Download e Eastern Cape Socio-Economic Review and Outlook 2013

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FORWARD
FORWARD
Hon. Mcebisi Jonas - MEC for Economic
Development, Environmental Affairs & Tourism
A review of the local economy and an assessment of social conditions cannot be divided. Promoting a vibrant, sustainable and growing economy is the cornerstone for achieving tangible
improvements in the standard of living for all. This is particularly relevant in terms of the historical economic and social inequalities that continue to influence the development pathway
of the Eastern Cape.
Critical reflection on socio-economic performance is vital for guiding, and where necessary,
re-making, the economic development trajectory of the province. The Socio-Economic Review and Outlook (SERO) provides a critical overview of the performance of the Eastern Cape
economy with a view towards fast-tracking economic development. Commissioned by the
Department of Economic Development, Environmental Affairs and Tourism (DEDEAT), this is
now the 4th edition of the Eastern Cape SERO, and has come to be recognised as a reference
point for understanding macro and micro economic trends within the province. This year’s
edition is undertaken by Business Enterprises, an independent and academically rigorous
task-team from the University of Pretoria.
The SERO focuses on development of the Eastern Cape Economy, but is framed within the
wider context of South Africa and the global economy. The geographical spread of development within the Eastern Cape is also strongly emphasised. Relevant social and economic
indicators are assessed at District Municipality Level, and in some cases at Local Municipality
Level. An important addition to the SERO 2013 is a specific review of the Eastern Cape Labour Market. The triple challenge of poverty, inequality and unemployment is rooted within
the structural inequalities embedded within the Labour Market. A specific analysis of labour
market trends highlights the urgent task of accelerating job creation within the province.
In conclusion, the SERO 2013 provides a critical review of social and economic performance
of the province, and is intended to be used as an accessible and accepted socio-economic
barometer of economic development within the Eastern Cape.
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Abbreviations................................................................................................................................. 9
Executive Summary................................................................................................... 10
Chapter 1: Global and National Economic Performance and Outlook....................... 14
1.1 Global Economic Review and Outlook............................................................................. 14
1.2. National Economic Review and Outlook......................................................................... 17
1.2.1. Overview................................................................................................................ 17
1.2.2. Sectoral Performance............................................................................................ 18
1.2.3. Consumer Spending.............................................................................................. 20
1.2.4. Gross Fixed Capital Formation.............................................................................. 22
1.2.5 The Foreign Sector.................................................................................................. 23
1.2.6. Monetary Policy..................................................................................................... 24
1.2.7 Fiscal Policy............................................................................................................ 26
Chapter 2: Socio - Economic Review and Outlook of the Eastern Cape................... 27
2.1 Demographic Profile of the Eastern Cape........................................................................ 27
2.1.1 Population............................................................................................................... 27
2.1.2 Fertility and Mortality............................................................................................... 29
2.2 Poverty, Inequality, and Food Security in the Eastern Cape............................................ 31
2.2.1 Poverty.................................................................................................................... 31
2.2.2 Service Delivery....................................................................................................... 33
2.2.2 Inequality................................................................................................................. 34
2.2.3 Food Security.......................................................................................................... 34
2.3 Economic Profile of the Eastern Cape.............................................................................. 35
2.3.1 Economic Performance........................................................................................... 35
2.3.2. Sectoral Analysis.................................................................................................... 36
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2.3.3 Trade Position......................................................................................................... 39
2.3.3.1 Export......................................................................................................... 39
2.3.3.2 Import......................................................................................................... 40
2.3.3.3 Trade Balance............................................................................................ 40
2.4 Sectoral Linkages in the Eastern Cape............................................................................ 41
Chapter 3: Socio - Economic Review and Outlook of District Municipalities in the
Eastern Cape............................................................................................................. 44
3.1 Demographic Profile of District Municipalities.................................................................. 44
3.2 Poverty and Food Security in District Municipalities........................................................ 48
3.2.1Poverty..................................................................................................................... 48
3.2.2 Food Security.......................................................................................................... 50
3.3 Economic Profile of District Municipalities....................................................................... 51
3.3.1 Introduction............................................................................................................. 51
3.3.1 Economic Profile..................................................................................................... 51
3.3.2 Sectoral Analysis..................................................................................................... 54
3.3.3.1 Sectoral shares.......................................................................................... 54
3.3.3.2 Growth performance.................................................................................. 56
3.3.3.3 Tracing Sources of Growth........................................................................ 58
3.3.4 Comparative Advantage of Activities...................................................................... 61
Chapter 4: Understanding the Labour Market........................................................... 62
4.1 Global Employment Performance..................................................................................... 62
4.2 National Employment Performance.................................................................................. 62
4.2.1 Introduction............................................................................................................. 62
4.2.2 Employment National.............................................................................................. 63
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4.2.3 Employment and Gross Value Added..................................................................... 66
4.3 Labour Market in the Eastern Cape.................................................................................. 67
4.3.1 Introduction............................................................................................................. 67
4.3.2 Status of Employment............................................................................................. 68
4.3.3 Employment and Gross Value Added..................................................................... 71
4.3.4 Employment Multipliers........................................................................................... 72
4.4 Labour Market in District Municipalities........................................................................... 73
Appendix..................................................................................................................................... 78
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List of Figures
Figure 1.1: Year-on-year GDP Growth......................................................................................... 14
Figure 1.2: Advanced Country Indicators.................................................................................... 15
Figure 1.3: Relationship between Fiscal Balance and Debt-to-GDP Ratio................................. 16
Figure 1.4: Relationships between Economic Performance of Advanced Economies and
External Positions of Developing Asia and Sub-Saharan Africa................................................. 17
Figure 1.5: Growth in the South African Economy...................................................................... 18
Figure 1.6: Sectoral Growth and Sectoral Shares in Total Value Added..................................... 19
Figure 1.7: Year-on-year Growth of FCEH and Contributions to FCEH Growth......................... 21
Figure 1.8: Trends in Gross Domestic Expenditure..................................................................... 22
Figure 1.9: Gross Fixed Capital Formation.................................................................................. 23
Figure 1.10: South Africa’s Export Destinations.......................................................................... 24
Figure 1.11: Monetary Policy and Inflation.................................................................................. 25
Figure 1.12: Government Finance Account................................................................................. 26
Figure 2.1: Population and Gender Distribution for the Eastern Cape........................................ 27
Figure 2.2: Share of Population Age Groups and Gender in the Eastern Cape.......................... 28
Figure 2.3: Population by Age Group for the Eastern Cape........................................................ 29
Figure 2.4: Poverty in South Africa.............................................................................................. 31
Figure 2.5: Poverty in the Eastern Cape...................................................................................... 32
Figure 2.6: Progress in the Delivery of Municipal Services......................................................... 33
Figure 2.7: Income Inequality in the Eastern Cape..................................................................... 34
Figure 2.8: Food Insecurity in the Eastern Cape......................................................................... 35
Figure 2.9: National and Provincial Growth Rates and Contribution of the Province to GDP
Growth......................................................................................................................................... 36
Figure 2.10: Provinces’ Contribution to GDP.............................................................................. 36
Figure 2.11: Sectoral Contributions to GDP................................................................................ 38
Figure 2.12: Export and Import Shares....................................................................................... 39
Figure 2.13: Balance of Trade in the Eastern Cape..................................................................... 41
Figure 2.14: Relative Strength of Sector Activities...................................................................... 43
Figure 3.1: Population and Gender Distribution for the District Municipalities........................... 44
Figure 3.2: The Share of Population Age Groups and Gender.................................................... 45
Figure 3.3: Population by Age Group.......................................................................................... 46
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Figure 3.4: Average Deprivation in Selected Indicators in Alfred Nzo District
Municipality (%) .......................................................................................................................... 49
Figure 3.5: Average Deprivation in Selected Indicators in O.R Tambo District
Municipality (%) .......................................................................................................................... 49
Figure 3.6: Average Deprivation in Selected Indicators in Amatole District
Municipality (%) .......................................................................................................................... 50
Figure 3.7: Food Insecurity in the Eastern Cape......................................................................... 50
Figure 3.8: District Municipalities Contribution to Provincial GDP.............................................. 52
Figure 3.9: GDP Growth Rates Provincial for Nelson Mandela Bay and Buffalo City................. 52
Figure 3.10: Provincial and District Municipality Growth Rates.................................................. 53
Figure 3.11: Year-on-Year Growth Rates of Manufacturing in District Municipalities................. 57
Figure 3.12: The Contribution of Activities to Growth in GVA in Nelson Mandela Bay
and Buffalo City........................................................................................................................... 58
Figure 3.13: The Contribution of Activities to Growth in District Municipalities GVA.................. 59
Figure 4.1: Sectoral Shares and Employment............................................................................. 63
Figure 4.2: Employment by Level of Education........................................................................... 64
Figure 4.3: Employment and Unemployment by Age................................................................. 64
Figure 4.4: Employment and Unemployment in South Africa..................................................... 65
Figure 4.5: The Growth Response of Employment..................................................................... 66
Figure 4.6: Share of Value Added and Employment by Sector................................................... 67
Figure 4.7: Employment and Unemployment by Age Groups..................................................... 69
Figure 4.8: Distribution of Working Age Population by Age Group............................................. 69
Figure 4.9: Employment by Activity in the Eastern Cape............................................................ 70
Figure 4.10: Employment by Occupation.................................................................................... 71
Figure 4:11: GVA and Employment Shares by Activity............................................................... 72
Figure 4.12: The Effect on Employment Multipliers of Changes in Labour Intensity.................. 73
Figure 4.13: Employment in the District Municipalities............................................................... 74
Figure 4.14: The Unemployment Rate in District Municipalities................................................. 74
Figure 4.15: Employment by Sector in District Municipalities.................................................... 75
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List of Tables
Table 1.1: Import Penetration, Export Intensity, and Labour Share in Output............................. 20
Table 2.1: Fertility Rates, Provincial and South Africa................................................................. 29
Table 2.2: Life Expectance at Birth, Provincial and South Africa................................................ 30
Table 2.3: Leading Natural Causes of Deaths, Eastern Cape and South Africa......................... 30
Table 2.4: Average Deprivation in O.R Tambo District Municipality............................................ 32
Table 2.5: Sectoral Contribution at Basic Prices......................................................................... 37
Table 2.6: Location Quotient....................................................................................................... 38
Table 2.7: Major Export Contributors in the Eastern Cape......................................................... 39
Table 2.8: Major Imports Contributors in the Eastern Cape........................................................ 40
Table 2.10: Gross Output, Value Added, Income, and Employment Multipliers in the Eastern
Cape............................................................................................................................................ 41
Table 2.11: Relative Strength of a Sector Compared to Other Sectors in the Economy............ 42
Table 3.1: Sectoral Shares of GVA by District Municipality in the Eastern Cape (%).................. 56
Table 3.2: Annual Sectoral Growth Rates by District Municipality.............................................. 58
Table 3.3: Contribution to Growth in Gross Value Added (%)..................................................... 59
Table 3.4: Location Quotient....................................................................................................... 61
Table 4.1: Eastern Cape Labour Market...................................................................................... 68
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Abbreviations
CCI: Consumer Confidence Index
CPI: Consumer Price Index
DA: Developing Asia
DEDEAT: Department of Economic Development, Environmental Affairs and Tourism
DM: District Municipality
FCEH: Final Consumption Expenditure of Households
FCEGG: Final Consumption Expenditure of General Government
FDI: Foreign Direct Investment
FIP: Fuzzy Index of Poverty
GDP: Gross Domestic Product
GFCF: Gross Fixed Capital Formation
GDE: Gross Domestic Expenditure
GDPR: Gross Domestic Product Regional
GVA: Gross Value Added
ILO: International Labour Organization
IMF: International Monetary Fund
LM: Local Municipality
MPC: Monetary Policy Committee
NMBM: Nelson Mandela Bay Metro
RoE: Rest of the Economy
SARB: South African Reserve Bank
SSA: Sub-Saharan Africa
UK: United Kingdom
US: United States of America
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Executive Summary
Introduction
This is the fourth publication of the Socio-Economic Review and Outlook (SERO) by the Department of Economic
Development, Environmental Affairs and Tourism (DEDEAT). In this document a wide variety of socio-economic
indicators, grouped into four chapters, are covered. Chapter 1 highlights issues related to global and national
economic performance and outlook. In Chapter 2, the performances of various socio-economic indicators in the
Eastern Cape are discussed. In Chapter 3, the focus shifts towards analysis of socio-economic performances at
District and Local Municipality Levels. And finally, Chapter 4 concludes with analysis of the labour market at various
levels of disaggregation, global, national, provincial, and District Municipality levels. Below is a summary of the major
findings from the four chapters.
The Global and National Economy Performance and Outlook
The global economy has been reeling from the continued effects of the economic crisis since 2007. A range of
approaches to economic recovery have been followed, ranging from financial bailout during the 2007/08 financial
crisis, to austerity measures in the most recent 2011/12 sovereign debt crisis, but each with limited success. The
global crisis continues to grow deeper and wider, with global growth predictions only marginal for 2013 and 2014.
The impact of the crisis has not been limited to advanced economies alone; the crisis has been characterised by
considerable spill-over effects into developing economies, due to the close international trade and financial market
linkages. South Africa has similarly experienced significant shockwaves from the meltdown. The South African
economy officially entered into recession in the second quarter of 2009. The economy was quick to emerge from
economic recession by the first quarter of 2010, but has been on a bumpy path of recovery since. Moreover, economic
recovery has been thwarted by the ensuing sovereign debt crisis in the Euro.
The effect of the crisis on South Africa can be manifestly observed in different forms: below par performance
by productive sectors, hikes in the rate of unemployment; lacklustre growth in consumer demand; lower private
investment; and an added pressure to an already ailing external sector. Indications suggest that the South African
economy will continue to be adversely affected by the poor performance of the global economy. Hence, growth
will more than likely continue to disappoint in 2013 and 2014. This is unless enough policy space is created (fiscal,
monetary, and international trade) to enable the government to run larger fiscal deficits in order to counteract lacklustre
global demand. However, to be effective, such public-sector driven investment must also ensure that it addresses
structural inefficiencies in the South African economy and initiate a new growth trajectory.
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Socio-Economic Performance of the Eastern Cape Economy
The Eastern Cape is home to 6.7 million people, equivalent to 12.8% of the national population. The following are
some of the salient demographic characteristics of the province: the Eastern Cape is comprised of a relatively young
population; a declining but higher than national average fertility rate; a working age population that is increasingly
female; and a below-average life expectancy rate.
The province faces significant social challenges: namely, addressing poverty, income inequality, food insecurity, and
unemployment. The Eastern Cape is frequently measured as the poorest province in South Africa. Within the Eastern
Cape, poverty is widespread, but is most prevalent within Alfred Nzo District Municipality (DM), followed by O.R
Tambo DM, Amatole DM, Ukhahlamba DM, Chris Hani DM, Cacadu DM and lastly NMBM (Nelson Mandela Bay
Municipality).1 On the positive side, the Eastern Cape has managed to reduce poverty levels between 2007 and
2011, particularly, in the O.R Tambo DM. In addition, average deprivation levels are examined by DM so that further
reductions in poverty can be achieved through proper targeting of interventions.
The province is also characterized by high levels of food insecurity; close to 78% of the provinces’ households may
be classified as food insecure. Food insecurity seems widespread among households in Alfred Nzo, Chris Hani, and
O.R Tambo DM. The study further highlights socio-economic characteristics of food insecure households. It is found
that the majority of the food insecure, reside in rural areas, are African, are headed by women, and have larger family
sizes, greater dependency ratios, and reside in places under-served in municipal services.
The economy of the Eastern Cape is strongly driven by the tertiary sector. The tertiary sector accounted for approximately
77% of the provincial Gross Domestic Product (GDP) in 2011, a 1.7 percentage point increase compared with its
2002 level. On the flip-side, the secondary and primary sectors, registered a 1.2 and 0.5 percentage point decline
respectively. In 2011, 60% of the 1.3 million employed people were employed in three sectors, namely, community
services (26.1%), trade (23.5%), and manufacturing (12.2%). The chapter further classifies activities as key, weak or
a combination of both depending on their backward and forward linkages with other activities in the economy. Key
activities include: utilities (electricity and water), construction, transport, and finance. Weak activities include: mining
and trade. Community services have strong backward linkages but weak forward linkages; whilst agriculture and
manufacturing have strong forward linkages but weak backward linkages. This classification is useful; as it can help
identify activities that need support to enhance their contribution to the provincial economy.
The Eastern Cape registered a trade deficit of R1.6 billion in 2011, mainly attributed to the poor performance of the
transport equipment sector. In 2011, the transport sector contributed 40% of the total provincial export, an increase
of 13.5% percentage points compared with the 2010 level. In the same year however, import of transport equipment
accounted for 56% of the total provincial import, an increase of 15.4 percentage points compared with its 2010 level.
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Buffalo City Metropole is sometimes treated separately in the analysis depemding on the date. Buffalo City was subsumed in the Amatole DM when the census was conducted
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Socio-economic performance of District Municipalities
According to the 2011 census, the majority of the population in the Eastern Cape reside in O.R Tambo DM, followed
by the NMBM, Amatole DM, Alfred Nzo DM, Chris Hani DM, Cacadu DM, and Ukhahlamba DM. By breaking down the
provinces’ population into three age groups and comparing it over four census years -1996, 2001, 2007 (Community
Survey), and 2011- the proportion of individuals which fall within the working age group of 16-64 years is increasing
and accounts for the majority of the population in all DMs. Further to this, poorer DM’s within the province are more
likely to have a relatively lower proportion of their population of working age.
The gender composition of the population is weighted towards females in all DMs. In the O.R Tambo DM, where
the difference is significant, females outnumber males by 4.5%. The gender composition also varies by age group.
Males outnumber females at lower age cohorts whilst females outnumber males at higher age cohorts. This can be
partially explained by patterns of migration, which are biased towards males in search of better opportunities in richer
provinces. Close to 218,815 people migrated from the province alone between 2006 and 2011; the Eastern Cape is
ranked number one in terms of net migration.
The Eastern Cape is commonly ranked as the poorest province in South Africa. However, there are also significant
differences in poverty levels between DMs. Ranked from the poorest to the relatively well-off, Alfred Nzo has the
highest rate of poverty, followed by O.R Tambo, Amatole, Ukhahlamba, Chris Hani, Cacadu, and NMBM. Furthermore,
the top three poorest Local Municipalities (LM) within the Alfred Nzo DM were Ntabankulu LM (44%), Mbizana LM
(40%), and Umzimvubu LM (36%). In addition, other indicators of wellbeing are explored within LMs, specifically
households’ access to services such as formal housing, electricity for lighting, sanitation, refuse removal, water, and
education levels. For example in Ntabankulu LM, the poorest LM within Alfred Nzo DM, average deprivation in refuse
removal was 85%; 62% in access to electricity for lighting; 76% in income and 60% in access to sanitation.
