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Theewaterskloof in 2030: A projection of socio-economic trends in the municipality

anton@econologic.co.za1

10 September 2010

1 Anita Christelis did valuable work in editing this report

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CONTENTS

SUMMARY OF SELECT INDICATORS AND PROJECTIONS ............................................................................ 3

1. INTRODUCTION ............................................................................................................................... 4

2. DATA AVAILABILITY AND PROJECTION MODELS ............................................................................... 5

3. POPULATION ................................................................................................................................... 6

4. ECONOMIC GROWTH ..................................................................................................................... 10

5. EMPLOYMENT ............................................................................................................................... 19

5.1 Employment by sector ........................................................................................................... 19

6. BUSINESS ENTITIES ........................................................................................................................ 23

7. FARM INSOLVENCY ........................................................................................................................ 25

8. WATER AVAILABILITY ..................................................................................................................... 25

9. INFRASTRUCTURE AND SERVICES ................................................................................................... 26

10. Human capital ............................................................................................................................ 30

11. CRIME ........................................................................................................................................ 32

12. HEALTH ...................................................................................................................................... 33

13. MORTALITY ................................................................................................................................ 34

14. GREENHOUSE GASES AND CLIMATE CHANGE ............................................................................. 36

15. CONCLUSION ............................................................................................................................. 37

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SUMMARY OF SELECT INDICATORS AND PROJECTIONS2

2010 2030

Population 103,150 126,586 – 240,091

Gross Domestic Product – Region (ZAR, 2000 prices) 1,900,000,000 3,380,000,000

Gross Domestic Product Per Capita (ZAR, 2000 prices)

12,090 - 16,810 14,010 – 26,740

Gross Value Added (ZAR, 2000 prices) 2,127,337 3,842,208

Gross Operating Surplus – Agriculture and Hunting 762,083,000 1,533,727,000

Number Unemployed 47,806 40,988 - 116,733

Dependency Ratio (Employed to Unemployed) 1:3.60 1:4.32

Water Resources Per Capita (m3) 1,809 777-1,474

Electricity Consumption (gWh) 150 ,000 506,344

Waste Disposal (tons per annum) 54,202 91,484-173,514

Population with TB 1,200 2,336

Population with HIV 7,696 14,598

AIDS Orphans 1,166 3,887

Infant Mortality (first year) per 1,000 Births 32 50

Greenhouse Gas Emissions (tCO2 equivalent per annum) 876,775 882,455 - 2,520,956

2 See text for underlying assumptions.

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1. INTRODUCTION

“All models are wrong, but some are useful” (Box and Draper, 1987)3.

This study models socio-economic trends in the Theewaterskloof (TWK) municipality based on the

available data for the past five to fourteen years. Projections are made for 2015, 2020, 2025, and 2030.

The modelled results will almost certainly –and in some cases, hopefully – be proven wrong. Economies

and societies are dynamic and in many instances the past is not a good proxy for the future, due to

historical trends being altered by innovation, reform, shifts in perception and even collapse – both

economic and environmental. As a small, open economy, the trends that define TWK can be influenced

by changes both within the municipality and outside it.4

The projections presented in this report should, however, be useful regardless of whether or not they

are proven accurate. Long-term strategic planning is a prerequisite for any municipality seeking to fulfil

its Constitutional obligation for “social and economic development” and a “safe and healthy

environment” (RSA Constitution, Section 152). Having a sense of what the future might hold is important

in creating a “relatively surprise-free” planning environment (Sondeijker et al., 2006)5 and in charting

the intermediate steps required to realise or avoid different futures. Identifying how “business as usual”

in 2010 needs to change is only possible once that business as usual and its consequences are

understood.

The report is descriptive as opposed to normative; presenting the projections as they are calculated

without providing much detail on their causes or consequences.

3 Box, George E. P.; Norman R. Draper (1987). Empirical Model-Building and Response Surfaces. Wiley. pp. p. 424.

ISBN 0471810339.

4 Stock theft in the Eastern Cape, for example, has affected the economic viability of sheep and cattle farming in

that region and seen some people migrate from the Eastern Cape to TWK towns such as Villiersdorp and Grabouw.

5 Sondeijker, S., Geurts, J., Rotmans, J. and Tukker, A. (2006) Imagining sustainability: the added value of transition

scenarios in transition management. Foresight. 8 (5),

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Figure 1: This study aims to present an image of what the TWK Municipality could look like in 2030 if the trends

of the last 14 years persist. (Source: Western Cape Provincial Treasury 20076)

2. DATA AVAILABILITY AND PROJECTION MODELS

Development planners in South Africa have long lamented the paucity of socio-economic data at the

local level (Woolard & Leibrand, 20017; Aliber, 20098; Lorentzen et al., 20099) something that Statistics

South Africa’s (SatsSA’s) forthcoming “Living Condition’s Survey” hopes to redress. The limited data

available for TWK has had implications for this study. The (1) under-reporting of population numbers

based on an under-estimation of inward migrants and the (2) under-reporting of informal employment

(including contracted farm labourers) represent a particular concern in TWK and in some instances, the

local data reported by Quantec and Global Insights is generated by interpolating district and provincial

data and has led to different estimates depending on the method applied. It should be noted, however,

that StatsSA has recently improved the provision of data, making greater coherence in the downscaling

of data to the local level by companies such as Quantec, possible. The majority of data accessed for this

6 Provincial Treasury (2007) Socio-Economic Profile, Overberg District.

7 Woolard, I. and Leibbrandt, M. (2001), "Measuring Poverty in South Africa", in Bhorat, H., Leibbrandt, M., Maziya, M., Van der Berg, S. and Woolard, I., eds. Fighting Poverty: Labour Markets and Inequality in South Africa. Cape Town: UCT Press, pp. 41-73. 8 Aliber, M. (2009) Exploring statistics South Africa's national household surverys as sources of information about

household-level food security. Agrekon. 48 (4) 384-409

9 Lorentzen, J, Cartwright, A and Meth, C (2009) Trade liberalisation, rural poverty and the environment: a case

study of sugarcane production in the Incomati basin in Mpumalanga, South Africa, in “Vulnerable Places,

Vulnerable People” J Cook (ed). Edward Elgar Press.

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report proved adequately consistent to create a representation of the nature and scale of change in

TWK.

In addition the Community Survey (StatsSA 2007), which is used extensively in this report, and StatsSA’s

agricultural, mortality, and “Census at School” surveys, all provide valuable additions to the seminal

Census 2001.

In this study projections have been made by finding the “best-fit” trend (linear, quadratic, polynomial or

exponential) on historical data. Best-fit has been discerned based on the R2 co-efficient for the

respective trends. Once established, this best fit trend has been extended into the future, so as to

generate projections up until 2030. In instances where the most appropriate trend was not obvious, due

to a lack of data or because two different trends presented equally good fits, two sets of projections

have been provided with an explanation as to their respective assumptions.

