPS Ukhahlamba 10.8% 13.7% 14.0%

EC 3.7% 2.2% 2.7%

WRTCA Ukhahlamba 6.3% 8.8% 9.6%




In 2009, around 40 percent of households still had access to water through dams,

boreholes and other modes. Almost 60 percent of households had access to piped water,

an increase of 3 percentage points when compared to the period 1995 to 2005.

Table 4.7.5: Access to Water in Ukhahlamba, 1995-2009

ACCESS TO WATER (%)

Average 1995 - 2005 2006 2007 2008 2009










115


Almost 60 percent of the population use candles and paraffin, with slightly more than 40

percent using electricity in 2009, an increase of about 4 percentage points compared to

the period between 1995 and 2005.

Table 4.7.6: Access to Energy in Ukhahlamba, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Electricity 36.8 40.2 40.1 40.6 41.0

Gas 0.4 0.3 0.3 0.3 0.3

Paraffin 23.4 20.9 20.9 20.6 20.3

Candles 38.3 37.5 37.6 37.5 37.3



Households with access to flushed or chemical toilets increased from about 16 percent

to 19 percent during the period 1995 and 2005, and 2009. The percentage of households

using pit- and bucket latrines, and unspecified waste management mechanisms, is still

slightly above 80 percent.

Table 4.7.7: Access to Sanitation in Ukhahlamba, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009


Pit latrine 34.3 32.2 31.9 37.1 31.5

Bucket latrine 9.3 9.3 9.3 1.3 9.3

None of the above 40.6 40.3 51.6 40.2 40.1




The number of households with access to telephones increased from 66 percent in

1995 and 2005 to about 73 percent in 2009.

Table 4.7.8: Access to Telephones in Ukhahlamba, 1995-2008


Average 1995 - 2005 2006 2007 2008 2009








116

UKH

AH

LAM

BA D

ISTR

ICT

MU

NIC

IPALIT

Y


More than two-thirds of the population of Ukhahlamba DM have no access to refuse disposal

facilities, and use their own refuse dumps or other methods of waste management.

Table 4.7.9: Access to Refuse in Ukhahlamba, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009





Own refuse dump 55.0 55.0 54.8 54.7 54.7




In 2009, about 91 percent of the population had access to housing, 32 percent of which

was traditional dwellings. Only 9 percent of the population lived in informal dwellings such

as shacks. This has been the case since the period 1995 and 2005.

Table 4.7.10: Access to Housing in Ukhahlamba, 1995-2009


Average 1995 - 2005 2006 2007 2008 2009











117


The tertiary sector provides 79 percent of employment, followed by the secondary sector

with 9 percent and the primary sector with 11 percent. Community services provided

about 38 percent of employment, while wholesale and finance recorded the highest

growth rates in 2008. The primary sector had the highest rate of job losses between 2006

and 2009. The tertiary sector grew from 68 percent in 1995 to 79 percent in 2008, with

the primary sector dropping from 20 percent in 1995 to 12 percent in 2008. The secondary

sector dropped by 3 percentage points, from 12 percent in 1995 to 9 percent in 2008.

Table 4.7.11: Formal employment by sector in Ukhahlamba, 1995-2008



AFF 7,351 -15.9 -13.8 5,390 4,953 -8.1

MQ 555 -48.8 -7.2 255 256 0.5

MAN 2,444 -10.2 -1.2 2,186 2,144 -1.9

EGW 147 -8.8 -3.1 132 132 -0.3

CON 2,082 -21.6 7.4 1,870 1,896 1.4

WRTCA 2,663 30.7 23.7 4,717 5,132 8.8

TSC 9.1 -28.4 2.1 654 649 -0.8

FIBS 1,272 41.8 20.7 2,493 2,710 8.7

CSPS 11,230 5.4 7.8 13,069 13,586 4.0

OGSS 4,635 14.4 4.2 5,614 5,825 3.8

GSS 6,595 -0.9 10.9 7,455 7,761 4.1



There has been growth in both formal and informal employment between 2006 and 2008.

Despite this growth, informal employment has been shedding jobs and only accounts for

21 percent of employment, down from 25 percent in 1995. This was accompanied by an

increase in the number of jobs in formal employment by skill. In 1995 the contribution was

75 percent, and increased by 4 percentage points in 2008. The contribution of the highly

skilled, semi-skilled and unskilled sectors declined since 1995.

Table 4.7.12: Informal and Formal Employment by Skill in Ukhahlamba, 1995-2008



Formal and informal

employment 38,200 -0.9 1.6 39,825 39,968 0.4



High skilled 3,606 -6.9 4.5 3,546 3,571 0.7

Skilled 10,118 4.7 10.7 12,432 12,973 4.4



118

Table 4.7.13: Dependency Ratio in Ukhahlamba, 1995-2009

UKH

AH

LAM

BA D

ISTR

ICT

MU

NIC

IPALIT

Y

DEPENDENCY RATIO

1995 (%) 2000 (%) 2005 (%) 2009 (%)

93.4 89.2 86.5 85.7



The number of people with no schooling has been decreasing over the years, resulting in

an increase of people receiving matric, and obtaining diplomas and degrees. The growth

rate of people who received Honours and higher degrees was almost 10 percent between

2006 and 2009. Overall, the literacy rate has increased in the district.