Food security is a second important socio-economic indicator for analysis. Unlike Chapter two, where the focus was
at district municipal level, food insecurity at the Local Municipal level is also assessed. Alfred Nzo is identified (in
Chapter two) as the most food insecure DM in the province, followed by Chris Hani, and O.R Tambo DM. Within the
Alfred Nzo, the study identifies Matatiele as the most food insecure LM at 86%, followed by Umzimvubu at 85%; and
Ntabankulu at 80%.
The study also analysed the performance of DMs in the Eastern Cape economy. It looked at the share of each DM
in the regional Gross Value Added (GVA); growth in the GVA of each DM; the contribution of each DM to growth in
the regional GDP; and finally it traced the contribution that each activity made to growth in its respective DM’s GVA.
NMBM is the largest economy in the province, and by some measure, accounting for 45% of the province’s total
GDP. This is followed by Buffalo City Metropolitan at 21% of the province’s GDP. On the other hand, Ukhahlamba
and Alfred Nzo DMs are the smallest economies, each accounting for only 5% of the province’s GDP. The province’s
GDP grew by 3% year-on-year in 2011, while the NMBM and Buffalo City Metropole, the two bigger economies in the
province, grew respectively by 3% and 2%. By decomposing the 3% provincial GDP growth, the study shows that
48% and 20% of the 3% provincial GDP growth came from NMBM and Buffalo City Metropolitan, respectively. The
remaining 34% was shared among the remaining six DMs.
In addition, the study examines the performance of economic sectors within each of the DMs. This is done by
comparing the relative size of each sector within their respective DM’s GVA, examining year-on-year growth rates, as
well as sectoral contributions to growth in DM’s GVA. The tertiary sector generally accounted for the largest share of
each DM’s GVA. In particular, community service’s share of the GVA was the greatest in all DMs and metros; its share
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was the lowest in DM’s with more developed economies like the NMBM (at 20% of GVA), and highest in relatively
small and underdeveloped DMs such as Alfred Nzo (at 47% of GVA).
Sector growth, measured in terms of a year-on-year growth rate, slowed down in the majority of the DMs. This is
particularly so, among others, in manufacturing. The tertiary sector performed better than other sectors in terms of
growth. The share of the tertiary sector increased in all DMs between 2002 and 2011, showing the importance of the
tertiary sector. Within the tertiary sector, community services and finance were among the best performers. The latter
performed particularly well in underdeveloped economies. The share of community services increased in five out of
the eight DMs (metros include). This happened on the back of declining shares in the secondary and primary sectors.
This trend was consistent across all DMs and metros. Community services stood out as the major drivers of growth
in all DMs. The fact that community services featured as the most important sector in terms of share in GVA, year-onyear growth, and as a major driver of growth highlights the dominance of government within economic activity in the
Eastern Cape, particularly within underdeveloped economies.
Employment in the Eastern Cape
Tracking job creation within the Eastern Cape is a primary indicator of progress and development. However,
employment levels within the Eastern Cape continue to disappoint. In addition, access to employment is highly
nuanced – dependent on age, race, gender, and education. The province accounted for 9.7% of the national
employment rate in the third quarter of 2012. Within the province, over half of the employed people were located in
the metros. The majority of the employed, regardless of the location, were employed in community services. The share
of community services in total employment at provincial as well as DM level showed marked increases compared
with other activities. This happened at the expense of production activities such as manufacturing and agriculture.
The chapter also shows that the majority of the employed, regardless of activities they are in, are employed in
elementary occupations. However the share of elementary occupation, together with others that fall in the same
category (unskilled job category), decreased while those in the semi-skilled and skilled categories increased. This
signals the presence of skill-biased employment growth.
The Eastern Cape is also characterised by an alarmingly high level of unemployment. In the third quarter of 2012,
the rate of unemployment rose to 28.8%. This is partly explained by an increase in the labour force over and above
the number of new jobs created. Increases in the total size of the labour force were caused, not only by increases in
the number of new entrants into the job market, but also by increases in the number of people who switched their
employment status from economically non-active to economically active.
The labour intensity of various sectors is also analysed for the province. Activities like manufacturing, transport,
finance, households, electricity, and water are shown to be highly capital intensive. This means that there is need for
structural transformation to change the existing growth-employment mix to promote a more labour-intensive path of
development. A simulation exercise was conducted to gauge how initiatives that promote increased labour intensity
(through structural transformation) affect employment multipliers. Results indicate that such changes will significantly
increase employment multipliers and provide an important bridge towards reducing inequality and hence promoting
shared economic growth and development in the Eastern Cape.
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Chapter 1: Global and National Economic Performance and
Outlook
1.1 Global Economic Review and Outlook
Growth in the global economy was sluggish in 2011 with an estimated growth rate of only 3.8% as calculated by the
International Monetary Fund (IMF). Much of the growth was contributed by the emerging and developing economies.
They included mostly developing Asia and Sub-Saharan Africa (although South Africa was an exception). Developing
Asia and Sub-Saharan Africa grew by 7.8% and 5.2% respectively. Advanced or developed economies grew by a
mere 1.6% in 2011.
Figure 1.1: Year-on-year GDP Growth
Source: Based on IMF World Outlook 2012 data
In 2011, the major cause for the sluggish growth in the global economy can be attributed to the ‘Sovereign Debt Crisis’,
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particularly in advanced economies, concentrated in the Euro area. The crisis together with other growth inhibiting
factors (such as poor macroeconomic policies, lack of competition, and less integration into the global economy)
were responsible for less than desirable growth in output, particularly in advanced economies (see Figure 1.2). Slow
growth in advanced economies was accompanied by weak private consumption, lower levels of investment (gross
fixed capital formation), higher levels of unemployment and lower levels of inflation. Unemployment in advanced
economies hit a high of 8% in 2009 and has not shown any sign of improvement since. Inflation is still lower than
its pre-crisis rate, symptomatic of low global demand. It rose moderately by 0.55 percentage points in 2011 from
1.95% in 2010. The global recession/slow-down has manifested itself in higher levels of public debt, increasing
fiscal deficits, weakening financial institutions, flagging investor confidence and rising risk premiums on international
bonds. To address this, advanced economies have embarked on massive fiscal consolidation drives, however, these
measures also limit the ability of government to directly intervene in the economy by stimulating domestic demand.
Figure 1.2: Advanced Country Indicators
Source: Authors computation based on IMF World Economic Outlook data
According to the IMF, the global economy is expected to grow at 3.6%, year-on-year in 2013. Much of the growth is
projected to come from emerging and developing economies. Sub-Saharan Africa would grow at 5.7% in 2013 while
developing Asia would grow at 7.2% in 2013. On the contrary, growth in advanced economies would disappoint;
developed economies are expected to grow at 1.6%. The Euro area in particular is expected to experience close
to zero economic growth in 2013 (at 0.2%). Depressed aggregate demand, lower fixed capital formation, and a
deepening crisis in the financial sector will continue to suppress growth prospects in the short-term.
In addition to deepening financial crisis, prospects for quicker recovery in 2013 are dampened by some uncertainties.
These include inter alia the concern that Eurozone policy makers will be unable to abate an escalating sovereign debt
crisis; and whether the US congress will be able to avert the gridlock that caused the ‘fiscal cliff’ in the US:
The euro area is deeply entangled within a sovereign debt crisis; and it is not yet clear how successful its policy
makers will be in dealing with it. In a recent meeting held by Euro leaders, an agreement was reached to establish a
banking union to break the adverse feedback loop between sovereigns and the banks. The success of this project is
yet to be seen.
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The world waited in anticipation for US policy makers’ handling of the ‘fiscal cliff’ – a sharp decline in government
expenditure alongside increased tax hikes – which was feared to cause a second US recession. The crisis was
averted, thanks to the compromise in the US congress around the extent of the tax hike. However disagreements
on an outstanding, yet contentious issue (i.e. raising the debt ceiling) is still looming. If unresolved, it could limit the
ability of the US to provide fiscal stimulus to propel its ailing economy on a path to recovery. A failing US economy
would have spill-over effects on the global economy.
Figure 1.3: The relationship between Fiscal Balance and Debt-to-GDP Ratio
Source: authors’ computation based on IMF data
Given that prospect for global economic growth remain severely muted, policymakers may choose to stimulate their
economies using countercyclical fiscal policy. However, this depends on the fiscal space and the debt level.
Figure 1.3 compares changes in the fiscal balances and debt levels (debt-to-GDP ratio) of South Africa’s major
trading partners. In 2011, most countries grappled with larger fiscal deficits, however, the majority of countries were
able to reduce their debt-to-GDP ratio (in other words, their GDP grew proportionately more than the total size of their
debt), with the notable exception of Japan. Figure 1.3 shows that public debt surged in Japan, South Africa, the US,
UK, Euro Area, and Sub-Saharan Africa. In the Euro area, where fiscal consolidation is already entrenched, public
debt rose from 70% in 2008, to 88% in 2011. The IMF estimates that debt levels will continue to rise in the Eurozone,
to approximately 95% of GDP in 2013. On the contrary, in 2011, emerging economies such as Brazil, India, and China
have managed to decrease the total size of their public debt. Overall,
Therefore, it appears that advanced economies have limited fiscal space to counter further economic slowdown
with increased fiscal spending. Some have little fiscal buffer, while others lack the fiscal credibility needed to finance
fiscal expansion, and still others lack monetary policy space and adequate foreign exchange reserves. The situation
is different for emerging markets. They have managed to maintain prudent fiscal policies, lower public debt, built
strong domestic financial markets, and accumulated reserves. This can give them the latitude to experiment with
complementary policies to counter future economic slowdown, if necessary.
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Figure 1.4: Relationships between Economic Performance of Advanced Economies and External Positions of Developing Asia and Sub-Saharan Africa
Source: Authors’ computation based on IMF data
The discussions hitherto suggest that emerging markets and developing economies have performed relatively
positively despite the sluggish global economy. Nevertheless, in today’s highly globalised economic system, no
country can be totally immune from the effects of sluggish global growth. This is because economies are intertwined
through highly interrelated financial markets and sophisticated trade linkages. For example, the crisis in the Euro
area tightened fiscal and financial conditions in the Euro and led to lower demand for exports sourced from emerging
markets and developing economies. This had direct effect on the external trade positions of exporting countries.
Figure 1.4 shows the close correlation between growth in advanced economies and the external trade balance of
emerging and developing economies.
1.2. National Economic Review and Outlook
1.2.1. Overview
South Africa is home to the largest economy in Africa. In the third quarter of 2012, the South African economy grew
by 2.3% year-on-year, a 1.1 percentage point decline compared with the third quarter of 2011 (see Figure 1.5). Figure
1.5 further shows that South African Gross Domestic Product (GDP) has been on a declining trend since the first
quarter of 2011. This is a reversal to the short spell of recovery that followed the aftermath of the 2009 economic
recession.
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Figure 1.5: Growth in the South African Economy
Source: Own computation based on Statistics South Africa. Projections were obtained from the IMF’s World Economic Outlook.
The sluggish performance of the South African economy is directly linked to both global and domestic economic
conditions, with strong feedback loops between domestic and international markets. The global economic outlook
has already been highlighted in the section above. In this section, the contributions of domestic factors to sluggish
South African GDP growth are discussed. These include understanding changes in the following key correlates
of economic development: namely, spending by households and government, represented respectively by Final
Consumption Expenditure of Households (FCEH) and Final Consumption Expenditure by the General Government
(FCEGG); private and public investment, as represented by Gross Fixed Capital Formation (GFCF); the external
trade sector; and macroeconomic policy. The analysis is further broken down in relation to particular key economic
sectors.
1.2.2. Sectoral Performance
The growth in South African GDP, albeit sluggish, was driven in particular by modest growth of the tertiary and
secondary sectors. These grew by 2.6% and 2.3% year-on-year between Q32011 and Q32012 respectively. On the
contrary, the poorly performing primary sector, actually contracted by 1.6% over the same period.
The tertiary sector is the largest sector in terms of its contribution to GDP and employment. It accounted for about
69% of GDP and created employment opportunity for about 72% of the labour force. In addition the tertiary sector is
composed of activities that are less import penetrated and less export intensive (Table 1.1), and as a result, the sector
is fairly shielded from events in the global economy. All sub-sectors of the tertiary sector registered positive growth
over the previous year (see Figure 1.6). The following activities, within the tertiary sector performed relatively well in
the third quarter of 2012: wholesale and retail, personal services, government services, and financial, real estate, and
business services. Each grew at 3.2%, 3.1%, 2.9% and 2.3%, respectively. However, in comparison with previous
year growth rates, growth remains slow; each has yet to reach its respective pre-economic recession growth rates.
Finance, real estate, and business services and government services managed to increase their relative shares of
total GDP. Shares of the other activities within the tertiary sector remained fairly constant.
18
Figure 1.6: Sectoral Growth and Sectoral Shares in Total Value Added
14.00
12.00
Construction
Year-on-year growth
10.00
8.00
Personel
6.00
Finance &
Transport
Wholsale &
4.00
Elect&water
gov't
Manufacturing
Personal
Wholsale
&
gov't
Construction
Manufacturing
Agriculture
Transport
2.00
Finance &
Mining
0.00
0.00
0.05
0.10
0.15
0.20
0.25
Mining
Elec&water
-2.00
Agriculture
-4.00
Share of total value added
2012Q3
2007Q3
Source: Own computations based on data from Statistics South Africa
The secondary sector also experienced positive, although slow, growth in the third quarter of 2012. Such growth was
derived from the manufacturing and construction sectors; each grew at 2.5% and 3.1%, respectively (see Figure
1.6). However growth in both the manufacturing and construction sectors have yet to attain their pre-crisis economic
growth rates (which averaged 5% and 11% respectively prior to 2009).
Manufacturing showed strong signs of recovery in the immediate aftermath of the economic recession; however, this
recovery tapered off soon after another wave of crisis hit, emanating from the Euro. Manufacturing is the most import
penetrated and highly export intensive activity and therefore more vulnerable to global economic conditions (Table
1.1). Such developments, inter alia, affected not only its growth but also its share in Gross Value Added (GVA).
19
Table 1.1: Import Penetration, Export Intensity, and Labour Share in Output
Activities
Primary Sector
Secondary
Sector
Import penetration
Labour share in output
Estimate
(%)
Rank
Agriculture, Forestry, and Fisheries
3.74
4
0.28
7
6.740
5
Mining and Quarrying
17.99
3
0.36
6
47.440
1
Manufacturing
25.96
1
0.46
4
13.650
3
Electricity and
water
–
–
0.36
6
0.002
8
Construction
–
–
0.58
1
–
–
Wholesale, retail,
and motor trade
–
–
0.49
3
29.100
2
Transport, storage,
and communication
–
–
0.38
5
10.330
4
Finance, real estate, and business
services
22.00
2
0.36
6
1.400
7
–
–
0.52
2
2.500
6
Tertiary Sectors Government services and personal
services
Estimate
Rank
Export intensity
Estimate
Rank
(%)
Source: Own computation based on Statistics South Africa 2006 Social Accounting Matrix (SAM).
Construction, the second strongest performing activity with the secondary sector, is more closely linked with
domestic, rather than international developments, given that construction is neither an import intensive, nor export
intensive, activity (see Table 1.1). Expectations are that growth in construction will soon start to accelerate, driven by
government commitment to create more jobs by investing heavily in public infrastructure: through economic services,
social services, justice and protection services, and central government administrative and financial services. A
public budget of R808.6 billion was allocated in the Medium-Term expenditure framework. This is to be implemented
over a period of three years and represents a large injection into the domestic economy (2010/11 to 2013/14).
Performance in the primary sector was disappointing. Growth decelerated by 1.6% year-on-year in the third quarter
of 2013. This was caused by a 2.5% and 1% year-on-year drag in the Agriculture, Forestry, and Fisheries and the
Mining and Quarrying sectors respectively (see Figure 1.6). The mining sector was the most affected by the crisis.
Not only did its growth rate reverse; but its share of the total value added also declined. The poor performance of the
mining sector can be attributed to a host of factors. These include sluggish growth in the global economy, especially
in the US and the Euro area, which comprises countries that are South Africa’s major buyers of mineral exports;
labour unrest that has specifically affected the mining and agriculture sectors; and political and policy uncertainty
surrounding land and mining ownership.
1.2.3. Consumer Spending
Consumers remain on the driver’s seat of growth in Gross Domestic Expenditure (GDE) and hence economic growth
in South Africa. However since the global economic crisis, their contribution to growth has been declining. Growth in
real final household consumption expenditure (FCEH) slowed to 3.4% year-on-year in the third quarter of 2012 and
20
has been on a clear path of slowdown (see Figure 1.7).
Prospects for the recovery of FCEH in the fourth quarter are pessimistic, despite the lower interest rate environment. A
lower interest rate has not been translated into total domestic credit extension and increased spending by households.
The slow pass-through could be owing to high consumer indebtedness, estimated in terms of household debt to
disposable ratio, at over 76%. Estimates show that 47% of the 19 million credit active consumers have impaired
credit records. The uncertain economic future outlook is yet another drag on household consumption. The consumer
confidence index (CCI) of FNB/BER, a measure of consumers’ level of optimism on the South African economy,
shows continued declines in consumer confidence.
Final Consumption Expenditure of Households (FCEH) is composed of households’ spending on durable goods,
non-durable goods, semi-durable goods, and services. Non-durable goods and services account for about 77% of
FCEH. A quick check on the contributions that each component made to growth in FCEH shows that non-durable
goods and services contributed respectively 22% and 21% of the 3.4% year-on-year growth in FCEH registered in
the third quarter of 2012. This is lower than the 26% and 33% contributions each made in the corresponding quarter
of 2011 (see Figure 1.7). On the contrary, spending on durable and semi-durable goods has risen (see Figure 1.7).
Durable goods accounted for 37% of the growth in FCEH, an improvement of 9 percentage points. Similarly, semidurable goods contributed 19% to total FCEH, a 6 percentage point increase from 13% in the same quarter of the
previous year.
Figure 1.7: Year-on-year growth of FCEH and Contributions of FCEH Growth
Source: Own computations based on data from the Reserve Bank of South Africa
The decline in households’ spending on non-durable goods and services can be attributed in part to inflationary
pressure. This is because inflation has been concentrated in non-durable goods and services more than other items
in the Consumer Price Index (CPI) basket. For example, prices of important non-durable goods such as bread and
cereals, electricity and oil fuel, petrol, water and other services surged on average by 6%, 10%, 11%, and 9%,
21
respectively. Food, energy, and electricity inflation are major drivers of headline CPI inflation. Service-related items
such as private transport operations and public transport also registered significant price hikes. Each increased by
13% and 14% respectively.