Crucially, the report does not attempt to model socio-economic parameters simultaneously even though

many of these parameters are inter-dependent. For example, the report concludes that TWK’s water

resources are likely to become highly constrained by future economic and population growth. Whilst it is

known that the economy of TWK is water dependent and that some of the population growth

experienced in TWK is a function of the region’s relative economic success, the projections made in this

study do not attempt to model how water constraints will affect population numbers via their impact on

economic growth, and how this slow-down in economic growth might actually relax the water

constraint. Instead the study examines each parameter discretely, but based on the assumptions that

past trends capture many of the inter-dependencies.

3. POPULATION

The population of TWK was estimated at 109,997 in 2006/7 (PDG, 2009)10, although the 2009/10

Integrated Development Plan (IDP) Review posits a population of 103,281 in 2007 and suggests this will

reach 107,009 by 2010, whereas the Community Survey estimated the population at 86,719 in 2007.

Based on the PDG estimate, the population was comprised of 23,676 households.

Clearly population estimates vary for TWK, as elsewhere in South Africa, and the Mayor is cited in the

2009/10 IDP as suggesting the population could have been as high as 135,000 in 2007 if all inward

migrants had been counted. The population of TWK is expanding at a declining rate, but at a rate that is

above that for the province and country. The data suggest that between 2001 and 2006, population

growth was experienced in all cohorts except for the 20-24 (-0.19 per cent) and 25-29 (-0.13 per cent),

possibly due to outmigration of skilled and semi-skilled job-seekers, and HIV-related mortality.

10 Palmer Development Group (2009) Theewaterskloof Viability Assessment, a study for the DBSA.

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Of the current TWK population, 55.8 per cent is estimated to be “urban formal”, 23.3 per cent is “rural

formal”, and 21.9 per cent is “rural informal”. Population projections for TWK are contingent upon the

basis on which Census data are interpolated (localised) and then projected into the future, as can be

seen in Table 1. If the current trends continue, population growth rate can be expected to decline, but a

major uncertainty is introduced by the so-called “informal” sector, and the relative economic success of

TWK may attract migrant labourers and see inward migration expanding the population.

Figure 2: TWK population distribution by age and gender (2006). The diagram illustrates the number of male

economic migrants (aged 30-34) living in TWK.

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Figure 3: Population growth rate 1996-2006, South Africa, Western Cape and TWK (source: Quantec data, 2008).

It is not clear, however, that the available data adequately capture the extent of inward migration.

Certainly the following information on country of origin contained in the population register seems to

under-report the extent of foreigners living in TWK.

Table 1: The breakdown of the TWK population by nationality suggests possible under-reporting of immigrants

living in the municipality which in turn would account for an under-reporting of population size. (Source:

Western Cape Provincial Treasury 200711)

WC031: Theewaterskloof

Local Municipality

African/Black Coloured Indian/Asian White

Male Female Male Female Male Female Male Female

South Africa 12,463 8,765 3,0738 3,0563 66 64 4,828 5,054

SADC countries 8 13 29 43 0 0 100 102

Rest of Africa 14 6 3 3 0 0 7 10

Europe 3 7 9 9 0 0 163 161

Asia 0 0 3 3 0 3 7 7

North America 0 0 0 0 0 0 0 7

Central and South America

0 0 0 0 0 0 5 8

Australia and New Zealand

0 0 0 0 0 0 4 0

11 Provincial Treasury (2007) Socio-Economic Profile, Overberg District.

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Inward migration from South Africa, and from outside South Africa influences the population and

population growth rate of TWK. Predicting migration trends in the future is difficult except to say that

increased mobility of people seeking economic opportunity or moving away from a lack of economic

opportunity (increasingly caused by environmental changes) is a global phenomenon. Continuing

inward migration to TWK could be the result of the region’s relative economic success and orderly

governance and exponential population growth (as a combination of organic growth and inward

migration) is probably the most likely population growth trend. Where this is the case, the population of

TWK could reach 240,000 by 2030 in spite of decreasing rates of fertility.

Table 2: Projected population growth rates in TWK (Source: StatsSA, 200712

)

2010 2015 2020 2025

Normal 3.35 3.35 3.35 3.35

High 5.90 5.90 5.90 5.90

Low 0.00 0.00 0.00 0.00

The same increasingly mobile population could, equally, present the converse scenario. A combination

of over-population, environmental degradation (particularly contamination and over-use of water

resources), governance collapse and economic decline, could see TWK becoming an undesirable place to

live and do business, with massive out-migration and complete population collapse (see Figure 4).

Figure 4: TWK population projections under the three different trend assumptions

12 StatsSA 2007, Community Survey.

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Table 3: TWK population projections 2010-2030 based on different assumptions

Population (based on 1996-2006 base period)

2010 2015 2020 2025 2030

“Normal” 103,150 114,834 119,420 123,262 126,586

Boom and collapse (polynomial) 113,424 112,424 94,786 53,623 196

Exponential growth 121,379 145,245 173,802 207,975 240,091

4. ECONOMIC GROWTH

The TWK economy has grown every year in both nominal (includingthe effects of inflation) and real

(stripping out the effects of a inflation) terms since 2000.

In 2005, the Gross Domestic Product per Region (GDP-R) of TWK was R1,467 billion. In 2006, GDP-R was

R1.510 billion and the economy had grown at an average of 3.43 per cent in real terms over the previous

three years (Provincial Treasury, 200713). Growth in a small, open and natural resource-dependent

economy is never smooth, nor guaranteed, but if TWK’s past growth is projected to 2030 it would result

in a GDP-R of R3.38 billion (in 2000 values). Based on this growth and the population projections

contained above, the Gross Domestic Product (GDP) per capita could increase from R16,810 in 2010 to

R26,740 per capita if conservative population estimates are assumed. If the more rapid population

growth scenario transpires, per capita income would move from R12,090 in 2010 to R14,010 in 2030 (all

values in 2000).

13 Provincial Treasury (2007) Socio-Economic Profile, Overberg District.

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Figure 5: Nominal and real (2000 prices) changes in TWK Gross Value Added (2000-2008) (Source: Quantec, 2009)

Figure 6: Real (2000 prices) Gross Value Added growth in TWK 2000-2008 (Source: Quantec, 2009)

At the local level detailed, longer-term time series are available for Gross Value Added (GVA) and for

Gross Operating Surplus than is the case for GDP-R. GVA can be converted into GDP by adding all taxes

and subtracting all State subsidies, but since GVA data are more abundant and provide a good indicator

of economic performance, they are used below.

In real terms, GVA grew at 3 per cent between 2000 and 2008 (2000 prices). This resulted in TWK having

a GVA of R2 billion in 2008. If this growth is sustained, GVA in 2000 prices would be R3.84 billion in 2030.