Table 4.7.14: Level of Education in Ukhahlamba, 1995-2009

LEVEL OF EDUCATION


Total 319,215 6.1 1.7 347,202 352,314 1.5

Grade 0 / No schooling 71,575 -5.8 0.6 66,103 65,572 -0.8

Grade 12 10,435 31.7 3.4 15,038 15,735 4.6

Less than matric and certificate / diploma 1,685 -30.5 -3.1 985 895 -9.1

Certificate with Grade 12 749 58.8 5.1 1,375 1,470 6.9


Bachelor’s Degree 646 24.2 5.9 898 947 5.5

Bachelor’s Degree and Diploma 171 138.5 4.8 491 535 9.0

Honours Degree 67 150.8 4.8 203 222 9.3


Doctorate) 65 97.1 5.0 151 164 8.3

Other (unspecified) 42,198 -7.8 1.2 38,225 37,967 -0.7


Table 4.7.15: Literacy level in Ukhahlamba, 1995-2009



Illiterate 71,575 -5.8 0.6 66,103 65,572 -0.8

Literate 74,091 13.8 2.3 88,404 90,706 2.6



The dependency ratio was about 86 percent in 2009, reflecting the huge percentage of

people who are dependent, and a very small labour force. The combination of this with

low income distribution results in high poverty levels, which is the case with Ukhahlamba

DM.

119


The percentage of the population characterised as poor decreased to almost 63 percent

in 2009, after reaching 79 percent in 2005.

Table 4.7.16: People in Poverty in Ukhahlamba, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

63.8 76.4 79.0 62.8


4.7.13 DISTRIBUTION Of HOUSEHOLD By INCOME

Households earning not more than R3 500 decreased from slightly more than 87 percent

in 1995 to 67 percent in 2009. The number of households within the income bracket of

more than R3 500 and R30 000 increased from 12 percent in 1995 to about 31 percent in

2009. This saw a 2 percent increase in households earning more than R30 000, from 0.5

percent in 1995.

Table 4.7.17: Distribution of Households by Income in Ukhahlamba, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

< R500 per month 13.9 15.2 7.7 3.3

R500 - R1000 30.9 25.1 23.0 14.8

R1001 - R3500 42.6 46.0 49.5 48.9

R3501 - R6000 5.6 6.2 9.6 16.2

R6001 - R11000 3.8 3.6 4.7 8.1

R11001 - R16000 2.1 1.6 1.9 2.9

R16001 - R30000 0.8 1.3 2.3 3.3

R30001 - R50000 0.3 0.7 0.9 1.6

R50000+ 0.1 0.2 0.4 0.9


4.7.14 HUMAN DEVELOPMENT INDICATOR

The Human Development Indicator in the district has remained constant at around 0.4,

signaling that the standard of living has remained unchanged since 1995. The district still

remains underdeveloped, with low life expectancy, high illiteracy rates and low standards

of living.

Table 4.7.18: Human Development Indicator in Ukhahlamba, 1995-2009


1995 (%) 2000 (%) 2005 (%) 2009 (%)

0.40 0.42 0.44 0.43


120

4.7.15 URBANISATION

Between 1995 and 2009 there was a 5 percentage point increase in the number of people

moving from rural areas to urban areas.

Table 4.7.19: Urbanisation in Ukhahlamba, 1995-2009

UKH

AH

LAM

BA D

ISTR

ICT

MU

NIC

IPALIT

Y

URBANIZATION (%)

1995 (%) 2000 (%) 2005 (%) 2009 (%)




123

5 STRATEGIC IMPLICATIONS

The data contained in this economic profile and outlook shows that the economy requires

drastic responses from both the private and the public sector. The impact of the 2008-

2009 economic crisis poses a threat of the reversal of the gains that have been made in

the past 10 years. The productive capacity that the economy has developed during this

period is under threat of being eroded; the projected long spells of unemployment have

serious implications for the skills base of the province.

The current trends in the economy require a well-resourced and capacitated developmental

state that responds to the challenges in practical terms. The public sector itself can go

a long way in intervening in the economy through refining the way it operates. Further

action is needed in facilitating:

Increased demand for local infrastructure inputs•

industrial development of labour absorbing and growth potential sectors •

Crowding in of private investment through more effective infrastructure•

Advancing a more inclusive economic growth, decent work and sustainable •

livelihoods

Rural development, food security and land reform•

Access to quality education•

Improved health care•

The fight against crime and corruption•

The fact is that we can’t afford not to act, our society needs strong action. If high

unemployment is allowed to persist, it will push millions more families into poverty, put

a drag on economic growth for years to come, and do serious damage to the province’s

long-term outlook. The collapse in global demand has caused businesses to scale back

activities and in some cases, shut down. A half-hearted economic programme of action

will only serve to extend the slowness of the recovery and will place the province behind

the curve of economic recovery.