Figure 1.8: Trends in Gross Domestic Expenditure
Source: Own computations based on data from the Reserve Bank of South Africa
Growth in the South African economy has so far been led by FCEH. This is because FCEH contributed to the
largest proportion of growth in Gross Domestic Expenditure (GDE). However, decomposition of GDE growth into
its constituent parts also shows that FCEH’s contribution to GDE growth has been declining (see Figure 1.8). GDE
grew by only 3.9% in the third quarter of 2012. FCEH contribution to 3.9% was 50%; much lower than the 70%
contribution it made to the same in the first quarter of 2011. Contributions to GDE growth by other components of
GDE on the contrary have increased. The contribution of Gross Fixed Capital Formation (GFCF) increased from 6% in
the first quarter of 2011 to 30% in the third quarter of 2012, while that of Final Consumption Expenditure of General
Government (FCEGG) slightly improved from 21% to 23%. Growth in GFCF was the product of increased investment
by the government and public corporations.
1.2.4. Gross Fixed Capital Formation
Real gross fixed capital formation (GFCF) grew by 7.1% year-on-year in the third quarter of 2012 (see Figure 1.9).
This accounts for 29% of the total 3.9% growth in GDE. The growth in GFCF was the product of accelerated growth
in capital spending by public corporations (18%) and the government (11%) (see Figure 1.9). Capital spending by
private and business enterprises grew by only 3.4%.
In terms of spending by type of investment, there was a fall in investments in machinery and other equipment. Growth
in other types of investment were however positive but weaker. Capital spending was weaker in the private sector
dominated activities such as mining and quarrying, manufacturing, and storage and communication activities. It was
stronger in public and government dominated activities namely transport and storage; electrical, gas and water;
financial intermediation; and community, social, and personal services.
22
Figure 1.9: Gross Fixed Capital Formation
Source: Own computation based on data from Statistics South Africa and Reserve Bank of South Africa
In general, it can be argued that GFCF is on a path of recovery; however this is driven by government spending
and has yet to attain its pre-crisis growth rate of 14% (see Figure 1.9). It is also interesting to note that the share
of the private business enterprises in GFCF has fallen. Public corporations, private business enterprises, and the
government contributed 22%, 62%, and 16% respectively of GFCF in the third quarter of 2012. This is in clear
contrast to pre-crisis levels in which the contribution of private business enterprises to GFCF was at a high of 75%.
Will GFCF grow weaker or will it contract in 2013 and beyond? This depends on the growth performance of advanced
economies and their ability to avert the current economic crisis. South Africa relies on foreign capital (portfolio
and foreign direct investment (FDI)) to finance its investments. For example, in 2011, total investment was 19.7%
of GDP, whilst national saving was only 16.4% of GDP. The shortfall was bridged by foreign capital, mainly from
portfolio investments. According to the IMF, advanced economies will grow by only 1.5% in 2013, attributed to
the deepening sovereign debt crisis. There are therefore very limited prospects for significant FDI investment from
developed economies in the near future. Furthermore, South Africa’s ability to attract foreign capital could also be
adversely affected by cuts in its sovereign ratings (three times so far). This could increase the cost of borrowing for
South Africa, which could in turn limit investment.
1.2.5 The Foreign Sector Growth in exports slowed to 5.43% year-on-year in the third quarter of 2012 (see Figure 1.10). That was a 13
percentage point decrease compared with the third quarter of 2011. This was caused by a decline in South Africa’s
export to Europe by 7%. The slow growth was also triggered by weaker exports into America and Asia. South Africa’s
export to America and Asia grew by only 5% year-on-year. This however is equivalent to a 20 and a 10 percentage
point decline respectively from the corresponding quarter in 2011. Nevertheless, South Africa’s export to Africa
accelerated in the third quarter of 2012. This has been increasingly so since the 2009 economic recession.
23
Figure 1.10: South Africa’s Export Destinations
Source: Own computations based on data from International Trade Center
South Africa’s import growth accelerated year-on-year to 19.4% in the third quarter of 2012. The trade balance, the
difference between total exports and imports of all goods and services, has been in deficit since the third quarter of
2004. The trade deficit averaged 5.2% of GDP between 2004 and 2008. The deficit decreased to 4% of GDP during
the 2009 crisis, and it fell to 3% of GDP in 2010 and 2011. The deficit is expected to widen reaching 5.5% of GDP in
2012. The deepening crisis in Europe coupled with developments in the domestic economy contributed to the further
deterioration of the trade balance in 2013. The IMF forecast a trade deficit of 5.8% in 2013 for South Africa.
South Africa has relied heavily on capital inflows to finance its trade deficit. Much of the capital inflow has arrived
in the form of portfolio investments rather than Foreign Direct Investment (FDI). However, portfolio investment is
often attributed to risk aversion and investor speculation and is not always classified as real investment per se. This
included investors’ search for safe heavens to try and escape the financial crisis in Europe; and the lower interest rate
in the US that encouraged capital outflow in the US as a result of quantitative easing.
Industrial actions and labour unrest have heavily affected the mining industry in the last two quarters of 2012. Moreover,
political impasse regarding the nationalization of mines, and the slowly recovering global economy are downside
risks that continue to threaten export growth in 2013. On the other hand, import bills could pileup in response to the
government’s massive investment in socio-economic infrastructure; the price of oil could surge in the international
market due to continued unrest in the Middle East. All of these factors could result in a wider trade deficit. To this
is the added pressure of a credit rating downgrade by rating agencies which could constrain the country’s ability to
attract foreign capital. Does South Africa have the necessary external buffer to potentially sustain wider trade deficits
in the foreseeable future? Thankfully, the current reserve coverage ratio is at 4 months of import, currently higher than
the widely cited benchmark of 3 months.
1.2.6. Monetary Policy
Inflation reached a concerning height of 11% during the world food price crisis in 2008 (see Figure 1.11). However
post-2008 inflation has been on the decline and has remained within the South African Reserve Bank’s (SARB) target
24
range of 3% – 6% since 2010. In November 2012, headline inflation reached 5.6% year-on-year. In general, changes
in food, energy, and electricity prices accounted for much of the inflationary pressure. Food, energy and electricity
are more heavily weighted items within the consumer basket.
Figure 1.11: Monetary Policy and Inflation
Source: Own calculations based on data from the South African Reserve Bank
The SARB’s application of monetary policy instruments played an important role in keeping inflation in-check within the
target band, with tighter monetary policies pursued when inflation surged above the 6% target, and monetary policy
relaxed when inflation moved steadily within the target band. The policies directly influence the cost of borrowing.
This is reflected on the year-on-year changes in private sector credit growth and total domestic credit extension (see
Figure 1.11).
Monetary policy has been more accommodative in recent years, in response to the modest global and domestic
growth environments. The Monetary Policy Committee (MPC) has arguably kept the repo rate low to boost economic
growth by encouraging higher spending by households and businesses. However, any further easing of monetary
policy depends on keeping inflation and exchange rate pressure in check, and inflation is already close to the 6%
upper target band. Would monetary policy easing result in inflation higher than 6%? If yes, how prepared are the MPC
is to tolerate that and for how long? What would the impact be on the exchange rate, etc? The effect of lower repo
rates on capital inflow is yet to be established for South Africa as much of the movements are caused by external
developments rather than actions by monetary authorities. But the relationships between net capital inflows and the
exchange rate has so far been direct. This is because much of the recent instability in the exchange rate was triggered
by fluctuations in capital inflows caused mainly by quantitative easing in the US and financial crisis in the Euro area.
25
1.2.7 Fiscal Policy
Government expenditure increased in 2012 to 32.3% of GDP. Government revenue on the other hand has not
responded in kind and stands at 27.3% of GDP (see Figure 1.12). The unbalanced growth trends in revenue and
expenditure have resulted in a widening fiscal deficit. The South African government has allowed its expenditure to
increase in order to stimulate growth in the context of a slow growth environment; however tax collection has not
kept pace with the expansion.
Figure 1.12: Government Finance Account
Source: Own calculations based on data from the South African Reserve bank
Can national government afford to finance larger fiscal deficits in the future if the road to recovery gets tougher? It
depends on the ability to build a fiscal buffer; or in the absence of that, on the ability to raise funds from the domestic
and foreign financial markets. The former depends on government savings while the latter on lenders perception
of South Africa as creditworthy. The fiscal balance has been in deficit since 2008, preventing the opportunity for
government savings. South Africa’s debt-to-GDP ratio increased from 27.4% before the crisis (i.e. in 2008) to over
40% in 2012. Furthermore, South Africa’s ability to access finance is currently eroded by recent credit ratings
downgrades. The unfavourable ratings could end the history of available and favourable finance from international
financial markets.
South Africa has so far been able to meet its fiscal deficit financing needs by selling government bonds in the domestic
market (see Figure 1.12). Would such financing options be available if actions are required to widen the fiscal deficits?
Would that create undue pressure in the domestic financial market? Would it crowd-out private investors? The answer
depends on the health of the domestic financial market. Reports by the Reserve Bank of South Africa attest that the
domestic financial market is still healthy. Indicators such as the capital adequacy ratio (14.7%), non-performing loans
to total loans (4.6%), returns to assets (1.6%), and return to equity (21.5%) all remain at acceptable levels.
26
Chapter 2: Economic Review and Outlook of the Eastern
Cape
2.1 Demographic Profile of the Eastern Cape
2.1.1 Population
This section analyzes demographic statistics of the Eastern Cape Province. Demographic statistics are crucial to
direct policies that affect socio-economic conditions of the province. According to the 2011 census, the province is a
home to 6.7 million people. This is equivalent to 12.7% of the national population. This makes the Eastern Cape the
third most populated province after Gauteng, with a population of 12.2 million and 23.7% of the national population;
and KwaZulu-Natal, with a population of 10.2 million and 19.8% of the national population. Compared with the 2001
census, the province grew by 4.5%.
Figure 2.1: Population and Gender Distribution for the Eastern Cape
Source: Own Computations based on Census 2011.
27
Figure 2.1 plots age and gender distributions of the province’s population. It shows that the 0-4 and 15-19 age
cohorts are the biggest contributors to the province’s population. Those under the age of 30, account for 57% of the
province’s population. The median ages of the province’s population is 22.4 years, lower than the national median
age of 24.4 years, which makes it the second youngest provincial population in South Africa. Limpopo however tops
the list with a median age of 21.3 years. The relatively wealthier provinces such as the Western Cape and Gauteng
occupy the last two spots in the ranking of ages with median ages of 27 and 28.6 years respectively.
Figure 2.2: Share of Population Age Goups and Gender in the Eastern Cape
Source: Own Computations based on Census 2011.
Figure 2.2 illustrates population distribution by gender. The male population is marginally greater than that of the
female population in the first four age cohorts. This comprises the ages between 0-4, 5-9, 10-14, and 15-19. If all the
age cohorts before the age of 20 are put together, males outnumber females by a mere 1.03%. This is not however
true for age cohorts above 15 to 19. In these age cohorts, the female population is noticeably larger than the male
population (see Figure 2.2).
The bias towards females in the province’s working age population alluded to above could be attributed, among
others, to migration of males to wealthier provinces in search of better opportunities. The Eastern Cape is ranked
number one in terms if the extent of net migration; close to 214,815 people migrated to other provinces from the
Eastern Cape Province alone between 2006 and 2011.
28
Figure 2.3: Population by Age Group for Eastern Cape
Source: Own Computations based on Census 1996, 2001, 2011 and Community Survey 2007.
Figure 2.3 compares population by age groups in 1996, 2001, 2007 and 2011. The figure shows that the proportion
of the population within the age group of 0-14 is falling while that of 15-64 is increasing and those over the age of 64
have remained more or less unchanged. The increase in the working age population (i.e. aged 15-64) may be good
news when viewed from the perspective of an increased workforce. However, given the high levels of unemployment
in South Africa, the potential of the workforce is already underutilized. This implies a greater challenge to the province
to match a growing workforce with growing job opportunities as well as greater capacity in municipal service delivery.
2.1.2 Fertility and Mortality
The fertility rate measures the ratio of live births in the province to the population of the province. The mortality rate
on the other hand is measured as the ratio of deaths to population. Both are expressed per 1000 per year. The
two together with migration are major components of population growth. In general, there is a negative correlation
between the level of development and the core components of population growth: fertility, mortality, and migration.
Hence, they indicate where the province stands in comparison with other provinces in terms of socio-economic
development.
Table 2.1: Fertility Rates, Provincial and South Africa
WC
NC
FS
KZN
NW
GP
MP
LP
EC
SA
2001-06
2.5
2.6
2.4
3.2
3.1
2.2
2.7
3.0
3.4
2.8
2006-11
2.3
2.4
2.3
2.8
2.8
2.1
2.5
2.9
2.8
2.5
Source: Statistics South Africa, Midyear population estimates, 2011; WC stands for Western Cape; NC for Northern Cape; FS for
Free State; KZN for KwaZulu-Natal; NW for North West; GP for Gauteng Province; MP for Mpumalanga; LP for Limpopo Province;
EC for Eastern Cape; and SA for South Africa
Table 2.1 gives fertility rates for all the provinces between two time periods: 2001-2006 and 2006-2011. South Africa
had an average fertility rate of 2.8 children per woman between 2001 and 2006. The Eastern Cape recorded the
highest fertility rate at 3.4 children per woman during this time, which was significantly above the national average.
The Eastern Cape was followed by KwaZulu-Natal, at 3.2; North West, at 3.1; and Limpopo, at 3.0. The fertility rate
for South Africa fell marginally to 2.5 children between the years 2006 and 2011. The Eastern Cape Province had the
second highest rate of 2.8 children per woman between 2006 and 2011, surpassed only by the Limpopo Province,
at 2.9 children. Hence, the fertility rate in the Eastern Cape declined significantly over the period, a positive sign of
development.
29
Table 2.2: Life Expectance at Birth, Provincial and South Africa
Males
WC
NC
FS
KZN
NW
GP
MP
LP
EC
SA
2001-06
57.6
52.9
43.7
47.4
49.0
54.3
49.4
54.5
49.1
50.6
2006-11
59.9
54.1
44.9
48.4
50.4
55.4
50.2
55.8
50.2
52.1
63.9
56.7
47.9
51.6
53.3
58.9
53.1
61.5
54.2
55.5
Females
2001-06
2006-11
65.8
57.4
47.9
52.2
53.2
59.1
52.8
61.4
54.4
56.2
Source: Statistics South Africa, Midyear population estimates, 2011 WC stands for Western Cape; NC for Northern Cape; FS for
Free State; KZN for KwaZulu-Natal; NW for North West; GP for Gauteng Province; MP for Mpumalanga; LP for Limpopo Province;
EC for Eastern Cape; and SA for South Africa
Table 2.2 provides a breakdown of life expectancy at birth by gender for all provinces. Average life expectancy for the
South African male population between the years 2006 and 2011 was only 52.1 years. Males in the Western Cape
Province had the highest life expectancy rate of 59.9 years followed by Limpopo at 55.8 and Gauteng at 55.4. For
the Eastern Cape Province, life expectancy of males was only 50.2 years, lower than national average of 52.1. Only
KwaZulu-Natal and the Free State Province had life expectancy estimates lower than the Eastern Cape Province at
48.4 and 44.9 years respectively. Life expectancy for males improved in South Africa as well as for the Eastern Cape
by approximately 1.5 years and 1.1 years respectively between the period 2006 and 2011, compared with the period
2001 to 2006.
Table 2.2 further shows life expectancy for the female population. Female life expectancy is general higher than that
of males. In addition, like their male counterparts, their life expectancy too had slightly improved over the years. Life
expectancy amongst females increased to 56.2 years between 2006 and 2011 from 55.5 years between 2001 and
2006, equivalent to a gain of approximately seven months. The following provinces registered above national average
life expectancy rates between 2006 and 2011: the Western Cape (65.8 years), Gauteng (59.1 years), and the Limpopo
(61.4 years). For the Eastern Cape, female life expectancy did not change significantly since 2001. It hovered at
around 54 years, lower than national average of 56 years.
Table 2.3: Leading Natural Causes of Deaths, Eastern Cape and South Africa
Rank
Eastern Cape
South Africa
Type
Number
1
Tuberculosis
11132
2
Influenza & pneumonia
3
Type
Number
15
Tuberculosis
69003
13.2
3689
4.9
Influenza & pneumonia
42964
8.2
Other forms of heart disease
3641
4.9
Intestinal infectious diseases
30675
5.9
4
Chronic lower respiratory diseases
3344
4.5
Other forms of heart disease
26462
5.1
5
Cerebrovascular diseases
3315
4.4
Cerebrovascular diseases
24835
4.7
6
Intestinal infectious diseases
3059
4.1
Diabetes mellitus
20523
3.9
7
Diabetes mellitus
2773
3.7
Human immunodeficiency virus
(HIV) disease
17570
Hypertensive diseases
15386
8
%
3.1
%
3.4
Human immunodeficiency virus
(HIV) disease
2349
9
Other viral diseases
2169
2.9
Chronic lower respiratory
diseases
14184
10
Hypertensive diseases
2112
2.8
Certain disorders involving the
immune mechanism
13096
Other natural causes
248519
Total
523217
2.9
Other natural causes
37333
Total
74916
50
100
2.7
2.5
48
100
Source: Statistics South Africa, Mortality and Causes of Death, 2011
Table 2.3 shows the ten leading causes of natural deaths in the Eastern Cape and South Africa in 2009. According
to the table, Tuberculosis is ranked number one cause of death in the Eastern Cape and South Africa; it accounted
30
respectively for 15% and 13% of all deaths. Influenza and pneumonia are ranked as the second most common
cause of death in both the Eastern Cape (5%) and in South Africa (8%). HIV is only the eighth leading cause of death
in the Eastern Cape; although this is a gross underestimation. Tuberculosis, influenza and pneumonia are all HIV
opportunistic diseases and death should actually be ultimately attributed to HIV in a large proportion of cases.
2.2 Poverty, Inequality, and Food Security in the Eastern Cape
2.2.1 Poverty
Poverty is an important headline figure in measuring changes in socio-economic development. In this study, poverty
levels in the Eastern Cape are estimated using a Fuzzy Index of Poverty (FIP) (See Appendix B for methodology).
The index does not use a monetary poverty line and hence avoids the controversy surrounding the determination of
a poverty line, its depth and breadth. The FIP allows for the inclusion of a holistic set of development measures in
assessing well-being. Twelve indicators of well-being are be considered: employment, municipal services (such as
refuse collection, access to water, access to toilet, and access to electricity for lighting, cooking, and heating), type
of dwelling, education, income, household size, and access to means of communication such as cell phones. The
FIP can be put to a variety of uses; most common is its use of average and individual deprivation indices to identify
people most vulnerable to poverty and therefore in most urgent need of assistance.
Figure 2.4: Poverty in South Africa
Source: Own computations based on data from Statistics South Africa – 2007 Community Surveys & 2011 census.