Table 4: Projected real GVA in TWK based on three per cent growth per annum

2010 2015 2020 2025 2030

2,127,337 2,466,167 2,858,963 3,314,322 3,842,208

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Figure 7: Percentage changes in Gross Value Added and population (2000-2005 and 2005-2008)

The TWK economy is largely dependent on the agricultural sector; a sector whose contribution has

remained signicant over the past ten years in spite of national and global declines. “Manufacturing”

(including food processing), “community, social and personal services” (government services) and

“wholesale and retail trade” (including the growing tourism sector) are the other lead sectors, while

“construction” and “financial, insurance, real estate, and business services” (mainly real estate in TWK)

have both grown by more than the municipal average since 2000. The expansion of community services

is associated with the roll-out of infrastructure, amenities, and social services. While the provision of

these services is a necessary prerequisite for growth, the sector is dependent on fiscal transfers and is

not a classic “engine of growth”.

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Figure 8: Sectoral contribution to TWK economy (GVA in 2000). Source: Quantec, 2009

Figure 9: Sectoral contribution to TWK economy (GVA in 2008). Source: Quantec, 2009

Figure 10: (The mean change in real GVA per sector in TWK 2000-2008 represented in blue, the mean change in

GDP 2000 -2008 (red), Quantec estimates for GDP growth 2000-2008 (green). The figure illustrates differences

between GDP, GVA and GOS and also highlights data discrepancies. However, in most instances the data for

significant sectors concur on the direction of changes. (Source: Quantec, 2009).

The future contribution of different sectors to the TWK economy will depend on the growth rates of

these sectors relative to each other.

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If it is assumed that sectoral growth will be linear, based on expansion over the past 14 years, a

conservative estimate of future growth is obtained (Table 5). Under this projection agriculture remains

the dominant sector in 2030, although real estate, retail and trade (including tourism), food and

beverage processing, and construction all constitute proportionately larger components of the

economy. The table below reports growth in GOS to 2030 under these assumptions.14

Table 5: Projected GOS of lead-sectors based on historical trends; a more conservative projection that does not

assume sustained exponential growth.

Sector 2010 2015 2020 2025 2030

Agriculture and hunting

762,083 954,993 1,147,903 1,340,813 1,533,723

Real estate 266,978 348,873 430,768 512,663 594,558

Retail trade and repairs of goods

217,012 278,487 339,962 401,437 462,912

Food, beverage, and tobacco

203,695 260,450 317,205 373,960 430,715

Electricity and gas 187,511 236,755 285,999 335,243 384,487

Construction 87,188 117,537 147,886 178,235 208,584

Public administration

and defence 44,576 57,882 71,188 84,494 97,800

Wood and wood products

20,423 23,910 27,396 30,883 34,370

If the general trend for economic growth over the past 14 years is judged to be exponential, and the different exponential trends are derived and extrapolated to 2030, a more optimistic view of the future economy is obtained (Table 6). Extrapolated under this assumption, the recent growth in construction ensures that this sector becomes dominant by 2030 (an unlikely but interesting finding).15

14

GOS represents the sum of all pre-tax profits and as such is a key determinant of GVA and GDP-R, but tends to

involve smaller figures than GDP.

15 The danger of such extrapolations is shown in the figure for “mining of metal ores””; a small and insignificant

sector in TWK that probably revolves around sandmining. The sector has, however, off its miniscule base, grown

quite rapidly since 1996. When extrapolated blindly it shows this sector becoming significant in TWK by 2030.

Realistically this growth cannot be sustained by the known resource and will remain small. Similarly, sectors that

have declined in the past, come to an end altogether if extrapolated blindly, whereas the more likely scenario is

that many of them will continue to operate at a low level of functioning.

15

Table 6: Projected gross operating surplus (GOS) by sector based on assumed exponential growth (own calculations, based on Quantec, 2009)

tor % Growth 1996-

2009

Average % growth 2004-

2009 2010 2015 2020 2025 2030

Agriculture & hunting 259% 13.0% 831,172 1,530,335 2,817,616 5,187,726 9,551,518

Forestry & logging 238% 2.9% 18,136 20,954 24,210 27,972 32,319

Fishing, operations on fish farms

611% 30.8% 3,693 14,119 53,975 206,348 788,872

Mining of metal ores 3149% 59.3% 27,837 285,261 2,923,262 29,956,691 306,986,948

Food, beverages, & tobacco 370% 11.3% 245,659 419,327 715,768 1,221,778 2,085,509

Textiles, clothing, & leather goods

78% -11.8% 1,371 734 393 210 112

Wood & wood products 165% 0.7% 17,771 18,415 19,082 19,773 20,489

Fuel, petroleum, chemicals, & rubber products

251% -1.7% 6,625 6,067 5,555 5,087 4,658

Other non-metal mineral products

393% 6.5% 5,429 7,452 10,231 14,045 19,281

Metal products, machinery, & household appliances

151% -7.4% 3,044 2,075 1,414 964 657

Electrical machinery & apparatus

146% -4.5% 6 4 4 3 2

Electronic, sound/vision, medical, & other appliances

234% -1.3% 1 1 1 1 1

Transport equipment 85% -13.1% 382 190 94 47 23

Furniture & other items NEC & recycling

185% 1.8% 8,377 9,159 10,014 10,949 11,971

Electricity, gas, steam & hot water supply

313% 15.9% 244,847 512,523 1,072,832 2,245,690 4,700,757

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Collection, purification, & distribution of water

733% 13.1% 34,356 63,501 117,372 216,943 400,986

Construction 758% 26.3% 139,468 448,413 1,441,715 4,635,337 14,903,322

Wholesale & commission trade

275% 4.6% 32,453 40,713 51,075 64,074 80,382

Retail trade & repairs of goods

455% 11.7% 252,431 439,108 763,838 1,328,712 2,311,323

Sale & repairs of motor vehicles, sale of fuel

374% 8.6% 40,819 61,578 92,894 140,136 211,404

Hotels & restaurants 325% 11.9% 31,392 55,080 96,640 169,561 297,504

Land & water transport 494% 20.5% 87,454 222,029 563,691 1,431,107 3,633,318

Air transport & transport supporting activities

362% 8.7% 8,027 12,164 18,433 27,932 42,326

Post & telecommunication 554% 3.1% 77,642 90,383 105,215 122,480 142,579

Finance & Insurance 569% 17.6% 62,596 140,665 316,097 710,322 1,596,212

Real estate activities 474% 12.4% 310,867 557,021 998,087 1,788,401 3,204,510

Other business activities 342% 6.9% 20,290 28,353 39,619 55,362 77,360

Public administration & defence activities

398% 7.5% 48,182 69,026 98,886 141,665 202,950

Education 334% 8.6% 44,287 66,949 101,206 152,993 231,279

Health & social work 479% 10.3% 24,534 40,086 65,495 107,010 174,840

Other service activities 376% 11.9% 15,968 28,070 49,344 86,741

152,479

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In reality, some sectors are likely to grow and others are likely to contract, and growth will follow a

wide variety of trends (linear, exponential, and logarithmic). Crucial for TWK, is what will happen in the

agricultural sector. The best-fit for this sector emerges as “linear nominal growth” at a relatively slow

rate (growth of R38.5 million per annum in 2000 values). The actual growth is unlikely to be “smooth” –

that is not the norm for agricultural sectors – but significantly linear growth is a more plausible trend

than the logarithmic stabilisation or decline that often defines other agricultural sectors in the country

(Figure 11).