124

Thus, balancing the imperative of closing socio-economic gaps and the need to mobilize

resources requires a strong development vision. The nature of economic interventions

within the province must derive from the socio-economic character of the province. This

report goes a long way in describing the character of our sectors.

The numbers show that this province is more than 60 percent rural and yet is driven by

the tertiary sector, which contributes more than 70 percent to the economy – a serious

anomaly. There is a dire need to reconfigure the structure of the provincial economy. The

starting point in this undertaking is to consider the natural comparative advantages of

the province, such as land endowment and favourable climatic conditions. In brief, the

province needs to reinvest in agriculture, forestry and fishing, and the following initiatives

need to be considered:

Investment in market-oriented agricultural infrastructure. This includes infrastructure •

that supports on-farm production (irrigation, energy, transportation, pre- and post-

harvest storage), ensures efficient trading and exchange (telecommunications,

covered markets), adds value to the domestic economy (agro-processing and

packaging facilities), and enables produce to move rapidly and efficiently from farmgate

to processing facilities, and on to wholesalers (transportation and bulk storage);

Building the economic case for developing road infrastructure to link elements in the •

agricultural value chain – suppliers, farmers, extension services, collection points,

wholesalers, agro-processors, and end-user markets. This would help to create new

corridors and to improve existing ones, and involve the development of strategies for

Public-Private Partnerships (PPPs) and farm-to-market roads;

Develop a framework for Public-Private Partnerships and water for irrigation; and•

Develop a framework for the participation of Public-Private Partnerships in wholesale •

markets and trading centres.

This is underpinned by a renewed commitment of government and donors to investment

in rural infrastructure, and an emerging bull market for global trade in cereals, horticulture,

meat and milk products – as well as experimentation with new forms of infrastructure

financing and contracting. There are real opportunities to broaden the role of the private

sector in infrastructure for agricultural development through PPP models.

Also, because of the absence of a mining sector in the Eastern Cape, this model would help

absorb unskilled labour and diversify the secondary and tertiary sectors in the province,

thus making households less susceptible to unemployment.

126

APPENDIX

FORECAST METHODOLOGYThe methodology adopted in this study to forecast the provincial macro-economy variable

is twofold. Given the scarcity of data availability at the provincial level, we have updated

our macro-econometric model and used the output at the national level to forecast the

Eastern Cape macro-variable. The first reason for such a methodology was based on the

fact that we found a strong correlation between the Eastern Cape macro-data and the

national one. The second motive is based on a strong, long-running relationship between

the Eastern Cape and national variables. From the two empirical evidences, and using

historical data, we derived and forecast the contribution of each variable to the national

macro-variable.

We then forecast the national macro-variable, using our in-house macro-econometric

model, and then estimated the province’s macro-variable from the forecast national

macro-variable. The estimations and forecasts of macro-data in this study are based

on a statistical technique called Seemingly Unrelated Regression (SUR). This statistical

technique allows for different error variances in each decade, and for the correlation of

these errors over time in a system of stochastic equations. Below is the outline of the

technique used to forecast the national macro-variables.

ESTIMATION TECHNIqUES

A seemingly unrelated regression (SUR) is a system comprising several stochastic

equations that are linked by the fact that their disturbances are correlated. There are

two main motivations for the use of SUR. The first is to gain efficiency in estimation

by combining information on different equations, while the second is to impose or test

restrictions that involve parameters in different equations. Below is a technical explanation

of the model.

127

Suppose that yit is a dependent variable, xit = (1, xit,1, xit,2, ..., xit,Ki−1)’ is a Ki-vector of

explanatory variables for observational unit i, and uit is an unobservable error term, where

the double index it denotes the tth observation of the ith equation in the system. Often t

denotes time and we will refer to this as the time dimension, but in some applications, t

could have other interpretations, for example as a location in space. A classical nonlinear

NSUR model is a system of nonlinear regression equations;

y1t = h1(βx1t) + u1t

...

yNt = hN(βxNt) + uNt

where i = 1, · · · ,N, and t = 1, ..., T. Denote L = K1+· · ·+KN.

Further simplification in notation can be accomplished by stacking the observations either

in the t dimension or for each i. For example, if we stack for each observation t, let Yt =

[y1t, ..., yNt]’, H(β,Xt) = [h1(β,x1t), …, hN(β,xNt)], Ut = [u1t, ...uNt]’ , and β = [β’1, ..., β’N]’.

Then, we write the NSUR model in a multivariate nonlinear regression form,

Yt = H(β,Xt) + Ut

In our case, we have estimated β using a Gaussian quasi-maximum likelihood estimator

(QMLE) assuming that Yt are Gaussian-conditioned on Xt for any measurable transformation

g of Xt.

DATA USED

The quarterly data used in the estimation of the model were obtained from Statistics South

Africa (StatsSA), the South African Reserve Bank and International Financial Statistics as

published by the WInternational Monetary Fund (IMF).

EC Economic Outlook Book

Documents

Transcript of EC Economic Outlook Book