Figure 2.4 plots average levels of deprivation in South Africa for 2007 and 2011. The Eastern Cape tops the list of
poor provinces in terms of exposure to average deprivation both in 2007 and 2011. Limpopo and KwaZulu-Natal
occupy second and third places in the list respectively. The figure shows that with the exception of the Western
Cape Province (which remained constant at a comparatively low level), average deprivation has been on the decline.
Notable amongst these declines is significant progress made in the Eastern Cape. Average deprivation in the province
fell by 5 percentage points between 2007 and 2011.
31
Figure 2.5: Poverty in the Eastern Cape
Source: Own computations based on data from Statistics South Africa – 2007 Community Surveys & 2011 census.
Figure 2.5 compares the level of poverty in the Eastern Cape by District Municipalities (DM) in 2007 and 2011.
The figure shows that poverty has fallen significantly with varied degree in all DMs except in the Amatole DM. In
2011, Alfred Nzo experienced the highest average deprivation, followed by the O.R. Tambo and Amatole DMs. The
ranking was slightly different in 2007; the O.R. Tambo DM experienced the highest average deprivation, followed by
Alfred Nzo and Ukhahlamba DMs. O.R. Tambo was among the DMs that achieved relatively significant reductions in
average deprivation between 2007 and 2011.
Table 2.4: Average Deprivation in O.R Tambo District Municipality
Fuzzy Proportion of Poor Households by Indicator (multiply by 100 to get percentage)
Empl-
oyment
Refuse
Water
Dwelling
Toilet
Lighting
Cooking
Heating
Education
Income
HH
size
Cell
phone
Cacadu
0.36
0.15
0.08
0.13
0.17
0.08
0.04
0.12
0.55
0.87
0.20
0.22
Amatole
0.48
0.71
0.26
0.26
0.51
0.18
0.17
0.29
0.64
0.95
0.25
0.22
Chris Hani
0.46
0.61
0.19
0.20
0.44
0.16
0.14
0.26
0.65
0.93
0.25
0.19
Ukhahlamba
0.44
0.61
0.20
0.22
0.45
0.20
0.14
0.25
0.65
0.93
0.23
0.20
O.R Tambo
0.48
0.77
0.39
0.29
0.51
0.22
0.23
0.31
0.67
0.95
0.34
0.19
Alfred Nzo
0.48
0.80
0.38
0.30
0.53
0.42
0.28
0.37
0.68
0.96
0.33
0.20
NMBM
0.08
0.08
0.05
0.12
0.08
0.05
0.02
0.06
0.45
0.82
0.22
0.15
Source: own computation based on data from Statistics South Africa, 2011 Census.
Table 2.4 breaks down the FIP into its respective eleven indicators of wellbeing. The table shows considerable
variation in deprivations experienced both across indicators and DMs. The differences are most significant in
indicators such as employment, refuse removal, access to water, and sanitation (access to a toilet). Levels of
deprivation are relatively lower for most indicators in Nelson Mandela Bay Metro (NMBM) and Cacadu. Respectively,
8% and 36% of their population experienced deprivation in refuse removal; 8% and 17% in sanitation; 5% and 8% in
access to water. Deprivations are relatively highest in Alfred Nzo and O.R.Tambo DMs. They respectively experienced
deprivation of 80% and 70% in refuse removal; 38% and 39% in access to water; and 53% and 51% in sanitation.
Levels of deprivation are high but more or less similar across DMs in education, income, and household size.
32
2.2.2 Service Delivery
Service delivery is an important aspect of wellbeing that can make a sizable dent on the level of poverty, particularly
in light of the responsibly of government to its citizens. Here the focus is on evaluating progress in services delivered
by municipalities between 2007 and 2011.
Figure 2.6: Progress of the Delivery of Municipal Services
Figure 2.6a: Deprivation in Refuse Collection
Figure 2.6b: Deprivation in access to water
Figure 2.6c: Deprivation in access to toilet
Figure 2.6d: Deprivation in access to electricity for lighting
Source: Own Computations based on data from 2007 Community Surveys and 2011 Census
Overall, figures 2.6a – 2.6d show that households in the Eastern Cape face high levels of average deprivation in terms
of municipal services. Households’ access to services deteriorated in some DMs while it improved in others. The
Amatole and Alfred Nzo DMs are among the DMs that failed to demonstrate significant improvement. In the Amatole
DM, deprivations in refuse removal (see Figure 2.6a) and sanitation (see Figure 2.6c) worsened to 71% and 51%; the
same applies to Alfred Nzo DM in terms of access to water (see Figure 2.6b). The O.R. Tambo DM was among the
33
DMs that performed relatively positively over the period. It managed to marginally reduce backlogs in most of the
municipal services.
2.2.2 Inequality
Various research reports describe South Africa as one of the most unequal societies in the world. The country has
made slight progress in addressing inequality between 2002 and 2011. According to data from IHS Global Insight,
income inequality, measured in terms of Gini Coefficient was 0.68 in 2002; and fell marginally to 0.63 in 2011.
Figure 2.7: Income Inequality in the Eastern Cape
Source: Own computations based on data from Global Insight
Figure 2.7 illustrates levels of income inequality in the Eastern Cape in 2002 and 2011. A point on Figure 2.7,
depending on its location above, on, or below the 450 line indicates respectively whether income inequality in the
province got worse, remained unchanged, or improved between 2002 and 2011. All the points fall below the line, thus
suggest that income inequality has fallen marginally in all DMs. But, the reduction was relatively higher in some DMs
than in others. Three DMs in particular, namely, the O.R. Tambo, Alfred Nzo, and Amatole DMs were among those
that managed to reduce inequality at a relatively higher pace than the others.
2.2.3 Food Security
In this section, levels of food insecurity in the Eastern Cape Province are analysed. The information contained allows
for efficient geographic targeting of food security efforts, through initiation of local economic development programs
(See Appendix A for methodology).
The Eastern Cape has one of the highest levels of food insecurity in South Africa. According to the estimates, about
78% of the households in the province may be classified as food insecure. This is much higher than national average
of 64%.
34
Figure 2.8: Food Insecurity in the Eastern Cape
Source: Own computation
Figure 2.8 plots food insecurity in the Eastern Cape Province. Vulnerability to food insecurity is widespread,
particularly among households in Alfred Nzo DM (86%) followed by Chris Hani DM (83%) and O.R Tambo DM (81%).
Food insecurity is relatively lower in Cacadu (66%) and NMBM (71%) and less prevalent in the Western regions of
the Eastern Cape. Further analysis of the characteristics of food insecure households show that the majority of food
insecure households in the province reside in rural areas, are Africans, are headed by females, have larger family
sizes, and have higher dependency ratios.
2.3 Economic Profile of The Eatern Cape
2.3.1 Economic Performance
As shown in Figure 2.9, GDP within the Eastern Cape increased by 3.42% in 2011, a 1 percentage point improvement
compared with 2010. Only three provinces (Gauteng, KwaZulu-Natal, and Western Cape) registered growth rates
higher than the Eastern Cape. Gauteng grew by 4.04%, KwaZulu-Natal by 3.64%, and the Western Cape by 3.64%.
However, growth in the Eastern Cape (and the rest of South Africa) is lower than the pre-crisis growth rate of
approximately 5%. In 2011, the Eastern Cape contributed 8% of the 3.46% national growth in GDP (see Figures 2.9
and 2.10). Gauteng, KwaZulu-Natal, and the Western Cape contributed comparatively much larger shares at 41%,
17% and 16% of the total growth respectively.
35
Figure 2.9: National and Provincial Growth Rates and Contribution of the Province to GDP Growth
Source: Own computations based on data from Statistics South Africa
Figure 2.10 shows the contribution to national GDP of the nine provinces in 2002, 2008, and 2011. The Eastern
Cape Province is the fourth largest economy in South Africa, although only marginally ahead of the North West,
Mpumalanga and Limpopo. It accounted for 7.8% of GDP in 2011. The Eastern Cape’s contribution to GDP has
remained unchanged at an average rate of 7.8% since 2002. Gauteng is still the biggest economy, contributing 35%
of GDP, followed by KwaZulu-Natal 16.4% and the Western Cape 14.8%.
Figure 2.10: Provinces’ Contribution to GDP
Source: Own Computations based on Statistics South Africa; WC stands for Western Cape; NC for Northern Cape; FS for Free
State; KZN for KwaZulu-Natal; NW for North West; GP for Gauteng Province; MP for Mpumalanga; LP for Limpopo Province; EC
for Eastern Cape; and SA for South Africa
2.3.2. Sectoral Analysis
Breaking down GDP growth into sectors helps to determine the relative importance of a sector within the provincial
economy, and the comparative advantage of a sector relative to the national economy. The results can be used to
prioritise sectors that do well and to provide support to others that perform otherwise.
Table 2.5 shows the contribution to the GDP regional (GDPR) of ten activities grouped into three sectors. The tertiary
36
sector is the largest sector in the Eastern Cape contributing 76.7% of the GDPR, followed by the secondary sector
at 21.2%, and the primary sector at 2.2%. Three activities account for 61% of the GDPR: finance, real estate and
business services at 22.4%, general government services at 21.2%, and manufacturing at 17.5%.
Table 2.5: Sectoral Contribution at Basic Prices
Sectors
2002
2011
% Point Change
Primary Sector
2.7
2.2
-0.5
Agriculture, forestry and fishing
2.5
2.1
-0.5
Mining and quarrying
0.2
0.1
-0.1
Secondary Sector
22.3
21.2
-1.2
Manufacturing
19.6
17.5
-2.2
Electricity, gas and water
1.1
1.1
0.0
Construction
1.6
2.6
1.1
Tertiary Sector
75.0
76.7
1.7
Wholesale & retail trade
14.5
13.8
-0.7
Transport, storage and communication
8.8
8.9
0.1
Finance, real estate and business services
20.1
22.4
2.4
Personal services
10.2
10.3
0.1
General government services
21.5
21.2
-0.2
All industries at basic prices
100
Source: Own computation based on data from Statistics South Africa
100
The contribution of the tertiary sector grew from 75% to 76.7%, an increase of 1.7 percentage points (see Table 2.5
& Figure 2.10). On the contrary the primary and secondary sectors contracted by 0.5 and 1.2 percentage points,
respectively.
The increase in the contribution of the tertiary sector to regional GDP was caused by the finance, real estate and
business services sector whose contribution to regional GDP rose by 2.4 percentage points. The contribution to
regional GDP of the agriculture, forestry, and fisheries as well as mining and quarrying sectors fell by 0.5 and 0.1
percentage points respectively. The secondary sector decreased in importance mainly due to a 2.2 percentage point
contraction in manufacturing. This is despite a 1.1 percentage point increase in construction. The electricity, gas
and water sector’s contribution to regional GDP remained unchanged. Overall, it can be argued that slight structural
transformation is happening within the province. This is driven by a growing commercial service sector. The share of
services in regional GDP is increasing year-on-year (Table 2.5 and Figure 2.9). In addition, the sector is emerging as
a major employer; the number of people employed in the sector is increasing year-on-year. In fact, the commercial
service sector overtook the government service sector as the largest contributor to regional GDP between 2002 and
2011.
37
Figure 2.11: Sectoral Contributions to GDP
Source: Own Computations based on data from Statistics South Africa
The location quotient indicates the presence of comparative advantage of a sector within the provincial economy.
It is measured as the ratio of the percentage share of a sector in the provincial economy to the share of the same
sector in the national economy. A province is suggested to have a comparative advantage if the location quotient
is greater than one. Table 2.6 provides location quotients for the Eastern Cape. According to the table, the province
has comparative advantage in personal services (1.69), and general government services (1.39), and an average
advantage in manufacturing (1.01). The fact that government services currently has a high location quotient shows
the relative importance of government as a direct driver of the regional economy. This is also an indication of the need
for growth in non-governmental sectors. The province has a high comparative disadvantage in mining & quarrying
(0.02), and electricity, gas & water (0.51). However, in terms of the electricity sector, there is still huge potential for
growth in terms of renewable energy (primarily wind and solar farms), ideally suited to the Eastern Cape. This should
mean that electricity will significantly raise its comparative advantage in the years to come.
Table 2.6: Location Quotient,
Industries
Agriculture, forestry and fishing
GDP, constant 2005 Prices
Percentage
Eastern
Cape
South
Africa
South-Africa
Eastern
Cape
Location quotients
2,731
41,580
2.44
2.1
0.84
139
99,672
5.86
0.1
0.02
Manufacturing
23,226
292,733
17.21
17.5
1.01
Electricity, gas and water
1,398
34,798
2.05
1.1
0.51
Construction
3,526
57,985
3.41
2.6
0.78
Wholesale & retail trade
18,382
235,404
13.84
13.8
1.00
Transport, storage and communication
11,827
172,549
10.15
8.9
0.88
Finance, real estate and business
services
29,847
402,500
23.66
22.4
0.95
Personal services
13,754
104,260
6.13
10.3
1.69
General government services
28,215
259,346
15.25
21.2
1.39
100
100
Mining and quarrying
Total
133,043
1,700,826
Source: Own computation based on data from Statistics South Africa
38
2.3.3 Trade Position
2.3.3.1 Export
The Eastern Cape exported R33 billion worth of goods and services in 2011, a 7.3% increase compared with 2010.
Figure 2.12 illustrates the inter-provincial export as a percentage share of the total export in 2011. It shows that the
province is the fourth largest exporter in South Africa, contributing 4.5% of the country’s total exports. However, the
province’s contribution to total export has been on a steady decline since 2008. This is true for all other provinces
except for Gauteng. Gauteng’s contribution to total export has steadily increased since 2002. The three largest
contributors to total exports in 2011 were Gauteng at 69.4%, KwaZulu-Natal at 11.3%, and the Western Cape at
7.5%.
Figure 2.12: Export & Import Shares
Source: Own computations based on data from Global Insight; WC stands for Western Cape; NC for Northern Cape; FS for Free
State; KZN for KwaZulu-Natal; NW for North West; GP for Gauteng Province; MP for Mpumalanga; LP for Limpopo Province; EC
for Eastern Cape; and SA for South Africa
Table 2.7: Major Export Contributors in the Eastern Cape
1996
2000
2005
2010
2011
Agriculture & hunting
7.2
2.5
2.5
5.8
8.5
Food, beverages, tobacco product
14.4
4.3
3.7
3.8
4.1
Fuel, petroleum, chemical & rubber 8.6
products
6.1
4.7
3.9
5.2
Metal products, machinery &
household appliances
11.7
16
18.3
25.9
29.5
Transport equipment
13.7
37.3
58.6
49
39.5
Source: Own computations based on data from global insight
Table 2.7 provides a list of industries making the largest contributions to the province’s total export between 1996
and 2011. In 2011, the major contributions to the province’s total export came from transport equipment at 39.5%;
metal products, machinery and household appliances at 29.5% and agriculture and hunting at 8.5%. Other products
with sizable contributions to total export in the same year included fuel, petroleum, chemical and rubber products at
5.2% and food, beverages, and tobacco products at 4.1%.
Table 2.7 further shows that the contribution of some industries to the province’s total export has changed significantly
39
since 1996. The metal products, machinery and household appliances sector and the transport equipment sectors
are two industries that have shown marked increases. The metal products, machinery and household appliances
contribution to total exports rose steadily. Its contribution to total exports was 11.7% in 1996; 16% in 2000, 18.3% in
2005, 25.9% in 2010, and 29.5% in 2011. This was not true for transport equipment though. The industry has gone
through steep increases, 1996 to 2005; followed by a strong decline post-2005.
2.3.3.2 Import
The Eastern Cape imported goods and services worth R35.5 billion in 2011, which is a 15.2% increase compared
with the 2010 level. This makes it the fourth largest importer in South Africa. In 2011, the province accounted for 4.5%
of the country’s total imports (see Figure 2.12).
Table 2.8: Major Import Contributors in the Eastern Cape
1996
2000
2005
2010
2011
Fuel, petroleum, chemical & rubber 10.1
products
10.5
8.4
14.4
13.7
Other non-metallic mineral products
1.5
3.6
1.8
4.6
4.3
Metal products, machinery &
household appliances
12.4
13.4
8.9
11.4
10.8
Transport equipment
59.8
50.9
70.9
56.3
56.1
Source: own computations based on data from Global Insight
Table 2.8 shows major import contributors by sector within the Eastern Cape. Transport equipment alone contributes
for over one-half of the province’s total import bill. In 2011, import of transport equipment contributed 56% of
the province’s total import, followed by fuel, petroleum, chemical and rubber products at 13.7%; metal products,
machinery and household appliances at 10.8%; and other non-metallic mineral products at 4.3%. The four together
contributed 85% of the province’s total import.
2.3.3.3 Trade Balance
The trade balance is the difference between total exports and total imports. The Eastern Cape registered a trade
deficit of R1.6 billion in 2011 (in other words, total imports exceeded total exports). This could be attributed to the
poor performance of the transport equipment sector, a major exporter, as well as a large importer to the province.
This sector contributed 40% of the total export and 56% of the total import of the province. In 2011, import of
transport equipment grew by 15.4% year-on-year while that of exports declined by 13.5%.
40
Figure 2.13:
Balance of Trade in the Eastern Cape
Source: Own computations based on data from Global Insight
Figure 2.13 plots balance of trade in the Eastern Cape since 1996. The province experienced consistent trade
surpluses, as seen in 2001, 2004, 2008, and 2010 (see Figure 2.13). The trade surplus was highest in 2008, attributed
to the relatively good performance of transport equipment. In 2008, the transport equipment sector contributed 57%
and 65% of the province’s total export and import, respectively. Hence, the balance of trade is very vulnerable to the
performance of the automotive industry.
2.4 Sectoral Linkages in the Eastern Cape
This cross-cutting nature of the economy, means that there are numerous sectoral linkages within the Eastern Cape
which need to be properly appreciated: this allows for identification of the role of each activity in the development
process; it helps to prioritise activities on the basis of their contribution to provincial development strategies, which in
general promote labour-absorptive growth; and it assists determination of the potential impact of investments channeled through a particular sector on the level of poverty, inequality, and unemployment.
Table 2.10: Gross Output, Value Added, Income, and Employment Multipliers in the Eastern Cape
Sectors
Output
GDP
Household Income
Employment
Estimate
Rank
Estimate
Rank
Estimate
Rank
Estimate
Rank
Agriculture
1.16
6
0.60
7
0.23
6
15
3
Mining
0.39
7
0.22
9
0.09
8
4
8
Manufacturing
1.16
6
0.45
8
0.19
7
6
7
Utilities
1.74
3
0.90
4
0.36
4
8
6
Construction
2.0
1
0.77
6
0.35
5
24
1
Trade
1.73
4
0.92
3
0.40
2
10
4
Transport
1.75
2
0.88
5
0.36
4
8
6
Fin. & Bus.