Figure 11: The TWK economy is dominated by the agricultural sector. The sector has experienced moderate

nominal growth in GVA between 1996-2009, although this growth has been erratic. (Source: Quantec, 2010)

In relation to the other sectors, the GOS figures show some economic diversification through growth in

“real estate” businesses, “food and beverage processing”, “retail trade and repair” (which includes

tourism), and the “energy sector” – all large sectors in their own right by 2009. The construction sector,

too, has grown rapidly and in terms of GOS, was ranked as the sixth largest sector in TWK in 2009.

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Figure 12: Gross operating surplus of “other” lead sectors in the TWK economy (1996-2008). (Source: based on

Quantec, 2009)

Figure 13: The mean nominal growth in GVA for all sectors in TWK (1996- 2009) shows sustained growth across a

wide variety of sectors; the highest growth is evident in some of the least significant sectors. (Source: based on

Quantec, 2009)

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5. EMPLOYMENT

TWK is reported as having employed 32,180 people in the municipal economy in 2009. Predicting future

unemployment requires predicting the percentage of the TWK population that will be of a working age.

This study makes the assumption that the current demographic breakdown in which two thirds of the

population is between 16- 65 is maintained until 2030.

Table 7 below projects unemployment based on past employment trends, but assuming different

population growth scenarios and growth of the nature that has been experienced over the past 14

years. The “favourable scenario” assumes lower population expansion. The unemployment figures

include people that have given up looking for work. This is a departure from the figures used by National

Government in South Africa, which exclude such people.

5.1 Employment by sector

Significantly for TWK, neither the agricultural sector which dominates (and is predicted to dominate for

some time should current trends continue) nor the other sectors that are expanding rapidly, are

particularly labour intensive. It is known, for example, that fruit and wine farms in the Western Cape

have become more labour efficient resulting in less labour being used per unit of land and revenue

(Ewert & Hamman, 200516; Bekker et al, 2005)17. In a statement on the South African labour market in

2010, StatsSA reported that at the national level formal-sector employment had fallen by 1.4 per cent in

the second quarter, driven by jobs lost in the construction, transport, and agricultural sectors, but

informal sector employment had increased by 5.7 per cent. The statement reflects a trend that almost

certainly applies in TWK. The jobless growth that characterises the TWK agricultural sector may in part

reflect the “casualisation” of labour on farms and in tourism ventures. Whilst seasonal or casual labour

is meant to be recorded in the official employment data there is a reasonable suspicion that it is not

completely captured.

16

Ewert, J and Hamman J (2005) Labour Organisation in Western Cape Agriculture: an ethnic corporatism? In The

Agrarian Question in South Africa, Henry Bernstein, Routledge Press.

17 Bekker, Dodds, M & S. Ewert, J (2005) A Strategic Social Analysis of the South African Wine Industry.

20

Figure 14: TWK employment in agriculture and hunting (1996-2009). (Source: based on Community Survey, and

Labour Force Survey, 2007)

After agriculture, the lead employer in 2008 was the public sector; in which employment doubled

between 1996 and 2008. “Retail trade and repairs” (which includes tourism), “construction” (which has

grown rapidly since 2000), “domestic work” and “education” (including training, private schools and

tertiary institutions), were the other major employers.

Table 7: Projected figures and percentages of unemployment. (Source: based on Community Survey, and Labour

Force Survey, 2007)

Estimated 2010 2015 2020 2025 2030

Normal

Percentage unemployed

39% 41% 42% 43% 44%

Number unemployed

47,806 56,277 64,749 73,220 81,691

Favourable

Percentage unemployed

34% 36% 35% 34% 32%

Number unemployed

35,533 40,780 41,295 41,314 40,988

Unfavourable

Percentage unemployed

39% 42% 45% 47% 49%

Number unemployed

47,723 61,099 77,604 97,855 116,733

Table 8 below reports the narrower, official South African extent of unemployment.

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Table 8: The “normal” unemployment scenario from Table 7 above; people who have given up looking for work

are excluded as per the “narrow” definition of unemployment applied by government. (Source: based on

Community Survey, and Labour Force Survey, 2007).

2010 2015 2020 2025 2030

Normal

Percentage unemployed

(narrow) 26% 27% 28% 29% 29%

Number unemployed

(narrow) 31,872 37,520 43,168 48,816 54,463

Employment creation must rank as a priority for local municipalities in South Africa seeking to fulfil their

developmental mandate. Increasing unemployment has many implications, one of which is an increasing

dependency ratio. Dependency, in this context, refers to the number of people that are dependent on

each income earner. The dependency ratio in TWK shifted from 2.85 in 1996 to 3.53 in 2006. If current

trends continue, the dependency ration will be 4.32 in 2030: every employed person will be responsible

for 4.32 other people.

Notably the other chief primary sector activity - forestry and logging – did not employ additional

people between 1996 and 2008.

Figure 15: TWK employment in “other” lead sectors 1996-2009. (Source: based on Community Survey, and

Labour Force Survey, 2007)

22

The sectors in which people are likely to be employed in the future can be projected based on the

relative growth and labour intensity of existing sectors. If labour absorption is assumed to be a linear

function of economic growth, then “agriculture” will continue to be the greatest employer in 2030,

although the proportion of the labour force employed by the agricultural sector will have declined.

Figure 16: Linear trends in employment by sector, based on 1996-2009 data. These trends have been used in

projections. (Source: based on Community Survey, and Labour Force Survey, 2007)

Table 9: Projected employment by sector for 2010-2030 based on respective linear trends from 1996-2007.

(Source: based on Community Survey, and Labour Force Survey, 2007)

Sector 2010 2015 2020 2025 2030

Agriculture and hunting

employment 14,178 13,623 13,069 12,514 11,960

Public sector 2,620 3,150 3,681 4,212 4,742

Retail 2,456 2,915 3,374 3,833 4,292

Construction 1,931 2,334 2,737 801 3,544

Households 1,539 1,728 1,916 2,104 2,292

Education 1,480 1,760 2,039 2,319 2,599

Motor vehicles sales and repairs

962 1,143 1,324 1,505 1,686

Wholesale and commission

traders 686 810 934 1,058 1,181

Wood and wood products

480 479 478 477 476

23

Forestry and logging

291 315 338 361 385

Food, beverage and tobacco

products 855 910 966 1,021 1,077

The intention of economic planning and innovation is to ensure that neither growth nor employment is

linear. In this sense, the challenge is to alter the linear trends contained in Figure 16. Knowing what will

ensue if such trends persist, can form part of the motivation for the types innovations and interventions

that will ensure this.

Combining economic and employment data suggests that the only “growth” sector capable of creating

intensive employment is “construction”. “Retail trade” (including tourism) is both growing and

employing a large number of people, but is not labour intensive in terms of jobs per unit of revenue. The

public sector is a significant employer. TWK has 19 public sector staff for every 1,000 households (PDG,

2010) which is higher than the average of 16 for B3 municipalities in South Africa. The majority of these

people are employed in “waste management”, “finance and administration” and “roads” - the building

and maintenance of roads; important services for the functioning of the economy. However, since this

sector is dependent on fiscal transfers that are themselves dependent on taxes, it is not typically

considered a growth sector.

6. BUSINESS ENTITIES

South Africa’s local economic development strategy has emphasised the role of private sector

businesses in creating jobs and stimulating growth. Cipro records are not available at the municipal

level, but provincial data show 279,776 registered business entities in the Western Cape (Cipro, 201018).

Over 85 per cent of these are Close Corporations. Based on the percentage of the provincial economy

that TWK contributes there are roughly 3,600 registered business entities in TWK in 2010. On average,

each of these employs 12 people, and would need to employ 19 people to ensure full employment in

TWK.

Information on “Regional Service Levies”, together with the Labour Force Survey, provides a partial

means of bridging the gap between Cipro data and local businesses. In TWK there have been roughly 20

new business entities registered every year, whilst the number of liquidated entities has been negligible

and not changed much between 2000 and 2009. Should this trend continue, there would be over 4,000

registered entities in the TWK municipality by 2030. It is unlikely that all those entities would be active,

but assuming they were the average employment per entity in 2030 would be 8.5 (based on

employment projections for 2030 in Table 7). Should TWK want to achieve “full employment” in 2030,

each of the 4,000 registered entities would need to employ almost 29 people on average by that date.

18 www.cipro.co.za/about_us/Web_Statistics_Version11.pdf

24

The numbers serve to highlight the challenge of enabling businesses to grow in a labour intensive

manner if they are to play a part in the socio-economic development of TWK.

Figure 17: The Registration of new companies (blue) and Close Corporation (red) in the Western Cape for 2000-

2009.

Figure 18: The liquidations of new companies (blue) and Close Corporations (red) in the Western Cape for 2000-

2009.

25

On average South Africa has 58 Small and Medium Micro-enterpirses (SMME) per 1,000 inhabitants. In

TWK this figure is 25 SMME’s per 1,000 people, with over 58 per cent of these businesses classified as

“informal” (FinScope, 2006)19. Understanding the scope of the informal economy is important in TWK

(and elsewhere in South Africa). Whilst the labour force survey provides some insights into informal

activity at the provincial level, no official data exist for at the local level. Collecting this data requires

complex methodological surveys at the local level (Caroline Skinner, pers comms. 2010); eThekwini

Municipality is the only municipality in South Africa to have undertaken such a survey to date.

7. FARM INSOLVENCY

The Terms of Reference for the study required projections on farm insolvency. The obvious source of

these data would be commercial banks and the Land Bank in South Africa, but they were not able to

release this information. It is known that the number of active farms in South Africa (and the Western

Cape) is declining and farm size is increasing. StatsSA reported in 2007 that farm debt in the Western

Cape was R9.06 billion, roughly 20 per cent of the province’s farming asset value. This ratio is marginally

better than the national average, but the data are not represented as a time series nor at the municipal

level.

8. WATER AVAILABILITY

Water is acknowledged as simultaneously being an economic, social, and environmental resource in

South Africa (DWAF, 1998)20 The TWK district represents a key water catchment area for the City of

Cape Town Metropolitan Area, but itself faces challenges in providing adequate water for its economy,

residents, and the environment.

TWK receives its bulk water from the Overberg Water Board (OWB) which operates three rural schemes

(Ruensveld West, Ruensveld East, and Duiwenhoks) that simultaneously supply the rural areas and

farms. The municipality is responsible for ensuring that water supply and sanitation services are

provided to the urban areas. In the rural areas, farmers take responsibility for water supply and

sanitation to their houses and employees’ houses.

Ensuring more efficient use of the available water resource (which in terms of legislation includes all

groundwater) will become a defining challenge for the TWK municipality prior to 2030. On average,

South Africa has a renewable water resource of just over 1,000 m3 per capita (roughly a seventh of the

19 Finscope (2006) Small Business Survey in, Information Sharing and SMME Financing in South Africa: a survey of

the Landscape. A Report for the National Credit Regulator of South Africa by Michael Turner et al. Published by

PERC Press, North Carolina.

20 DWAF (1998) National Water Act, Republic of South Africa, Pretoria.

26

global mean). Water figures are reported at the catchment level, but if the total resource available in the

Breede-Overberg catchment (of which TWK is part) once transfers out have been subtracted, is divided

by the number of people living in the area, then 1,809 m3 is available per capita. This is more than the

national average, but TWK also has greater demands on this water and already has one of the highest

levels of run-off capture in the world due to the abundance of dam and farm dams in the region. The

available water is a finite resource. If anything, the size of TWKs resource will decrease as demands in

the City of Cape Town increase and necessitate further water transfers from TWK. If the TWK population

grows to 240,000 by 2030, as predicted under the high population growth scenario, the available water

resource would decrease to 777 m3 per capita, making the sustainability of the agricultural base of the

TWK economy impossible with current water use technologies.

The Department of Water Affairs and Forestry (DWAF)’s Internal Strategic Perspective (2004) suggests

that a 30 per cent saving on urban water use is possible through demand side management measures in

the Breede-Overberg catchment. Ensuring these efficiency gains are forthcoming in both Cape Town and

TWK is one way in which the looming water constraint and resulting impacts might be mitigated.

Table 10: Changing water resource per capita in TWK under different population projections

2010 2015 2020 2025 2030

Water resource per capita low population growth (m3/capita)

1,809 1,625 1,563 1,514 1,474

Water resource per capita high population growth (m3/capita)

1,537 1,285 1,074 897 777

9. INFRASTRUCTURE AND SERVICES

StatsSA’s Gapminder simulation provides an excellent ilustration of service delivery fluctuations within

South Africa’s municipalities 1996 and 2007. The most obvious observation is that TWK performs well

relative to most municipalities in the country. The numbers do not concur with those reported

elsewhere, but the flux shows a general trend of declining services in TWK between 1996 and 2001,

followed by improvements until 2007 that collectively surpass the 1996 level. On the Gapminder

simulation access to piped water, including a standpipe for example, declined from 87 per cent to 84 per

cent before increasing to 91 per cent. Similarly, the percentage of the population without electricity

increased from 15 per cent in 1996 to 20 per cent in 2001 before falling to 9.8 per cent in 2007.