1.74
3
0.97
2
0.39
3
9
5
Rest of the Econ1.66
5
0.99
1
0.45
1
22
2
omy*
Source: Own computation
* The Rest of the Economy represents general government services and community, social and personal services
Table 2.10 provides multipliers for gross output, value added, income, and employment. The multiplier (an economic
41
concept that indicates the overall effect of investment on the economy) shows the effect of a R1 million change
in investment on additional output produced by all production activities in the economy, additional value added
produced, additional household income earned, and additional direct and indirect employment created. The table
reveals that the construction sector is ranked the highest in terms of gross output and employment creation. A R1
million investment in construction would increase gross output by R2 million. It would also create about 24 jobs.
Table 2.10 further shows that all the multipliers for the manufacturing activity are the smallest. It is ranked 8th in
output, 8th in GDP, 7th in income, and 4th in employment. This is indicative of the capital-intensive and importintensive nature of manufacturing in the Easter Cape. The same is true for agriculture except in terms of employment
creation. A R1 million change in investment in agriculture has the potential to create about 15 jobs. In general, in
terms of sectors, the tertiary sector tops the list for ability to impact the economy, followed by the secondary, and
the primary sectors.
Table 2.11: Relative Strength of a Sector Compared to Other Sectors in the Economy
Sectors
Backward Linkage
Forward Linkage
Estimate
Rank
Index
Estimate
Rank
Index
Agriculture
2
8
0.9
2.3
1
1.3
Mining
1.3
9
0.6
1.6
7
0.9
Manufacturing
2
7
0.9
1.7
6
1
Utilities
2.4
2
1.1
1.9
2
1.1
Construction
2.7
1
1.3
1.7
4
1
Trade
1.9
6
0.9
1.5
8
0.9
Transport
2.3
3
1.1
1.7
5
1
Finance & Business
2.3
4
1.1
1.7
3
1
Rest of the economy
2.2
5
1.1
1.6
7
0.9
Source: Own Computation
Table 2.11 calculates the relative strength of a sector compared with other sectors of the economy. Relative strength
is measured here by the size of backward and forward linkages within the Eastern Cape economy. It also provides
linkage indices so activities can be classified into four groups: key, weak, and a mixture of weak and strong backward
and forward linkages. This is important information to guide policy making in supporting sector development in the
province.
An activity is said to have backward linkages if it buys inputs directly or indirectly from other activities in the economy,
called upstream activities. This can create additional investment or increased capacity utilization in the upstream
activities. The construction industry within the Eastern Cape is estimated to have strong backward linkages followed
by utilities (electricity and water), and transport. Agriculture, mining, and manufacturing on the other hand are among
those with the least backward linked.
Forward linkages on the other hand arise through a sale of output to other sectors of the economy referred to as
downstream activities. Downstream activities have the potential to expand production motivated by lower cost of
production brought about by increased supply. The figure shows agriculture ranked as top in terms of forward
linkages, followed by utilities and finance and businesses. Mining and manufacturing are the least forward linked.
42
Figure 2:14: Relative Strength of Activities
Source: Own computation
Figure 2.14 classifies activities into key (top right quadrant), weak (bottom left quadrant), and a combination of strong
and weak forward and backward linkages (top left and bottom right quadrants respectively). Figure 2.14 shows that
the utilities (electricity and water), construction, transport, and finance and business activities are largely interrelated
with both strong upstream and downstream activities. They are classified as key activities. Investment in these
sectors has the potential to stimulate economic growth in the Eastern Cape, more than in any other sector. Mining
and trade are weak activates, while agriculture and manufacturing have strong forward but weak backward linkages.
From the discussion hitherto, it can be argued that policies that promote linkages across activities are crucial to
achieve greater economic growth into the future. This can be achieved, among others things, by increasing the
local content of inputs used in production. For example, manufacturing is an important economic activity within the
province. It contributes 18% of the regional GDP and 11% of employment. It is also one of the activities with great
potential to contribute further to economic growth. However, its potential to contribute significantly to the province’s
economy is constrained by leakages. Manufacturing is one of the most import penetrated sectors as it imports about
26% of its intermediate inputs. Its contribution to regional GDP can be increased if its linkages with domestic input
suppliers can be enhanced.
The second important area for consideration is structural transformation within the labour market in order to ensure
equitable labour-absorptive growth. The tertiary sector is currently the driver of job creation within the province. This
is unlike the experience of the now developed countries, where manufacturing played the primary role. Manufacturing
has great potential to achieve equitable labour-absorptive growth provided that it is offered the necessary support.
Only two sectors are relatively labour intensive within the province: namely, the construction sector, and government,
community and personal services sector. A shift in input intensity, away from capital and towards labour, could take the province a long way in addressing its employment challenges.
43
Chapter 3: Socio-Economic Review and Outlook of District
Municipalities in the Eastern Cape
3.1 Demographic Profile of District Municipalities
Although economic development can be meaningfully discussed at the aggregated provincial level, development is
always location specific. Understanding the geographical spread of development is paramount in properly supporting
specific regions within the Eastern Cape. In this chapter, District Municipalities (DM) are analysed in greater detail.
The Eastern Cape is a home to two metros and six DMs: Nelson Mandela Bay Metro (NMBM), Buffalo City Metro,
Cacadu (DC10), Amatole (DC12), Chris Hani (DC13), Ukhahlamba (DC14), Oliver Tambo (DC15), and Alfred Nzo
(DC44). Note that Buffalo City Metropole was subsumed within Amatole DM before May 2011 and is therefore not
always separated from Amatole in the data.
District Municipalities
Figure 3.1: Population and Gender Distribution of the District Municipalities
NMA:Nelson Mandela Bay Metropolitan
DC44: Alfred Nzo
DC15: O.R.Tambo
DC14: Ukhahlamba
DC13: Chris Hani
DC12: Amatole
DC10: Cacadu
0
100000 200000 300000 400000 500000 600000 700000 800000
Number
Male
Female
Source: Own computation based on Census 2011.
Figure 3.1 gives population and gender distribution for the Eastern Cape by DM in 2011. Three DMs account for the
greatest proportion of the province’s 6.7 million population. These include O.R Tambo, Nelson Mandela Bay Metro,
and Amatole (which includes Buffalo City Metropolitan). The Cacadu and Ukhahlamba DMs are among the less
populated. Figure 3.1 further classifies the population by gender. It shows that females outnumber males in all DMs.
44
This is significantly so in the O.R Tambo DM wherein females outnumbered males by 4.5%.
6.0
Percentage of population
Percentage of population
Figure 3.2: The Share of Population Age Groups and Gender
5.0
4.0
3.0
2.0
1.0
0.0
0-4
10-14 20-24
30-34
40-44
50-54
60-64
9.0
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
70-74
0-4
10-14
20-24
Age groups
Male
Male
Female
6.0
5.0
4.0
3.0
2.0
1.0
0.0
20-24
30-34
40-44
50-54
60-64
6.0
5.0
4.0
3.0
2.0
1.0
0.0
70-74
0-4
10-14
20-24
Female
5.0
4.0
3.0
2.0
1.0
0.0
30-34
40-44
50-54
60-64
70-74
50-54
60-64
70-74
Female
8.0
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0-4
10-14
20-24
Male
Female
Figure 3.2e: Ukhahlamba District Municipality
30-34
40-44
50-54
60-64
70-74
Age groups
Age groups
Male
40-44
Figure 3.2d: Chris Hani District Municipality
Percentage of population
Percentage of population
6.0
20-24
30-34
Age groups
7.0
10-14
70-74
7.0
Male
Figure 3.2c: Amatole District Municipality
0-4
60-64
Female
Age groups
Male
50-54
Figure 3.2b: Alfred Nzo District Municipality
Percentage of population
Percentage of population
7.0
10-14
40-44
Age groups
Figure 3.2a: Cacadu District Municipality
0-4
30-34
Female
Figure 3.2f: O.R. Tambo District Municipality
45
Percentage of population
7.0
6.0
5.0
4.0
3.0
2.0
1.0
0.0
0-4
10-14
20-24
30-34
40-44
50-54
60-64
70-74
Age groups
Male
Female
Figure 3.2g: Nelson Mandela Bay Metro
Source: Own computations based on Census 2011.
Figure 3.2, panels a through g, illustrate the age and gender composition by DM. According to the figure, the female
population was consistently higher than that of males at higher age cohorts. However, the gender deviation by age
varied from one DM to the other. For example, the variation started at a higher age cohorts in Nelson Mandela Bay
Metro, Cacadu, and Ukhahlamba DMs whilst in the others, it started off at lower age cohorts. It is also apparent from
the figure that the gap between the female and male population is wider at higher age brackets.
The youth constitute the largest share of the population in all DMs. In some, the dominant age cohort is 0-4 years
of age, while in others it is relatively higher, at 25-29. DMs such as Alfred Nzo, O.R Tambo, Ukhahlamba, Chris Hani,
and Amatole fall in the former, while the remaining the NMBM and Cacadu DM fall in the later.
80.0
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
aged 0 - 14 year
aged 15 - 64 years
Percentage of population
Percentage of population
Figure 3.3: Population by Age Group
65 year and above
60.0
50.0
40.0
30.0
20.0
10.0
0.0
aged 0 - 14 year
aged 15 - 64 years
65 year and above
Age groups
Age groups
1996 2001 2007 2011
1996 2001 2007 2011
Figure 3.3a: Cacadu District Municipality
Figure 3.3b: Alfred Nzo District Municipality
46
Percentage of population
Percentage of population
70.0
60.0
50.0
40.0
30.0
20.0
10.0
0.0
aged 0 - 14 year
aged 15 - 64 years
40.0
30.0
20.0
10.0
0.0
aged 0 - 14 year
aged 15 - 64 years
65 year and above
Age groups
1996 2001 2007 2011
1996 2001 2007 2011
70.0
Figure 3.3d: Chris Hani District Municipality
Percentage of population
Percentage of population
50.0
Age groups
60.0
50.0
40.0
30.0
20.0
10.0
0.0
aged 15 - 64 years
65 year and above
60.0
50.0
40.0
30.0
20.0
10.0
0.0
aged 0 - 14 year
aged 15 - 64 years
65 year and above
Age groups
Age groups
1996 2001 2007 2011
1996 2001 2007 2011
Figure 3.3e: Ukhahlamba District Municipality
Percentage of population
60.0
65 year and above
Figure 3.3c: Amatole District Municipality
aged 0 - 14 year
70.0
Figure 3.3f: O.R Tambo District Municipality
80.0
60.0
40.0
20.0
0.0
aged 0 - 14 year
aged 15 - 64 years
65 year and above
Age groups
1996
2001
2007
2011
Figure 3.3g: Nelson Mandela Bay Metro
Source: Own computations based on Census 1996, 2001, 2011 and Community Survey 2007.
Figures 3.3a through 3.3g classify the population of each DM into three age groups 0-14, 15-64, and 65 and above
years of age and further track changes in the age distribution over time (1996 – 2011). The classification of ages
is important from a labour market monitoring perspective. For example, it helps to monitor closely changes in the
working age population (15-64), as this guides the total number of jobs that are required within the local economy
and is an important predictor of employment and unemployment rates. Those in younger age cohorts (aged 14
and under), indicate the future size of the labour force, while older age cohorts (aged 64 and above) represents
47
pensioners who have typically exited the workforce (and are largely supported by the government provided South
African Old Age Pension).
Figure 3.3 shows that the proportion of people of working age (i.e. 15-64 years) is largest within all DMs and is also
growing. The proportionate size of the working age population is largest in Nelson Mandela Bay Metro and Cacadu
DM, each accounting for close to 70% of their respective populations. In the remaining DMs, the proportion is
relatively less; falling within a range of 50% to 60%. Figures 3.3 also shows the proportion of people that fall within
the 0-14 age bracket. This group is the second largest. However, unlike the other age groups, its share of total
population is falling. In 2011, individuals aged 14 and below accounted for 20%-45% of the population. In Alfred Nzo
and O.R Tambo DMs, the proportion is highest, at 45%, whilst in Nelson Mandela Bay Metro and Cacadu DM, it is
below 30%.
Individuals over the age of 65, represent about 10% of the total population in all DMs. However, this proportion is
increasing over time. DMs with relatively higher elderly populations include Ukhahlamba, Amatole, O.R Tambo, Chris
Hani, and Alfred Nzo DMs, whilst the proportion of the elderly is relatively lower in the Nelson Mandela Bay Metro and
in Cacadu DMs (see Figure 3.3).
In summary, results show that the female population is more than that of male’s in all DMs. Males dominate at lower
age cohorts, mostly in age groups below 24 years. On the contrary, females outnumber makes in higher age groups;
where the shift towards females is relatively lower in poorer DMs than richer DMs. This can be attributed, as alluded to
earlier, to male-biased migration to other provinces in search of better opportunities. The proportion of the population
that falls between the 15-64 age-band is the largest within all DMs and is increasing; those that fall within the 0-14
age group are proportionally the second largest but are decreasing over time; and the proportion of people age 64
and above is relatively small but is increasing.
3.2 Poverty and Food Security in District Municipalities
3.2.1Poverty
In this section, the level of poverty at DM and LM level is discussed. The objective is to rank Local Municipalities (LM),
within each DM, on the basis of their average exposure to average deprivation computed using eleven indicators of
wellbeing (using the Fuzzy Index of Poverty). The methodology is explained in Appendix B.
It is to be recalled that the seven DMs were already ranked in Chapter 3. Alfred Nzo, O.R Tambo, and Amatole DMs
were ranked top in the list of poor DMs in the province. Within Alfred Nzo DM, average deprivation of the LM’s
ranged from between 33% and 44%; in O.R Tambo, 28% to 40%; and in Amatole, 18% to 39%. In the Alfred Nzo
DM, the poorest DM in the province, deprivation was the highest in Ntabankulu LM, at 44%; followed by Mbizana
LM, at 40%; and Umzimvubu, at 36% (see Figure 3.4b). In the Ntabankulu LM, higher levels of average deprivation
were registered in refuse removal, at 85%; incomes, at 76%; electricity for lighting, at 62%; and sanitation (toilet), at
60% (see Figure 3.4 a). In Mbizana, second in the list of poor LMs in the DM, households experienced higher level of
average deprivation in refuse removal, at 84%; incomes, at 74%; water, at 64%; and sanitation, at 56% (see Figure
3.4). In Umzimvubu, the third poorest LM, higher levels of average deprivation came as a result of deprivation in
refuse removal, at 79%; incomes at 74%; and access to electricity for lighting, at 43%.
48
Figure 3.4: Average Deprivation in Selected Indicators in Alfred Nzo District Municipality (%)
Figure 3.4a: level of deprivation in selected indicators
Figure 3.4b: Average level of deprivation
Source: Own computations based on Statistics South Africa, Census 2011
In the O.R Tambo DM, the second poorest DM in the province, average deprivation was the highest in Port St John’s
LM, at 40%; followed by Qaukeni LM, at 38%; and Nyandeni LM, at 37% (see Figure 3.5b). In the Port St John’s
LM, the deprivation was driven by limited access to refuse removal, 85%; incomes, 77%; access to toilet, 58%;
and education, at 52% (see Figure 3.5a). In the Qaukeni LM, deprivation was the highest in refuse removal, at 83%;
income, 73%; water, 58%; and toilet, 53%. In Nyandeni, yet again refuse removal topped the list at 86%; incomes,
at 75%; sanitation, at 60%; and education, at 53%.
Figure 3.5: Average Deprivation in Selected Indicators in O.R Tambo District Municipality(%)
Figure 3.5a: Level of deprivation in selected indicators
Figure 3.5b: Average deprivation in O.R Tambo DM
In Amatole DM, the third poorest DM in the province, Mbhashe was the poorest LM with average deprivation level of
35% (see Figure 3.6b). Mbhashe was followed by Mnquma, at 32%; and Amhlathi at 26%. In Mbhashe, the poorest
LM in the Amathole DM, the deprivation was driven by higher levels of deprivation in individual indicators of wellbeing
such as refuse removal, at 86%; incomes, at 73%; access to water, at 55%; and education, at 52% (see Figure 3.6a).
In Mnquma LM, the second poorest LM in the O.R Tambo DM, higher level of exposure to average deprivation were
caused by refuse removal, at 72%; income, at 71%; education 51%; and access to water, at 42% (see Figure 3.6b).
Amhlathi LM is the third poorest LM in the O.R Tambo DM. It experienced significant deprivation in incomes, at 70%;
refuse removal, at 66%; education, at 52%; and sanitation, at 46%.
49
The reader is referred to Figure B1 in Appendix B for detailed information on average and individual levels of deprivation experienced by the remaining LMs within other DMs.
Figure 3.6: Average Deprivation in Selected Indicators in Amatole District Municipality (%)
Figure 3.6a: Deprivation in selected indicators
Figure 3.6b: Average deprivation
3.2.2 Food Security
In this section, the level of food insecurity at the DM level is discussed. The methodology applied is provided in
Appendix A. The Eastern Cape is one of the most food insecure provinces in South Africa. All the DMs experienced
high level of food insecurity at more than 66% of households. However, the three most food insecure DMs were
Alfred Nzo, at 86%; Chris Hani, at 83%; and O.R Tambo, at 81%. The two metros, NMBM and Buffalo City each
experienced food insecurity level of 70% and 57%, respectively.
Figures 3.7a through 3.7f depict the level of food insecurity in each DM in the province. In Alfred Nzo, the most
food insecure DM, all the four LMs have food insecurity levels at above 80%. These include Matatiele, at 86%;
Umzimvubu, at 85%; Mbizana, at 83%; and Ntabankulu, at 80% (see Figure 3.7f).
In Chris Hani, the second most food insecure DM in the province, higher concentrations of food insecure households
are found in Intsiki Yethu, Sakhisizwe, and Emalahleni LMs each at 86%; Engcobo at 85%; Lukanji, at 83%; Tsolwana,
at 82%; Lukanji, at 83%; Inkwanca, at 81%; and Inxuba Yethemba, at 72% (see Figure 3.7c).
Figure 3.7: Food Insecurity in the Eastern Cape
Figure 3.7a: Cacadu District Municipality
4.7b: Amatole District Municipality
50
Figure 3.7c: Chris Hani District Municipality
Figure 3.7e: Oliver Tambo District Municipality
Figure 3.7d: Ukhahlamba District Municipality
Figure 3.7f: Alfred Nzo District Municipality
Source: Author computation based on combination of Community Survey and Income & Expenditure Survey of Statistics South
Africa
In O.R Tambo, the third most food insecure DM, the Nyandeni LM is the most food insecure, at 87%; followed by
Qaukeni and Mhlontlo LMs, each at 81%; Port St John’s, at 79%; and King Sabata, at 76% (see Figure 3.7e).
Analysis of socio-economic characteristics of the food insecure households in affected DMs indicate that the majority
of the food insecure households reside in rural areas, they have higher numbers of dependants, they are headed by
women, they are Africans, and they reside in places with underdeveloped essential socio-economic infrastructure.