27

Figure 19: Deterioration in piped water in TWK 1996-

2001 (Source:

http://www.gapminder.org/communityproxy)

Figure 20: Improvement in piped water in TWK 2001-

2007 (Source:

http://www.gapminder.org/communityproxy)

If these trends continue, it would be possible to eradicate many service backlogs by 2030. The

alternative, however, is that high levels of services and economic growth induce inward migration that

outpaces the municipality’s budget for service delivery.

Projecting infrastructure and sevices depends on the extent of population growth, the nature of this

growth, and the finances and capacity to increase service delivery. The PDG Viability Assessment (2010)

makes some assumptions in these regards: surplus on high income consumers increases from 23 per

cent to 40 per cent; property rates increase in real terms at 7 per cent; an increase in the Equitable

Share allocation by 7 per cent per annum. Assuming these assumptions apply, PDG show that it might be

possible to retain the service delivery deficit at current, manageable levels in spite of population

increases, but would involve the municipality incurring significant debt.

Housing provides an illustrative example. The PDG report assumes that 500 house units can be built per

annum given existing allocations. This would keep apace of conservative population growth estimates

(126,586 people by 2030) but would see up to 30,000 households (half of all the total number of

households) without formal housing by 2030 if the population continued to grow exponentially (as per

recent trends) and reached 240,091 people by 2030. These estimates assume that the number of people

per household decreases from 4.5 to 3.5 over the same period, which would result in 65,000 households

in 2030. The realisation that housing delivery is beginning to lag may, of course, act as a deterrent to

inward migrants, but the analysis nevertheless provides a sense of scale to the housing challenge and

highlights the dynamic nature of this challenge.

The same scenarios – the eradication of service delivery backlogs or massive increases in service delivery

backlogs – are possible for almost all services. Table 11 below reports an increase in informal housing

based on the difficulty in finding land, builders and budget for the constuction of government houses. It

28

does, however, assume that recent gains in the provision of services such as water and electricity to all

households (formal and informal) continue.

A clear and related finding to emerge from the services projection is the scale of the challenge of

providing adequate bulk sewerage for houses in TWK as the population numbers grows. The provision of

bulk sewerage services is linked to the availablity of water in TWK; failure to meet the bulk sewerage

challenge would see a contamination of, and reduction in, the availability of water.

A further significant finding involves the increase in solid waste disposal requirements as population and

the consumption of goods increases. Currently TWK’s rural waste is disposed of by farmers, while urban

waste is subject to municipal removal. Finding sites for the disposal of the 91,484 -173,514 tons of waste

that will be generated per annum by 2030, and the creation of mechanisms for the collection, sorting,

and managing (hopefully recycling) of this waste will become a priority. Solid waste generation

estimates are based on Palczynsky (2002)21 who reported that Cape Town’s solid waste disposal could

be expected to increase from 1.65kg per person per day in 2002 to 1.95kg per person per day in 2030

should current trends persist.

Table 11: Infrastructure and services (based on the 23,040 -24,650 houses captured in the 2001 Census and the

23,676 houses reported in the PDG Viability Assessment, 2010)

1996 2001 2010 2015 2020 2025 2030

Electricity consumption - low growth and population (MWh)

150,000 220,000 412,600 459,336 477,680 493,048 506,344

Electricity consumption – high growth and population (MWh)

150,000 240,000 485,516 726,225 1,042,812 1,455,825 1,920,728

% Informal households if delivery rate stays at 500 per annum and population reaches 240,000 in 2030.

29% 16%

(9.9% in CS 2007)

25% 36% 44% 50%

% Informal households if population growth is moderate and delivery is accelerated to meet population growth

29% 16% 9% 5% 3% 1%

% Households with no water or standpipe 19% 9%

4% 2% 1% 0%

% Households without water in house 39% 35% 31% 28% 25% 23%

21 Palczyncski, R (2002) Study on Solid waste Disposal Options for Africa

http://www.bscw.ihe.nl/pub/bscw.cgi/d1354356/SOLID%2520WASTE%2520MANAGEMENT%2520STUDY.pdf

29

% Households with VIP toilets or less 19% 26% 36% 49% 67% 91%

% Households not using electricity for lighting 20%

15% (9.2% in CS 2007)

11% 8% 6% 5%

% Households without weekly refuse collection 28%

18% (9.3% in CS 2007)

12% 7% 5% 3%

Annual waste disposal low population growth (ton per annum)22

54,202 62,031 74,547 82,991 86,305 89,081 91,484

Annual waste disposal high population growth (ton per annum)

54,202 62,031 87,721 104,969 125,607 150,304 173,514

Figure 21: TWK household sewerage systems (2001)

30

Figure 22: Access to water in TWK by type (percentage of total access in 2001).

10. Human capital

Human capital estimates for TWK are patchy and insufficient for the generation of trends. In 2001 40 per

cent of the population between 5 and 24 had “no formal schooling” (Census 2001) and only 12.8 per

cent of the working population had a Matric pass. Illiteracy is reported to be high, but decreasing. In the

instance of human capital (education, skills and literacy), the lack of data is not a major impediment on

policy formulation. The links between education (and particularly female education) and socio-economic

improvement are sufficiently well charted as to warrant efforts to improve human capital in the absence

of major data or projections.

Grade 6 literacy in TWK in 2005 was reported at 41 per cent, while numeracy in Grade 6 was 13.2 per

cent (The Western Cape Department of Education, 2007)23 . For Grade 3, TWK reported 49 per cent for

literacy and 28 per cent for numeracy.

Literacy is a complex concept, and data are not available at the municipal level. The Western Cape

Department of Education records literacy levels against target standards of literacy for Grades 3 and

Grades 6. Under this measure, learners that obtain 50 per cent or more for an age-specific literacy test

are considered “literate”. Applying this measure, the Department pronounced that for the enture

Western Cape, 37 per cent of Grade 3 learners and 15 per cent of Grade 6 learners were literate based

on a 2002/3 survey. By 2004, only 34 per cent of Grade 6 learners were at the target level of literacy; a

finding that was considered sufficiently alarming to warrant the implementation of a literacy strategy

called “LITNUM”. By 2009 literacy levels among Grade 6 learners in the province had increased to 48.6

23 Cited in Provincial Treasury (2007) Overberg District Socio-economic Profile.

31

per cent, and a target of 65 per cent by 2014 is attainable. If this trend is continued, 100 per cent literacy

amongst Grade 6 learners could be attained within the province by 2030, which would imply that the

TWK would have a 100 per cent literacy among Grade 6 learners. What this statistic does not report, is

the number of students in TWK who do not make it to Grade 6 or those that have passed a school going

age without becoming literate.