3.3 Economic Profile of District Municipalities in the Eastern Cape Province
In this section, the economic contributions of DMs to the Eastern Cape Economy are evaluated. This is undertaken
within two subsections. The first sub-section analyses the share of each DM to the province’s Regional Gross
Domestic Product (GDPR); trends in the GDP growth of each DM; and results from a decomposition model, with a
particular focus on DMs’ contribution to GDPR growth.
The second section analyses specific sectors within each DM. This includes sectoral contributions to GVA, growth
performances, and DMs’ contributions to overall GDP growth of the province. Finally, it concludes by looking at
location quotients to determine the comparative advantage (or lack thereof) of sectors within a DM.
3.3.1 Economic Profile
Figure 3.8 shows the contribution of DMs to the GDP of the Eastern Cape, and shows the relative size and importance
51
of the local economy within each DM. Together, the NMBM and Buffalo City Metros contribute the lions share of
the Province’s GDP in 2011 (at 66%), with NMBM contributing 43% and Buffalo City Metro contributing 23%. The
remaining DMs together account for only 34% of the Province’s GDP. The Amatole and the O.R Tambo DMs are
the third and fourth largest economies. They contributed 10% and 9% of the province’s GDP respectively. Figure
3.8 further shows that the Ukhahlamba and Alfred Nzo DMs are the smallest economies in the province; each
contributing approximately 5% of the province’s GDP. Secondly, the contributions of the DMs to GDPR remained
fairly unchanged between 2002 and 2011.
Figure 3.8: District Municipalities Contribution to Provincial GDP
Source: Own Computations based on data from Global Insight
Figure 3.9: GDP growth rates of Nelson Mandela Bay Metro and Buffalo City
Figure 3.9a: Nelson Mandela Bay Metro
Figure 3.9b: Buffalo City Metropolitan
Source: Own Computations Based on data from Global Insight
Figure 3.9 illustrates the growth performance of the NMBM and Buffalo City Metropolitan. The growth of the Eastern
Cape GDP is the sum total of growth in the GDP of the eight DMs, metros included. Here, we decomposed growth in
GDPR to trace major sources of growth in the province’s economy. Results are reported in Figure 3.9, for the NMBM
and Buffalo City, and in Figure 3.10, for the remaining DMs.
According to Figure 3.9, in 2011, the chunk of the province’s growth came from the NMBM (compare this with that
52
provided in Figure 3.10, panels a through f). Figure 3.9 further shows that NMBM’s contribution varied across time.
It fell within the range of 41% to 45% prior to the 2009 recession; surged to a high of 74% during the recession;
and has been on a decline ever since (although the NMBM trend line is towards an increasing contribution). In 2011,
they registered a 3% and 2% year-on-year growth rate, respectively. Hence the economies of the two metros have
been on a path of recovery since 2010; however, they are yet to attain their pre-crisis growth rates of 5%. Just like
other economies, their economies were affected by the 2009 economic recession, which resulted in a 2% and 1%
contraction.
Figure 3.10: Provincial and District Municipality Growth Rates
Figure 3.10a: Cacadu District Municipality
Figure 3.10b: Amatole District Municipality
Figure 3.10d: Ukhahlamba District Municipality
Figure 3.10c: Chris Hani District Municipality
53
Figure 3.10e: O.R Tambo District Municipality
Figure 3.10f: Alfred Nzo District Municipality
Source: Own Computations based on data from Statistics SA and Global Insight
Figure 3.10, panels a through f, depict growth performances for the remaining DMs. The figure shows that GDP
growth in the DM and in the province moved in tandem only in bigger economies such as the Cacadu DM (see Figure
3.10a). However, in smaller economies, wide growth differentials were observed. This was particularly so prior to the
2009 crises. The growth differentials narrowed significantly thereafter.
The Amatole DM is the third largest contributor to the growth in the provincial GDP. In 2011, Amatole grew by 3%
year-on-year. This translates into 10% contribution to the 3% growth in provincial GDP. This makes it the third largest
contributor to provincial GDP growth after the Nelson Mandela Bay Metro and Buffalo City Metro; followed by the
Cacadu DM, at 7%, and Chris Hani, at 6%. Alfred Nzo’s contribution to GDPR growth was the smallest, at 1%.
In addition, Figure 3.10 presents trends in DMs’ contribution to GDPR growth since 2003. The Nelson Mandela Bay
Metro (see Figure 3.9) and Ukhahlamba DM are the only whose contribution to the growth of the Eastern Economy
has increased since 2003. In the case of the NMBM, its contribution grew from 30% in 2003 to 48% in 2011. On the
contrary, contributions coming from the other DMs have declined over time.
3.3.2 Sectoral Analysis
The performance of DMs is broken further down into their sectoral contributions. This includes analysing the primary,
secondary and tertiary sectors as well as specific sub-sectors within each, including agriculture, mining, manufacturing, electricity, construction, trade, transport, finance, and community services. Sectoral performance is gauged
by analysing the share of each activity in GVA, by inspecting growth trends, and by measuring the contribution each
sector makes to GVA growth. This helps to determine the importance of a sector in a DM’s economy, to assess current and future growth prospects of sectors, and to trace sources of growth.
3.3.3.1Sectoral Shares
Table 3.1 provides a summary of sectoral shares of the nine activities in each DM in 2002 and 2011. The activities
are classified into three aggregated sectors (primary, secondary, and tertiary). The tertiary sector is the largest sector
54
in all DMs. In 2011, its share in each DM’s GVA ranged between 63% (in Nelson Mandela Bay Metro) to 82% (in O.R
Tambo DM). The secondary sector is the second largest sector. Its contribution to GVA is the greatest in NMBM (at
25%); and lowest in the Alfred Nzo DM (at 5%). The primary sector is ranked last. Its share in GVA was the highest in
Cacadu, at 8.7%; and the lowest, at 0.3% in Nelson Mandela Bay Metro.
The share of the tertiary sector in GVA grew in all DMs in 2011 compared with 2002. Tertiary sector growth was
greatest in Ukhahlamba DM, followed by NMBM, and lastly Chris Hani DM. In particular, tertiary sector growth was
driven by growth in community services and finance. Table 3.1 shows contrasting relationships between the shares of
activities in GVA and the level of development of a DM. The share of finance in GVA is higher in relatively affluent DMs
such as Nelson Mandela Bay Metro, at 20%; and Cacadu, at 19%. It is lower in relatively poorer DMs. For example,
in Alfred Nzo and Ukhahlamba DMs, the share of finance is only 7% and 12% of GVA, respectively. In the case of
community services, the reverse is true. The share of community services is the highest in poorer DMs. For example,
this was 51% in Alfred Nzo DM, 46% in Ukhahlamba DM, and 44% in O.R Tambo DM.
The share of the secondary sector in GVA increased in four of the DMs between 2002 and 2011. These included
Cacadu, Chris Hani, O.R Tambo, and Alfred Nzo (Table 3.1). However within the NMBM, the economic hub of the
province, the share of the secondary sector showed a large decline. Its share in the metro’s GVA declined by 1.7
percentage points. This was due to dismal performance by manufacturing whose share in GVA declined in all DMs.
In DMs where the share of the secondary sector to GVA increased, the decline in the share of manufacturing was
offset by growth in construction. Construction is one of the activities whose share in GVA increased across all DMs.
The primary sector is composed of agriculture and mining. It is the smallest sector in all DMs. In 2011, its contribution
to DMs’ GVA was the lowest in the Nelson Mandela Bay Metro, at 0.3%; and the highest in Cacadu DM, at 8.7%.
Agriculture is the largest activity within the primary sector. Table 3.1 further shows that the share of GVA accruing
to the primary sector declined between 2002 and 2011. This happened across all DMs. This decline was most
significant in the Cacadu and Ukhahlamba DMs.
55
Table 3.1: Sectoral Shares of GVA by District Municipality in the Eastern Cape (%)
Cacadu
2002
Amatole
Ukhahlamba
O.R Tambo
Alfred Nzo
NMBM
Buffalo City
2002
2011
2002
2011
2002
2011
2002
2011
2002
2011
2002
2011
2002
2011
Primary
10.4
8.7
3.0
2.6
4.9
4.3
8.3
7.2
4.4
4.3
3.5
2.9
0.4
0.3
1
1
Agriculture
10.4
8.6
2.8
2.5
4.8
4.2
8.3
7.2
4.4
4.3
2.6
2.5
0.2
0.2
1
1
Mining
2011
Chris Hani
0.0
0.0
0.2
0.1
0.1
0.1
0.0
0.0
0.0
0.0
0.9
0.4
0.2
0.1
0
0
12.2
13.4
15.6
15.1
8.6
8.6
13.4
12.2
5.5
5.7
4.6
4.9
26.5
24.8
20
20
Manufacturing
8.2
7.7
13.9
12.7
5.6
4.6
11.4
9.5
3.6
3.1
2.7
2.2
24.2
21.7
17
16
Electricity
2.0
2.1
0.5
0.5
1.1
1.1
0.6
0.6
0.7
0.6
0.6
0.6
1.0
1.0
1
1
Construction
2.0
3.6
1.2
1.9
1.9
2.9
1.4
2.1
1.3
2.0
1.3
2.0
1.3
2.2
2
3
Tertiary
68.1
68.9
70.6
71.2
76.7
77.8
66.5
68.7
81
81.5
79.3
79.8
61.5
62.9
69
69
Trade
12.6
13.6
12.7
11.8
15.2
13.5
9.0
7.9
18.2
16.2
23.9
20.5
11.6
11.4
12
12
5.9
6.6
3.2
3.1
6.1
5.2
3.6
3.1
3.6
3.3
2.9
2.2
11.3
11.4
7
7
Finance
18.8
19.0
14.6
17.0
10.8
13.7
9.1
12.2
15.7
17.8
5.4
6.7
18.4
19.9
22
25
Community
30.7
29.8
40.1
39.2
44.6
45.4
44.7
45.5
43.5
44.2
47.1
50.5
20.2
20.2
28
25
Total GVA
90.7
91.0
89.2
88.9
90.2
90.7
88.1
88.1
90.9
91.4
87.4
87.6
88.4
88.0
89
89
Taxes less Subs
9.3
9.0
10.8
11.1
9.8
9.3
11.9
11.9
9.1
8.6
12.6
12.4
11.6
12.0
11
11
Total
100
100
100.0
100.0
100
100
100
100
100
100
100
100
100
100
100
100
Secondary
Transport
Source: Computation based on data from Global Insight
3.3.3.2 Growth Performance
The results of year-on-year growth rates by sector are summarised in Table 3.2. The table provides a comparison of
year-on-year growth rates compared between 2003 and 2011.
A number of DMs registered negative growth rates in agriculture in 2011. Agriculture contracted in DMs where
agriculture’s share was relatively larger. For example, in Cacadu and Ukhahlamba DMs, where agriculture’s share was
as high as 7% of GVA, it contracted by 1%. Agriculture showed positive growth in O.R Tambo DM, while its growth
remained unchanged in Nelson Mandela Bay Metro, Amatole DM, and Alfred Nzo DM.
The manufacturing sector grew by 3% year-on-year in the Eastern Cape Province in 2011, a 1 percentage point
contraction compared with its 2010 level. Manufacturing registered a 3% increase in the Nelson Mandela Bay Metro.
Similarly, it did not disappoint in other DMs where its contribution to GVA was relatively larger. In the Amatole and
Ukhahlamba DMs, where manufacturing was relatively important, manufacturing grew by 2% and 1%, respectively.
56
Figure 3.11: Year-on-year Growth Rates of Manufacturing in District Municipalities
Source: Own Computations Based on Global Insight
A look at growth trends also reveals that manufacturing growth contracted in all DMs during the economic recession.
For example, it contracted by 7% in the Nelson Mandela Bay Metro, 7% in Amatole DM, and 6% in Ukhahlamba
DM (see Figure 3.11). In 2010, it looked as though manufacturing was a path of recovery. However manufacturing
again slowed to 3% in 2011. It remains to be seen whether growth in manufacturing can reach its pre-crisis level in
the near future.
The tertiary sector constitutes trade, transport, finance and community services. It performed relatively better
than the other sectors. However, growth performances varied: some activities performed better than others. The
community and finance activities were among the best performers. Community services grew on average by 4% in
2011, a 3 percentage point increase compared with its level in 2010. All DMs grew by similar amounts, except Alfred
Nzo DM whose growth slowed to 2% from 4% in 2010.
At provincial level, finance grew on average by 1% year-on-year in 2011. The picture at the DM level however was
somewhat different. NMBM, the largest contributor to the province’s total financial output, contracted by 1% yearon-year (see Table 3.2). Similarly, all the six DMs registered positive growth; but growth slowed compared with 2010
levels.
57
Figure 3.2: Annual Sectoral Growth Rates by District Municipalities
Cacadu
Amatole
Chris Hani
2002
2011
2002
2011
Agriculture
-1
-1
-2
Mining
10
6
6
Manufacturing
1
3
Electricity
16
Construction
34
Trade
Ukhahlamba
O.R Tambo
Alfred Nzo
NMBM
2002
2002
2002
2002
2011
2002
2011
0
-1
-1
-1
-1
2
16
-2
0
2
-1
2
-4
-1
15
1
14
0
1
31
2
30
2
4
2
1
2
0
Transport
5
3
3
2
Finance
7
2
7
2
Community
2
4
2
Total GVA
4
2
Taxes less Subs
4
5
100
100
Total
2011
2011
Buffalo City
2011
2002
2011
-2
1
-2
0
0
0
1
1
-19
-12
-20
-13
13
-2
0
0
1
-1
2
-5
0
-3
3
17
17
11
-2
14
-1
2
-5
11
0
1%
1
27
1
32
1
25
-1
30
2
2
2
2
-3
1
1
2
-2
-3
0
4
12
12
2
1
-1
1
4
2
-1
-3
1
2
7
7
5
2
2
2
9
2
0
1
5
1
22
22
4
2
4
2
4
2
3
2
2
3
4
28
27
3
3
2
3
1
2
3
3
0
1
2
3
89
89
6
5
5
4
2
4
5
4
3
2
3
5
11
11
100.0
100.0
100
100
100
100
100
100
100
100
100
100
100
100
Source: Own computations based on data from Global Insight
3.3.3.3Tracing Sources of Growth
In this section, the focus moves to the contributions that each sector made to growth in GVA at the DM level. Results
are provided in Table 3.3 and Figures 3.12 and 4.13.
Figure 3.12 compares the contributions that the nine activities made to growth in GVA in their respective metros. In
2011, GVA increased by 3.04% and 2.04% in the NMBM and Buffalo City Metro, respectively. It shows that 24.7%
of the 3.04% growth in GVA in NMBM came from manufacturing, followed by community services at 24.3%; trade at
13.5%; transport at 9.4%, and finance at 6.1%. In the same year, in Buffalo City Metro, 44% of the 2.04% growth in
the metro’s GVA came from community services, followed by manufacturing, at 15%; and trade at 11%.
Figure 3.12: The contribution of Activities to Growth in GVA in Nelson Mandela Bay and Buffalo City
Figure 3.12a: Nelson Mandela Bay Metro
Figure 3.12b: Buffalo City Metro
In 2003, NMBM’s GVA increased by 1.77%. Growth in finance contributed the greatest proportion, at 51.3%. This
58
was followed by community services at 33.8%; construction at 21.7%; and transport at 9.5%. In the same year,
Buffalo City’s GVA grew by 3.54%. The greatest proportion of the growth was attributed to growth in finance at 41%;
followed by community services at 19%; and construction at 15% of GVA.
Table 3.3: Sector Contribution to Growth in Gross Value Added (%)
Cacadu
Agriculture
Amatole
Chris Hani
Ukhahlamba
O.R Tambo
2002
2011
2002
2011
2002
2011
2002
2011
2002
2011
-12.3
-4.0
-2.7
1.7
-0.1
0.0
-0.6
2.5
-2.6
-2.9
-1.6
-0.2
-2.6
-1.6
Mining
0.0
0.0
0.4
0.1
0.5
0.0
Manufacturing
1.0
8.2
-0.5
8.1
-3.0
3.6
Electricity
-50.1
2.6
Alfred Nzo
2002
NMBM
2011
2002
-5.4
-0.7
-22.7
-7.2
-18.6
-1.1
Buffalo City
2011
2002
2011
0.0
0.0
-0.3
0.0
1.2
-0.1
0.1
0.3
-45.5
24.7
-4.1
20.2
7.9
-0.7
2.4
0.1
6.1
0.1
7.6
-0.5
2.7
-0.4
2.0
-3.7
6.3
-0.1
2.8
1.4
Construction
17.1
1.7
12.2
1.4
20.8
1.6
40.2
0.6
11.6
0.9
42.1
-3.0
21.7
1.3
14.8
3.7
Trade
11.6
11.1
3.7
9.5
-0.6
11.0
-25.7
4.8
7.2
12.2
-72.5
-69.5
0.1
13.4
3.4
14.6
Transport
7.0
7.2
3.2
2.6
5.3
2.1
-2.6
1.6
3.9
2.0
-4.6
-9.4
9.5
9.4
5.9
6.3
Finance
31.3
13.3
34.7
11.0
20.4
12.1
21.7
11.1
38.2
11.3
0.3
6.8
51.3
6.1
41.4
37.5
Community
16.8
45.0
25.5
49.3
35.9
57.8
97.0
64.7
26.5
57.3
127.7
150.7
33.8
24.3
19.2
7.6
Total GVA
90.1
83.0
79.8
81.8
82.6
86.5
75.9
81.1
86.7
87.6
48.3
62.9
78.4
78.9
83.2
91.6
Taxes less Subs
9.9
17.0
20.2
18.2
17.4
13.5
24.1
18.9
13.3
12.4
51.7
37.1
21.6
21.1
16.8
8.4
Total
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100.0
100.0
Source: Own Computations based on data from Global Insight
Figure 3.12 panels a and b further depict that the importance of manufacturing and trade as growth drivers grew
significantly between 2003 and 2011. On the contrary, the contribution of finance, construction, and electricity to the
metro’s GVA growth declined. For example, in 2003, community services contributed 33.8% of the NMBM’s GDP
growth. The figure dropped to 24.3% in 2011. In the case of finance the drop was significant; it fell from 51.3% in
2003 to 6.1% in 2011, in the NMBM; and from 41% in 2003 to 3% 2011 in the Buffalo City Metro.
Figure 3.13: The Contribution of Activities to Growth in District Municipality GVA
Figure 3.13a: Cacadu District Municipality
Figure 3.13b: Amatole District Municipality
59
Figure 3.13c: Chris Hani District Municipality Figure 3.13d: Ukhahlamba District Municipality
Figure 3.13e: O.R Tambo District Municipality
Figure 3.13f: Alfred Nzo District Municipality
Figure 3.13, panels a through f look at the contributions of activities in the remaining six DMs in 2003 and 2011.