Nonetheless, it seems clear that literacy levels are improving. Improvements are off a low base; the

2001 Census reported that 40 per cent of TWK people between the ages of five and 24 were not in any

formal education institution. The 2005 IDP claimed that only 12.8 per cent of the employed labour force

had a Matric Pass (a symptom of the high level of agricultural employment). Continuing to equip the

labour force, and future labour force, with skills is central to meeting the challenge of creating an

innovative and adaptive economy capable of adjusting to the changes that affect it.

Table 12: Human capital indicators

Human Capital Indicators

2001 2010 2015 2020 2025 2030

Indigent population

14,575 36,266 (2006) 42,000 48,000 54,000 60,000

Indigent households

3,642 5,774 (IDP 2005/6) 8,059 (WC Treasury 2006) 9,333 11,294 13,500 16,000

Projecting indigent populations is equally complicated. The projection in Table 12 assumes indigent

numbers will increase to 60,000 by 2030 – a pessimistic, and by no means foregone, prognosis. Such a

scenario would be associated with a 2030 population of 240,000 driven by high levels of inward

migration caused by economic or environmental collapses elsewhere in South Africa, continued

economic growth in TWK and high levels of service delivery in TWK.

South Africa’s indigent policy applies to households earning less than R800.00 per month and lacking key

services. According to the 2001 Census 14 per cent of households and 16.5 per cent of the population in

TWK were indigent. Currently there are over 23,000 households in TWK and 18,616 properties officially

subject to rates. However not all rated properties are used for residential purposes, implying that more

than 21 per cent of households do not pay rates.

The inflow of economic migrants to TWK complicates the forecasting of indigent numbers. Some of

these migrants may end up being classified as indigent, but others have come to TWK to deliberately

earn income that will see them escape the indigent classification. Service delivery is improving, and if it

32

continues to improve it will reduce the number of indigent households. There is, however, the possibility

that service delivery capacity will be inundated by growing demand. Whilst this would see a spike in

indigent numbers, it would probably also see a reduction in the number of inward migrants.

11. CRIME

Violent crime has, according to South African Police statistics, been decreasing since 2007 throughout

South Africa, while burglaries at homes and commercial properties have been increasing. In the Western

Cape between 2008/9 and 2009/2010 murder declined by 4.9 per cent in the province to 2,274, while

robbery decreased 0.9 per cent and sexual offences increased 8.4 per cent.

In TWK homicide was the greatest cause of premature death in 2006; 13.5 per cent of all premature

deaths (Boland/Overberg Report 2006) and 45 per cent of all premature death due to injuries in 2006

(English, 2007/824).

There was a 19 per cent increase in the absolute number of murders in TWK between 2005 and 2006,

which is an increase of 10.2 per cent relative to population size. Two years is an insufficient basis for the

extrapolation of a trend; should this trend continue there would be 233 murders by the year 2030. The

Overberg IDP reported a 144 per cent increase in drug-related crime between 2002 and 2005, although

this figure is reported to have declined between 2006 and 2007.

Figure21: Crime statistics for 2006 & 2007, and extrapolated values for 2030

24 Boland/Overberg Regional Annual Health Status Report, 2007/08. Report for the Information Management

Office, Worcester.

33

Other crime statistics are reported to be on a decline in the TWK. It is not prudent to extrapolate based

on limited records from 2006-2007, except to show that if the gains made in that year can be sustained,

crime can be overcome.

12. HEALTH

Most health data are collected at the provincial level and are collated but not reported by district health

wards. The Overberg IDP (2007-2010) reported that 997 people died from HIV-related causes in 2005 in

the TWK. For HIV, provinces perform an antenatal HIV survey that produces estimates at the district

level. The survey showed that 26.7 per cent of the women attending antenatal clinics in TWK were HIV

positive in 2007, while only 14.9 per cent of women tested at the clinics in 2008 were HIV positive.

Collating back to provincial data, TWK was home to 3.2 per cent of the province’s HIV infected people in

2008. According to an HIV research medic in the province25, this translated into roughly 10,000 people

living with HIV. The same data for 2009 was much lower; the variation is most likely the result of low

sample size and random variation.

Combining these data with other reported data from the Health Systems Trust26 (much of which relies

on the same survey but adjusts the data differently) produced the figures in Table 13 below.

Nationally, 30 per cent of HIV positive people also have Tuberculosis (TB) in 2007, although as with any

communicable disease the infection rate is dynamic. In TWK the proportion appears to be less (16 per

cent) probably due to effective treatment (the municipality has a higher “smear-conversion rate” - a

measure of effectiveness of TB treatment) than the region and TB has a 77 per cent “cure rate” in TWK

according to the Overberg IDP (2009/10). However, assuming the infection dynamic remains the same,

then as the number of people living with HIV increases, so too will the number of people living with TB.

Table 13: HIV and TB in TWK

2010 2015 2020 2025 2030

% of provincial population with reported TB 2.41 2.61 2.81 3.01 3.21

TWK people living with TB 1,200 1,452 1,673 2,115 2,336

TWK reported TB deaths per 100,000 1 1 0 - -

Population living with HIV 7,696 9,076 10,457 13,217 14,598

AIDS orphans 1,166 1,847 2,527 3,207 3,887

Population living with AIDS defining condition 864 1,259 1,654 2,049 2,445

25

Dr Andrew Boulle is a UCT based medic involved in the province’s HIV/TB monitoring and treatment programme.

26 http://www.hst.org.za/healthstats/264/data%20

34

Tuberculosis as a cause of mortality is almost certainly under-reported. The StatsSA data source

provides an official disclaimer: “Note that the estimates calculated from the StatsSA cause of death data

are not corrected for underreporting or poor coding, and are thus an underestimate of mortality due to

TB.”27

13. MORTALITY

Figure 22: First year infant mortality in TWK (English, 2007/8)28

South Africa is one of 12 countries in the world that has reported an increase in under-5 child mortality

in the past 30 years (MRC, 2009). The increase is almost entirely attributable to HIV. The trend for TWK

is difficult to discern. In 2005 there were 38 deaths of children under 5 in TWK for every 1,000 babies

born. In 2006 there were 30 deaths and in 2007 there were 44 deaths per 1,000 babies born.

Extrapolating from these data is not possible, but it seems unlikely that TWK will meet its Millennium

Development Goal target of reducing infant mortality by two thirds by 2015.