They show that in 2011, the tertiary sector made positive and significant contributions to GVA. However, the sources
and magnitudes of the contributions varied. In poor DMs, the greater proportion of the contributions came from
community services: Alfred Nzo (see Figure 3.12g), Ukhahlamba (see Figure 3.12d), O.R Tambo (see Figure 3.12e),
and Chris Hani (see Figure 3.12c). In relatively better-off DMs, community services still played a leading role; but
others sectors such as manufacturing, trade, transport, and finance also featured.
In Cacadu, a relatively well-off DM in the province (see Chapter 3 for rankings based on average deprivation),
community services contributed 45% of the growth followed by finance, at 13.3%; trade, at 11.1%; manufacturing, at
8.2%; and transport, at 7.2%. Finance contributed 31.3% of the growth in 2003; followed by construction, at 17.1%;
trade, at 11.6%; and electricity, at 7.9%. Community and manufacturing made big jumps. They registered increase
of 28 and 7 percentage points, respectively. The contribution of trade declined from 31.3% in 2003 to 13.3% in 2011,
a decrease of 18 percentage points.
Figure 4.13 also shows changes in growth contributions, if any, that an activity had made between 2003 and 2011.
The figure shows that agriculture’s contribution to growth in GVA declined in all DMs; that of manufacturing increased,
although by smaller margins, in Cacadu, Amatole, Chris Hani, and O.R Tambo; finance’s contribution, though positive,
declined in all DMs except in Alfred Nzo; trade’s contribution increased in Amatole, Chris Hani, Ukhahlamba, and O.R
60
Tambo and decreased in Cacadu DM.
The results so far indicate that the tertiary sector is not only an important sector in terms of contribution to GVA;
it is also a major driver of growth in all DM’s. Within the tertiary sector, community services prominently feature
as an important driver – this highlights the dominance of government within economic activity of the Eastern
Cape, particularly within underdeveloped local economies. Other activities such as trade, transport, finance, and
manufacturing are also important drivers; but mostly in DMs with bigger economies.
3.3.4 Comparative Advantage of Activities
Location quotients indicate whether an activity has a comparative advantage in a given sector or not. A DM is said
to have comparative advantage in an activity if its location quotient is greater than one.
Table 3.4 provides a summary of location quotients for the Nelson Mandela Bay Metro and the six DMs. According
to the table, the Nelson Mandela Bay Metro, Cacadu, and Chris Hani each had comparative advantages in five
activities; Alfred Nzo in four activities; Amatole and O.R Tambo in three activities; and Ukhahlamba in only two
activities.
The Nelson Mandela Bay Metro has a comparative advantage in mining, manufacturing, electricity, transport, and
finance; Alfred Nzo in agriculture, electricity, construction, and trade; Amatole in agriculture, mining, and community
services; and Ukhahlamba in agriculture and community services.
Table 3.4 further shows that all DM’s except Nelson Mandela Bay Metro have a comparative advantage in agriculture
and community services; only Nelson Mandela Bay Metro has a comparative advantage in manufacturing, transport,
and finance; and only Cacadu and Chris Hani show a comparative advantage in construction. This provides policy
makers with a clear starting point in terms of understanding which sectors currently have a head-start in which
sectors and should therefore be prioritised.
Table 3.4: Location Quotients
Cacadu
2002
Amatole
Chris Hani
2011
2002
2011
2002
Ukhahlamba
O.R. Tambo
Alfred Nzo
Nelson Mandela
2012
2002
2011
2002
2011
2002
2011
2002
2011
Agriculture
4.81
4.69
1.30
1.35
2.22
2.29
3.82
3.89
2.01
2.32
1.19
1.38
0.11
0.11
Mining
0.06
0.06
1.24
1.45
0.59
0.80
0.00
0.00
0.16
0.08
6.36
3.93
1.10
1.10
Manufacturing
0.48
0.49
0.81
0.81
0.33
0.30
0.66
0.61
0.21
0.20
0.16
0.14
1.41
1.39
Electricity
2.13
2.21
0.53
0.55
1.19
1.16
0.64
0.62
0.72
0.68
0.67
0.60
1.09
1.08
Construction
1.40
1.50
0.81
0.79
1.29
1.20
0.95
0.85
0.87
0.81
0.92
0.85
0.89
0.89
Trade
0.98
1.10
0.99
0.96
1.18
1.10
0.70
0.65
1.41
1.31
1.86
1.66
0.90
0.93
Transport
0.75
0.84
0.41
0.39
0.77
0.66
0.46
0.40
0.45
0.42
0.36
0.28
1.42
1.44
Finance
1.07
0.96
0.83
0.86
0.61
0.69
0.52
0.61
0.89
0.90
0.31
0.34
1.04
1.01
Community services
1.07
1.06
1.39
1.40
1.55
1.62
1.55
1.62
1.51
1.58
1.64
1.80
0.70
0.72
Source: Own Computations based on Global Insight Data
61
Chapter 4: Understanding the Labour Market
4.1 Global Employment Performance
The world economy has been in crisis since 2007. A host of factors could be blamed for this: poor macroeconomic
policies, low private investment, faltering demand, fiscal austerity, and many others. The crisis, among others, has
adversely affected employment. A recent report by the International Labor Organization (ILO) indicated that 4 million
people lost jobs in 2012; this increased the number of unemployed people to a total of 197 million people since the
crisis started. The global rate of unemployment reached an all time high in 2012.
The economic slowdown has so far been confined to major economic players in the Euro Zone, the USA, some
advanced economies outside the Euro Zone, emerging economies and some developing countries. Various reports
project that this time around it would spread into previously unaffected economies through second round effects.
This would mean further growth in the rate of unemployment. The ILO (2013) projects that close to 5.1 and 3
million more people will be unemployed in 2013 and 2014, respectively. This, if realized, will increase the number
of unemployed people further to 202 and 205 million in 2013 and 2014, respectively. The global unemployment
crisis has impacted on all age groups; but it has had profound effects on the youth. Youth unemployment increased
to 12.6% in 2012, a rise of one percentage point from its 2007 level (ILO, 2013). The rate of unemployment could
have risen to 13.6% if the youth had not given up the job search (or if discouraged work-seekers were included in
the definition of the unemployed). Many individuals have dropped out of the labour market discouraged by such
extended spells of unemployment. The percentage of the youth who have been unemployed for a period of six
months or longer increased to 35% in 2012 from 26% in 2007 (ILO, 2013).
4.2 National Employment Performance
4.2.1 Introduction
The South African economy has not been spared from the global crisis; in fact, when compared to other emerging
economies, South Africa has been hard hit by the crisis. This is in particular through significant trade and financial
market linkages between South Africa and the Euro Zone which have recently become the centre of the crisis.
The 2008-2009 global financial crisis pushed South Africa’s economy into recession officially in the first quarter of
2009. This resulted in the loss of 900,000 jobs. No estimates are yet made available that assessed the effect on
62
employment of subsequent crisis; but one can imagine they have had compounded effect on current and future
employment outcomes.
Taking cognizance of world economic situation and also noting the importance of addressing unemployment problems
in South Africa, which has been recognized as structural in nature, the government of South Africa introduced its
National Development Plan in 2010. The National Development Plan has been in full swing since then. The program
gives credence to infrastructural development in areas touted to have greater employment creation potential. The
section that follows analyses how South Africa fared in terms of shielding its labour market from the crisis.
4.2.2 Employment National
About 13.6 million people were employed in South Africa in the third quarter of 2012. This is a 2.5% year-on-year
increase compared with the same quarter in the previous year. The majority of them, about 70%, were employed in
the tertiary sector (see Figure 4.1). Two activities within the tertiary sector: the wholesale and retail sector and the
community, social, and personal services each absorbed 22% of the labour force.
Figure 4.1: Sectoral Shares and Employment
Agriculture
Electricity, Gas & Water
1%
Services
22%
Construction
8%
Wholesale & Retail
22%
Transport
6%
Source: own calculations based on data from Statistics South Africa
The majority of the employed, about 85%, had attained above primary school level education. Those with secondarylevel education (completed or not completed) accounted 66%, tertiary level of education 19%, and primary level of
education (completed and not completed) 12% (see Figure 4.2). In addition, employment was gender, race, as well
as age biased: the majority of the employed were men; the percentage of blacks/Africans employed was smaller than
other races; and only about 22% of the employed were between the ages of 15 and 30.
63
Figure 4.2: Employment by Level of Education
Source: Own computations based on data from Statistics South Africa
Close to 4.67 million South Africans were unemployed in the third quarter of 2012, a 5.1% year-on-year increase
compared with the same quarter in the previous year. This resulted in a higher rate of unemployment of 25.5%. In
terms of gender and race, 28.2% of females compared with 23.3% of their male counterparts were unemployed. In
terms of race, the Africans and Coloureds dominated faced higher risks of unemployment; unemployment was 29%
for Africans and 25% for Coloureds. Only 6% of whites and 12% of Indians were unemployed. In terms of age, the
majority of the unemployed were relatively young. For example, about 52% of the unemployed were between the
age of 15 and 30 years of age (see Figure 4.3).
Figure 4.3: Employment & Unemployment by Age
Age 5054, 4.1
Unemployed %, 2012Q3
Age 55-59, 2.1
Age 60-64, 0.2
Age 1519, 4.6
Age 55-59, 7.2
Age 45-49,
6.1
Age 40-44, 8.5
Age 20-24, 25.0
Figure 4.3a: Unemployment by Age
Age 50-54, 10.5
Age 60-64, 2.8
Age 2024, 7.9
Age 25-29, 13.6
Age 45-49, 12.4
Age 35-39, 11.4
Age 30-34, 15.3
15Employed %, 2012Q3 Age
19, 0.9
Age 25-29, 22.3
Age 40-44, 14.1
Age 30-34, 14.7
Age 35-39, 14.7
Figure 4.3b: Employment by Age
Source: Own computation based on data from Statistics South Africa
64
In addition to the global economic crisis, the high rate of unemployment was caused by a change in labour market
dynamics and a low employment-growth nexus. The labour force expanded in the third quarter of 2012; it added
552,000 new job seekers, which is a 3.1% year-on-year increase, compared with the same quarter in 2011. This is
more than the 327,000 new jobs created. This means that the unemployment rate may increase despite net increases
in the total number of jobs created. Employment needs to increase by at least 4.2% year-on-year (in order that half a
million new jobs are added into the labour market every year) in order to bring about any significant reduction in the
rate of unemployment.
The bulge in the size of the labour force was also caused by an increase in the labour force participation rate (i.e.
people who change their employment status from economically non-active to economically active). The economically
non-active population decreased by 0.6% year-on-year in the third quarter of 2012 (see Figure 4.4)
Figure 4.4: Employment and Unemployment in South Africa
Source: own calculations based on data from Statistics South Africa
The stubbornly high level of unemployment can also be attributed to the low growth-employment nexus. In South
Africa, due to unintended effects of labour regulation and at times misguided policy actions, capital goods have
been made relatively cheaper in comparison to labour. This is reflected by the fact that today the majority of South
African industries are dominated by capital intensive production (Table 1.1). The problem is compounded by rigid
labour policies. These are blamed for escalating the cost of labour in comparison to other countries in similar stages
of development.
65
Figure 4.5: The Growth Response of Employment
Rage of growth required to create
500000 jobs every year
25
20
15
10
5
0
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Employment Intensity of Growth
Source: Own computations
Therefore, given the current state of production organization, wherein the employment intensity of growth is 0.31%
(estimate obtained from DBSA, 2011), the task of increasing employment by 4.2% or more year-on-year is going to
be difficult as it entails tripling the current GDP growth rate (see Figure 4.5).
Thus, to achieve the required growth in employment with a moderate GDP growth rate of between 4%-7%, the
employment intensity of growth must be raised to at least 0.5 (as shown in the figure). This entails an overhaul of
the structure of the economy, which requires concerted effort from policy makers, the business fraternity and social
formations alike.
Both the New Growth Path (NGP) and the National Development Plan emphasize the need for structural transformation
through the implementation of a host of policy responses. They plan to create five million job opportunities in ten
years, equivalent to one-half a million job opportunities a year by investing in activities with proven higher labourabsorptive capacity.
4.2.3 Employment and Gross Value Added
In this section, an attempt is made to relate the contribution of the nine major economic activities in South Africa
to Gross Value Added and employment: the sectors evaluated include agriculture, mining, manufacturing, utilities
(electricity plus water), trade, transport, mining, finance, and RoE (government plus social plus private services. This
is to determine the relative importance of an activity from an employment and/or value added perspective. This helps
determine where efforts should be directed for fast-tracking employment and GVA. .
66
Figure 4.6: Share of Value Added and Employment by Sector
Sectoral share of Employment
30
RoE
25
Trade
20
15
Manuf
10
Fin & Bus's
Agriculture
5
Utilities
0
0
5
Transport
Mining
10
15
20
25
30
Sectoral Share of Value Added
Source: Own Computations based on Statistics South Africa Data.
RoE represents government and, community, social and private household services. They are collectively referred to as community services in this study.
Utilities represent electricity and finance. It is referred to as electricity in other sections of this study.
Figure 4.6 illustrates each sector’s share in total GDP and employment. The 450 line classifies activities into three
groups. The first represents activities that fall on the line which are considered to be balanced in their bias towards
value added or employment. The second group represents those that are biased towards value-added. These group
of activities fall below the 450 line. The last group are directly opposite to the second group; these represent those
sectors that are biased towards employment creation. These group of activities fall above the diagonal line.
The following activities are weighted towards value-added with smaller employment effects: mining, transport,
finance utilities, and manufacturing. Because they contribute more to value added than to employment, balanced
employment-output outcome can be achieved by increasing the employment content of these activities.
The remaining activities: the Rest of the Economy (RoE), construction, trade, and agriculture have higher employment
creation potential for each Rand of Value Added. Activities in the third group have high labour absorptive capacities
than those in the second group. For example, except agriculture, others, namely, construction, trade, and RoE have
higher labour absorptive capacities. Labour intensity is 0.58 for construction, 0.49 for trade, and 0.52 for community
services (RoE). The results further collaborate with earlier discussion in Chapter 3 that these same sectors have
greater employment multipliers. Thus, a balanced employment-output outcome can be envisaged by, among others,
promoting efficiency in these activities.
4.3 Labour Market in the Eastern Cape
4.3.1 Introduction
The labour market of the Eastern Cape is discussed in greater detail. This includes assessment of employment
demography such as gender, age, education, and race; labour market indicators such as unemployment rate, labour
force participation, and labour absorption; employment classification by sectors and occupation; relative importance
of an activity in employment versus GVA creation; employment multipliers; and finally the section concludes with
67
simulation exercises of a structural change in the labour intensity of output and the implications for job creation within
the province.
4.3.2 Status of Employment
Employment grew year-on-year by 2.5% in the third quarter of the 2012, a 1 percentage point increase compared
with similar quarter in 2011 (Table 4.1). The province employed 1.3 million people in total, which is equal to 9.7% of
the total number of people employed in the whole country. This makes the Eastern Cape the fourth largest employer
after Gauteng (30.7%), KwaZulu-Natal (18.6%), and the Western Cape (13.2%).
Table 4.1: Eastern Cape Labour Market
2011Q3
2012Q3
Change
%
Working age population
4179
4255
46
1.1
Labour Force
1781
1870
89
5
Employed
1298
1330
32
2.5
Unemployed
483
539
56
11.6
Discouraged Job Seekers
388
396
8
2.1
Not Economically Active
2399
2356
-43
-1.8
Rates/Ratios
Point Change
Unemployment Rate
28.2
30
1.8
Labour Force Participation Rate
53.7
56.7
3.0
Employed to population ratio (absorption)
38.6
39.7
1.1
Source: Statistics South Africa, Quarterly Labour Force Survey
Table 4.1 also compares rate of unemployment in the province between the third quarters of 2011 and 2012. The
rate of unemployment increased from 28.2% in the 3rd Quarter of 2011 to 30% in the 3rd Quarter of 2012, an
increase of 1.8 percentage points. This is despite a 2.5% increase in employment. A simultaneous increase in both
the unemployment rate and employment levels is explained by an increase in the total size of the labour force (by
5%), in excess of the increase in the total number of new jobs.
This is also reflected in the rates of absorption and labour force participation. The labour force participation rate is
measured as the ratio of the labour force to the working age population. It increased from 53.7% in the 3rd Quarter
of 2011 to 56.7% in 2012, an increase of 3 percentage points. Similarly, the absorption rate, the ratio of employment
to the working age population, increased from 38.6% to 39.7%, an increase of 1.1 percentage points.
According to the International Labour Organization (ILO), a 70% or more labour absorption rate is considered good
while a rate less than 50% is considered low. The Eastern Cape’s absorption rate was one of the lowest of all
provinces at 39% (compared to a 41% absorption rate for the country), very revealing of poor employment prospects
within the province. The increase in the labour force was caused by a decrease in the number of not economically
active population by 1.8% (Table 4.1), implying that a greater proportion of the population were willing to work.
68
Figure 4.7: Employment & Unemployment by Age Groups
50-54 yrs, 12.7
60-64 15-19 Yrs,
55-59 yrs, 8.3 yre, 2.6 0.8
20-24 yrs, 7.1
55-59 yrs, 2.8
25-29 Yrs, 13.4
30-34 yrs, 12.8
45-49 Yrs, 13.5
45-49 Yrs, 7.6
40-44 Yrs,
8.2
Panel a: Employment
15-19 Yrs, 6.2
50-54 yrs, 4.8
20-24 yrs, 23.6
35-39 Yrs, 12.2
35-39 Yrs, 15.2
40-44 Yrs, 13.7
60-64 yre, 0.5
25-29 Yrs, 21.2
30-34 yrs, 13.0
Panel b: Unemployment
Source: Own computations based on data from Statistics South Africa
Figure 4.7 panels a and b illustrate rates of employment and unemployment by age groups respectively. It shows that
both rates of employment and unemployment are not balanced among age groups. This mirrors the situation at the
national level. The majority of the employed, about 68%, were between the ages of 30 and 55 (see Figure 4.7, panel
a). The youth, constituting the ages between 15 and 30 years, on the other hand accounted for only 21.3% of the
employed. This is despite the proportionally skewed nature of the working age population towards the youth – the
youth comprised 51% of the working age population in 2012 (see Figure 4.7). This is an increase of 5 percentage
points from the estimate for 2002 (at 46%), highlighting a fall in average age in the working age population. The same
trends are true when the base comparison group is the labour force.
Figure 4.8: Distribution of Working Age Population by Age Group
2002
55-59 yrs, 5
60-64 rys, 4
60-64 rys, 4
50-54 yrs, 6
15-19 yrs, 21
45-49 yrs, 7
40-44 yrs, 9
2012
55-59 yrs, 6
50-54 yrs, 6
15-19 yrs, 20
45-49 yrs, 6
20-24 yrs, 14
40-44 yrs, 7
20-24 yrs, 17
35-39 yrs, 8
35-39 yrs, 10
30-34 yrs, 10
25-29 yrs, 13
30-34 yrs, 12
25-29 yrs, 14
Source: Own computations based on data from Statistics South Africa
69
Figure 4.9 below illustrates the distribution of employment by activities in the third quarters of 2002 and 2012.
More than 60% of the 1.3 million people employed in the province in the third quarter of 2012 were employed in
three activities: government social and personal services (26.1%), wholesale and retail (23.5%), and manufacturing
(12.2%). On the contrary, the primary sectors, comprising mining and quarrying (0.1%) and agriculture, forestry,
hunting and fisheries (4.5%) employed far fewer numbers of people.
Figure 4.9: Employment by Activity in the Eastern Cape
Source: Own computations based on data from Statistics South Africa
It is also interesting to note that agriculture, forestry, hunting & fisheries, mining and quarrying, and private households
decreased their shares of employment, all the other activities increased their share of employment in the third quarter
of 2012 compared with 2002. The government social and personal services increased to 26.1% from 19.9%, an
increase of 6.2 percentage points; wholesale and retail increased to 23.5% from 18.4%, an increase of 5.1 percentage
points; finance and business services increased to 9% from 4.3%, an increase of 4.7 percentage points. The share
of Agriculture, forestry, hunting and fisheries declined to 4.5% from 21.1%, a very large decline of 16.6 percentage
points.
70
Figure 4.10: Employment by Occupation
Source: Own computation based on data from Labour market dynamics
Figure 4.10 gives employment by occupation category in 2008 and 2011. In 2008, elementary occupations made up
of 28.4% of total employment, followed by service workers and shop and market sales at 13.4% and technical and
associate professionals at 11.4%. In 2011, elementary activities decreased to 24.1% while employment in service
workers and shop and market sales workers as well as technical and associate professionals increased respectively
to 14.9% and 14.4%. Between the two years, employment declined in the unskilled job categories while employment
in the semi-skilled and skilled categories increased – evidence of skill-biased employment growth. This aligns with
the fall in proportion of individuals employed within agriculture, forestry, hunting and fisheries sectors which would
have been biased towards creating employment for the unskilled.
4.3.3 Employment and Gross Value Added
In this section, as in section 4.3.2, the contributions of the nine sector activities to value added and employment
are compared, however with a focus on the Eastern Cape. The results help determine the relative importance of
employment in relation to the value added of activities.
Figure 4.11 compares the contributions of activities to provincial GVA with that of their contribution to total provincial
employment. It finds the following activities important from a value added perspective: finance, manufacturing,
households, transport, and electricity. This means that these activities contribute more to GVA than employment.
71
Figure 4:11: GVA and Employment Shares by Activity
Source: Own Computations based on Statistics South Africa Data
The following activities are important from an employment creation perspective: mining, agriculture, construction,
trade, and community services. Except in mining, results are similar to that reported earlier (see Figure 4.6). Mining
is a less important economic activity in the province; in 2011, it accounted for about 0.1% of the GDP and for less
than 1% of total employment. In addition, mining is one of the activities in which the province has a comparative
disadvantage.
In general, the results suggest that significant job creation can be achieved in the short term by improving efficiency
and channeling support to activities with greater labour absorptive capacity and, in the medium to the longer term,
by embarking on structural transformation drives. The latter should target activities that are contributing less to
employment relative to value added (indicative of a bias towards capital). These include manufacturing, electricity,
transport, and finance. The results in general underscore the need to implement a balanced approach that harnesses
employment creation potentials on the one hand and promotes economic growth on the other.
4.3.4 Employment Multipliers
In this section employment multipliers by economic sector are analysed. An employment multiplier is defined as
the effect of a R1 million change in investment on total employment numbers. Results reported earlier in Chapter 2
Table 2.10, show that an injection of R1 million into the economy will create 24 jobs if located within the construction
industry; 22 jobs if in the RoE, and 15 jobs if injected into agriculture.
The main objective of this section is not to repeat what was discussed in Chapter 2. It is rather to analyse employment
responses of sectors if the labour intensity of sectors is increased. Labour intensity is proxied by the labour-to-capital
ratio. The higher the ratio, the greater the labour intensity. A change in labour intensity can affect the number of
new jobs created directly and indirectly; directly as a result of direct operations, and indirectly through backward,
forward and consumption linkages. Backward and forward linkages occur as a result of linkages with upstream
and downstream activities, respectively. Consumption linkages on the other hand occur as a result of a change in
households’ share in factor income caused by changes in the labour-to-capital ratio.
72
Figure 4.12: The Effect on Employment Multipliers of Changes in Labour Intensity
Source: Own Computations Based on Eastern Cape Social Accounting Matrix
The average labour-to-capital ratio in the province is estimated at 0.42. This means that on average production
activity in the province is capital intensive. Construction has the highest labour-to-capital ratio, at 0.58; followed by
RoE (community plus private services), at 0.52; trade, at 0.49; and manufacturing, 0.46. In the remaining, the ratios
are lower than the provincial average.
Figure 4.12 illustrates the effect of a, one-half, and three quarter increase in the labour-capital ratio on employment
multipliers. This is made under the assumption that the change causes little impact on the quantity and proportion of
intermediary inputs used in production. Relaxation to this assumption could result in higher employer multiplier. It is
not done here due to lack of data.
A quarter change to the labour-to-capital ratio across all activities will result in an increase in the provincial labourto-capital ratio to 0.51. The change will result in an increase in employment multipliers in all activities. The increases
are higher in construction, trade, transport, and RoE (see Figure 4.12). See Figure 4.12 for the effect on employment
multiplier of one-half (an increase to 0.63) and three quarter (an increase to 0.74) increases in the labour-to capital
ratio. In general, results show that structural transformation along the lines of labour intensive production can result
in an increase in employment. The outcome will be significant if it can be complemented by increase in efficiency.
4.4: Labour Market in District Municipalities
In this section, employment and unemployment situations at DM level are assessed. This will be divided into two
parts. The first part will break down provincial employment and unemployment figures into the two Metros and the six
DMs. In addition, changes in employment shares over a nine year time horizon are discussed (2002 to 2011).
In the second section, total employment at DM and metro levels are further broken down into nine activities: households,
community services, finance, transport, trade, construction, electricity, manufacturing, mining, and agriculture. This
will help identify drivers of employment growth at activity level. Note that agriculture represents agriculture, forestry,
and fishing; mining refers to mining and quarrying; electricity represents electricity, water and gas; trade, refers to
wholesale and retail trade as well as motor trade, catering and accommodation; transport represents transport,
storage and communication; and finance refers to finance, real estate and business services.
73
Figure 4.13: Employment in the District Municipalities
Source: Own computations based on data from Global Insight
Figure 4.12 presents employment at DM level, in 2002 and 2011. Close to 1 million people were employed in the
Eastern Cape Province in 2011. Over half of them were in two cities: NMBM and Buffalo City Metropole. They
accounted for 30% and 23% of the total provincial employment. The remaining, namely, Cacadu, Amatole, and O.R
Tambo DMs each accounted for 10.9%, 10.8%, and 9.5% of the total provincial employment.
Figure 4.13 further shows that in 2011, 191,000 new jobs were created compared with 2002 level, an increase of
23%. The increase was significant in the NMBM and Buffalo Metropolitan City; each accounted for 29.4% and 28%
of the increase. The remaining was shared among the eight DMs as O.R Tambo, 0%; Amatole, 10%; Cacadu, Chris
Hani, 7%; Alfred Nzo, 4%; and Ukhahlamba, 3%.
Figure 4.14: The Unemployment Rate in District Municipalities
Source: Own Computations based on data from Global Insight
In 2011, the national rate of unemployment was 24.7%. The Eastern Cape Province was one of the provinces that
registered above average rates of unemployment at 31%. Figure 4.14 brakes down this rate of unemployment
by DMs and Metros. Only Cacadu DM had a rate of unemployment lower than the national average; its rate of
unemployment was 23.7%.
The following DMs registered higher rates of unemployment: Alfred Nzo DM, 51.8%; followed by O.R Tambo DM,
74
48.4%; Amatole DM, 46.8%; Chris Hani, 39.1%; Ukhahlamba DM, 37.4%; and Cacadu, 23.7%. The metros, Buffalo
City and NMBM, were also showed high rates of unemployment. This was 35.8% in Buffalo City and 35.3% in NMBM.
In 2011, compared with 2002, all the DMs plus the Metros managed to reduce the rate of unemployment. The
reduction was relatively higher in Buffalo City Metropolitan, a decrease of 8 percentage points; followed by the O.R
Tambo DM, a decrease of 7.6 percentage points; and NMBM, a decrease of 6.6 percentage points.
Next attempt is made to identify major employers. This is provided in Figure 4.15, panels a through h. The Community
Services is major employer in all the metros and DMs. Community service employment is the highest in Alfred Nzo
and Amatole DMs, each accounting for 45% of total employment; followed by O.R Tambo and Chris Hani DMs, at
43% and 42%, respectively. In Cacadu, NMB, and Buffalo City Metro, it accounted for 22%, 26%, and 32% of the
employment. Trade is the second largest employer in the majority of the DMs. This is true in the NMBM (20.7%),
Buffalo City Metro (22.6%), Amatole (17%), O.R Tambo (17.1%), and Alfred Nzo (20%).
Figure 4.15:
Employment by Sector in District Municipalities
Figure 4.15a: NMBM
Figure 4.15c: Cacadu DM
Figure 4.15b: Buffalo City
Figure 4.15d: Amatole DM
75
Figure 4.15e: Chris Hani DM
Figure 4.15f: Ukhahlamba DM
Figure 4.15g: O.R Tambo DM
Figure 4.15h: Alfred Nzo DM
Manufacturing accounted for sizable proportion of employment in the province. It is higher in NMBM, at 20.5%;
Buffalo City Metro, at 18.7%; Amatole DM, at 10.2%; and O.R Tambo DM, at 5.8%. Agriculture accounted for sizable
share of DMs’ employment in Cacadu, at 21.9; Ukhahlamba, at 17.4%; and Chris Hani, at 9.7%
Figure 4.15 also provides employment figures in 2002 and 2012. Results show that the share of community services
in total employment increased across all DMs and metros. But results are mixed in other activities.
Despite their size in output and employment, total employment in manufacturing significantly declined in the metros.
Its share in total employment contracted by 4.4 and 3.1 percentage points in the NMBM and Buffalo City Metros,
respectively.
The share of agriculture in total employment also declined in all the metros and DMs. The decline was massive in the
Cacadu and Ukhahlamba DMs, where it is relatively important economic activity. In Cacadu DM, it dipped by 10.8
percentage points while it dipped by 7.9 percentage points in Ukhahlamba DMs.
76
In summary, the discussions hitherto indicate, though with varied degree, the presence of relatively high level of
concentration of employed people in the metros (NMBA and Buffalo City) and Cacadu DM. These are relatively betteroff places, measured in terms of average deprivation. These same places are also characterized by relatively lower
rates of unemployment (although unemployment is still high by absolute standards).
The majority of people in the DMs were employed in community services. This includes the two metros despite their
relatively larger and mature economies. The role of productive activities and those that render services, with the
exception of community services, in employment creation varied from one DM to the other. For example, manufacturing
played a second-place role in employment creation in the NMBM; agriculture in the Cacadu DM; and trade in Buffalo
City Metropolitan. In addition, results show that the province is dependent (and becoming more dependant) on
community services for its employment. For example, the share of community services in total employment showed
marked increase between 2002 and 2011. This happened at the expense of manufacturing, agriculture, and other
activities whose share in total employment had significantly declined.
77
Appendix
Appendix A: Measuring Food Security
It is hard to obtain data to estimate food security status of households at LM level from a single source. Here we
combine two data sources to address the problem. We use the Income and Expenditure Survey with the Community
Survey (CS). The former contains rich information about households’ expenditure on food but lacks representativeness
to conduct the study at the LM level. The CS avoids the problem of representativeness but it is not rich enough as the
IES in terms of availability of important data such as household expenditure on food. Here, we combine the CS and
IES to determine food security status of households. We estimate equation 1 using IES by restricting the variables
to those found in the IES and CS. Parameters from equation 1 are next applied to the CS to determine headcount
indices at the LM level. Equation 1 to 4 provides the steps followed.
The reduced form of Straus’ (1983) demand equation was further reduced to a logistic regression format to obtain
equation 1:
Where,
and
is a vector of explanatory variables,
[1]
is vector of k+1 parameters to be estimated,
is a discrete variable taking a value of 0 if a household is food insecure (
To determine the food security status of households, the variable
calculating households’ expenditure on food as
prices of food items,
as
, where,
<0) and 1 otherwise
in equation 1) is estimated by
stands for household size,
unit
is a matrix of food items consumed. Households’ status on food security is then determined
, where
is estimate for per capital calorie availability at household level in logarithms obtained
by applying a conversion factor to
secure when
(estimate of
is the error term,
is per capital calorie consumption in logarithms. A household is food
.
The probability that a household is food secured is then given by:
Where,
[2]
is conditional probability of food security.
The log odds are given by:
[3]
Rearranging [3], we derive a formula for conditional probability as:
[4]
Equation [1] is then fitted to compute headcount indices. The food security status of households is then determined
by following the following steps: Step 1, draw n-variate binomial distributions; Step 2, use the simulated disturbances
and parameter estimates from the logistic regression (equation 1) to estimate predicted odds ratio for each house-
78
hold in a local municipality (equation 3); Step 3: manipulate predicted odds ratios to determine household status on
food security (equation 4); Step 4, replicate the procedure r times (r=100); Step 5, determine predicted food security
status of households by calculating the mean and variance values for each estimate over all the 100 simulations.
Appendix B: Fuzzy Index of Poverty
This study used the fuzzy sets method, as demonstrated by Cerioli and Zani (1990). Given that the population is
represented by a set X and x represents households, then x ∈ X . A fuzzy subset of poor households represented
by A of X is defined as:
A = {x, µ A ( x)} Where
[1]
µ A is a membership function and A is a set of poor households with i=1,…,n. The membership function gives
information about the degree of belongingness of x to A. For an ordinary set, the function may take a value of either
0 or 1, i.e.:
[2]
In cases where sets are fuzzy, however, µ A ( x )
= 1 if x is a member of A entirely, whereas µ A ( x) = 0 if x is not a
member of A. If x belongs to A partly, then 0 < µ A ( x) < 1 . The degree of membership of x to A increases as the
value of
µA
gets closer to 1.
More than one dimension or indicator of well-being can be added to the basket to calculate indices of poverty in a
multidimensional setting. These indicators come in different forms, namely dichotomic, polytomic, or continuous.
This affects how we define our membership function.
Say,
ω = {ω1 ,...ω k }
represents indicators of well-being for j=1,…,k. For a dichotomic indicator, the membership
function is given by:
Where,
ω ij
[3]
takes a value of zero when individual i is deprived of good j. It takes a value of 1 otherwise.
An indicator added to the basket could be qualitative in nature, as above, but it could comprise more than two
categories of responses. This is where the application of fuzzy sets becomes helpful for calculating membership
functions. For example, a household’s response to a question about their access to basic services might have m
categories, i.e. either very good or fairly good or average or fairly bad or very bad. In cases like this,
value from the set
order
{θ
(1)
j
m
j
θ < θ <,...θ
1
j
2
j
,..., θ
(m)
j
ωj
may take its
}. Responses have to be first converted to scores and then arranged in an ascending
by assigning higher values to situations of declining well-being. A membership function
which takes value in the interval [0,1] and increases linearly as the risk of poverty rises is thus given by:
[4]
79
Where,
θ ij
represents the score for indicator j by individual i, and
θ jmin and θ jmax are scores of values corresponding
to the lower and upper bounds.
For continuous indicators, a slightly different membership function could be formulated. Here, instead of scores we
and ω max
values representing minimum and maximum values. For example,
ω j , with ω min
j
j
use actual values given by
income is one good example of a continuous variable. In this study, we used income poverty lines of R312 and R593
per person per month as lower ( ω min
) and upper ( ω max
) bounds of poverty, respectively:
j
j
[5]
After membership functions are defined, the next step would be to aggregate deprivation indicators, i.e. aggregating
the k indicators
{ω1 ,...ω k } to a single value to help us evaluate the degree of belongingness of each household i to
the fuzzy subset of the poor households A. The question is how to find a function h(.) , as shown by equation [6]
below, to assist in the aggregation process:
µ A (i ) = h( µ A (i ), µ A (i ),..., µ A (i )
1
k
2
[6]
There are several methods of defining the function h(.) . They may be compactly written as:
k

hσ ( µ A1 (i ),..., µ A1 (i ) = ∑ϖ j ( µ A1 (i ) δ 
1

1/ δ
[7]
δ ≠ 0 is a parameter referring to the type of mean to be used. When δ → 0 , a geometric mean is obtained,
whereas when δ = −1 , h(.) corresponds to a harmonic mean. In the case where δ = 1 , h(.) reduces to an arithmetic
k
mean, ϖ j represents the weight assigned to indicator ω j in the aggregation process ∑ j =1ϖ j = 1 . The degree of
Where
belongingness of each household to the fuzzy subset of the poor is given by:
µ A (i ) = ∑ϖ j µ A (i )
j
[8]
Cerioli and Zani suggest the following specification for weights:
ln( 1
ϖj =
µA )
∀j = 1,..., k
j
k
∑ ln( 1
j =1
Where
µA )
[9]
j
µ A = 1 n ∑i =1 µ A (i ) represents the fuzzy proportion of deprived individuals according to indicator ω j . This
n
j
j
means that the weights are the inverse function of the average deprivation level. As mentioned earlier, this method of
calculating weights gives more importance to indicators associated with less frequent symptoms of poverty.
The results from equation [8] could be aggregated to obtain a summary of the measure of poverty, known as the fuzzy
index of poverty, FIPε [0,1] , for the whole population as:
FIP =
1 n
∑ µ A (i)
n 1
[10]
80
FIP measures the proportion of individuals that belong to the poor subset (Cerioli and Zani, 1990). If no poverty,
FIP=0. FIP takes a value of one in the case of extreme condition of poverty. Under normal circumstances FIP takes
a value between zero and one. In general, other things remaining unchanged, higher poverty corresponds to an
increase in the FIP.
Figure B1: Average Deprivation in the District Municipalities in the Eastern Cape
Panel a: Cacadu District Municipality
Panel c: Alfred Nzo District Municipality
Panel b: Chris Hani District Municipality
Panel d: Ukhlahlumba District Municipality
Panel e: O.R Tambo District Municipality
Panel f: Amatole District Municipality
81
82