27

http://www.hst.org.za/generic/45

28 Boland Overberg: Health Status Report 2007/8.

35

The data for deaths of children in their first year of life is more extensive and shows a gradual increase

between 1999 and 2008. If this trend continues unabated – most obviously without better prevention of

mother to child HIV transmissions, which itself is linked to the provision of Antiretroviral (ARV)

medication - then the following increases in first year infant mortality can be expected.

Table 14: Projected number of infants per 1,000 births dying within their first year should current trends persist.

2010 2015 2020 2025 2030

32 36 42 46 50

A study for the Overberg and Winelands District combined, showed that the most common cause of

premature death in men aged 20-49 were “injuries”, whereas in women aged 20-34 it was Human

Immunodeficiency Virus (HIV).

Figure 23: Existing cause of death among females for different age groups in TWK

36

Figure 24: Existing cause of death among males for different age groups in TWK

14. GREENHOUSE GASES AND CLIMATE CHANGE

A gigawatt hour (GWh) of Eskom electricity emits 1 ton of carbon dioxide (CO2) - an intensive global

warming impact relative to other countries. The International Energy Agency estimates that South Africa

produces US$622 of GDP per ton of CO2 emitted and that on average each resident of South Africa is

responsible for 8.5 tCO2equivelent per annum. In 2009, 21 per cent of TWK still did not have access to

electricity, but a Western Cape study reported increasing numbers poor resorting to wood and paraffin

burning to save costs and make up for a lack of grid connections.

In 2006 the municipality distributed 61.1 gWh to urban consumers. This electricity is only responsible for

62,000 tCO2, but over 10,000 rural consumers purchase electricity directly from Eskom in TWK, and off-

grid users burning wood and paraffin, people driving cars, the ploughing of land and the removal of

vegetation and most industrial activities result in further emissions.

Table 15: Greenhouse gas emissions for TWK (energy, transport and land use sources)

2010 2015 2020 2025 2030

“Normal population and static CO2 per capita”

103,150 114,834 119,420 123,262 126,586

tCO2

37

876,775 976,089 1,015,070 1,047,727 1,075,981

Potential liability if carbon is priced at

R100/tCO2

87,677,500

97,608,900

101,507,000

104,772,700

107,598,100

Social cost of CO2 (R850/tCO2)

745,258,750

829,675,650

862,809,500

890,567,950

914,583,850

Exponential population & growth, & linear

carbon growth 121,379 145,245 173,802 207,975 240,091

tCO2

1,031,722

1,307,205

1,651,119

2,079,750

2,520,956

Potential liability if carbon is priced at

R100/tCO2

103,172,150

130,720,500

165,111,900

207,975,000

252,095,550

Social cost of CO2

(R850/tCO2)

876,963,275

1,111,124,250

1,403,451,150

1,767,787,500

2,142,812,175

tCO2 emissions based on projected GDP growth and 1tCO2 per $ of GDP

488,594 566,414 656,629 761,213 882,455

If current trends continue the “carbon footprint” of TWK is set to increase dramatically as the

population grows and as emissions per capita grow. At some stage South Africa post 2012, as a signatory

to the United Nations Framework Convention on Climate Change, is likely to confront either a tax on CO2

emissions or a “cap and trade” scheme to restrict emissions. Where such schemes currently function

carbon is priced at (roughly) R100 per ton and by applying this price it is possible to project a potential

carbon “liability” for the municipality.

Similarly the acclaimed Stern Review (2006)29 established that a ton of CO2 imposes costs on the earth’s

population of (roughly) R850 per ton – this cost the Stern Review defined as the “social cost of carbon”.

Apply this figure it is possible to project the “social costs of TWK’s carbon”.

15. CONCLUSION

Things seldom stay the same, but this study enquires as to what the future may look like if the current

trends persist in TWK. The projections above use historical trends to model future socio-economic

scenarios for the TWK municipality.

29 Stern, N et al (2006) The Economics of Climate Change. Defra, UK.

38

In many ways the commissioning of this report by a local authority in South Africa is remarkable.

Municipalities in South Africa (and elsewhere) are known for their reactive approaches to development

and short-term perspectives in planning. The foresight in commissioning a long-term forecast is, in itself,

one of the resources that will enable TWK to meet future challenges. The information contained in this

report provides an illustration of a potential future in TWK. Being aware of this future, and the

intermediate steps (2015, 2020, 2025) that would result in its realisation, is part of the preparedness to

cope with a wide range of different futures.

The performance of TWK against a wide range of socio-economic indicators over the past 14 years

suggests it is well-equipped to make the required changes relative to other municipalities in South

Africa. It is precisely this relative success that will create and compound many of the challenges that

TWK will confront in the lead-up to 2030. Economic, social and environmental collapse elsewhere in

Southern Africa is likely to see economic migrants move into TWK in this period. It is in this context that

population emerges as a definitive variable in TWK. The extent of unemployment within the indigent

population and the backlog in services are all a function of the population assumptions applied in the

model. This is an accurate reflection of TWK; in which inward migration of work seekers is one of the

defining characteristics of the municipality and gives rise to a range of opportunities (most obviously

labour with which to sustain agricultural growth) and challenges (most obviously the provision of

housing and services).

Although population growth is reported to be slowing in TWK, it continues to grow at a rate above that

of the province and country. The fact that population is determinant of such a wide range of other

variables, makes the uncertainty over population numbers a cause for concern. That most variables

appear “better” under lower population projections, however, does not imply that population growth is

undesirable. Population growth is not necessarily “good” or “bad” for development. Where people are

effectively integrated, they provide the market demand, human resources, rates, and institutions for

sustained economic development. As is suggested in much of the development literature, it is how the

population is skilled, employed, accommodated and satisfied, that is determinant of development

outcomes – with a particular emphasis on the skills of female population members (Dysen et al, 199630).

The role of the built environment in mediating between people and the natural environment is also

critical in this regard, and an effective and resource efficient built environment would appear to be an

essential part of TWK’s future success.

Ensuring the effective integration of TWKs population is probably a more appropriate and effective

means of managing an influx than attempted restrictions n migrants. As the World Bank (2010, p.15)

30 Dysen, T, Cassen, R & Visaria, L (1996) 21st century India; Population, Economy, Human Development and the

Environment. Oxford University Press.

39

notes, “Policies designed to restrict migrants rarely succeed, are often self-defeating and increase the

cost to migrants and to communities of origin and destination”31.

The study highlights that in some instances, most notably the use of water, the provision of housing,

refuse removal and the creation of employment and a skilled labour force, a departure from past trends

is necessary. Knowing this should enable the introduction of the technologies and activities – many of

which exist elsewhere or exist in TWK but on too small a scale – that will ensure a deviation from past

trends and continued socio-economic progress of the municipality.

31 World Bank Group (2010) World Development Report 2010: Development and Climate Change. Washington DC.